Tropical forest mapping and change detection using ALOS PALSAR data

Transcription

1 Tropical forest mapping and change detection using ALOS PALSAR data Wenmei Li a, Qi Feng a, Erxue Chen a, Zengyuan Li *a a The research institute of forest resources information technique, Chinese Academy of Forestry No.1 Dongxiaofu, Haidian district, Beijing, China, ABSTRACT Tropical forests represent a significant carbon store in the global carbon balance. Over recent years, substantial forests have been cut or burned. To investigate the changes of deforestation in tropical forests, ALOS PALSAR 50 m Orthorectified mosaic product (FBD) in Borneo from 2007 to 2009 was used, as SAR data can overcome the shortcoming of optical images. Pixel based unsupervised classification and object-oriented classification methods were applied to choose better method for classification. The comparison showed the object-oriented classification got the better result and its accuracy was higher. In the following step, object-oriented classification method was employed to monitor the changes of deforestation in Borneo during two years. The results indicated the deforestation increased sharply, especially the areas in the coastal and next to non-forest land cover/use. The change law of land cover/use in Borneo is forest to shrub, grassland and bare soil. There are also inverse changes from non-forest to forest by reforestation around settlement and water areas. Overall, forest decreases gradually and non-forest areas increases simultaneously in Borneo. This research indicates clearly that ALOS PALSAR Ortho-rectified FBD product can be used to classify land cover/use and two different time images could be employed in change detection without in situ data. Keywords: ALOS PALSAR, change detection, object-oriented classification, unsupervised classification, equation based on polynomial distribution 1. INTRODUCTION Land use/cover change is an important field in global research laboratory currently [1]. Forest is a part of land use/cover and it plays a critical role in global change study. Monitoring changes of forest is a useful mean to study global change and forest variation reflects law of land use/cover change. Therefore, now forest investigation and forest change research are significant for further global change study. Southeast Asia is generally located between latitudes 28 N and 13 S on the southeast of Asia. This area is famous for its forest. Every year it produces many woods and sells to all over the world. Borneo is an island in the Southeast Asia, which is the only island, belongs to three countries on the world. There are plenty of forests in Borneo and they have changed a lot in recent years. Southeast Asia forest area is one hot point of the global forest biomass research. However, this area is covered by cloud all the year around. Traditional optical remote sensing cannot get clear images and cannot attain land use classification and forest mapping in region scale [2]. Meanwhile, most tropical forest areas are untraversed and field reference data cannot be acquired. Under this condition, how to make forest mapping in southeast topical forest areas is one important problem in the world. SAR gets the attention of forest researchers as its advantages of work in day and weather with higher penetrability. SAR can penetrate cloud to make up the deficiency of optical remote sensing [3] to produce tropical forest map. However, Southeast Asia is outside China with wide coverage complex surface. De Grandi and Rosenqvist got the tropical forest map using JERS-1 SAR images in tropical forest project [4]. Li obtained northeast forest distribution map of China using ENVISAT and ERS SAR Data [5]. SIBERIA-1 &2 [6], GRFM [3], GBFM [7] and ESA-MOST Dragon cooperation program [4] are large application projects, which are showing the advantages of SAR in forest application. These projects acquired many important results for further research and all these researches are supported by ground reference data. * lizy@caf.ac.cn International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, edited by Xiufeng He, Jia Xu, Vagner G. Ferreira, Proc. of SPIE Vol. 8286, 82861U 2011 SPIE CCC code: X/11/$18 doi: / Proc. of SPIE Vol U-1

2 There are mainly three classification methods for SAR images in current, simple data driven method-traditional based on spectral classification, pure method based on scattering mechanism and classification method based on rules [8]. Simple data driven method was used in early years and ISODATA, fuzzy c means and k means were used in detail operation [9]. Pure method based on scattering mechanism was on the base of back scattering mechanism, eigenvector and parameters were used in classification [10]. Classification method based on rules has been employed to classify types of land use recently [11]. All of the methods are based on PolSAR or PolInSAR data, but method with only intensity information, no ground reference data or expert knowledge and large areas of forest mapping using SAR data is little involved. Only with intensity information ALOS PALSAR FBD is Ortho-rectified mosaic data. After geo-rectification and radiometric calibration, it can be used to provide origin data with optical classification method to produce forest maps of large areas. Pixel-based unsupervised classification and object-oriented unsupervised methods were used without in situ data in this paper, results of the two methods were compared and the better one was employed to forest change detection. 2.1 Data description 2. MATERIALS AND METHODS ALOS PALSAR is an active micro sensor, which is using L band to make earth observation all-time and all-weather. This research used K&C PALSAR Ortho-rectified synthesis of product data called FBD (Fine Beam Dual), which contains 272 scenes in 2007 and 208 scenes in 2009 HH/HV strip data. Its spatial resolution is 50 meters parameter -83 for radiometric calibration and polarization is HH and HV. What s more, it is multi-looked processed and covering tropical forest for change detection of forest biomass and carbon cycle [12]. The purpose of our research is using ALOS PALSAR product to classify types of land use in Borneo and finally get the change detection of forest cover during 2007 and Classification system Land cover in Borneo is complex and diverse. Forest contains evergreen coniferous forests, evergreen broad-leaved forests, deciduous coniferous forests, deciduous broad-leaved forests and Mangrove, Peat swamp forests and so on. It is hard to classify these detail types only with PALSAR dual polarization data. According to the characteristics of Borneo and classification method, 6 classes were determined: forest, shrub, grassland, crop, water and bare land / burned areas with tree cover. 2.3 Methods and operating procedures Pixel-based unsupervised classification method Pixel based unsupervised classification method is always associated with K means or ISODATA. The method is on the base of pixel and spectral distance between adjacent pixels and it is a traditional classification method Object-oriented unsupervised classification method Object-oriented unsupervised classification method was involved by Hoekman [11], which is for agricultural application using polarization data. Object-oriented classification method assembles adjacent pixels as object to identify interested spectral factors, and then uses space, texture and spectral information to segment and classify images, outputs classification results with high accuracy [13], [14]. It usually contains two parts---image object construction and object classification. Image object construction mainly means segmentation techniques, which include multi-scale segmentation, gray-scale segmentation, texture segmentation, knowledge segmentation and watershed segmentation and so on. Multi-scale segmentation is common used in classification, this method combines spectral features and shape facility of remote sensing image calculates the Syndrome value of Spectral heterogeneity and Shape heterogeneity of every band in image, subsequently computes all bands weights value according to weights of every band. When composite weights of segmented object less than Proc. of SPIE Vol U-2

3 specified threshold start repeat iteration until the composite weights of all segmented object more than specified threshold and multi-scale segmentation is finished then. There are two methods used in object classification---supervised classification and based rules classification method. Not only the spectral information, but also space and texture information are used in object-oriented classification. In this paper, pixel based ISODATA unsupervised classification and visual interpretation combined method were used to classify PALSAR FBD data in 2007 in Borneo. On the other hand, object-oriented classification method (unsupervised classification) was used to get the classification result of Borneo. In the end, two results from different classification methods were compared and the better method was selected to monitor forest changes during 2007 and 2009 in Borneo (See Fig. 1). PALSAR Ortho-rectification products Preprocess HH HV HH/HV layer-stack HH HV layer-stack Color-composite images Images to be classified Pixel-based unsupervised classification Object-oriented unsupervised classification Construct image objects Create rule sets Results from pixel-based method Qualitative comparison Results from object-oriented classification Precision evaluation Quantitative comparison Precision evaluation Chose better method for quantitative change monitoring Fig. 1. Flow chart of operation. Proc. of SPIE Vol U-3

4 2.3.3 Operating procedures Unsupervised classification method (ISODATA) was used to make classification for SAR images in 2007 in Borneo. The maximum iterations were set 120 and convergence threshold was to get a primary result. Visual interpretation was used to merge the primary result to attain final unsupervised classification result. Interpretation was on the base of classed results in 2007 in Borneo from Hoekman [11]. Object-oriented unsupervised classification method (Based on rules) was used to classify PALSAR FBD data in 2007 in Borneo on the other hand. To construct image object means to determine segmentation scale. A split is a subdivision or a union operation and in consequence there are two methods in segmentation----big-endian subdivision and little-endian merge [15]. Multi-scale segmentation and little-endian merge method were employed in concrete operations. This method is local optimization procedure and iteration is over when heterogeneity is more than given threshold. Heterogeneity is determined by spectral heterogeneity and shape heterogeneity [16]. Different segmentation scales were used for different cultures. After large numbers of experiments good segmentation factors were obtained, for water scale parameter, shape heterogeneity and compactness factor were 200, 0.1 and 0.5 respectively, while the others were 150, 0.1 and 0.5 accordingly. Spectral heterogeneity and smoothness were 0.9 and 0.5 accordingly. The important procedure is rules establishment in object classification. After image object construction, the polygon composed by homogenous pixels became basic unit and classification operation was executed on objects. Every object contained spectral, shape, texture, location and topology information, which were combined to establish classification rule sets for culture distinction. Interactively determine threshold method was employed in this thesis and rule sets were determined by object (polygon) information and different levels transmission. Rule sets were definite as followed: Water: SP = 200 and MBC <= db; Land: SP = 150 and MBC > db; Forest: First condition was in land class and HV/HH >=0.51 and Std (HH)>= , Second condition was db <= MBC < db and (HV/HH <0.4 or HV/HH> 0.49), otherwise the object was Non - forest; Bared soil: In non-forest areas, 0.4<=HV/HH<=0.49 and db <= MBC <= db; Crop: In non-forest areas, Std (HH)>= and MBC <= db; Grassland: In non-forest areas, db <= MBC <= db; Shrub: In non-forest areas, the last was shrub. In these rule sets, SP (Scale parameter) is a factor reflects segment scale, large SP reflects boundary of huge types without detail information, while small SP mirrors detail boards of land use/cover. MBC (Mean backscattering coefficient) is average value of HH and HV backscattering intensity, which reflects spectral information. HV/HH is ratio of HV and HH backscattering coefficient, which means ratio of volume scattering and surface scattering. HV/HH reflects proportion of volume scattering and more HV/HH means more volume scattering. This ratio usually mirrors texture and shape information. Std (HH) is standard deviation of HH polarization, which reflects dispersion degree of surface scattering and it generally images shape, location and topology message. Proc. of SPIE Vol U-4

5 2.3.4 Classification accuracy evaluation Because of shortage of ground reference data and valid statistic data, relative validation was adopted in classification accuracy evaluation. Hoekman has got the classification results using ALOS PALSAR FBD, DEM, Land-use map and in-situ data in Borneo in 2007 [11]. Sample size for classification precision evaluation was calculated through equation based on polynomial distribution and its function is N B P ( 1 P ) i i = 2 (1) bi where N is sample size, P i the ratio of areas of class i and total areas, b i the expected error of class i and B can be found in 2 χ distribution table with 1 as freedom and B= In practical operation, P i can be alternative by the class which is near 50%. Forest was the one 79.77% close to 50%, then P i =79.77%. b i was 6.5% accordingly confidence was 93.5%. The sample size of accuracy evaluation for 2007 classification result in Borneo was about 250. Samples were chosen by uniform distribution method according to the reference result [11]. Three classes stationing were employed in verification point selection. First step sample points were distributed on the intersection of longitude - latitude grid with one degree intervals. As the research on the land-use classification in Borneo, points lay in ocean were removed. Longitude-latitude grid was double encryption in category concentrative areas in second step stationing. Third step 8 bared areas were selected by visual interpretation for bare soil s evaluation as original and doubled grids intersection did not in bared areas. The results of pixel-based unsupervised classification and object-oriented unsupervised classification were validated by this method. 3. RESULTS 3.1 Classification results The results of Borneo in 2007 by using pixel-based unsupervised classification and object-oriented unsupervised classification method were shown in Fig. 2. In order to choose the better method for change detection these results were evaluated using relative validation and Hoekman s [11] result as reference. According to the graph overall classes can be classified by both methods. However, there was difference for two methods in SAR data classification. From qualitative point of view, there were lots of outliers in results using pixel-based method especially in forest class. What s more, bared land / burned areas with tree cover could not be classified by this method. Tables 1, 2, 3 and 4 list the error matrix accuracy assessment and accuracy evaluation of results by two methods. From quantitative point of view, the total accuracy was 61.6% and Kappa coefficient was for pixel-based method. Cropland, grassland and shrub were not classified correctly and their user accuracies were lower than 50%. Meanwhile, the total accuracy was 78.4% and Kappa coefficient was for Object-oriented method. Bare land/burned areas with tree cover was classified by object-oriented method and cropland, grassland and shrub could be classified clearly. Their user accuracies were more than 50%. Both methods had higher accuracy in forest and non-forest classification with total accuracy 83.2% for pixelbased method and 93.6% for object-oriented method. However, the two methods were affected by speckle, which caused outliers in final result. Cropland, grassland and shrub were mixed at different degree. From qualitative and quantitative point of view, object-oriented method was better than pixel-based method in SAR image classification. Proc. of SPIE Vol U-5

6 (a) Fig. 2. Classification results of Borneo in (a) Used pixel-based unsupervised classification method. (b) Used objectoriented unsupervised classification method. (b) Table 1. Error matrix accuracy assessment of classification result of Borneo in 2007 using Pixel-based unsupervised classification method Types of testing samples Types after unsupervised classification Water Forest Cropland Grassland Shrub Bared land/burned areas with tree Water Forest Crop land Grassland Shrub Bared land/burned areas with tree Total Total Proc. of SPIE Vol U-6

7 Table 2. Accuracy evaluation of classification result of Borneo in 2007 using Pixel-based unsupervised classification method. Precision Types Water Forest Cropland Grassland Shrub Bared land/burned areas with tree User accuracy 75% 90.48% 28% 45.45% 39.06% 0% Product accuracy 50% 88.79% 46.67% 14.71% 60.98% 0% Total accuracy 61.6% Kappa coefficient Table 3. Error matrix accuracy assessment of classification result of Borneo in 2007 using Object-oriented unsupervised classification method. Types of testing samples Types after object-oriented unsupervised classification Water Forest Cropland Grassland Shrub Bared land/burned areas with tree Water Forest Crop land Grassland Shrub Bared land/burned areas with tree Total Total Table 4. Accuracy evaluation of classification result of Borneo in 2007 using Object-oriented unsupervised classification method. Precision Types Water Forest Cropland Grassland Shrub Bared land/burned areas with tree User accuracy 100% 100% 64.52% 55.56% 52.31% 100% Product accuracy 80% 91% 66.67% 44.12% 83% 75% Total accuracy 78.4% Kappa coefficient Change detection Object-oriented unsupervised classification method was used to monitor land changes in Borneo from 2007 to Fig. 3, Fig. 4 and Table 5 show the changes in qualitative and quantitative point of view. It can be seen from Fig. 3, the classed in classification map during 2007 and 2009 changed when types in the color composite images from 2007 to 2009 varied. This character explained classification map with high stability and facticity could be used for change detection. Fig. 4 shows the changes of forest from 2007 to 2009 in Borneo and it can be seen that forest decreased during this period. Reduced forest centered on the edge of forest and non-forest, coastal areas. According to Table 5, forest has Proc. of SPIE Vol U-7

8 been reduced 1.56% and cropland fell rapidly from 2007 to While bare land/burned areas with tree cover and grassland rose sharply with 94.56% and 87.26% respectively. Area of grassland increased km2, bare land/burned areas with tree cover rose km2 and forest decreased km2, which indicated decreased forest may become grassland or bare land/burned areas with tree cover. These changes concentrated in the coastal and next to cropland, grassland and other non-forest land cover/use. The change rule of forest and non-forest in Borneo is forest to shrub, grassland and bared soil or residential areas Color composite image Classification map Fig. 3. Changes in Borneo from 2007 to 2009 in qualitative point of view. Fig.4. Changes of forest in Borneo during 2007 and Proc. of SPIE Vol U-8

9 Table 5. Changes of major land cover types in quantitative point of view in Borneo from 2007 to Type Area(km 2 ) (2007) Area(km 2 ) (2009) Area variation(km 2 ) ( ) Change rate(- reduction, +increasing) Water % Forest % Cropland % Grassland % Shrub % Bare land/burned areas with tree cover % 4. CONCLUSION The compared results in qualitative and quantitative point of view in Borneo indicate Object-oriented unsupervised classification method is better than Pixel-based unsupervised classification method in ALOS PALSAR FBD classification. Equation based on polynomial distribution and uniform distribution combined special points were used for accuracy evaluation. Change detection results illustrate that there is deforestation in Borneo from 2007 to 2009, forest has been changed to shrub, grassland and bared soil/burned areas with tree cover in the coastal and near cropland, grassland and other non-forest land cover/use. All these results show that Object-oriented unsupervised classification method is a better solution for SAR image classification and ALOS PALSAR FBD product is a useful data for change monitoring research in Borneo. Borneo is a typical tropical forest area and its study method could be used in the whole tropical forest. Experiments on classification also indicate that ALOS PALSAR Ortho-rectified data can be used for classification and change monitoring. Object-oriented unsupervised classification method can reduce the effects of speckles in SAR images. However, the accuracy of classification only with SAR Ortho-rectified data was lower. Wetland and urban could not be classified and forest type was not distinguished either in this research. Change rules need to be studied further to reflect detail variation between different types. ACKNOWLEDGMENTS Thanks to K&C Initiative Project for providing ALOS PALSAR FBD product used in this study. REFERENCES [1] [2] [3] [4] Caoxue, Ke Changqing. Dynamic remote sensing monitoring of land use in Nanjing based on TM images. Geomatics and information science of Wuhan University. Papers 31(11), (2006). Dirk H. Hoekman, M.J.Quinones, R. Verhoeven, M. A. M. Vissers, V. Schut and N. Wielaard. PalSAR tropical forest cover mapping, mosaicing and validation, case study Borneo. Proc. of 4th Int. Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry PolInSAR 2009, January 2009, Frascati, Italy. ESA SP-668 (2009). Chen Erxue. Development of forest biomass estimation using SAR data. World forestry research. Papers 12 (6), (1999). De Grandi G., Mayaux P., Rauste Y., Rosenqvist A., Simard M and Saatchi S.S. The Global Rain Forest Mapping Project JERS-1 radar mosaic of tropical Africa: development and product characterization aspects [J]. IEEE Transactions on Geoscience and Remote Sensing. Papers 38(5), (2000). Proc. of SPIE Vol U-9

10 [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] Li Z., Pang Y., Schmullius C. and Santoro M. Forest mapping using ENVISAT and ERS SAR data in Northeast of China [J]. IEEE Trans. Geosci. Remote Sensing. 8, (2005). Schmullius C., Holz A., and Vietmeier J. SIBERIA -results from the IGBP Boreal Forest Transect [J]. Proceedings of IGARSS 99 Conference, Hamburg, Germany (1999). Rauste Y. Multi-temporal JERS SAR data in boreal forest biomass mapping [J]. Remote Sensing of Environment. Papers 97(2) (2005). Oliver C.J. and Quegan S. Understanding synthetic aperture radar images [M]. Boston, MA: Artech House. (1998) Kong J.A., et al. Identification of terrain cover using the optimal terrain classifier [J]. Journal of Electromagnetic Waves and Applications, (1988). Rignot E., Chellappa R., and Dubois P. Unsupervised segmentation of polarimetric SAR data using the covariance matrix [J]. IEEE Transactions on Geoscience and Remote Sensing. 30(4), (1992). Hoekman D.H. and Martin A.M. A New Polarimetric Classification Approach Evaluated for Agricultural Crops [J]. IEEE Transactions on Geoscience and Remote Sensing. 41(12), (2003). Guo Lin, Pei Zhiyuan, Wu Quan, et al. Application of method and process of object-oriented land use-cover classification using remote sensing images [J]. Transactions of the CSAE. 26(7), (2010). Benz U C, Hfmann P, Willhauck G, et al. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information [J]. ISPRS Journal of photogrammetry & Remote sensing. 58, (2004). Zhang Jianguo, Li Xianwen, Wu Yanlei. Object oriented estimation of winter wheat planting area using remote sensing data [J]. Transactions of the CSAE. 24 (5), (2008). Lin Xiancheng, Li Yongshu. Classification research on Object-Oriented high-resolution remote sensing images in Chengdu Plain [J]. Journal of southwest Jiaotong University. Papers 45 (3), (2010). Proc. of SPIE Vol U-10