ENVISAT forest monitoring Indonesia. D.H. Hoekman M.A.M. Vissers R.A. Sugardiman J. Vargas

Transcription

1 ENVISAT forest monitoring Indonesia D.H. Hoekman M.A.M. Vissers R.A. Sugardiman J. Vargas

2 ENVISAT forest monitoring Indonesia D.H. Hoekman M.A.M. Vissers SarVision BV R.A. Sugardiman Indonesian Ministry of Forestry J. Vargas Wageningen University NRSP-2 report 01-XX NRSP-2 project 3.3/DE-08 ISBN December 2001 This report decribes a project carried out in the framework of the National Remote Sensing Programme (NRSP-2) under responsibility of the Netherlands Remote Sensing Board (BCRS)

3 Content Content 1 Summary 3 1. Introduction Background and Objectives Approaches adopted Structure of this report 7 2. ERS-1/2 map production Introduction The test site Initial validation study at the test site Monitoring the extended area Approach for Palangkaraya ENVISAT ASAR monitoring Introduction The ASAR instrument Overview operating modes ASAR Product Quality Description of land cover classes and database ASAR classification simulation Full legend ASAR classification simulation Aggregated legends Class-pair analysis Selection of classes to be grouped Other aggregation levels Analysis for selected ASAR modes and selected land cover legends Influence of the striping in the C-band VV AirSAR image on the results Workshops and discussion Introduction Discussion Jakarta Workshop Minutes of the workshop Discussion with Head of Forestry Planning Agency Brief discussion other workshops and meetings Workshop Balikpapan Discussion with Balikpapan Orangutan Survival Foundation Donor organisations workshop Conclusions and recommendations 57 References 61 Appendices 63 Appendix I. Jakarta workshop program, invitation and participation 63 Appendix II. Balikpapan workshop 68 1

4 Scope: To support the introduction of an operational Indonesian radar forest monitoring system a demonstration is executed at a test site in East-Kalimantan based on long ERS time series. Future use of ENVISAT ASAR is simulated using AirSAR C-band PolSAR data acquired recently in the NASA PacRim-2 campaign. Information products are evaluated with representatives from the main user communities. (Note that this project also prepares for approved ENVISAT AO proposal 599). 2

5 Summary To support the introduction of operational radar forest monitoring systems in Indonesian a demonstration is executed at the Tropenbos study area in East-Kalimantan. Interest focuses on fulfilling information needs relating to land cover change, fire risk and fire damage monitoring, with main emphasis on early detection. The terrain is very hilly and typical for the rugged topography encountered in most Indonesian forest areas. A series of 21 ERS-1 and ERS-2 images covering the period is processed. The modulating effects of slope angle and slope aspect on the backscatter intensity complicate processing of data of hilly terrain. New multi-temporal segmentation techniques and Iterated Conditional Modes (ICM) techniques, in combination with backscatter change classification techniques have been applied to deal with this problem. Results show that land cover classes such as mangrove and nipah (palm mangrove) can be classified well in a pre-processing step. The remaining terrain shows swift changes in land cover type and extent and notably periods of drought, such as the El Niño event, have pronounced impacts. Several types of maps have been made for several points of time within the monitoring period, showing the changes in forest, tree plantation and agricultural areas, the post-fire damage and the pre-fire forest fire susceptibility (risk). The main problem encountered remains the effect of relief in areas with steep slopes such as the Sungai Wain reserve or the Meratus mountain range. Explicit use of DEM's seems to be unavoidable for good interpretation. Future use of ENVISAT ASAR is simulated using AirSAR C-band PolSAR data acquired recently in the 2000 NASA PacRim-2 campaign. It could be shown that the ASAR Alternating Polarisation (AP) mode, especially the HH-HV polarisation combination, performs significantly better than ERS SAR. The collection of field data from 122 field plots and its organisation into a structured legend of 18 classes at four levels of hierarchy is described. The results of ASAR classification simulation as a function of polarisation combination, speckle level and the effect of choice of legends in terms of similarities in land cover and/or radar characteristics are discussed. Using the techniques developed and demonstrated, SarVision started to contribute to programmes of the Indonesian Government, the Balikpapan Orangutan Survival Foundation (BOS) and the Gibbon Foundation for continued improved safeguarding of remaining wild orangutan populations and their habitat. A key area is located in Central-Kalimantan, east of Palangkaraya. This newly established Mawas forest reserve is 700,000 ha in size and is part of a proposed dept-for-nature-swap program and carbon offset trading agreement. Because it is a swampy and perfectly flat area some of the problems encountered in the Balikpapan are not present. The focus here is on fast detection of illegal logging in support of effective law enforcement. 3

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7 1. Introduction 1.1 Background and Objectives During the last 6 years several Dutch and Indonesian organisations worked closely together with the objective to develop a radar based tropical forest monitoring system. The work was initiated on invitation by the Indonesian Minister of Forestry (MOF), Mr. Djamaludin, during his visit to Wageningen University (WU) in May Significant progress has been made during recent years, including successful execution of the ESA INDREX 1996 airborne radar campaign and the NASA PacRim airborne radar campaign. ERS time series have been studied as part of ESA s INDREX campaign. WU and MOF co-ordinated the radar research, training and development programme under the umbrella of the Tropenbos Foundation. Other Dutch and Indonesian partners, such as Fokker Space, NLR, TNO-FEL, ITC, APHI and PT Mapindo participated in one or more of the projects carried out in this program. Main conclusions are reported in two BCRS studies (BCRS and BCRS report NRSP ). These studies recommend the use of a radar satellite monitoring system to collect upto-date nation-wide information and, once this system is in place, the use of high resolution airborne InSAR for tree mapping in selected areas of interest. Identification of the latter areas is steered by information obtained by the satellite products, thus making the airborne system operations much more effective. Based on recommendations made during the Siramhutan-Demo (BCRS) Workshop (Jakarta, November 1999) and the Tropenbos-MOF-NWO Workshop (Balikpapan, December 1999) new activities are being developed for a follow-on programme ( ). Recent discussion (September 2000) with the current Minister of Forestry, Dr. Nur Mahmudi Ismail, re-confirmed the recommendation to prepare for implementation of the operational nationwide satellite monitoring system as a first step. Moreover it was stressed to execute related activities as soon as possible considering the current enormous information needs of the government, concession holders and many other parties. Important areas of application are timber certification, forest rehabilitation, fire prevention, nature conservation and, in the future, certification of carbon sequestration. Building an operational system would mean that besides the existing (research) co-operation at government level between MOF, WU and Tropenbos an additional co-operation between Dutch and Indonesian industry would be welcomed. While WU would continue giving support in new developments and training, the Wageningen based new company SarVision could lead the way to the introduction of the operational systems, capitalising on results obtained during the programme. Further preparations were made in the period September-October 2000 by discussing the desired monitoring system with representatives of the main user communities. Very briefly stated it can be concluded that all parties consider the new monitoring techniques as very promising. They would like to give support and to participate in further development or implementation steps. Organisations like the state-owned concession holder companies PT Inhutani III and V need information for management, forest rehabilitation and timber certification. Other organisations like WWF Sundaland consider monitoring of illegal logging and fire risk of utmost importance. The national accreditation organisation for timber certification Lembaga Ekolabel Indonesia (LEI) expressed interest in joint exploration of radar s capabilities as a practical tool towards eco-labelling. 5

8 1.2 Approaches adopted Many recent studies showed the appropriateness of ERS SAR, and in particular ERS SAR time series, to provide useful information. The current monitoring system in Indonesia is based on LANDSAT. Cloud cover prevents frequent observation. At the East-Kalimantan test site, for example, the probability of acquiring an optical image (SPOT or LANDSAT) is less than 4%. Though LANDSAT provides very useful information it fails delivering information in time and, thus, is of limited use in many areas of application mentioned above such as early detection of illegal logging and fire risk, or for applications related to timber certification. In principle it would be advisable to combine both sources of information but appropriate geometric registration would require the use of accurate DEM s. It is envisaged that in the near future ERS SAR will be replaced by ASAR, enhancing observation capabilities considerably and that geometric registration with LANDSAT-7 ETM will be facilitated by the DEM s to be generated from the data acquired during the recent and successful (February 2000) Shuttle Radar Topography Mission (SRTM). (a) The ERS SAR based monitoring system Also at the East-Kalimantan test site the use of ERS SAR time series has been investigated. A new combination of existing and new techniques resulted in unprecedented high values for classification accuracy. These techniques comprise new multi-temporal segmentation procedures, image ratio-ing and new physically based classification schemes. Even in this very rugged terrain with steep hills an accuracy of over 90% was reached for most land cover types. In severe ENSO (El Niño Southern Oscillation) induced fires occurred in this area. Both in the pre-fire period as in the post-fire period very good results were obtained. Moreover, it was shown that fire risk could be mapped well in advance of the fires and that meaningful evaluations of fire damage could be made. The accuracy levels could be assessed by utilisation of two independent ground data sets acquired in April New ground data sets have been acquired in September 2000 and June The small-scale study area of 18x20 km 2 is extended to a large 70x90 km 2 area (applying 12-dimensional joint segmentation!) to produce and demonstrate products similar to the products the proposed operational system could produce. During this study the already available time series of 12 images could be extended with another 11 more recently acquired ERS images ( ). In this way the functioning of a monitoring system is well demonstrated in this study, i.e. by showing large products at the proposed 50x50 km 2 standard size at several times: (a) pre-fire (until February 1997), post-fire (until May 1999) and (c) the current status (end 2000). (b) The ENVISAT ASAR, SRTM, LANDSAT-7 based monitoring system In the near future the capabilities can be improved by utilising additional polarisations and/or using a larger incidence angle, new possibilities offered by ASAR. This will be studied in future experiments with ENVISAT over the East-Kalimantan test site. An ESA AO study entitled Retrieval of Geophysical Parameters from Multi-scale Active Microwave Remote Sensing for the Monitoring of Regional-scale Land Surface Dynamics (ID: 599, P.I.: Prof. Cracknell, Dundee) was accepted. It is noted that this study also covers two other non-tropical sites. This year (2001) ENVISAT data could be simulated already, one year before they will become available, by using data collected in September 2000 at the East-Kalimantan test site during the NASA PacRim-2 campaign, which have become available in January Besides L and P-band, fully polarimetric C-band data (AirSAR, 9 looks, 4 m pixel size) and DEM s (C-band TopSAR data, 10 m horizontal resolution, +/- 3 m height accuracy) will become available for a flat to hilly 10x60 km 2 and for a hilly to mountainous 30x40 km 2 area. The AirSAR data can be used to study the enhanced capabilities additional polarisation may offer and shed light on the relevance of an appropriate choice for incidence angle. The 6

9 TopSAR data can be used to simulate SRTM DEM s that will become available soon for the whole of Indonesia and will play an important role, both in SAR data interpretation as well as a means to meaningfully integrate SAR data with LANDSAT-7 data. (c) Evaluation with representatives of users in main areas of application To warrant optimal product development future users have actively participate in this project, supported the field work, contributed to image interpretation and generated feedback on the usefulness of the products within their organisations (see also appendices I and II). In this phase it is still well possible to adapt the products to user s specific information needs and optimise classification procedures, legends and define a set of product types before the next step, standardisation, will be taken. It is also anticipated that larger scale pre-operational activities (e.g. a whole province or 10 million ha) will follow shortly after successful execution of this project. In the mean time a start has been made in Central-Kalimantan where SarVision started operational and permanent monitoring of the newly established 700,000 ha Mawas reserve. It is of large importance to perform a cost-benefit analysis that will produce cost estimates per square km of the end products (produced results in map format). Since no alternatives for the products developed are available and the benefit depends upon the current development regarding e.g. timber certification or carbon sequestration the profits may only be expressed in general terms. R. Sugardiman has been assigned by the Indonesian government as the (Indonesian) coordinator for the proposed operational system. He will commence this task full-time after completion of his PhD-study in Wageningen in the year During the execution of this project he will be based in Wageningen. 1.3 Structure of this report Chapter 2 focuses on the use, and further refinements of the use, of the current ERS-1/2 SAR PRI time series. A time series of 21 images is processed to produce several types of maps at different points of time within this period. The basic processing tools used are multi-temporal segmentation, physical model based pre-classification and Maximum Likelihood classification. As an alternative for the computationally intensive multi-temporal segmentation the use of iterated conditional modes is investigated. The latter proves to be a viable alternative for fast and accurate map updating. Chapter 3 describes the ASAR radar image modes, which will become available in 2002 and the approaches adopted to simulate such image products from C-band polarimetric AirSAR data. The simulated products are evaluated, using a newly acquired extensive field data set (June-July 2001), in terms of polarisation combinations, speckle level and legend structure. It will be shown that the ASAR Alternating Polarisation mode, notably the HH-HV combination, performs significantly better than ERS SAR. It is noted that, in principle, a similar analysis could have been performed for the PALSAR instrument onboard the Japanese ALOS satellite. It is expected that PALSAR, being an L-band polarimetric system, will even outperform ASAR. This may be shown in future studies. Chapter 4 describes the feedback and recommendations obtained from decision-makers and users communities. Meetings were held with the new Minister of Forestry (i.e. the successor of Dr. Nur Mahmudi Ismail), H.E. Mr. Marzuki Usman at June 2001 in Balikpapan, the Governor of East-Kalimantan, Mr. Asmawi Agani at 5 and 7 November 2001 in 7

10 Palangkaraya, and the current Minister of Forestry, H.E. Dr. Prakoso at 9 November 2001 at the ministry in Jakarta. Workshops were held at the ministry in Jakarta at 24 October 2001 for users communities, at the town hall in Balikpapan at 31 October 2001 for local users and, additionally, another workshop was held in Jakarta at 8 November 2001 for donor organisations (EU, World Bank, GTZ, DFID, NRM and JICA). The Balikpapan Orangutan Survival Foundation was given the mandate for management of the 700,000 ha Mawas reserve in Central-Kalimantan, partly as a result of their use of the SarVision service to monitor this area permanently and frequently. 8

11 2. ERS-1/2 map production 2.1 Introduction The ERS-1 and ERS-2 SAR monitoring at the Balikpapan test site is presently in its third phase. Initially, a large number of ERS SAR scenes acquired in the period of the East-Kalimantan Tropenbos test site were studied in support of the INDonesian Radar Experiment (INDREX-96) (Wooding et al., 1998), with the objective to study its potential for land cover change monitoring. Subsequently, an additional series of ERS SAR scenes was acquired in support of studies to assess fire damage caused by the severe El Niño event, occurring at the same test site in the period June 1997 April Finally, another series of ERS SAR data was acquired to extend the period until the year 2000, thus including the time of the PacRim-2 NASA airborne radar data acquisition campaign. The latter campaign would enable preparation for monitoring with future spaceborne radar systems, such as the ESA ASAR (see also chapter 3) and the NASDA PALSAR. ERS SAR data have been used in many landcover change monitoring studies before, also at the Guaviare Tropenbos site in Colombia and in Indonesia. In the thesis of Bijker (1997) a monitoring system for Guaviare is described linking land cover change models and multitemporal ERS SAR observations. Mapping accuracy in the order of 60-70% was obtained for forest, secondary vegetation, pastures and natural grasslands. The study of Kuntz and Siegert (1999) along the Mahakan river in the Indonesian province East-Kalimantan describes how several land cover types such as undisturbed Dipterocarp forest, heath forest, secondary forest, clear-cuts/shifting cultivation and selective logging can be mapped successfully using texture analysis and a time series of observations. In general optical systems, like SPOT or Landsat-TM, yield higher accuracy than ERS SAR. For example, Trichon et al. (1999) shows that high accuracy, i.e. in the order of 70-80%, can be obtained for mapping a large number of vegetation types in the Indonesian province Jambi. However, cloud cover prevents optical systems to make the repetitive and frequent observation that is needed for monitoring. Statistics derived from the study of spatial and temporal distribution of cloud cover using geostationary meteorological satellites reveal that the probability of acquiring SPOT or Landsat-TM images with less than 30% cloud cover are definitely very low (Gastellu-Etchegorry, 1988a,b). For the Kalimantan test site, the mean probability to acquire images with less than 70% cloud cover is only 4% per month. The current study at the East-Kalimantan Tropenbos test site relates to land cover change and fire damage monitoring with main emphasis on early detection. The terrain is very hilly and typical for the rugged topography encountered in most Indonesian forest areas. The modulating effects of slope angle and slope aspect on the backscatter intensity complicate processing of data of hilly terrain. New multi-temporal segmentation techniques (Oliver and Quegan, 1998) and Iterated Conditional Modes (ICM) techniques (Besag, 1986, Hoekman and Quiñones, 2001), in combination with backscatter change classification techniques have been applied to deal with this problem. 9

12 2.2 The test site The test site is located in East Kalimantan Indonesia in the neighbourhood of the Tropenbos research station Wanariset-Samboja, approximately between Longitude 116 o 40 to 117 o 00 East and Latitude 0 o 50 to 1 o 15 South. The test site covers about 18 by 20 km. The central part of the test site is covered with secondary forest, rubber plantation and forest with enrichment planting. A small part of the secondary forest is still under protection, namely the Bukit Bangkirai conservation area. Part of it has been saved from the fires. In the northern part the transmigration areas Semoi and Sepaku are located. These are surrounded by agricultural areas (wet and dry rice fields, also mixed farming occurs). In the western part of the test site the Bay of Balikpapan can be found, which has mangrove and nipah (palm mangrove) forests on its shores. The southern part is covered with secondary and swamp forest, which is part of the Sungai Wain protected forest area. In the south-east corner, close to the road Balikpapan- Samarinda, small villages occur which have mixed farming gardens ( kebun/ladang ). The appearance of these landcover types on an ERS multi-temporal composite is indicated in figure 2.1. The terrain of the test site varies from undulating to very hilly and is typical for the rugged topography encountered in most Indonesian forest areas. There are almost no flat areas except for the mangrove part in the west. This condition makes classification with ERS-SAR radar images quite difficult because of the influence of relief on radar backscatter. (ERS multitemporal color composite: ) Figure 2.1. The test area with the main land cover types. 2.3 Initial validation study at the test site Preliminary results have been published previously in Grim et al., 2000 and Sugardiman, et al., In this section the most important results are summarised to prepare for the 10

13 discussion on the production of large maps (covering a 70x90 km 2, including the test site) over a much longer period of time, and utilising additional and/or new processing techniques. The initial time series comprises 12 images (table 2.1). The images numbered 1 to 9 were collected in the pre-fire period. Multi-temporal composites of those images mainly show greyish colours since backscatter changes were relative small, which means there are no intense land cover or soil moisture changes). Combinations including images 10 and 11, acquired in the fire period, and image 12, directly after the fire, are very colourful, indicating the drastic changes that took place. Figure 2.1 is a clear example. Table 2.1. ERS-SAR PRI image data sets, initial set No Satellite Date Remarks 1 ERS-1 October 22, 1993 Pre-fire 2 ERS-1 November 26, ERS-1 September 10, ERS-2 April 07, ERS-2 May 13, ERS-2 July 22, ERS-2 September 30, ERS-2 November 04, ERS-2 February 17, ERS-2 June 02, 1997 Severe fire 11 ERS-2 April 13, 1998 Severe fire 12 ERS-2 May 18, 1998 Post-fire Also measurements at rain gauges in the test area indicate the extremely dry conditions encountered in the early 1998 period, featuring a complete absence of rain in a 3-months period, i.e. mid-january until mid-april (see also figure 2.2). Rainfall (mm) Months Rainfall 1996 Rainfall 1998 ERS ERS ERS ERS ERS Figure 2.2. Rainfall at the test site and the acquisition dates of ERS-1/2 11

14 Figure 2.3. Averaged mean temporal signatures derived from ERS data The annual 1996 and 1998 trends of rainfall at the test area (as has been recorded in the Tropenbos research station Wanariset-Samboja) are strongly contrasting and reflect the amount of soil moisture and vegetation vigour. The image of April 13, 1998 (no. 11) is dated just before the end of the extensive fire period (rainfall way under the monthly average of 160 mm). This image shows the largest areas of damage to vegetation, and the radar backscatter in this image shows the lowest mean, some 4 db lower than normal (figure 2.3). The last image, May 18, 1998 (no. 12) has been taken after several days of heavy rainfall when almost all fires were extinguished. Unlike the image before, in this image the soil is wet. Increased soil moisture and start of regrowth a few weeks before, have a large effect on backscatter, which already returned to values within 1 db of the normal levels. To be able to study classification potential in a systematic way and to enable proper validation two independent databases have been collected in 1999 (table 2.2). At this time the damage done by the fires was still well visible. Interviews with local people and study of historical data enabled construction of a history record, notably the assessment of vegetation conditions before the fire is very important in this respect. Table 2.2. Two independent databases for training and evaluation Database Ruandha April 1999 Database Vincent April 1999 Land cover #ROI s Land cover #ROI s Mangrove, including nipah (palm mangrove) 10 Pre-fire Code Mangrove 6 1 Nipah 7 2 Forest, unburnt 4 Forest, unburnt 4 3 Forest, burnt 4 Forest Enrichment planting (f.e.p.) burnt 2 Rubber plantation 4 Forest, burnt 5 3 Forest, severely burnt 5 3 Forest Enrich plant, burnt 4 4 Forest Enrich plant, severely burnt 4 4 Rubber plantation, burnt 5 5 Rubber plantation, severely burnt 4 5 Kebun (mixed farming gardens) 4 Kebun (mixed farming gardens) 4 6 Agriculture 4 7 Water 11 Water 5 8 Total 43 Total 53 12

15 Results show that fire affected areas can be delineated well, but that it is sometimes hard to estimate the intensity of the fire damage accurately. Combining ERS SAR observations during the fire period with land cover class information obtained by ERS SAR in the pre-fire period and observations of hot spots (i.e. fires) by NOAA-AVHRR, together with knowledge of the types of fire occurring in this area, can be shown to yield very reliable results. An example of ERS SAR data and its classification is given in figure 2.4. The result of an independent validation through fieldwork campaigns showed high accuracy for almost all land cover types, before as well as after the fire period (table 2.3). For agricultural areas the result may seem poor. However, these areas comprise a mixture of gardens, rice fields, (fruit) tree plantations and forest remnants. Since agricultural areas are confused with plantations and forests, which also occur within the agricultural areas, the result may be much better when interpreted accordingly. Another result is that burnt forests are not always detected (only 85.2%). This is believed to be the result of ground fires, leaving the upper canopy largely intact during several months after the ground fire, thus disabling the C-band SAR to detect such a condition. Table 2.3. Pre-fire and post-fire classification accuracy of multi-temporal ERS SAR data (shown in figure 2.4). Pre-fire Post-fire water 100% water 100% mangrove 100% mangrove 100% nipah 100% nipah 100% swamp forest unknown swamp forest unknown forest 89.4% non-burnt forest 98.8% burnt forest 85.2% plantation 88.5% burnt plantation 98.4% agriculture 34.5% burnt agriculture 28.5% Since many forests are degraded, and therefore vulnerable to fire, and extreme dry seasons occur regularly, forest fire become a major problem in Indonesia. Fire Damage Assessment Maps derived from ERS-SAR data would be a very useful contribution to support forest rehabilitation and reforestation activities. There are clear indications that ERS SAR can map fire susceptibility and, consequently, fire risk/hazard maps can be made in order to plan preventive actions before forest fires start. It was found, for example, that susceptibility to fire might be well assessable by using the stability of the radar backscatter level in the pre-fire period as an indicator. An example for the test area is given in figure 2.5. More technical details on this technique are given in Grim et al., 2000 and Sugardiman, et al., Several features in this image are striking. An evaluation of the pre-fire period fire risk and the actual damage done, for example, reveals that all forest was under these extremely dry conditions actually burned except for the protected forest areas Wanariset, Bukit Bangkirai and Sungai Wain, where active fire fighting took place and for small areas close to the mangroves. The latter may be related to more favourable terrain drainage conditions. Good forest, not actively protected from fires, did burn. An example is the stretch of forest in the lower-left corner between forest plantations. The latter are extremely vulnerable and, apparently the fire easily spreads. 13

16 Figure 2.4. ERS SAR data fragments of an 18x20 km 2 area near Balikpapan (East-Kalimantan) located directly west of the Wanariset research station. (a) SAR image of 13 April 1998, (b) multitemporally segmented SAR image of 2 June 1997 (red), 13 April 1998 (green) and 18 May 1998 (blue), (c) pre-fire period classification, (d) post-fire period classification. Forest fire risk 1995 Fire damage evaluation 1998 High risk Low risk High risk - Burnt Low risk - Burnt High risk - Not Burnt Low risk - Not Burnt Figure 2.5. Fire risk map and fire damage evaluation. 14

17 2.4 Monitoring the extended area New approaches In the initial approach underlying the results discussed in the previous section a multitemporal segmentation technique (Oliver and Quegan, 1998) was utilised prior to a strongly heuristic physically based classification. This approach had three features that are important to consider now. Firstly, the segmentation parameters were set to yield relatively large segments. Secondly, because of the different legends of the two independent field data sets for training and validation, these data sets, necessarily, have been mixed to some extent to be able to execute full training and full validation of the final maps. Thirdly, the Maximum Likelihood (ML) procedure was driven by the averaged values of the training areas cf. Hoekman and Quiñones (2000), thus limiting the dimensionality of the feature space for classification. Consequently, a strongly heuristic and hierarchical approach to classification emerged. This result is undesirable however since it is hard to generalise and to extrapolate to larger areas or different periods of time. Moreover, it was noted that as a result of the large segments, and the delineation of the training areas in this segmented image, a biased classification accuracy result might have been obtained, overestimating certain classification accuracy rates. Considering these factors it was decided to develop a more formal and more generalised approach, which could be developed into standard methods which could be applied much more generally. Several steps were recognised. 1. The segmentation should be optimised to reveal the true underlying backscatter level. In our case this resulted in much smaller segments, giving better information to enter the classification process but leaving more aggregation necessary in subsequent steps. This segmentation was repeated for the initial 12 images, but now over the large 70x90 km 2 area. 2. The size of the training set in terms of number of points per class should be larger to be able to take advantage of the full dimensionality of the acquired image database. This point gets progressively more importance when the length of the monitoring period increases (as is the case now). It was decided to take all pixels of the segmented images within the training areas as training point, rather then the averaged values of the training areas only. This allows use of all data simultaneously if required, even for extreme long periods of observation. 3. Since multi-temporal segmentation is computationally excessive, and a new multitemporal segmentation would be required every time a new image is recorded, it is of importance for an operational system to develop faster methods. Even if this initially may lead to a somewhat sub-optimal result, it would enable meeting the important requirement of timeliness. In this demonstration project the use of ICM is introduced as a viable method. Technical details are given in a next section. The ICM approach An image showing clear segments with uniform levels of backscatter and variation only caused by speckle is easy to segment compared to most natural scenes encountered. Fuzzy boundaries, transition zones, texture, relief or fine drainage patterns complicate such a processing into uniform segments. To mitigate such averse conditions encountered in real images, or to utilise these as potential sources of additional information, image-processing techniques can be applied. However, because of the complexity of many scenes, many of the commonly applied techniques fail to a large extent. For example, image segmentation techniques (Oliver and Quegan, 1998) are not always very appropriate because of the absence 15

18 of well-defined boundaries between many forest types. Texture analysis seems only partly useful because of the limited image resolution of space-borne radar. Also the relief poses problems resulting in many classification errors. In the next part of this section the Iterated Conditional Modes (ICM) method (Besag, 1986) will be briefly introduced. This approach, combining ICM with several types of prior information, can yield very good classification results. In (Hoekman and Quiñones, 2000) the likelihood of a pixel i belonging to class c, li i, c, is based on the (multi-frequency or multi-temporal) radar signal properties in terms of intensities, phases and coherences (Here we limit to intensities only. The classification of a pixel simply is the selection of the class for which li i, c is the highest (the Maximum Likelihood or ML solution). In the ICM method the likelihood li i, c is modified to mli i, c by multiplication with a conditional probability exp( β u i, c ), where u i, c is the current number of neighbours of pixel i having class c, and β is a parameter determining the relative importance of neighbourhood information. In the approach adopted here the eight surrounding pixels form the neighbourhood. Now the classification of a pixel is changed by selecting the class for which the modified likelihood mli i, c is the highest (the ICM(1)-solution). Usually a number of cycles of ICM is required to reach a stable solution, and usually it is better to start with a lower value of β. By relaxing the value of β to the final value, more and more neighbourhood information is used. Note that the process is reversible, i.e. as soon as β is zero again the initial ML-solution is recovered. The logarithmic version of the modified likelihood mli, for ICM-cycle n is denoted as i c ( i, c, n ) = ln( lii, c ) + ui, c, n 1 ln mli β (2.1) For appropriately chosen values of β, the number of cycles and the relaxation scheme, usually determined by trial-and-error, this approach is found to yield major improvements for the classification results. Evaluation of the remaining misclassifications reveals that most of the remaining error is induced by the relief or can be related to rough texture. Moreover, the overall accuracy can be increased further by taking the dominance of certain cover types into account. This knowledge can be included by adding additional priors to the (logarithmic version of the) modified likelihood as ln ( mli i, c, n) ln( lii, c) + β1 ui, c, n 1 + β 2 ln( Pc) + β 3 ln( Ri, c) β 4Ti, c = (2.2) with where T Min ( t Tm ) β 2 i, c i c =, 2Tvc T Pc is the relative occurrence of class c, R, is the relief factor for pixel i and class c, i c T i, c is the texture factor for pixel i and class c, β 2, β 3, β 4 are factors defining the relative influence of prior information, β T is a factor defining a threshold for the influence of texture information, 16

19 ti c Tv c is the (logarithmic version of ) the coefficient of variation (CV) and Tm, are the mean and variance of the CV for class c. In this chapter the factors of relief and texture are not yet included, consequently the factors β, β 3 4 are set to zero. It may be concluded that the approach chosen is highly heuristic. Moreover, as yet, it is not clear how the values for the influence factors ( β 1, β 2, β 3, β 4 ), their relaxation schemes and other factors can be optimised. However, it can be shown that this approach yields major improvement in classification (see next sections). New data and validation sets Table 2.4 shows the 9 new images acquired in the post-fire period, and bridging the period almost until the PacRim-2 airborne campaign event of September The extended averaged mean class signatures are shown in figure 2.6. Two new data sets were collected in the year 2001 in the framework of this project. The first is a new and completely independent data set, the Kemal 2001 set, comprising 50 areas of the PacRim-2 Sungai Wain strip. The second is an extension of the data set initially collected in 1999 in the test area, but now extended to the full 70x90 km 2 area, including five new cover types. Table 2.4. ERS-SAR PRI image data sets, extended set No Satellite Date Remarks 13 ERS-2 June 22, 1998 Post-fire 14 ERS-2 October 05, ERS-1 November 08, ERS-2 November 09, ERS-1 December 13, ERS-1 February 21, ERS-2 February 22, ERS-1 February 06, ERS-2 February 07,

20 Figure 2.6. Averaged mean temporal signatures derived from ERS data. Table 2.5. One additional new database (Kemal 2001 set) for training and evaluation. Database Kemal June 2001 Land cover #ROI s Code Water 5 1 Shrimp ponds 6 2 Primary forest 9 3 Mangrove 6 4 Urban area 4 5 Rubber plantation 5 6 Palm oil plantation 4 7 Secondary forest 4 8 Shrub/Alang-Alang 7 9 Total 50 18

21 Table 2.6. Newly extended database (Ruandha 2001 set) for training and evaluation. Database Ruandha June 2001 Land cover #ROI s Code Water 11 1 Mangrove 10 2 Forest unburned 4 3 Forest burned 4 4 Agriculture 4 5 Unproductive 4 6 Plantation 6 7 Primary forest 5 8 Sec. Forest 5 9 Palm oil plantation 5 10 Grassland 5 11 Logged over forest 5 12 Bare 5 13 Total 73 New results Results are listed in tables 2.7 and 2.8 and illustrated in figures 2.7, 2.8 and 2.9. The approach taken is largely simplified and based on the three points raised in the beginning of this section. As a first step, and largely identical to the approach taken previously for the test area, the areas with land cover types that are not expected to change are singled out in a pre-processing phase. This pre-processing step results in the mapping, at least in this area, of the land cover types mangrove and nipah and of the water (sea and rivers). Such a pre-processing step can be repeated once every few years when desired and may result in new products such as coastal zone erosion monitoring, for example. Such applications are not of interest here. By making these areas and excluding them from the monitoring process more accurate results may be obtained in the areas of main interest, thus enhancing the monitoring function which focuses on early detection of illegal logging and fire risk or damage in the forests and plantations The results of the pre-fire map are shown in table 2.7a and may be compared to previous results shown in table 2.3. The differences are caused by several factors. The first is the better and more fragmented segmentation which leads to better results for more fragmented areas such as the agricultural areas. Also the use of the full data set, applying a simple ML procedure, may have a positive influence. Though the results for forest are comparable for the plantations the results are significantly lower, however. This may also be the result of the better, more fragmented, segmentation. Patters of relief are maintained much better, causing large and obvious errors. The only way to circumvent this is by explicitly accounting for the relief by including a DEM in the processing. There may also be more subtle causes for the observed differences. The first is related to registration. It was noted that the test area, being small, was geometrically registrated well. For the large area this seems much harder however, resulting in mismatches in other parts of the larger area. The combination of the strong relief in this area and orbital shifts of the satellite may cause this. The mismatch, of course, affects the finer structured segmentation much more than for the initial coarse segmentation. Secondly, as noted before, because of the coarse segmentation used earlier, accuracies may initially have been biased somewhat to too optimistic estimations. 19

22 Table 2.7a. Pre-fire year 1995 classification results using the new segmentation approach. Pre-fire1995 Segmentation Water very good Mangrove very good Confusion matrix (pixels) Nipah very good Total Forest (1) 86.8% Plantation (2) 69.3% Kebun (3) 63.8% The same results and same conclusions may apply to the post-fire year 1998 classification. Again the agricultural (kebun) area seems to show an improvement while the other classes show a decrease in results. The latter may be strengthened by the fact that most of the forests are in hilly terrain and the agricultural areas are more often located in flat terrain. This undesirable effect is well illustrated by the forests in Sungai Wain which very clearly show the hilly ridges present in this area and which were far less pronounced in the previous and coarser segmentation. The need to include a DEM again is very apparent. Table 2.7b. Post-fire year 1998 classification results using the new segmentation approach. Post-fire1998 Segmentation Water very good Mangrove very good Confusion matrix (pixels) Nipah very good Total Forest (1) 82.5% Forest burned (2) 53.6% Plantation burned (3) 75.8% Kebun burned (4) 46.2% The post-fire year 2000 results are not based on segmented images. When a pixel-based approach is adopted the results, not surprisingly, are much worse as compared to the ones of for example tables 2.3 and 2.7a. Good results however, are easily obtained when applying ICM. This is illustrated in tables 2.8b, c and d. It may be concluded that this simple and computationally fast technique yields results which are comparable to results obtained after application of the multi-temporal segmentation. That results for forest and plantations are a little worse than in the initial results may be an artefact as discussed above. Explicit application of a DEM may circumvent such problems and eventually result in approximately optimal results. Apparently segmentation is not a crucial step and may be replaced by ICM WHFKQLTXHV)URPWKHUHVXOWVSUHVHQWHGKHUHDSUHIHUHQFHOHYHOIRUWKHIDFWRU WKLVLV 1 of eq. 2.2) is not obvious. Apparently this level is not very sensitive and simple and robust methods may emerge. Another big advantage of ICM, besides being computationally simple, is that it can easily be expanded utilising other factors, as described in eq. 2.2! Table 2.8a. Post-fire year 2000 classification results using a pixel-based approach. Post-fire2000 Pixel-based Water very good Mangrove very good Confusion matrix (pixels) Nipah very good Total Forest (1) 63.2% Plantation (2) 46.3% Kebun (3) 42.7%

23 Table 2.8b.3RVWILUH\HDUFODVVLILFDWLRQUHVXOWVXVLQJWKH,&0 DSSURDFK Post-fire2000 ICM Beta 1.0 Water very good Mangrove very good Confusion matrix (pixels) Nipah very good Total Forest (1) 83.8% Plantation (2) 64.0% Kebun (3) 53.7% Table 2.8c.3RVWILUH\HDUFODVVLILFDWLRQUHVXOWVXVLQJWKH,&0 DSSURDFK Post-fire2000 ICM Beta 2.0 Water very good Mangrove very good Confusion matrix (pixels) Nipah very good Total Forest (1) 85.3% Plantation (2) 63.8% Kebun (3) 53.2% Table 2.8d.3RVWILUH\HDUFODVVLILFDWLRQUHVXOWVXVLQJWKH,&0 DSSURDFK Post-fire2000 ICM Beta 5.0 Water very good Mangrove very good Confusion matrix (pixels) Nipah very good Total Forest (1) 86.5% Plantation (2) 62.6% Kebun (3) 54.3% As a final illustration of the application of the results a confusion matrix is shown for the forest cover change in the period in table 2.9. According this table 30.6% of the forest is converted into non-forest. Most of the forest remains forest and most of the nonforest remains non-forest. A slight proportion of the non-forests is now classified as forest. These areas mainly coincide with the transmigration areas were, apparently, new plantations reach high levels of crown coverage. There are also non-consistent results, such as areas that are forest (in 1999) that are classified as non-forest remaining non-forest. Many of these inconsistent results relate to topographic effects and the misclassifications very commonly encountered on the slopes facing away from the radar. The hill ridges of Sungai Wain provide a clear example. Another example is the steep slopes of the Meratus mountain chain. Table 2.9. Forest cover change between years 1995 and 2000 derived from the field data sets DQGXVLQJWKHSRVWILUH\HDU,&0 DSSURDFK Forest cover change Water none Mangrove none Nipah none Change non-forest Å non-forest non-forest Å forest forest Å non-forest forest Å forest Total Forest Plantation Kebun

24 Figure 2.7. Multi-temporal ERS-2 composite; red: , green , blue Figure 2.8. Post-fire year 1998 classification results 22

25 Figure 2.9. Forest cover change map between years 1995 and Approach for Palangkaraya SarVision contributes to programmes of the Indonesian Government, the Balikpapan Orangutan Society (BOS) and the Gibbon Foundation for continued improved safeguarding of remaining wild orangutan populations and their habitat. Key areas are located in Central- Kalimantan, at Sebanggau and east of Palangkaraya. The latter area is a newly established 700,000 ha forest reserve and is part of a dept-for-nature-swap program. An integrated approach has been developed to protect wild orangutans and their habitat: 1. Orangutan habitat monitoring with space based and airborne systems, combined with ground surveys and habitat quality assessment by botanical specialists; 2. Swift and direct law enforcement through the involvement of a flying team authorised to do law enforcement related to protected area destruction and poaching of orangutans or other protected animals and through attacking any illegal trading of orangutans and other protected animals from orangutan habitats; 3. A training, education and extension part, which includes improving the performance of local law enforcement through training of local forest police, conducting education campaigns amongst people living close to forests with wild orangutans and conducting a wide extension campaign directed to schools and the general public, while involving and stimulating local environmental NGO s. SarVision focuses on the first of these points mainly by fast delivery of information on the location of suspected illegal logging and encroachment sites. Information will be provided to local managers on the state of forest and land cover, reforestation, forest regeneration and degradation and fire susceptibility and damage. Information on Internet WebPages on the status of the protected area will be constantly updated to inform donors and the general public. 23

26 Because it is a perfectly flat area, within the new area to be monitored near Palangkaraya some of the problems encountered in the Balikpapan are not present. Moreover, the focus here is on detection of illegal logging. Land cover changes are not very likely. Some variation in time however is to be expected in relation with reforestation programs and drainage characteristics. The latter is partly natural and may support classification in a range of natural forest types. It is also partly because of human interference. Parts of this area are (or were) artificially drained by canals and may suffer drought, followed by loss of forest trees, especially on the so-called peat domes. The approach therefore is both to fast detection of illegal logging as well as to land cover change, the latter being supportive to the first. A phased approach has been adopted. Archives will be searched for useful available recent historic satellite remote sensing data, preferably from ESA s ERS SAR (radar) and LANDSAT-7 ETM (optical) sensors. These images will be processed to produce an initial map, which will be linked to existing topographical and thematic maps to be collected in Indonesia, in order to make a planning for a field survey. During this survey selected areas will be visited to be able to develop suitable mapping legends. Navigable rivers and roads will be measured by GPS, preferably DGPS. The radar images will be processed in two ways: (a) optimised for spatial detail, in order to detect small road and river systems and (b) optimised to show temporal change, in order to differentiate land cover classes, fire susceptibility classes and anthropogenic activities. It is noted that radar is not hindered by clouds and, therefore, map updates can be made frequently. Illegal logging activities can be detected timely, enabling effective inspection and law enforcement operations by the local field teams. Phase II, Monitoring Map updates will be generated frequently (4-8 times/ year), fast (within two months) and will be shown on the internet. This service will enable the authorities and general public to follow the condition of the protected areas. Relevant changes will be highlighted, such as expansion of road networks, (illegal) clearing areas, suspected areas, fire damage and vegetation development. Publication on the web may contribute to the prevention of illegal activities and may encourage conservation activities in these areas in general. 24

27 3. ENVISAT ASAR monitoring 3.1 Introduction Chapter 3 describes the ASAR radar image modes, which will become available in 2002 and the approaches adopted to simulate such image products from C-band polarimetric AirSAR data. The simulated products are evaluated, using a newly acquired extensive field data set (June-July 2001), in terms of polarisation combinations, speckle level and legend structure. It will be shown that the ASAR Alternating Polarisation mode, notably the HH-HV combination, performs significantly better than ERS SAR. In section 3.2 the five ASAR instrument modes of operation will be described. Section 3.3 describes ASAR image product simulation from an AirSAR image of the Sungai Wain strip acquired at 16 September 2000 during the PacRim-2 NASA campaign. In section 3.4 the collection of field data from 122 field plots and its organisation into a structured legend of 18 classes at four levels of hierarchy is described. Sections 3.5 and 3.6 describe the results of classification simulation and the effect of choice of legends in terms of similarities in land cover and/or radar characteristics. 3.2 The ASAR instrument Overview operating modes ASAR ASAR has five mutually exclusive modes of operation, which can be classified in two categories. I. Global mission 1. Global monitoring (GM) 2. Wave mode (WM) These two modes have a low data rate and hence an operation capability up to 100% of the orbit. II. Regional mission Narrow swath modes 3. Image mode (IM) 4. Alternating polarisation mode (AP) Wide swath mode 5. Wide swath (WS) These three modes have a high data rate and operation time is limited either through data relay satellite by the instrument up to 30 minutes per orbit (including 10 minutes in eclipse), or by ground station visibility, depending on the transmission scheme selected. These five modes can be described briefly as follows. Global Monitoring Mode (GM) 25

28 The Global Monitoring Mode provides low resolution images (1 km) using ScanSAR technique over a 405 km swath at HH or VV polarisation. The mode has a low data rate due to a slightly reduced along-track duty ratio and the use of digital filtering for reduction in the across-track direction. The same sub-swaths as defined for the Wide Swath Mode are used. Wave Mode (WM) In Wave Mode, the ASAR instrument, measures changes in backscatter from the sea surface due to ocean wave action. Therefore it will generate vignettes, minimum size of 5x5 km 2, similar to ERS AMI wave mode, spaced 100 km along-track in HH or VV polarisation. The position of the wave vignette across track being selected as either constant or alternating between two across-track positions over the full swath range. Image Mode (IM) In Image Mode the ASAR will generate high spatial resolution products (30 m) similar to the ERS SAR. It will image one of the seven swaths located over a range of incidence angles spanning from 15 to 45 degrees in HH or VV polarisation. In table 3.1 below the range of the values is due to the different orbit positions. The values are given for Level 1b products. Table 3.1. ASAR image swath geometry. ASAR Swathes Swath Width [km] Near Range Incidence Angle Far Range Incidence Angle IS IS IS IS IS IS IS Alternating Polarisation Mode (AP) Alternating Polarisation Mode provides high-resolution products in any swath as in Image Mode but with polarisation changing from sub-aperture to sub-aperture within the synthetic aperture. Effectively a ScanSAR technique is used but without varying the sub-swath. The results are in two images of the same scene in different polarisations combination (HH/VV or HH/HV or VV/VH) with approximately 30m resolution (except IS1). Radiometric resolution is reduced compared to Image Mode. Wide Swath Mode (WS) In the Wide Swath Mode the ScanSAR technique is used providing images of a wider strip (405 km) with medium resolution (150 m) in HH or VV polarisation. The total swath consists of five sub-swaths and the ASAR transmits bursts of pulses to each of the sub-swaths in turn in such a way that a continuous along-track image is build up for each sub-swath Product Quality In terms of radiometric and geometric resolution the ASAR high-resolution products are described in table 3.2, and the ASAR browse, medium resolution and global monitoring products in table

29 Table 3.2. ASAR high resolution product quality ASAR HIGH RESOLUTION PRODUCTS Precision Image Image IM Pixel = 12.5 m Res. < 30 m ENL > 3 Single Look Complex Pixel = natural spacing Alternating Polarisation AP Pixel = 12.5 m Res. < 30 m ENL > 1.8 Pixel = natural spacing Wave WV Ellipsoid Geocoded Single Look Complex Wave Imagette Resolution: ~ 6 m (Azimuth), transmitted chirp dependant (Slant Range) Pixel = 12.5 m Res. < 30 m ENL > 3 Resolution: ~ 12 m (Azimuth), transmitted chirp dependant (Slant Range) Pixel = 12.5 m Res. < 30 m ENL > 1.8 Pixel = natural spacing Resolution: ~ 6 m (Azimuth), transmitted chirp dependant (Slant Range) Table 3.3. ASAR browse, medium resolution and global monitoring product quality. ASAR BROWSE AND MEDIUM RESOLUTION PRODUCTS Medium Resolution Image Global Image Monitoring Browse Image ** Image (narrow swath) IM Res. < 150 m Pixel = 75 m ENL ~ 40 Pixel = 225 m ENL ~ 80 Alternating Polarisation AP Res. < 150 m Pixel = 75 m ENL ~ 50 Pixel = 225 m ENL ~ 75 Wide WS Swath Res. < 150 m Pixel = 75 m ENL ~ 11.5 * Pixel = 900 m ENL ~ 30 to 48 (TBC) Global Monitoring GM Notes to table 3.3 Pixel = pixel spacing Res. = spatial resolution ENL = equivalent number of looks (gives the radiometric resolution) * instrument setting dependent ** browse are derived from medium resolution images Res < 1000 m Pixel = 500 m ENL ~ 7-9 * Pixel = 1000 m ENL ~

30 3.3 ASAR image simulation from AirSAR For the purpose of high-resolution monitoring IM and AP ASAR images are of major interest. In principle such products can be simulated from C-band polarimetric AirSAR images collected during the PacRim-2 campaign in September 2000 over the same test area. As a first step the nominal image specifications of ASAR high-resolution products given in table 3.4 may be compared with those of AirSAR given in table 3.5. The simulation procedure is a sequence of steps, which roughly include the following elements. Creating a larger pixel size (by resampling, block averaging, etc) and a lower spatial resolution (by low-pass filtering) is not very difficult. To get a low number of ENL (3 and 1.8, for IM and AP, respectively) is less straightforward. This step is elaborated in more detail hereafter. Another difficult problem is the correlation in slant and ground range of ASAR pixels. It is not clear from the documentation what these values are, or whether they still have to be determined precisely in the commissioning phase. For the moment we may assume that the figures do not differ significantly from ERS SAR. Another problem is the incidence angle. It is not possible to simulate other incidence angles than those measured by AirSAR. Consequently the range will be roughly between degrees, within a 10 km strip. This coincides with ASAR swaths IS3 until IS7. Table 3.4. ASAR high-resolution product quality. ASAR HIGH RESOLUTION PRODUCTS Precision Image Image IM Pixel = 12.5 m Res. < 30 m ENL > 3 Alternating Polarisation AP Pixel = 12.5 m Res. < 30 m ENL > 1.8 Table 3.5. Some relevant AirSAR image specifications for the PacRim-2 campaign. mode PolSAR XTI2 full polarimetry C, L, P-band P-band interferometry C, L band height resolution 1-3 for low-relief terrain (m) height resolution 3-5 for high-relief terrain (m) approx. DEM resolution (m) 10 x 10 bandwidth 40 MHz approx. SR pixel size (m) 4 x 3 approx. SR resolution (m) 5 x 5 m independent looks per pixel 8 (?) NE Sigma0-45 db (C, L, P) absolute calibration < 3dB relative calibration between < 1.5 db channels relative polarisation calibration < 0.5 db within channel 28

31 Proposed processing scheme The following sequence of steps has been taken. 1. Make C-band HH, VV and HV ground range images, resampled to 2.5x2.5 m pixels. 2. Apply 27.5 m x 27.5 m (11x11) low-pass filter (moving average). The resulting resolution 2 2 would be approx m (= ). 3. Box average to 12.5 x 12.5 m pixels (5x5). pixel _ area _ ASAR 4. The ENL is around * ENL _ AirSAR * Correlation _ AirSAR *sin( θ inc ) : pixel _ area _ AirSAR therefore the ENL *8*0.7*sin( θ ) inc = 56.74* sin( θ inc ) or roughly 24 at 25 degrees and 49 at 60 degrees of incidence angle. The pixel intensities thus can be regarded as an estimation of the underlying intensity value with a limited amount of speckle. In a next subsection the mathematical expressions are given to increase the speckle in such a way that ENL s of 3 and 1.8, resp. are obtained, independent of the incidence angle 5. The last step is to add pixel correlation. Here it is assumed that the values of ERS-1 as mentioned in Oliver and Quegan, (chapter 4.7), 1998, well approximate values for ASAR image correlation. Note that this procedure, with some minor modifications, may be applicable to simulate radar images of the PALSAR instrument for the same strip also. PALSAR belongs to the payload of the NASDA ALOS satellite scheduled for launch in Adding speckle (step 4) Suppose there is a uniform Rayleigh fading area. Then the variance is the equivalent number of looks (or ENL). Thus, for AirSAR 2 var = I / N, with N as and for ASAR 2 varairsar = I /(57 sinθ inc ) 2 2 var ASAR = I / 3 (IM mode) or var ASAR = I /1. 8 (AP mode). To correct the variance of the simulated image extra speckle (extra variance) will be added according (taking the IM image as an example): 2 var ASAR = I / 3 = constant = 2 I 57 sinθ inc 2 I + x. Since 2 I 3 2 I = 57 sinθ inc 2 I +, x 29

32 it follows that 1 = sinθ inc 1 +, x and x, thus, equals: 3 57 sinθ inc x = 57 sinθ inc 3 (for IM mode), and sinθ inc x = (for AP mode), 57 sinθ inc 1.8 Adding speckle thus simply is multiplication of the averaged AirSAR signal resulting from step 3 (see above) by a random draw fac (factor) from the x-look gamma distribution with mean I = 1 Using PI ( I I ) i N 1 N N 1 NI / = I e Ii ( N ) I, i with I i = I, i.e. the underlying mean of the homogeneous area, cf. Hoekman and Quiñones, 2000, it follows that 1 x x 1 x fac P( fac) = x fac e x ( ) The value of x is roughly in the range 3.19<x<3.43 (for IM mode) and 1.87<x<1.95 (for AM mode), depending on AirSAR image incidence angle. Results Simulated AP mode data are shown in Figure 3.1. Note there is a dark azimuthal band in the VV image, an artefact resulting from the original AirSAR C-band polarimetric image. The image size is 10 km (in range) x 60 km (in azimuth). Of course the resulting image can be inspected for its desired properties in terms of ENL and correlation, as a check on the appropriateness of the simulation procedure described above. For such a check it would have been ideal to use a large flat non-textured uniform area. However, unlike the Flevoland test site in The Netherlands, for example, such areas are hard to find at this Indonesian test site. Some extended areas of grass (alang alang) were selected as the optimum choice for such a test. The results are compared with the corresponding area in an ERS-2 image and are shown in Figure 3.2. The autocorrelation functions in range and azimuth are nearly similar. Also the number of ENL (2.30 versus 2.55) is nearly similar. The reason that these numbers are not identical and lower then the nominal value of 3.0 may be a result of the different time of acquisition and the presence of some structure and/or texture, respectively. 30

33 Figure 3.1. Simulated ASAR AP mode images in HH, HV and VV polarisation (1.8 looks) with the NRCS at linear scale and a colour composite of the same images with the NRCS in db. Note there is a dark azimuthal band in the VV image, an artefact resulting from the original AirSAR C-band polarimetric image. The image size is 10 km (in range) x 60 km (in azimuth). The near range is at the left. 31

34 ERS-2 sub area: ENL = 2.30 ASAR IM sub area: ENL = 2.55 Figure 3.2. Comparison of ENL and autocorrelation functions in range and azimuth between an ERS-2 SAR image and the Envisat ASAR IM mode simulated image. The test is done over a large homogeneous flat area, supposedly uniform in backscatter and Rayleigh fading. The autocorrelation functions in range (solid line) and azimuth (dotted line) are nearly similar. Also the number of ENL (2.30 versus 2.55) is nearly similar. The AirSAR image was collected at 16 September 2000 and the ERS-2 image at 7 January

35 3.4 Description of land cover classes and database During the fieldwork 122 areas (Regions of Interest or ROI s) were visited and 18 classes were distinguished. Those classes were grouped according to their natural composition, structure and / or function (real or potential use) in four levels of division. Table 3.6 shows the land cover classification system that is used in the present study. Table 3.6. Classification of land cover types considered in this study. Bold characters indicate classes determined in the fieldwork. Level I Level II Level III Level IV Number of ROI s Man-made structure (C 1) 9 Small and mixed 9 agricultural areas (C 2) Alang alang (C 3) 4 Non-forest Potential and real production areas Forest Potential and real agricultural areas Coconut plantation 4 (C 4) Shrubs (C 5) 12 Shrubs-trunks (C 6) 5 Shrubs-leguminocae (C 7) 4 Swamp (C 8) 4 Industrial production Rubber plantation 7 areas (C 9) Mangrove (C 10) 7 Primary forest (C 11) 5 Primary forest-trunks (C 12) 5 Secondary forest (old) (C 13) 11 Secondary forest-trunks (C 14) 5 Secondary forest Secondary forest-young (C 15) 8 Secondary forest-young-trunks (C 16) 9 Secondary forest-wet (C 17) 7 Water (C 18) 7 Land cover classes Land cover types defined in this study are divided in three main groups: Non-forest, Forest and Water. In the same way Non-forest is subdivided in Man-made structures (C 1) and Potential and real production areas. The first one includes industrial facilities, harbours in the Bay of Balikpapan and settlements; and the second one includes Potential and real agricultural areas, and Industrial production areas. Potential and real agricultural areas are characterised by a high change dynamic (even through the year) related with human activities. In this one it is possible to group: Small agriculture areas (C 2) conformed by two to four hectares parcels (family farm lands) in which a mix of crops (rice, pepper, banana, etc.) is cultivated, sometimes associated with alang-alang grassland and / or shrubs. Alang alang (C 3) (Imperata cylindrica) is an aggressive grass that invades and propagates over areas of forest cutting, incorrect soil management and abandoned agricultural areas after two or three harvests of crops. Often, it is associated with shrubs. Coconut plantation (C 4). Mono-cultivation of coconuts in areas bigger than 2.5 hectares, approximately. 33

36 Shrubs (C 5) are characterised by small trees and low vegetation forms (< 2 m). Most of the time associated with alang-alang and / or agricultural areas. When affected by fires, it was named Shrubs-trunks (C 6) following the explanation that will be given for burnt forest. Shrubs-leguminocea (C 7) is a special type of shrubs dominated by a single species of Leguminocea that covers most of the area. This typically is formed after clearing and abandoning the nearby areas of rubber plantation. Swamp (C 8). Although this is a natural ecosystem, it has in the study area a high potential use, mainly for rice cultivation. Industrial production areas are represented only by Rubber plantation (C 9). The main rubber plantation is localised in the south part of the study area. The plantation program started in the early 90 s and nowadays covers a considerable extension of terrain. The area is subdivided in areas depending on the plantation year. Small sectors are mixed with plantation of Acacia (Dipterocarpaceae sp.). On the other hand, Forest is subdivided into Primary forest, Mangrove and Secondary forest. Primary forest (C 11) is the forest that has not been degraded, keeping its structural and functional pristine characteristics: high density, tall trees with large diameter, large number of species and several layers. Mangrove (C 10) is the typical forest in zones were fresh water and seawater mix. It occurs along the river corridors with seawater influence, and in the Balikpapan bay. The mangrove can spread roughly m wide into the main land depending of the water mix. In the study area Rhizopora sp. is the most dominant, building a homogeneous canopy of m in height. Secondary forest is the forest that does not have the pristine shape as a consequence of human impact or natural disturbances (i. e. fires). This type occurs in the study area in many ways: The first one consists in primary forests that have been intensively logged resulting in low densities, loss of canopy layers and even some species. However, they still conserve big trees and the canopy comprises two or three layers. Although the structure has been modified the main functions of the forest still remains. In this study this kind of forest is termed Secondary forest (old) (C 13). The second way is the re-growth or recovery of forest. This kind was forest, totally or partially logged, years ago. Since that time, regeneration processes have been taken place, so the abundance of pioneer and fast growing species and the absence of big trees (more than 30 m of height) are their main characteristics. It is termed here Secondary forestyoung (C 15). When one of these two kinds of forest was found in a really wet area, generally associated with water springs, it was classified as Secondary forest-wet (C 17). The fourth one is derived from the impact of the intense fire period in 1997/1998. Remnants of fire events are standing trunks in areas where forest was burnt. Where primary forest was burnt the class used was Primary forest-trunks (C 12) (it is not anymore a primary forest because it was severely disturbed by fire). In the same way, it was possible to detect: Secondary forest (old) trunks (C 14) and Secondary forest-young-trunks (C 16). Finally, Water (C 18) represents natural water bodies as rivers and the Balikpapan Bay, and artificial ones as ponds dedicated to grow shrimps. 34

37 Radar database For all 122 ROI s summarised in table 3.6 radar data characteristics were derived from the simulated ASAR AP and IM products. These are expressed as the number of pixels, mean incidence angle, the mean radar backscatter γ in db, and its standard deviation within the ROI. 0 / i The backscatter parameter γ ( γ = σ cos( θ ) ; 0 σ is the differential radar cross section) is preferred over 0 σ because of its lower incidence angle dependence. In earlier studies (Hoekman and Quiñones, 2000) the use of logarithmic values was shown to be preferable since it leads to near Gaussian distributions for the area averaged backscatter. Figure 3.3a. Location of 122 field work plots. 35

38 3.5 ASAR classification simulation Full legend For the study of classification possibilities the same method as presented in Hoekman and Quiñones (2000), a Maximum Likelihood solution, is applied. This method assumes Gaussian distributions for the area averaged backscatter (in db), and speckle following the gamma distribution. It is particularly useful for the analysis of relative classification possibilities as a function of polarisation and number of independent looks. In this case application of this method is appropriate since the differences between IM (HH or VV, 3-looks) and AP products (HH/HV or HH/VV or VV/VH, 1.8-looks) are described in terms of polarisation combinations and ENL mainly. The relation between speckle level and ENL is shown in table 3.7. Table 3.7. Speckle level as function of number of independent looks (cf. Hoekman, 1991). ENL Speckle level [db] It is stressed once more that the results to be presented should be interpreted with care as they represent relative classification accuracy. On the one hand the limited number of training sets may give a bias towards a result that may be too optimistic, on the other hand high figures of accuracy may only be obtained for time series of certain length. In this study however, there is only one time of observation (the AirSAR data take), which leads to the expectation that actual results for an operational monitoring system may be much higher than the ones that can be presented here. Table 3.8 illustrates the potential of different combinations for polarisation and number of looks (ENL) for the classification of the land cover types studied. The values given in the second column represent the number of class pairs for which the percentage of wrong classification in the absence of other classes is lower than or equal to 25%. This number is arbitrary. In case two classes can be discriminated well this percentage would be near zero. In case two classes can not be discriminated at all the confusion is maximum and, just by chance, a near 50% result is to be expected. The total number of class pairs is 153. The closer to 153 the better is the polarisation-enl combination. Additionally, the average misclassification in percentages is shown in the third column. This value represents another measure of classification success. The highest classification potential in terms of numbers of class-pairs successfully discriminated, as can be expected, originated from the speckle free data set (ENL = 0) and the HH-HV-VV polarisation combination (151 of 153 class-pairs). Next bests are the 3 sets of HH-HV, HH-VV, and HV-VV in the absence of speckle (147, 143, and 143 of 153 class-pairs respectively). In general, the top ten positions in table 3.8 include only data sets with no speckle or 0.4 db of speckle (100 looks), no matter the polarisation combination utilised. These results suggest a strong influence of speckle level on the classification results. On the other hand, the bottom of the table, representing the less suitable combinations, is characterised by low numbers of looks (from 2 to 20), involving mainly one or two polarisation combinations. 36

39 The number of class-pairs correctly discriminated decreases considerable from 151, for the HH-HV-VV with no speckle, to 28 for the HH 3.5 db (2 looks) combination. Therefore, the speckle-polarisation combination has a huge influence on the discrimination of class-pairs and in turn on the classification results. This result will be elaborated in tables 3.9, 3.10 and 3.11, to be discussed later. Table 3.9 shows the classification accuracy for each polarisation-enl combination studied. From kappa-statistics, i.e. kappa and the variance of kappa, delta kappa is calculated cf. the method of Hoekman and Quiñones (2000). When delta kappa exceeds the value of 1.96 results are different at the 95% level of confidence. At the 95% level of confidence, the results can be divided into four groups. The first group (bold characters) comprises only one data set combination, i.e. HH-HV-VV with no speckle. With a reasonable 68 % of accuracy, the classification from that data set is significantly the best. This combination also had the most pair-classes successfully discriminated. The second group (shaded boxes) includes the second best result, i.e. HH-VV with no speckle and the results that are not significantly different from the second best result. Those results correspond to HH-HV and HH-VV with no speckle and HH-HV-VV with 0.4 db of speckle (100 looks). Hence, those polarisation-looks combinations may be regarded as equally suitable second best information sources for classifying the land cover types studied. The third group (underlined characters) consists of the worst result plus the results that are not significantly different from the worst result. Those results correspond to combinations with high speckle influence (low number of looks 2 to 20) with one and two polarisations. The last group (normal characters) represents an intermediate level of accuracy, varying from 38 % for HH-HV (100 looks) to 25 % for VV (100 looks). Generally speaking, as in table 3.8, the results of table 3.9 show a tendency: The lower the level of speckle, the higher the classification accuracy and the higher the number of classpairs correctly classified. Although less evident, the higher the number of polarisation combinations (i.e. HH=1, HH-HV=2, HH-HV-VV=3) the higher the classification accuracy. The relations mentioned above are shown more clearly in tables 3.10 and 3.11, and are commented below. In tables 3.10 and 3.11 bold characters indicate the best result plus the results that are not significantly different from the best result at the 95% level of confidence, and the underlined numbers indicate the worst result plus the results that are not significantly different from the worst result at the 95% level of confidence. Taking into consideration only the number of looks in table 3.10, it is possible to distinguish three levels of significant differences in classification results, as follows: No speckle proves to be significantly better than the other levels of speckle (high level). Classification results for 100 looks and 20 looks are not significantly different in any case (intermediate level). Finally, two and / or three looks present the worst results (low level). Hence, the more speckle added the lower the classification accuracy. That pattern is present over all studied combinations, however the significance of the differences varies according to the information contained on the polarisation combinations. For HH-HV-VV, HV-VV, and HH-HV polarisation combinations the three levels of significant differences are clearly defined; HH-VV, HV and HH also show those three levels but the low level (two and / or three looks) does not present a significant difference with the 37

40 classification results from 20 looks (lowest accuracy on the intermediate level); finally, VV polarisation involves only two significant levels. Since HH-VV was categorised on a lower level than HH-HV and HV-VV, and VV on a lower level than HH and HV, the potential of the polarisation information to classify land cover types on the studied area is related not only to the number of polarisations but also on the specific polarisations used. In table 3.11 the influence of the number of looks is evident. No matter the polarisation combination, the data sets with two and three looks do not have significant differences. When using a low number of looks (two or three) the number of polarisations and the specific polarisations used do not have any significant relevance on the classification results. Using 20 looks, only the classification results from VV polarisation is significantly worse than the others, except HH, which is not significantly different. Using 100 looks, the polarisation information gains relevance on the classification results. Here the classification results from HH, HV and VV are significantly worse than the others, except HH-VV. Finally, in absence of speckle, it is possible to define three significant levels of differences in accordance with the polarisation information. According to these results, it is possible to conclude that reducing speckle is an effective process to emphasise the differences in the polarisation information contained in the polarisation combinations studied. Hence, the higher the number of looks the higher the influence of polarisation information in the classification results. 38

41 Table 3.8. Potential of polarisation and speckle combination to discriminate between classpairs. Polarisation and speckle level combination Numbers of class pairs successfully discriminated (from 153 pairs) Average misclassification (%) HH-HV-VV, 0 db HH-HV, 0 db HH-VV, 0 db HV-VV, 0 db HH-HV-VV, 0 db HH-HV, 0.4 db HH-VV, 0.4 db HV, 0 db HH, 0 db HV-VV, 0.4 db VV, 0 db HH-HV-VV, 1.0 db HV, 0.4 db VV, 0.4 db HH, 0.4 db HH-HV, 1.0 db HH-VV, 1.0 db HV-VV, 1.0 db VV, 1.0 db HV, 1.0 db HH, 1.0 db HH-HV-VV, 3.5 db HH-HV, 3.5 db HH-VV, 3.5 db HV, 3.0 db HV-VV, 3.5 db HH, 3.0 db VV, 3.0 db VV, 3.5 db HV, 3.5dB HH, 3.5 db

42 Table 3.9. Classification results for the polarisation and speckle level combinations studied. Delta Kappa expresses the differences between classification results. Bold characters indicate the best result. Shaded boxes indicate the second best result and the results that are not significantly different from the second best result. Underlined characters indicate the worst result and the results that are not significantly different from the worst result. The level of confidence used is 95%. Polarisation and speckle level combination Classification accuracy (%) Kappa Variance of Kappa Delta Kappa Best result vs. the others Second best result vs. the others Worst result vs. the others HH-HV-VV, 0 db HV-VV, 0 db HH-HV, 0 db HH-VV, 0 db HH-HV-VV, 0.4 db HH-HV, 0.4 db HV-VV, 0.4 db HV, 0 db HH-VV, 0.4 db HH, 0 db HH-HV-VV, 1.0 db VV, 0 db HH-HV, 1.0 db HV-VV, 1.0 db HV, 0.4 db HH, 0.4 db HH-VV, 1.0 db VV, 0.4 db HV, 1.0 db HH, 1.0 db VV, 1.0 db HH-HV-VV, 3.5 db HH-HV, 3.5 db HV-VV, 3.5 db HH-VV, 3.5 db VV, 3.0 db HV, 3.0 db HH, 3.0 db HH, 3.5 db HV, 3.5 db VV, 3.5 db

43 Table Classification results for each polarisation combination with different levels of speckle. Bold characters indicate the best result and the results that are not significantly different from the best result in each polarisation combination. Underlined characters indicate the worst result and the results that are not significantly different from the worst result in each polarisation combination. The level of confidence used is 95%. Polarisation combination Level of speckle (db) Classification accuracy (%) Kappa Variance of Kappa Delta Kappa Best result vs. the others Worst result vs. the others HH-HV-VV HV-VV HH-HV HH-VV HV HH VV

44 Table Classification results for each speckle level with different polarisation combinations. Bold characters indicate the best result and the results that are not significantly different from the best result in each level of speckle. Underlined characters indicate the worst result and the results that are not significantly different from the worst result in each level of speckle. The level of confidence used is 95%. Level of speckle (db) Polarisation combination Classification accuracy (%) Kappa Variance of Kappa Delta Kappa Best result Worst result vs. vs. the others the others 0 HH-HV-VV HV-VV HH-HV HH-VV HV HH VV HH-HV-VV HH-HV HV-VV HH-VV HV HH VV HH-HV-VV HH-HV HV-VV HH-VV HV HH VV VV HV HH HH-HV-VV HH-HV HV-VV HH-VV HV HH VV

45 3.6 ASAR classification simulation Aggregated legends In this section the effect of class aggregation on classification accuracy is studied. The aggregation is done, arbitrarily, for the HH-HV-VV case at the 0.4 db level of speckle. HH- HV-VV proves to be the best polarisation combination in terms of classification accuracy and number of class-pairs right discriminated. Secondly, the use of the 0.4 db level of speckle (100 looks) simulates realistic results that may be obtained from a segmentation process on a real image of ENVISAT or ERS. Although the 43% of accuracy achieved for 18 classes by HH-HV-VV at the 0.4 db level of speckle is not the best one, it may represents a promising data set to improve the classification accuracy since it contains the maximal polarisation information and a realistic level of speckle. In the following five aggregation levels, with eleven, eight, seven, six and five classes, and eight types of aggregation (namely, AG 0, AG 1, AG 2, AG 3, AG 4, AG 5, AG 6, AG 7) will be taken into consideration Class-pair analysis First aggregation level The results of class-pair misclassification for the data set of the HH-HV-VV 0.4 db speckle combination identify 14 class-pairs with a percentage of misclassification in absence of other classes in excess of 25% (Table 3.12). Table Class-pairs with more than 25% of wrong classification in absence of other classes for the HH-HV-VV 0.4 db combination. Bold characters indicate class-pairs potentially suitable to be merged according to the classification system. Class numbers Class-pairs % of wrong classification C2 C5 Agriculture Shrubs 29.4 C2 C13 Agriculture Secondary forest 33.3 C5 C13 Shrubs Secondary forest 27.7 C6 C9 Shrubs trunks Rubber plantation 25.3 C7 - C14 Shrubs leguminocea Secondary forest trunks 27.1 C9 C10 Rubber plantation Mangrove 25.2 C9 C11 Rubber plantation Primary forest 29.7 C9 C13 Rubber plantation Secondary forest 26.1 C10 C11 Mangrove Primary forest 26.2 C11 C13 Primary forest Secondary forest 29.7 C12 C14 Primary forest trunks Secondary forest trunks 37.1 C13 C14 Secondary forest - Secondary forest trunks 25.1 C14 C16 Secondary forest trunks Secondary forest young trunks 33.1 C15 C16 Secondary forest young Secondary forest young trunks 31.8 The first aggregation level was restricted to the classes belonging to the lowest subdivisions defined in the classification system (see section 3.4) because they have more natural characteristics in common. As a result only five class-pairs are potentially suitable to be merged (bold characters in Table 3.6). The C2 C5 class-pair share the lowest subdivision of none forest classes, while the C12 C14, C13 C14, C14 C16 and C15 C16 class-pairs belong to the lowest level of forest classes. 43

46 3.6.2 Selection of classes to be grouped First aggregation level (AG 1) Once the potential class-pairs to be merged are detected it is necessary to analyse their similarities with other classes belonging to the same subdivision level. Those similarities may be characterised in two ways: natural similarities, considered in the land cover classification system utilised (table 3.6), and radar system perception similarities, expressed as percentages of misclassification. Based on those similarities, the final selection of the classes to be grouped has been made. As a result two sets of classes were aggregated: Agriculture (C2), Shrubs (C5), Shrubs-trunks (C6), and Shrubs-leguminocea (C7) were grouped under the name Agricultural Mix. And Primary forest-trunks (C12), Secondary forest (old) (C13), Secondary forest-trunks (C14), Secondary forest-young (C15) and Secondary forest-young-trunks (C16) were grouped as Secondary forest (except Secondary forest-wet). The Agricultural Mix aggregation was based on the following reasons. Firstly, class-pair misclassification among Shrubs, Shrubs-trunks, and Shrubs-leguminocea is higher than 20 % in all the cases. Secondly, those three classes represent variation of the same vegetation type (shrubs), hence the natural similarities are very high. Thirdly, agricultural areas are sometimes associated with shrubs (see section 3.4), meaning that a small section(s) of an agricultural area sometimes includes some shrubs. And finally, class-pair misclassification of agricultural classes with the different classes of shrubs ranges from moderate (13 %) to high (29 %). In the same way, Secondary forest (except Secondary forest wet) aggregation was based on the following two main reasons. First, these classes represent variation of the same vegetation type (secondary forest) and, hence, the natural similarities are very high. Functional similarities are especially relevant in that case. Regulation of the water and nutrient cycles, habitat to fauna species, biodiversity reservoirs are some of the functions that this range of secondary forest types have in common. Secondly, class-pair misclassification among them range from 20 % to 37 %. An exception is the class-pair C15 C12 and C15 C13, with a misclassification of 9 % and 11 %, respectively. This relatively low misclassification is related to the differences in species composition and forest structure as result of their different origin (see section 3.4). At this point in the analysis, it is important to mention the case of the Secondary forest-wet class. Although Secondary forest-wet shares some functional and structural characteristics with the other secondary forest types, it presents a very low misclassification with them (> 0.4 %). The most relevant characteristic that distinguishes this type of forest from the others is its wetness condition. Since moisture content is one of the most important object conditions influencing the reflection of the radar waves, wetness is without doubt the cause of the low misclassification among Secondary forest-wet and the other forest types. With a total of eleven classes the classification results after this aggregation show an increment in accuracy from 43.2% to 48.2% and a reduction of the average of class-pair misclassification from 10.9 % to 7.8 %. Although those figures show a classification improvement, they are not significantly different. Hence, additional aggregations were done in order to obtain significant improvement in classification results. Those aggregations are discussed in the next section. 44

47 3.6.3 Other aggregation levels After the first aggregation level (AG 1), four additional class aggregations were done. The process of selecting classes to be merged is as described in sections and for AG 1. The results of the classification for each aggregated database are summarised in table Table Classification results for HH-HV-VV polarisation and 0.4 db speckle level with different aggregation levels. Bold characters indicate the best result and the results that are not significantly different from the best result. Underlined characters indicate the worst result and the results that are not significantly different from the worst result. The level of confidence used is 95%. Aggregation code Kappa Variance of Kappa Classification accuracy (%) Number of classes average of misclassification (%) AG AG AG AG AG AG AG AG Second aggregation level Based on the classification results after AG 1 and taking into account the same parameters of class selection, a second aggregation (AG 2) was performed. In this case the aggregation was restricted to the lowest subdivision of non-forest classes (see table 3.6). The new Agricultural Mix, generated in AG 1, was merged with the other classes belonging to the subdivision Potential and real agricultural areas (Alang alang, Coconut plantation and Swamps). As a result, a database with eight classes was used in the Maximum Likelihood classification. The AG 2 database achieved a 56.3% of classification accuracy, 8.1 points more than AG 1 and 13.1 points more than the classification accuracy obtained from the original data base (AG 0). Because the omission and commission errors were higher than in AG 1, the average of misclassification was also higher. Once again, the improvement in classification accuracy is not statistically significant. Third aggregation level Once, the lowest subdivision levels were merged, the process of aggregation involves higher subdivisions. In this case, based on AG 2 classification results, Primary forest was merged with Secondary forest (except Secondary forest-wet) in a so-called Mixed forest class to form the AG 3 data set. Also at this level, Secondary forest-wet shows very different backscatter behaviour from the other forest types. 45

48 Classification accuracy for the AG 3 data set was 58.6% and the average of misclassification 7.2%. Although the accuracy is 15.4 points higher than the classification accuracy of the original data set (AG 0), the differences are not significant. This situation is caused by the statistic employed to detect differences. Kappa statistics is not only based on the total percentage of correct classification (classification accuracy) but also on the errors of commission and omission. Fourth aggregation level At this level of aggregation three different databases of six classes each are created based on AG 3 classification results. In each database, the aggregation of just one pair of classes was done in order to test the influence of individual class pair aggregations on the classification results. The AG 4 data set is the product of aggregating Mangrove and Mixed forest, obtained by the previous aggregation. The AG 5 database results from aggregating Potential and real agricultural areas and Industrial production areas (only represented by Rubber plantation). And the AG 6 database originates from aggregating Mixed forest and Secondary forest wet. The AG 4 and AG 5 aggregations are based on both natural similarities and radar system perception similarities as stated in section and valid for all former aggregation levels. The AG 6 aggregation is just based on natural similarities and is intended to test the importance of taking into account the radar system perception similarities in the selection of classes to be merged. None of the three classification results has significant differences with the original data set classification results (AG 0). The AG 4 and AG 5 have a small improvement in the overall classification accuracy (60.1% and 61.0%, respectively) and in the average of misclassification (5.9% and 6.1%, respectively) compared with 58.6% classification accuracy and 7.2% of average misclassification for the AG 3 data set. In contrast, the AG 6 data set classification results show a decrease in classification accuracy (52.3%), which is even lower than the AG 2 and AG 3 accuracy. The average of misclassification increases in relation with the former results. This relapse is clearly the consequence of aggregating two classes that although having high natural similarities (Mixed forest and Secondary forest wet) hardly have radar system perception similarity (only 1.1% of wrong class-pair classification in absence of other classes). Fifth aggregation level Taking into consideration the results from the fourth aggregation level, the fifth aggregation level was achieved by grouping, from the AG 3 data set, on the one hand Potential and real agricultural areas with Industrial production areas (Rubber plantation) as in AG 5, and on the other hand Mixed forest (except Secondary forest wet) with Mangrove as in AG 4. The results of this aggregation (AG 7) shows five classes related with the highest subdivision level of table 3.6. These classes are: Man made structures, Potential and real production areas, Forest (except Secondary forest wet), Secondary forest wet, and Water. 46

49 The classification results of the AG 7 data set shows a 69.8% of overall classification accuracy and a 4.2% of misclassification average. These results, finally, are significantly better than the classification results from the original data set (AG 0). Hence, the land cover classes aggregation process seems to be an adequate option to improve classification accuracy, and in general, the classification results. Taking the classification results of the AG 3 database as a starting point, it is possible to make a final analysis: The class aggregation in AG 4 and AG 5 improves the classification accuracy with 1.5 points and 2.5 points, respectively, comparing to the AG 3 results, while the class aggregation in AG 7 improves the classification accuracy in 11.2 points. Since the two couples of classes merged in AG 7 are the same couples merged individually in AG 4 and AG 5, it can be noted that the total improvement of classification accuracy is not the sum of the individual improvements caused by single class pair aggregations. A similar result is obtained comparing the average of misclassification. That statement has a direct relation with the aggregation system utilised. When two classes are aggregated, a new class results with new statistical figures (Mean and Standard deviation). Although derived from the two original classes, the new class is different from them. Therefore, the new class will have its own percentage of misclassification with other classes, different from the percentage of misclassification of the two original classes with the others. 3.7 Analysis for selected ASAR modes and selected land cover legends This analysis is based on the classification results from the AG 7, AG 2 and AG 0 data bases, that include five, eight and 18 classes, respectively. Once again, the level of speckle used is 0.4 db (100 looks) which is the expected level of speckle remaining after a segmentation process on a real image with an original 3 to 3.5 db of a speckle, as ENVISAT and ERS images. Table 3.14 shows a summary of the classification results for all the possible polarisation combinations that may be obtained from ENVISAT-1. HH-HV, HH-VV and HV-VV in the alternating polarisation mode, and HH or VV in the image mode (see chapter 3.2). VV also represents the polarisation used by ERS-2. The HH-HV combination represents the highest overall classification accuracy in the three data sets. However, its classification results do not show significant differences from HV-VV and HH-VV results in AG 7 and AG 2, nor from HV-VV, HH-VV and HH results in AG 0. In all the three databases, results from VV polarisation are significantly worse than those obtained from the HH-HV combination. Since VV represents the polarisation used by ERS-2, it is possible to state that ENVISAT simulated products (at least the HH-HV polarisation combination) are significantly more suitable to classify the land cover in the study area than the ERS simulated product. This indicates that ENVISAT products would have a higher potential to land cover classification and in turn to support land cover and forest assessment than ERS products. Although HH-VV does not present significant differences from HH-HV and HV-VV in any case, the overall classification accuracy is much lower than theirs. Therefore, only HH-HV and HV-VV are considered the ENVISAT polarisation combinations with more potential to support land cover and forest assessment by means of their classification capacities. 47

50 Table Classification results using the possible polarisation combinations of ENVISAT and ERS instruments for three different aggregation levels with 0.4 db of speckle. In each aggregation level results, bold characters indicate the best result and the results that are not significantly different from the best result. Underlined characters indicate the worst result and the result that are not significantly different from the worst result. The level of confidence used is 95%. Database Polarisation Kappa Variance Classification Average of Code combination of Kappa accuracy (%) misclassification (%) AG 7 HH-HV HV-VV HH-VV VV HH AG 2 HH-HV HV-VV HH-VV VV HH AG 0 HH-HV HV-VV HH-VV HH VV Influence of the striping in the C-band VV AirSAR image on the results Table 3.15 shows the 22 ROI s that are placed inside the dark bands in the VV image. These polygons were excluded from databases AG 7 and AG 2 to compare their classification results. 48

51 Table ROI s placed inside the dark bands. Polygon number Class name 3 Agriculture 6 Agriculture 7 Agriculture 12 Secondary forest wet 13 Agriculture 35 Shrubs 36 Shrubs 37 Secondary forest young 38 Shrubs 41 Secondary forest (old) 48 Secondary forest young 49 Alang alang 50 Secondary forest young-trunks 51 Secondary forest young-trunks 52 Primary forest 62 Shrubs 70 Water 86 Man made structure 101 Secondary forest wet 102 Shrubs 103 Secondary forest wet 119 Coconut plantation Table 3.16 shows the classification results for different polarisation combinations using the AG 7 and AG 2 databases with the original 122 ROI s and with 100 ROI s produced by excluding the ROI s inside the dark bands. Delta Kappa represents the statistical differences between classification results with and without dark band influence. The differences are not significant in any case. Therefore it can be concluded that the dark bands in the VV image do not have a significant influence on the classification results. 49

52 Table Influence of dark bands in the VV image over the classification results for two different aggregation levels with 0.4 db of speckle. Data code base Polarisation combination Darkband influence Kappa Variance of Kappa Delta Kappa Classification accuracy (%) Average misclassification (%) AG 7 HH-HV Yes No HV-VV Yes No HH-VV Yes No HH Yes No VV Yes No AG 2 HH-HV Yes No HV-VV Yes No HH-VV Yes No HH Yes No VV Yes No of 50

53 4. Workshops and discussion 4.1 Introduction Chapter 4 describes the feedback and recommendations obtained from decision-makers and user communities. Meetings were held with the new Minister of Forestry (i.e. the successor of Dr. Nur Mahmudi Ismail), H.E. Mr. Marzuki Usman at June 2001 in Balikpapan, the Governor of East-Kalimantan, Mr. Asmawi Agani at 5 and 7 November 2001 in Palangkaraya, and the current Minister of Forestry, H.E. Dr. Prakoso at 9 November 2001 at the ministry in Jakarta. The mandate for management of the 700,000 ha Mawas reserve in Central-Kalimantan was given to the Balikpapan Orangutan Survival (BOS) Foundation by the governor and current minister. This was partly a result of the use of BOS of the SarVision service to monitor this area permanently and frequently. Workshops were held at the Ministry in Jakarta at 24 October 2001 for user communities and at the town hall in Balikpapan at 31 October 2001 for local users. Additionally, another workshop was held in Jakarta at 8 November 2001 for donor organisations (EU, World Bank, GTZ, DFID, NRM and JICA). Section 4.2 describes the discussions during the BCRS Workshop in Jakarta. Agenda, invitation and participants of this workshop are listed in the appendices. Section 4.3 describes briefly the other meetings and workshops, but exclude details of the meetings with the two ministers of forestry and the governor of Central Kalimantan. 51

54 4.2 Discussion Jakarta Workshop Minutes of the workshop Agenda Minutes of the BCRS Workshop Jakarta, October 24th, Opening Speech by the Head of Forestry Planning Agency (Dr. Untung Iskandar) 2. Introduction by Dr. ir. D.H. Hoekman 3. Presentation on Mr. Ruandha Agung Sugardiman s work 4. Future Development by Dr. ir. D.H. Hoekman 5. Closing by Mr. Oesman Yoesoep Opening Speech by Dr. Untung Iskandar Dr. Untung Iskandar as the Head of Forestry Planning Agency pointed out several points during his speech as follows: 1. It is important to use radar imagery because of the cloud cover problems in Indonesia; 2. Radar has a capability to acquire data/images during day and night time; 3. During his visit at SIRAD he noted that young timber plantation could be detected by using multi-spectral analysis; hotspots could be detected; regeneration after fire might be detected; he mentioned that the cleverer a person the more information he or she could get. 4. When he met Dr. Indroyono Susilo (Deputy Minister of Ocean and Fishery), Dr. Untung Iskandar received a lot of information regarding space-borne and airborne radar sensors; 5. Dr. Untung Iskandar knew Dr. Hoekman from Dr. Smits and recognised his research works; 6. The radar co-operation enhanced capabilities to obtain timely information, especially when the minister was hungry for information; 7. Dr. Untung Iskandar has a long experience with Wageningen University, especially with Prof. Karsen from PROSEA. Discussion on Dr. Hoekman Introduction Chapter Question(s) 1. Prof. Suhardi asked whether radar remote sensing could differentiate until species that would be important for obtaining information on biodiversity aspects, and he raised the issue of biomass estimation (tall versus short tree with similar biomass). 2. Mr. Oesman Yoesoep knew that radar was an oblique and grey image and he was impressed with the AirSAR C, L, P band image, that the image was so colourful, differentiating vegetations. He wanted to know the scale of the AirSAR image and how much it costs. In addition, he indicated that it would be useful when the data could be used in daily MoF operations. Answer(s) 52

55 1. Dr. Hoekman informed Prof. Suhardi that it is still difficult to identify species at the botanical level using current remote sensing technology. Because each species contains many specific factors, e.g. tree canopy structure, moisture, leaf colour, leaf orientation differentiation up to a certain level is possible, however. Dr. Hoekman indicated that remote sensing could make a forest type mapping because it has the capability to assess information on 3D canopy structure, tree height, etc.; For Biomass Estimation, L and P-band backscatter were used to estimate the biomass in colonisation and primary forest areas and the model itself is very complex; In the near future, our group will use JERS data from Japan and just a few weeks ago, we also start to collaborate to propose a future P-band satellite along with ESA (EEOM New Call), NASA and NASDA (this would link to the Kyoto Protocol). 2. Answering Mr. Oesman Yoesoep, Dr. Hoekman explained a little bit of radar theory concerning the incidence angle and radar shadows; Dr. Hoekman informed that in the near future, Digital Elevation Models (DEM) from SRTM NASA will be available covering below 60 degree North Latitude and above 60 degree South Latitude; For AirSAR data a 10x60 km strip costs USD 12,000 and this would be cheaper than IKONOS data that are expensive and cover only small areas, but gives detailed information. For hyperspectral data, the classification process is still needed, but radar gives completely different information, e.g. foliar moisture and water content, roughness, etc. Discussion on Mr. Ruandha Agung Sugardiman s presentation Question(s) 1. Mr. Oesman Yoesoep informed about the GTZ research results on the forest fire damage where there were different outputs between Forestry Planning Agency (Baplan) 3.2 million Ha and GTZ 5.2 million Ha. He would like to have some ideas why these results were so much different; He asked about the measurement of biomass. In addition, he needed clarification on the costs of operational radar monitoring. 2. Mr. Sunuprapto indicated that he is sceptic about Mr. Sugardiman s research because in his research he showed that optical is more accurate than radar, in his swamp forest the backscatter is high, there are more damaged areas than in Mr. Sugardiman research. 3. Dr. Nengah suggested that Mr. Sugardiman could include an additional information feature related to the dielectric constant of because of the different site characteristics, vegetation cover type, and moisture or water content of tree and soil. 4. Mrs. Belinda requested explanation on details of the current methodology and development of the operational methodology to overcome different sensor, e.g. ERS, ENVISAT, PALSAR etc. The real thematic product would be vector data therefore there would be errors since image data are raster. Therefore, Baplan is doing manual digitising on screen for feature interpretation. Data fusion would require small time intervals due to temporal changes. 5. Mr. Kustanta wondered about the 95 percent of accuracy on biomass and 98 percent on land cover classification and questioned whether it could be caused by the (small number of) training sets. He asked about the amount of changes in two years interval. He also asked about the biomass measurement. Answer(s) 1. Mr. Sugardiman informed Mr. Oesman Yoesoep that the price is only for the data, but in comparison to the Landsat TM type acquisition, radar requires a time series of acquisitions. To investigate the different results, obviously similar parameters should be used to be able to compare both results. Biomass measurements are based on 53

56 samples of the transect plot type and they are not a real measurement involving felling down the trees. 2. Referring Mr. Sunuprapto, Mr. Sugardiman told that actually all the questions have been answered by Mr. Sunuprapto who obviously agrees that radar at present is only a complement for the current optical data. In addition, radar is very sensitive to water and moisture content of soil and tree components. 3. Mr. Sugardiman fully agreed with Dr. Nengah suggestions. 4. Answering Mrs. Belinda, Mr. Sugardiman informed that his method would be tested ranging from small sample areas to large areas and is to be validated at different site locations. For multi-scale and fusion, Mr. Nugroho added that it is obviously true that such errors would occur during the fusion and vectorisation processes. The most crucial is that such a research should investigate which method(s) or algorithm(s) would produce the least errors. In addition, such aggregation and generalisation approach should also be investigated during data integration phase. 5. Answering Mr. Kustanta, Dr. Hoekman informed that there were over 700 training and validation sites in the Colombia case study. It may be true that in such an area small temporal interval are needed when vegetation changes rapidly. The biomass measurement was derived from field measurement of tree parameters, e.g. DBH, Tree Height, Crown Perimeter etc. Discussion on Dr. Hoekman Future Development Chapter Question(s) 1. Mr. Toni from PT. Bumi Prasaja asked why the radar group did not use Radarsat data since many researchers also used Radarsat. 2. Dr. Wardoyo suggested that parallel to the radar research at the specialist level, it would be fruitful for operational use of remote sensing at MoF. The reason is that MoF has lack of human resources for remote sensing and this could be mitigated by starting pilot project(s) at common sites. Answer(s): 1. Dr. Hoekman informed Mr. Toni that the use of ERS SAR data was simply because of the much lower standard costs plus the 90 percent discount for research and application development. There is no reason not to use Radarsat or other radar data, when affordable. 2. Dr. Hoekman agreed with Dr. Wardoyo and suggested to have further discussion concerning the local staff for radar training in practice. It may be useful that MoF, at central and regional levels, could send Master level students through competition for NEC scheme fellowships. Dr. Hoekman is willing to supervise 2-5 Master students per year at Wageningen University. Reported by: Muljanto Nugroho 54

57 4.2.2 Discussion with Head of Forestry Planning Agency One day after the workshop an evaluation meeting was held with Dr. Untung Iskander, Director-General, Head of the Forestry Planning Agency. Two main conclusions were drawn. (1) The training of staff through Wageningen University and the Netherlands Education Centre of NUFFIC has no priority because of the limited number of eligible staff available and their low TOEFL scores. (2) The preferred training mechanism should be through joint execution of pilot projects. The preferred study area is the border of Malaysia and East Kalimantan. A second priority is the border of Papua. It was noted that also AirSAR PacRim-2 data are available at both sites! Funding mechanisms were discussed. SarVision and MOF together could establish a new mapping centre with ISO 9000 certification. Customers would be mining companies and foreign organisations/companies. 4.3 Brief discussion other workshops and meetings Workshop Balikpapan This workshop was held at the premises of the Balikpapan town hall at BAPEDALDA (Regional Environmental Agency) at 31 October for 17 participants. The invitation list is included in the appendices. The presentation was largely similar to the BCRS Jakarta workshop however put more emphasis on application of the results that were obtained in the area around Balikpapan. The decision making process is largely executed at the level of provinces and even regions (the so-called Kabupatan) since the recent shift of power to the provinces in the political pursue of decentralisation and regional autonomy. The meeting was largely informative to the local users since it was the first of such a presentation in Balikpapan. The success of the meeting is an encouragement to have similar meetings on a more regular basis and also to have one in Samarinda shortly. A lot of useful feedback was obtained on details of the current work. Interest to application is at several levels and ranges from management of the local watershed to monitoring of the large mountain ranges of Meratus and Sankaliurang Discussion with Balikpapan Orangutan Survival Foundation Discussion on application of the techniques was held with BOS on several occasions during the June and the November 2001 missions to Kalimantan. It could be concluded that spaceborne radar is a crucial element in the overall approach of nature management as envisaged by BOS. Early detection of illegal logging would enable effective law enforcement and pave the way to large debt swap for nature programs and carbon offset trading. This would be of interest to nature, notably the orangutan population, local people and, eventually, Indonesian economy. More details already have been given in section 2.5. When this formula proves to be successful for the Mawas reserve near Palangkaraya there is interest to extend the area to be monitored with the Meratus mountain range. 55

58 4.3.3 Donor organisations workshop A final workshop was held in Jakarta at 8 November at the request of the Natural Resources Management (NRM) organisation, and was especially organised to involve the large donor organisations. The Attendees included representatives of NRM, EU, World Bank, GTZ, DFID and JICA (note that the latter three are the German, British and Japanese International Development Co-operation organisations. Without going in details on the follow-on steps agreed upon it could be stated that these organisations were particularly pleased by SarVision s approach (as also stated on the WebPages) by stimulating technology transfer and the foundation of new independent local independent companies to apply the techniques locally. The advantages being continuity of services, cost reduction and making maximal use of local knowledge. At the same time continuing support by SarVision would support and warrant this continuity by quality control and service upgrading. 56

59 5. Conclusions and recommendations Conclusions The current monitoring system in Indonesia is based on LANDSAT. Cloud cover prevents frequent observation. At the East-Kalimantan test site, for example, the probability of acquiring an optical image (SPOT or LANDSAT) with less than 30% cloud cover is only 4% per month. Though LANDSAT provides very useful information it fails delivering information in time and, thus, is of limited use in many areas of application such as early detection of illegal logging and fire risk, or for applications related to timber certification. As an illustration of the poor temporal coverage it can be noted that the last useful LANDSAT image of this site dates back to December ERS SAR time series provide a viable solution, with or without the additional use of optical sensors like the current LANDSAT ETM. This was shown at the Tropenbos East-Kalimantan test site. The terrain of this test site is very hilly and typical for the rugged topography encountered in most Indonesian forest areas. A series of 21 ERS-1 and ERS-2 images covering the period has been processed. The modulating effects of slope angle and slope aspect on the backscatter intensity complicate the processing. New multi-temporal segmentation techniques and Iterated Conditional Modes (ICM) techniques, in combination with backscatter change classification techniques have been applied to deal with this problem. Results show that land cover classes such as mangrove and nipah (palm mangrove) can be classified well in pre-processing steps. The remaining terrain shows swift changes in land cover type and extent. Notably periods of severe drought, such as the El Niño event, have pronounced impacts. Several types of maps have been made for several points of time within the monitoring period, showing the changes in forests, tree plantations and agricultural areas, the post-fire damage and the pre-fire forest fire susceptibility (risk). Despite all processing techniques utilised the main problem encountered remains the effect of relief in areas with steep slopes, such as the Sungai Wain reserve or the Meratus mountain range. Explicit use of DEM s seems to be unavoidable for reaching good interpretation. Even in this very rugged terrain with steep hills an accuracy of over 64-87% could be reached for most land cover types. The computationally intensive multi-temporal segmentation technique used previously may be replaced by the faster ICM technique, which generates results comparable to segmentation and is superior to pixel-based classification. Another advantage of ICM is that other factors, in addition to the multi-temporal backscatter variation used in the segmentation, such as texture or terrain slopes, may be incorporated easily. DEM data may be obtained from NASA s Space Shuttle Radar Topography Mission (SRTM) in the near future. It is envisaged that major improvements still can be made when the SAR monitoring is supported by the use of such a DEM and by data from LANDSAT ETM, even if the latter can only be acquired sporadically. Agricultural or garden (kebun) areas show a somewhat lower accuracy which may be a result of the aggregation level adopted. In reality it is not a single physically well-defined class but a mixture of agriculture, forest remnants and small plantations, and is classified as such. The real accuracy, therefore, may be significantly higher but is difficult to validate. The same applies to forest fire damage. Completely burned forest is detected with very high accuracy. Often, however, the fire spreads along the ground through the dry litter layer (ground fire) damaging trunks near the surface. From the air the forest might seem to be in good condition (green canopy) and is classified as such by LANDSAT or ERS SAR. The real condition of the forest only becomes apparent about 6 months later, when the canopy trees die off. 57

60 Since many forests are degraded, and therefore vulnerable to fire, and extreme dry seasons occur regularly, forest fire becomes a major problem in Indonesia. Fire Damage Assessment Maps derived from ERS-SAR data would be a very useful contribution to support forest rehabilitation and reforestation activities. There are clear indications that ERS SAR can map fire susceptibility and, consequently, fire risk/hazard maps can be made in order to plan preventive actions before forest fires start. It was found, for example, that susceptibility to fire might be well assessable by using the stability of the radar backscatter level in the pre-fire period as an indicator. An evaluation of the pre-fire period fire risk and the actual damage done, for example, reveals that actually almost all forest was burned under these extremely dry conditions. The only exceptions were the protected forest areas Wanariset, Bukit Bangkirai and Sungai Wain, where active fire fighting took place and for small areas close to the mangroves. The latter may be related to more favourable terrain drainage conditions. All good forest (i.e. high forest with high crown closure), not actively protected from fires, did burn. In the near future the capabilities can be improved further by utilising additional polarisations and/or using a larger incidence angle, new possibilities offered by ASAR. To study this, ENVISAT data have been simulated using data collected in September 2000 at the East- Kalimantan test site during the NASA PACRIM-2 campaign. A method to simulate ASAR Alternating Polarisation (AP) mode data was introduced. The resulting image is 10 km (in range) x 60 km (in azimuth) in size, corresponding to the area covered by AirSAR. The resulting image was inspected for its desired properties in terms of ENL and correlation, as a check on the appropriateness of the simulation procedure adopted. Some extended areas of grass (alang alang) were selected as the optimum choice for such a test. The results are compared with the corresponding area in an ERS-2 image acquired in the same year. The autocorrelation functions in range and azimuth were shown to be nearly similar. Also the number of ENL (2.30 versus 2.55) are nearly similar. The reason that these numbers are not identical and lower then the nominal value of 3.0 may be a result of the different time of acquisition and the presence of some structure and/or texture, respectively. It could be shown that the ASAR Alternating Polarisation (AP) mode, especially the HH-HV polarisation combination, performs significantly better than ERS SAR. The collection of field data from 122 field plots and its organisation into a structured legend of 18 classes at four levels of hierarchy has been described. The results of ASAR classification simulation as a function of polarisation combination, speckle level and the effect of choice of legends in terms of similarities in land cover and/or radar characteristics have been discussed extensively. Using a single date of observation, and assuming data pre-processing steps such as segmentation can confine speckle variation within the 1.0 db level, a map with 6-7 legend units might be produced at a 60-70% accuracy level, which is a significantly better performance than may be obtained with a single ERS SAR image. Using the techniques developed and demonstrated, SarVision started to contribute to programmes of the Indonesian Government, the Balikpapan Orangutan Survival Foundation (BOS) and the Gibbon Foundation for continued improved safeguarding of remaining wild orangutan populations and their habitat. A key area is located in Central-Kalimantan, east of Palangkaraya. This newly established Mawas forest reserve is 700,000 ha in size and is part of a proposed dept-for-nature-swap program and carbon offset trading agreement. Because it is a swampy and perfectly flat area some of the problems encountered in the Balikpapan are not present. The focus here is on fast detection of illegal logging in support of effective law enforcement. To warrant optimal product development future users have actively participate in this project, supported the field work, contributed to image interpretation and generated feedback on the usefulness of the products within their organisations (see chapter 4 and appendices), and they will remain doing this in the future. In the framework of this project workshops have been 58

61 held in Jakarta (2 times) and Balikpapan, and meetings were held with two Ministers of Forestry, H.E. Mr. Marzuki Usman and H.E. Dr. Prakoso, the Director-Generals of Forestry Planning Agency, Dr. Untung Iskander, and of Nature Conservation, Mr. Wahjudi, and with the Governor of Central-Kalimantan. In this phase it is still well possible to adapt the products to user s specific information needs and optimise classification procedures, legends and define a set of product types before the next step, standardisation, will be taken. Without going in details it can be reported that many ideas and options have arisen for implementation of the techniques, for a range of applications and with several counterpart organisations and funding mechanisms. The acceptance of the technique is large and benefits seem to outweigh costs considerably. Cost-benefit analyses that will produce cost estimates per square km of the end products (produced results in map format) have been made resulting in a range of 1-5 US$ cent per ha per year (depending on type of product, quality and raw data costs). These costs can be reduced in the future when properly trained local staff in sufficient numbers is present. Since no alternatives for the products developed are available and the benefits depend upon the current developments regarding e.g. timber certification or carbon sequestration the profits may only be expressed in general terms. In the mean time a start has been made in Central-Kalimantan where SarVision started operational and permanent monitoring of the newly established 700,000 ha Mawas reserve. Discussions on the application of the techniques were held with BOS on several occasions during the June and the November 2001 missions to Kalimantan. It could be concluded that spaceborne radar is a crucial element in the overall approach of nature management as envisaged by BOS. Early detection of illegal logging would enable effective law enforcement and pave the way to large debt swap for nature programs and carbon offset trading. This would be of interest to nature, notably the orangutan population, local people and, eventually, Indonesian economy. More details already have been given in section 2.5. When this formula proves to be successful for the Mawas reserve near Palangkaraya BOS is interested to extend the area to be monitored with the Meratus mountain range. A final workshop was held in Jakarta with the objective to involve the large donor organisations. Without going in details on the follow-on steps agreed upon it could be stated that these organisations were particularly pleased by SarVision s approach (as also stated on the WebPages of SarVision) of stimulating technology transfer and the foundation of new independent local companies to apply the techniques locally. The advantages being continuity of services, cost reduction and making maximal use of local knowledge. At the same time, continuing support by SarVision would support and warrant this continuity by quality control and service upgrading. 59

62 Recommendations At the technical level some clear recommendations could be derived in order to further the monitoring techniques. In the first place the integration with DEM s may result in significant improvements in accuracy for some of the land cover types, notably in rugged terrain. For most protected forests accurate DEM s have been acquired by the Ministry of Forestry in 1997 during the airborne radar protected forests campaign resulting in 3 m accuracy height maps. Moreover, shortly, SRTM data will become available giving complete coverage with lower but sufficient accuracy. Integration with LANDSAT ETM is advisable since good quality and complementary information may emerge. This information supply however is irregular and far less frequent with an estimated 2 years average time interval. As soon as the ENVISAT ASAR is available its products should be preferred over ERS SAR PRI data. Notably the AP HH-HV product seems to be superior to ERS SAR PRI. Real tests with time series of this ASAR product, and other ASAR products, should be conducted to get good assessments of ASAR s performance. This could most easily be done at the East-Kalimantan test site by continuing the already long existing time series. In fact this has been proposed for the approved ENVISAT AO-599 study. Initial tests with JERS-1 time series in the Mawas reserve show great potential for L- band. Also AirSAR data in several studies (in the Amazon, and at this Balikpapan test site) show clearly L-band s superiority over C-band for the currently intended applications. It would be advisable to prepare for the L-band polarimetric PALSAR radar on-board the Japanese ALOS satellite to be launched in 2003 along lines as have been carried out for ENVISAT in this study. Other technical advancements include the use of texture, which can easily be integrated in the proposed ICM-method, and the study of legend structures. Eventually legends have to be optimised to make full use of radar s capabilities to discriminate certain physical structures and its sensitiveness for certain parameters such as plant and soil moisture variations. At the same time these capabilities should be translated in useful information to fulfil the identified user needs. Legend optimisation thus should result in a compromise between desired accuracy level and information needs. 60

63 References Besag, J., 1986, On the statistical analysis of dirty pictures, Journal Royal Statistical Society B, Vol.48, No.3, pp Bijker, W., 1997, Radar for rain forest, Ph.D. Thesis Wageningen Agricultural University, The Netherlands. Hoekman, D.H., 1991, Speckle ensemble statistics of logarithmically scaled data, IEEE Transactions on Geoscience and Remote Sensing, Vol.29, pp Hoekman, D.H. and M.J. Quiñones, 2000, Land cover type and biomass classification using AirSAR data for evaluation of monitoring scenarios in the Colombian Amazon, IEEE Transactions on Geoscience and Remote Sensing, Vol.38, pp Hoekman, D.H., and M. J. Quiñones, 2001, Biophysical forest type characterisation in the Colombian Amazon by airborne polarimetric SAR, Proceedings of the Third International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications, September 2001, University of Sheffield, UK, pp. Gastellu-Etchegorry, J.P., 1988a, Cloud cover distribution in Indonesia, Int. J. of Remote Sensing, Vol.9, pp Gastellu-Etchegorry, J.P., 1988b, Predictive models for remotely-sensed data acquisition in Indonesia, Int. J. of Remote Sensing, Vol.9, pp Grim, R.J.A., F.M. Seifert, D.H. Hoekman, C. Varekamp, Y.A. Hussin, M.A. Sharifi and M. Weir, 2000, SIRAMHUTAN Sistem Informasi untuk Manajemen Hutan A demonstration project for forestry in Indonesia, BCRS report NRSP , 83 pages. Kuntz, S, and F. Siegert, 1999, Monitoring of deforestation and land use in Indonesia with multi-temporal ERS data, International Journal of Remote Sensing, Vol.20, pp Oliver, C and S. Quegan, 1998, Understanding Synthetic Aperture Radar Images, Artech House. R.A. Sugardiman, D.H. Hoekman, V. Schut and M.A.M. Vissers, 2000, Land cover change and fire damage monitoring using ERS-1/2 SAR, Proceedings INDREX Final Results Workshop, ESTEC, 9 November 1999, Jakarta, 30 November 1999, ESA Report SP-489, Noordwijk, ISBN , pp Trichon, V., D. Ducrot and J.P. Gastellu-Etchegorry, 1999, SPOT4 potential for the monitoring of tropical vegetation. A case study in Sumatra, International Journal of Remote Sensing, Vol.20, pp Wooding, M.G., A.D. Zmuda, D.H. Hoekman, J.J. de Jong and E.P.W. Attema, 1998, The Indonesian Radar Experiment (INDREX-96), Proceedings of the Euro-Asian Space Week Co-operation in Space, November 1998, Singapore, ESA SP-430, pp

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65 Appendices Appendix I. Jakarta workshop program, invitation and participation AGENDA WORKSHOP RADAR SATELLITE MONITORING Jakarta, 24 October 2001 No Time Program Speaker Opening DR. Untung Iskandar Introduction DR. Ir. Dirk. H. Hoekman Radar Satellite Monitoring 1 DR. Ir. Dirk. H. Hoekman Discussion Coffee break Radar Satellite Monitoring 2 Ir. Ruandha A. Sugardiman, M.Sc Discussion Radar Satellite Monitoring 3 DR. Ir. Dirk. H. Hoekman Discussion Closing and Lunch - 63

66 DEPARTEMEN KEHUTANAN BADAN PLANOLOGI KEHUTANAN Gedung MANGGALA WANABAKTI Blok I, Lantai 7 Jalan Jenderal Gatot Subroto PO. Box No. 7 JKWB J A K A R T A Fax. : Telephone: , Telex : dephut ia Nomor : Jakarta, Oktober 2001 Lampiran : - Perihal : Undangan Lokakarya Pemantauan Penutupan Lahan dengan Citra Satelit Radar Kepada Yth.: Dalam rangka pengkajian aplikasi penggunaan citra satelit Radar untuk sektor Kehutanan khususnya pemantauan penutupan lahan, kami mengundang Saudara untuk hadir pada Lokakarya Pemantauan Penutupan Lahan dengan Citra Satelit Radar, yang akan diselenggarakan pada: Hari, tanggal : Rabu, 24 Oktober 2001 Waktu : Pukul Tempat : Ruang rapat Utama Menteri Kehutanan Gedung Manggala Wanabakti Blok I Lantai 4 Pembicara : Dr. Ir. Dirk H. Hoekman (Wageningen University, The Netherlands) Demikian, atas kehadiran Saudara kami ucapkan terima kasih. KEPALA BADAN UNTUNG ISKANDAR NIP

67 Daftar Undangan: 1. Direktur Jenderal RLPS 2. Direktur Jenderal BPK 3. Direktur Jenderal PKA 4. Kepala Badan LITBANG Kehutanan 5. Kepala PUSDIKLAT Kehutanan 6. Kepala Pusat Inventarisasi dan Statistik Kehutanan 7. Direktur Perlindungan Hutan 8. Direktur Penanggulangan Kebakaran Hutan 9. Direktur Pengelolaan Daerah Aliran Sungai dan Rehabilitasi Lahan 10. Direktur Bina Rencana Pemanfaatan Hutan Produksi 11. Direktur Bina Pengembangan Hutan Alam 12. Direktur Utama PT INHUTANI I 13. Direktur Utama PT INHUTANI II 14. Direktur Utama PT INHUTANI III 15. Direktur Utama PT INHUTANI IV 16. Direktur Utama PT INHUTANI V 17. Direktur Utama PERUM PERHUTANI 18. Dekan Fakultas Kehutanan, Institut Pertanian Bogor 19. Dekan Fakultas Kehutanan, Universitas Gadjah Mada 20. Deputi Bidang Survei Dasar dan Sumber Daya Alam, BAKOSURTANAL 21. Dr. Iwan Gunawan (BPPT) 22. Dr. Mukhlisin Arif (LAPAN) 23. Dr. Drajad Wibowo (LEI) 24. Direktur APHI 25. Direktur APSPI 26. Kepala Pusat Rencana Kehutanan 27. Kepala Pusat Inventarisasi dan Statistik 28. Kepala Pusat Pengukuhan dan Penatagunaan Kawasan Hutan 29. Kepala Pusat Pembentukan Wilayah Pengelolaan dan Perubahan Kawasan 30. Kepala Pusat Perpetaan Kehutanan 65

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