White Paper Status of X-Band SAR Applications in Forestry

Transcription

1 White Paper Status of X-Band SAR Applications in Forestry Steffen Kuntz & Felicitas v. Poncet (Astrium GEO) Ralf Knuth (Univ. Jena) Josef Kellndorfer (Woodshole Research Centre) Michael Köhl & Thomas Baldauf (Univ. Hamburg / Institute for World Forestry) Dir k Hoekmann (Univ. Wageningen / SARVision) Svein Solberg (Norw egian Forest and Landscape Institute) Abstract Since the launch of new very high resolution SAR systems in 2007, i.e. the Ger man TerraSAR-X and the Italian Constellation of Small Satellites for Mediterranean Basin Observation - COSMO SkyMed the international science community and commercial service providers investigated extensively the potential of spaceborne X-Band SAR imagery for various application domains. With increasing international attention to mitigate the impacts of climate change the role of tropical forests for reducing global carbon emissions has lead to the REDD+ initiative ( Reduction of Deforestation and Forest Degradation and sustainable forest management) and, subsequently, to the need to monitor tropical forests more intensively and more frequently. Hence, the aim of this White Paper is to provide an overview on the use of X-Band SAR imagery from Space for forestry applications. More specifically the paper describes successful demonstrators on forest cover mapping, change detection monitoring of forest cover and forest degradation, and contributions to combined forest inventories. In addition, a short chapter looks into evolving technologies such as the assessment of forest biomass from bi-static interferometric SAR data from the TerraSAR-X / TanDEM-X mission. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 1

2 1. Introduction Today in the REDD+ context in most cases in developing countries w all-to-w all monitoring of land use, land use change and forestry (LULUCF) is carried out by means of optical imagery. Here, the heritage of long-ter m time series from optical imagery (Landsat and SPOT) allows the creation of baseline scenarios starting from the 70ies of the last century. How ever, there are still large regions w here the availability of optical imagery is rare due to frequent cloud coverage. Here, since the launch of the ERS-1 and -2 satellites (on July 17, 1991 and April 21, 1995, respectively) and RADARSAT ( November 4, 1995) microw ave C-Band imagery has been proven as a pow erful tool for ARD (afforestation, reforestation, deforestation) monitoring from Space. The cloud penetrating capability of spaceborne SAR (synthetic aperture radar) allows the monitoring of large perpetually cloud covered tropical regions. In the meantime there exist operational approaches which can take benefit from the all-w eather capability of SAR systems for large area land cover and forest monitoring. With the event of very high resolution SAR systems such as the TerraSAR-X (launched June 15, 2007) and the Constellation of Small Satellites for Mediterranean basin Observation - COSMO SkyMed (first launch June 7, 2007) new opportunities for improved mapping and monitoring of tropical environments are available. TerraSAR-X is the first Ger man satellite implemented as a Public- Private Partnership (PPP) betw een DLR (German Aerospace Centre) and Astrium GmbH. Since June 2010 it flies in a so-called Tandem- Mission w ith its Tw in Tandem-X, w ith the goal to provide a unique digital elevation model from the Earth s surface. COSMO-SkyMed is a four spacecraft constellation, funded by the ASI (Agenzia Spaziale Italiana), and the Italian Ministry of Defense. Each of the four satellites is equipped w ith a SAR instrument and is capable of operating in all visibility conditions at high resolution and in real time. The first satellite w as launched on June 8, Besides that there are several other SAR missions under construction or planned. Table 1-1 provides an overview of the operational missions and of missions under construction or planned. Hence, it can be concluded that at least for the next decade the total number of missions w ill guarantee that operational forest monitor ing services can rely on data continuity on a global level. Table 1-1: Overv iew on operational spaceborne SAR-Missions and SAR-Missions under construction or planned. Mission Frequency Country Resolution Status ENVISAT C-Band ESA HR Operational RADARSAT C-Band Canada HR Operational Sentinel 1a, 1b C-Band ESA HR Under construction ALOS follow-up L-Band Japan HR Under construction SAOCOM L-Band Argentina HR Planned DESDynI / Tandem-L L-Band US / Germany HR Planned TerraSAR-X / TanDEM-X X-Band Germany VHR Operational TerraSAR -2 X-Band Germany VHR Under construction Cosmo SkyMed X-Band Italy VHR Operational PAZ X-Band Spain VHR Under construction TerraSAR -2 WorldSAR X-Band Germany VHR Planned GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 2

3 With respect to the relatively new X-Band sensors available operationally now for approximately 4 years in the follow ing focus will be on the new opportunities for forestry applications offered by VHR SAR imagery. 2. State of the Art From radar backscatter theory it is obvious that in the X-Band most of the energy is backscattered from the surface. At the same time the backscatter increases nearly linearly w ith increasing biomass until it saturates (Jensen, 2000; Dobson et al., 1992; Figure 2-1) Figure 2-1: Theoretical response of a pine stand to L-, C- and X-band frequencies: The shorter the wavelength the greater the contribution from surface scattering (from: Jensen, 2000; p. 315) Table 2-1 shows a comparison of the information content of X-Band and L-Band SAR data. It emphasis the fact that both frequencies are sensitive to different object features leading to a high potential for synergistic applications (i.e. multi-frequency data analysis). Table 2-1: Comparison of X-Band and L-Band SAR data Application X-Band L-Band Thematic content Interferometry Surface scattering sensitive to small changes on surface due to higher resolution Early saturation of signal with increasing biomass More information within vegetation classes due to sensitivity to surface roughness (e.g. forest types, regrowth, crops, etc.) Small objects recognition due to VHR resolution Impacted by temporal decorrelation in vegetation loss of coherence Higher resolution of 3-D features (DSM) Deeper penetration of surfaces Less impacted by vegetation cover Better correlated to biomass easy differentiation between forest / non-forest cover Lower resolution does not allow recognition of small objects (e.g. building, ships, infrastructure) Less temporal decorrelation Closer to DEM Lower resolution GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 3

4 Application X-Band L-Band Combination Combination of surface (X) and volume (L) scattering increases thematic content higher automation of thematic mapping X-band DSM + L-Band DEM allows more frequent 3-D change detection and improved overall DEM In the follow ing chapters examples from successful forestry applications deploying X- Band SAR data are show n Forest cover mapping First attempts shortly after the launch of TerraSAR-X showed the potential of the VHR data for forest cover assessment in Indonesia ( Figure 3.2). In this case the forest cover was derived from backscatter intensity from 3 m StripMap data in VV polarisation. Figure 2-2: Multitemporal TerraSAR-X ScanSAR image show ing a part of Sebangau National Park in Kalimantan, Indonesia. Many details can be detected e.g. old fire scars, forest and vegetation types and changes in land cover. Figure 2-3: Examples of regional forest maps from SAR data. On the left a forest cover map from TerraSAR-X 3m Stripmap data shows the Sebangau National Park and Palankaraya, Kalimantan, Indonesia. Overall accuracy: 87,6 % based on validation with 145 sample plots. On the right a forest type map from the same area combining ALOS-PALSAR (allowing a higher automation of forest cover classification) and TerraSAR-X Stripmap (improving delineation geometry and forest type / land cover mapping) is show n. Infoterra GmbH. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 4

5 Although it is difficult to derive a simple forest non-forest map from raw X-band data by using texture filtering the information content of the data can be significantly improved allow ing a higher automated classification approach. For instance Santos et al (2010) carried out a study using a StripMap image from TerraSAR-X. The results indicate that it is possible to discriminate forest from other land cover and land use targets. The discrimination of these landscape aspects was improved w ith the use of the coherent attribute entropy as a component of the classification model, because it is associated to the degree of randomness of the scattering process, w hich defines the target. Using other classifiers which consider textural SAR attributes in such high resolution images (X-band), w ould help to advance landscape studies w ith TerraSAR-X images in the Brazilian Amazon. The results obtained indicate the potential of TerraSAR-X data for the assessment and monitor ing of tropical forests. How ever, more research is needed in order to clarify the potential of X-band backscatter and polarimetry for forest non-forest mapping. The low degree of penetration into vegetation may produce a similar backscatter from tall trees and ground vegetation. Knuth et al. (2011) analysed the potential of monotemporal TerraSAR-X High Resolution SpotLight data in the framew ork of the FRA-SAR study. Aim w as to analyse whether or not such data can contribute to FAO s Forest Resource Assessment (FRA) Project as a source for validation in areas frequently covered by clouds. The results were recently validated w ith data from FAO and JRC. Based on that experience FAO now considers to use such data in the next FRA campaign scheduled for Figure 2-4: Example of an highly automated classification of forest cover based on TerraSAR-X 2 m High Resolution SpotLight data. Left: original TerraSAR-X StripMap image, centre: selected texture features to facilitate the automated classification, right: final classification results showing the forest cover (Knuth, 2011) Change detection monitoring Forest Cover Monitoring The sensitivity of X-band SA R data for surface changes is w ell know n. Hence, the major strength of using X-band data is on change detection monitor ing using time series which is even more important than forest cover mapping. Figure 2-5 show s results from an early study on change detection monitoring in a highly dynamic forest area in Nicaragua. The sensitivity of the X-band on surface changes has been demonstrated as w ell identifying successfully defoliation caused by the pine sawfly (Diprion pini) in Northern Ger many using object based classification means (Figure 2-6) GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 5

6 Figure 2-5: Potential clear cuts between 07/02/2008 and 16/05/2008. Left: multitemporal color composite (change areas are indicated in red colours). Right: potential change areas identified from classification results (light green areas.) Figure 2-6: Mapping of defoliation caused by pine saw fly in Northern Germany (from a presentation of K. MARTIN (2011). To exploit the capabilities of VHR X-band data for forest and land cover monitoring ASV GEO / Infoterra GmbH has developed a highly automated change detection processor which was tested in various regions all over the world. The algorithm does identify changes in the image intensity and in the phase (coherence change) from TerraSAR Single Look Slant Range Complex imagery (for more details on TerraSAR-X products: ). Based on image segmentation the potential change areas identified are automatically delineated and can be provided to customers in different formats (e.g. shape, kml or pixel information). The algorithm is running automatically. However, as environmental and forest management conditions are changing from region to region the operator has to apply certain adjustments e.g. with respect to sensitivity and the size of objects to be detected. The latter depends for instance on the pixel size and on the size of the tree canopies or exploitation pattern occurring in a region. The trade-off between the sensitivity of the algorithm to detect GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 6

7 degradation and the increase of unwanted noise (i.e. the identification of too many false alarms stemming e.g. from radar speckle or over water bodies) is a sensitive issue and may require several iterations together with forest experts, knowing the local environmental conditions and the management practices (Figure 2-7). t0 t0 t1 t1 EROS panchromatic < 1 m TerraSAR-X SpotLight (1 m) provided under EC/ESA GSC-DA Figure 2-7: Monitoring of clear-cut activ ities w ith TerraSAR-X SpotLight data. The highly automated change detection process developed by ASV GEO provides a change indicator map show ing the progress of tree felling activ ities (right). Left: the original TerraSAR-X images from tw o points in time in direct comparison w ith optical EROS data. Data acquisition and analysis carried out in 2011 over a Panama Channel Construction Site in the framework of the GMosaic project. Forest cover and its changes may be monitored by the coherence betw een X-band images. At least, this is the case w here the ground vegetation is sparse. This has been demonstrated w ith 11-days repeat-pass TerraSAR-X acquisitions in a pine forest in Finland. New clear-cuts were identified as having sudden increases in coherence (Solberg et al. 2011), see Figure 2-8. Figure 2-8: New clear-cut detected as a sudden increase in coherence (inside circle) in time series of 11-days repeat-pass TerraSAR-X SpotLight. Areas that remained as clearcuts or peatlands maintained high coherence v alues. A stand border map is overlaid as red polygons. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 7

8 Forest Degradation Monitoring Even more challenging than forest cover monitoring is the assessment of forest degradation, i.e. the removal of single trees w hereby the area remains as a forest. Besides the fact that there is still an on-going debate on the definition of forest degradation, it is generally acknow ledged that it means at least a reduction of the total carbon stock over a certain time. Forest degradation is one of the major sources of greenhouse gas (GHG) emissions although its significance has not been estimated on a global scale. In 2000 the total area of degraded forests and forest land in 77 tropical countries w as estimated to be about 800 million hectares, of which degraded primary forest and secondary forest covered about 500 million hectares. (ITTO, 2002 cited by SIMULA, 2009). The REDD Sourcebook ( recommends to use at least very high resolution (VHR) data as forest degradation can range from the removal of single trees to vast destructive logging practices, w hich results in a severe reduction of the canopy cover and secondary damages. Recent investigations have show n that TerraSAR-X data is of high geometric accuracy and its flexible tasking capabilities can be deployed even for fast progressing deforestation and forest degradation monitoring tasks. For instance Baldauf (2009) demonstrated that logging of individual trees in tropical rainforests in Brazil could be detected and automatically identified (Figure 2-9). Figure 2-9: Multi-temporal TerraSAR-X High Resolution SpotLight image (1m resolution) from a selectively logged stand in the Amazon rainforest / Brazil. HR SpotLight Mode in RGB: R: 2008/04/20; G: 2009/08/17; B: 2009/08/17 (left image). The logging of indiv idual trees can be detected and automatically identified (red spots; right image). Such information is mandatory for monitoring forest degradation from space (BALDAUF 2009). Based on these encouraging results in a test site in Colombia a study w as carried out in 2011 in cooperation betw een IDEA M (Institute of Hydrology, Meteorology and Environmental Studies) and Astrium GEO Information Services (Kuntz et al., 2011). The aim w as to demonstrate the use of TerraSAR-X imagery in a Colombian pacific tropical rainforest region, w here optical image availability is very restricted due to nearly permanent cloud coverage. In addition to its remoteness, the lack of appropriate infrastructure and political constraints makes is quite difficult to access the region. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 8

9 The analysis started w ith the creation of a forest cover map in the test site based on TerraSAR StripMap data from The change detection process for degradation monitoring used TerraSAR SpotLight data (acquisition dates: & ). The identification of the changes and the area delineation is highly automated. It requires only at the beginning the definition of thresholds w rt. to sensitivity and size of the change areas to be detected. The highlighted regions show that even in very small spots tree crowns have been removed (Figure 3-8). Figure 2-10: Forest cover map derived from TerraSAR-X StripMap data (left) and a very high resolution change map from TerraSAR-X SpotLight from a test site in West Columbia. In the SpotLight data removal of single trees can be detected (right). Approximately 40 change areas were identified and checked on ground and documented by IDEAM experts (below) Combined Forest Inventories As remote sensing currently does not allow the direct measurement of carbon stock in forests field measurements are required. How ever, for efficient, stratified sampling strategies a-priori know ledge of forest cover, forest conditions and dynamic areas (hot spots) is important. This information can be obtained from other HR imagery (both optical and SAR) or it can be replaced by recorded forest management actions. Based on this information permanent sample plots can be established in order to reduce the effort for expensive field measurements significantly (Figure 3-9). Furthermore, the implementation of per manent sample plots is likely to reduce the error propagation in repetitive inventories. By combining VHR imagery w ith field surveys the in-situ measurements can be reduced to a minimum in those regions w here no changes are detectable or w here the degree of changes (i.e. removal of single trees) can be directly observed by remote sensing. Thus the effort for repetitive sampling can GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 9

10 be significantly reduced after the initial inventory. In areas frequently covered by clouds this task can be done by VHR X-Band SAR data, as demonstrated above. T ree S pecies P rop ort io n 10,0% 6,7% 3, 3 % 6, 7 % 20, 0% 23, 3% 30,0% Figure 2-11: Principle of combined inventories taking benefit of remote sensing and optimised field survey. sfm consultants Outlook Evolving Technology Forest biomass monitoring remains a challenge in X-band remote sensing. Biomass appears to be strongly correlated to canopy surface height, w hich can be derived from stereo SAR imaging (InSAR or radargrammetry). This has been demonstrated w ith airborne SAR (Santos et al. 2008), the spaceshuttle SRTM mission (Solberg et al a. b), and Tandem-X is, hence, a promising method. At the beginning of 2011 the global TerraSAR TanDEM mission started. It is aimed to create a global digital elevation model from bi-static interferometric SAR technology deploying TerraSAR-X and TanDEM-X flying in a dedicated orbit. Until 2013 the solid surface of the Earth w ill be covered several times by that mission offering a unique opportunity to use this data set for the assessment of forest height and relate this to biomass and timber volume in an unprecedented quality (Figure 2-12). Hence, in DLR s science announcement of opportunity (see Annex 6.2) several proposals have been submitted and w ere accepted to investigate this issue. First results on achievable accuracy are expected from the scientific investigators in. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 10

11 Figure 2-12: Relationship between InSAR height and biomass. InSAR height was derived from the SRTM X-band (left) and airborne SAR (right). From a presentation given by S. Solberg at the Norwegian Space Centre, April Conclusions REDD+ services go far beyond simple forest monitoring and need to include at least: Deforestation / afforestation monitoring (activity data) Forest degradation monitor ing requiring VHR imagery and field survey C-Stock modeling based on in-situ sampling for biomass assessment and carbon stock model calibration The pre-condition for that is that the data have to be accessible for the countries in charge (in terms of budgets and existing data analysis capacities), that they available when needed in a reliable w ay and that their technical performance in terms of revisit, geometry and resolution is fitting for purpose. Here, very high resolution X-Band SAR data can play an important role in the international REDD+ monitoring context, for both deforestation and forest degradation. Especially in the framew ork of multi-stage forest inventories they can guarantee the implementation of permanent sampling schemes even in regions w hich are permanently covered by clouds. How ever, w ith highly variable ecological and socio-economic conditions in REDD+ countries there is no one size fits all solution for these tasks. But today s optical and SAR satellite systems together offer synergistic information w rt. scales and information content. Hence, follow ing the REDD Sourcebook recommendations it is expected that: Optical and SAR free data w ill constitute the baseline for national-w ide LULUCF monitoring and for forest type stratification on an annual or bi-annual update cycle. Combined inventory schemes w ill require additional VHR imagery to become most effective in order to allow the assessment how the carbon stock is changing. Here commercial V HR optical and SAR data w ill be required to install a reliable monitoring approach, especially w hen forest degradation monitoring is envisaged. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 11

12 SAR data processing and interpretation is much more demanding than the analysis of optical imagery for both, the data pre- and post-processing. Hence, respective training of national experts on SAR data analysis has to be foreseen from the beginning to enable the full exploitation of such data in interested countries. 4. References Baldauf, T.; Köhl, M. (2009): Use of TerraSAR-X for Forest Degradation Mapping in the context of REDD. Presentation at the World Forestry Congress, Buenos Aires, 2019 Dobson M.C., Ulaby F.T., LeToan T., Beaudoin E.S., Kasischke E.S., & Christensen N. (1992): Dependence of RADA R backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30(2), pp GOFC-GOLD (2010): A sourcebook of methods and procedures of monitoring and reporting anthropogenic greenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation. GOFC-GOLD Report version COP15-1 (GOFC- GOLD Project Office, Natural Resources Canada, Alberta, Canada) Jensen, J. R. (2000): Remote Sensing of the environment: an earth resource perspective. Prentice-Hall Inc. series in geographic information science. Knuth, R., N. Richter, R. Ec kardt & C. Schmullius (2011): Tropical forest mapping using single date TerraSAR-X high resolution SpotLight data. - Proc. 34th ISRSE Conf., April 10 15, 2011, Sydney, Australia Kuntz, S. (2010): Potential of spaceborne SAR for monitor ing the tropical environments. Tropical Ecology 51(1): p. 3-10, 2010 Kuntz, S, von Poncet, F, Baldauf, Th., Plugge, D., Kenter, & B., Köhl, M (2011): A multi-stage inventory scheme for REDD inventories in tropical countries. Proc. 34th ISRSE Conf., April 10 15, 2011, Sydney, Australia Martin K. (2011): Using TerraSAR-X data for mapping of damages in forests caused by the pine sawfly (Dprion pini). Presentation given at the 4. TerraSAR-X Science Team Meeting; DLR, 15 February 2011 Santos, J.R. dos, Freitas, C. da Costa, Araújo, L., Dutra, L.V., Mura, J.C., Gama, F., Soler, L., Sant anna, S.J.S. (2003). Airborne P-band SAR applied to the above ground biomass studies in the Brazilian tropical rainforest. Remote Sensing of Environment, 87(4), Santos J. R., Mura J.C., Kux H.J.H., Garcia C. E., Kuntz S., Brow n I.F., Pantoja N.V. (2010): Classification of TerraSAR-X imagery for the characterization of Amazon tropical forests. Proc. EA RSeL 2010 Simula, M, (2009): Tow ards Defining Forest Degradation: Comparative Analysis of Existing Definitions, Forest Resources Assessment Working Paper 154, FAO, Rome, Italy Solberg, S., Astrup, R., Gobakken, T., Næsset, E., & Weydahl, D.J. (2010a): Estimating spruce and pine biomass w ith interferometric X-band SAR. Remote sensing of environment. 114: GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 12

13 Solberg, S., Astrup, R., Bollandsås, O.M., Næsset, E., & Weydahl, D.J. (2010b): Deriving forest monitoring variables from X-band InSAR SRTM height. Canadian Journal of remote sensing. 36(1): Annex Forestry related R&D projects using TerraSAR-X / TanDEM-X data supported by DLR 6.1 Fourth TerraSAR-X Science Team Meeting; DLR, 15 February S. R. Cloude, A. Marino: New Forestry Products from Dual and Quadpol Terrasar-X Data; 2. Sandra Englhart, Vanessa Keuck, Florian Siegert: Aboveground biomass assessment in tropical forests Regression modeling versus Neural Networks using combined X- and L-band data; 3. Leif E.B. Eriksson, Maciej J. Soja, Lars M.H. Ulander, Johan E.S. Fransson, Andreas Pantze: Mapping of Clear-Cuts and Wind-Thrown Forest with TerraSAR-X ; 4. Juan Ygnacio López Hernández & Barbara Koch: LANDSAT and TerraSar-X data in cloud tropical forest of San Eusebio, Mérida, Venezuela 5. Sonia Ortiz, Johannes Breidenbach, Geral Kändler, Barbara Koch: Mapping forest types with TerraSAR-X imagery 6. R. Knuth, R. Eckardt, N. Richter, M. Bindel, T. Heyer & Christiane Schmullius: Using single date TerraSAR-X High Resolution SpotLight data for tropical forest cover classification 7. Klaus MARTIN: Using TerraSAR-X data for mapping of damages in forests caused by the pine sawfly (Dprion pini). 8. Svein Solberg, Rasmus Astrup, Päivi Lyytikainen-Saarenmaa, Tuula Kantola, Markus Holopainen, Dan Weydahl, Harri Kaartinen (2011). Testing TerraSAR-X for forest disturbance mapping. GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 13

14 6.2 Tandem-X Science AO XTI_VEGE031 5 Forest resource monitoring Prof. Dr. agric Solberg, Svein - Norwegian forest and landscape institute, National forest inventory XTI_LAND0333 REDD rainforest biomass monitoring Prof. Dr. agric Solberg, Svein - Norwegian forest and landscape institute, National forest inventory XTI_VEGE033 0 XTI_VEGE036 0 XTI_VEGE037 6 XTI_VEGE038 9 XTI_VEGE041 9 XTI_VEGE042 4 XTI_VEGE043 5 XTI_VEGE047 5 XTI_VEGE051 5 XTI_VEGE063 5 XTI_VEGE066 6 NTI_POLI0258 NTI_POLI0356 Estimation of vertical and horizontal structural properties of tropical and sub-tropical woody vegetation by combining SAR interferometry and two-point spatial statistics (texture measures) of SAR observations. Estimation of forest variables at plot level from TanDEM-X data 3D FOREST PARAMETER RETRIEVAL FROM TANDEM-X INTERFEROMETRY Forest Dynamics Monitoring with TanDEM-X Interferometry and Auxiliary Data in Boreal Zone Tropical Forest Structure and Biomass with X-band Interferometry at Multiple Vertical Wavelengths (Baselines) Forest height measurements from X- and P-band interferometric SAR Tropical Forest Degradation and Change Monitoring with TanDEM-X Characterization of Vegetation Structure in the Mixed Temperate Forests of the Northeastern United States Forest Surface Height Recovery using Volumetric Decorrelation Estimation Effect of Terrain Relief and Vegetation Cover on the Accuracy of TanDEM-X DEM Influences of forest biophysical parameters and topography variations on a DEM derived from X-band bistatic SAR interferometry Retrieval of forest height using singlepass POLInSAR Tree Height Estimation and Forest Biomass Mapping in Tasmania using Dr De Grandi, Gianfranco - European Commission - Joint Research Centre, Institute for Environment and Sustainability Dr. Karjalainen, Mika - Finnish Geodetic Institute, Remote Sensing and Photogrammetry Adj Prof Ulander, Lars - Chalmers University of Technology, Earth and Space Science Dr. Rauste, Yrjö - VTT Technical Research Centre of Finland, TK802 Dr. Treuhaft, Robert - Jet Propulsion Laboratory, California Institute of Technology, Tracking Systems and Applications Mrs Pourthie, Nadine - CNES, DCT/SI/AR Dr. Rauste, Yrjö - VTT Technical Research Centre of Finland, TK802 Associate Professor Siqueira, Paul - University of Massachusetts, Electrical and Computer Engineering Dr Williams, Mark - Independent Scientific Consultant - Radar Remote Sensing Professor Rao, Y.S. - IIT, CSREXTI_VEGE0657 Development of Remote Sensing based methods for damage mapping in forests within the FP7 project European Forest Downstream Services- Improved Information on Forest Structure and Damages (EUFODOS) Mr. Ermert, Jörg - Albert-Ludwigs-Universität-Freiburg, Department of Remote Sensing and Landscape Information Systems FeLis Mr. Ackermann, Nicolas - Friedrich Schiller University of Jena, Remote Sensing Dr. Cloude, Shane - AEL Consultans Dr Zhou, Zheng-Shu - CSIRO, CMIS GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 14

15 NTI_POLI0496 NTI_POLI0537 NTI_POLI0617 NTI_POLI0640 OTHER0681 High Coherent Polarimetric Data Ground Topography Retrieval on Forested Areas Based on Polarimetric SAR Interferometry Bistatic polarimetric interferometry of forest structure Vegetation structure characterization from space borne InSAR data using model-based techniques TanDEM-X Data for vegetation biomass estimation in a fire-disturbed Mediterranean area FRA-SAR: Application of TerraSAR-X, TanDEM-X and multi parametric radar data to support the FAO Forest Resource Assessment 2010 (FRA- SAR, Approved AO-project funding ref. FK 50EE0802) Ph.D. López-Martínez, Carlos - Universitat Politècnica de Catalunya, Signal Theory and Communications Dr. Neumann, Maxim - Jet Propulsion Laboratory, Radar Science and Engineering Mr Praks, Jaan - Aalto University, Department of Radio Science and Engineering Mrs Anastasia, Polychronaki - Aristotle Uni versi ty of Thessal oni ki, Facul ty of Forestry and Natural Environment Mr. Knuth, Ralf - Friedrich-Schiller- Universität Jena, Geografie GEO-FCT, 3 rd Science & Data Summit, Arusha, Tanzania, February, 6-10, ; updated August 15