ADB. IN THE 6 CI's RIVER BASIN TERRITORY - PACKAGE B. Final Report B.2 - Strategic Spatial Planning Appendix 5

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1 ADB TA 7189-INO: INSTITUTIONAL STRENGTHENING FOR INTEGRATED WATER RESOURCES MANAGEMENT (IWRM) IN THE 6 CI's RIVER BASIN TERRITORY - PACKAGE B Final Report B.2 - Strategic Spatial Planning Appendix 5 Summary Report: Mapping land use land cover in Java with Alos PALSAR May 2012

2 A P P L I E D G E O S O L U T I O N S, L L C Summary Report: Mapping land use land cover in Java with Alos PALSAR TA No INO Institutional Strengthening for Integrated Water Resources Management in the 6Ci s River Basin Territory Asian Development Bank Points of Contact Poul Grashoff Demis poulg@demis.nl Nathan Torbick Applied Geosolutions torbick@ags .com or torbick@gmail.com Executive Summary Demis is developing land use change models for regions of Java to improve water resource planning under a project funded by the Asian Development Bank. Applied GeoSolutions (AGS) carried out mapping tasks to parameterize the land use change models. The mapping tasks utilized Jaxa Alos PALSAR finebeam and mosaic imagery. Multitemporal finebeam single (hh) and dual pol (hh:hv) was collected for four distinct temporal periods during 2010 in Single Look Complex (SLC) to map the 6Ci watershed at a fine-scale (~12.5m) spatial resolution. Jaxa mosaic products from 2009 were utilized to map land use land covers for Java at 50m scale. A Classification And Regression Tree (CART) and operational thresholding approach was executed to classify the imagery. Geofield photos were used as calibration/validation to train the CART model and enhance land characterization. Final products focused on urban, water, rice, and paddy hydroperiod classes along with additional classes in standard Level 1 classification maps. This document summarizes the tasks executed by Applied Geosolutions in collaboration with Demis.

3 1.0 Why PALSAR for mapping land use land cover in Java? The tropical location of Java, Indonesia tends to have high cloud cover throughout much of the year limiting optical sensors ability to map land surface conditions. A primary advantage of Synthetic Aperture Radar (SAR) data is its ability to penetrate canopies and its sensitivity to vegetation structure, water content, and biomass independent of weather conditions. This makes the use of SAR for mapping land use land cover a logical choice for regions with high cloud cover. In general, mapping accuracy is somewhat dependant on spatial and temporal resolutions, band sensitivity, and land types when utilizing SAR data as saturation thresholds and sensitivity are largely dependent on community composition and structure. The acquisition strategy of the Jaxa Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) platform has been developed with a goal of having spatially and temporally consistent data scales with adequate revisit frequency and timing to enable the development of large-area maps. Therefore, this study focused on the application of Alos PALSAR for mapping land use land cover in Java. 2.0 Description of Data Fine-beam PALSAR. A complementing set of multitemporal and multiscale Jaxa Alos PALSAR satellite observations were used to map land us eland covers across the 6Ci river basin and Java. ALOS orbits in a Sun synchronous pattern at an altitude of km and an inclination of For the 6Ci river basin we used finebeam single (FBS) and dual (FBD, hh:hv) pol imagery (Rosenqvist et al 2004). FB imagery was collected in Single Look Complex (SLC) to optimize the complete signal and adjust the effective number of looks considering the ground range resolution, the pixel spacing in azimuth, and incidence angle. FB data were co-registered using a cubic convolution crosscorrelation approach considering shifts and range and azimuth dependency. Multilooking was applied with a four or five to one ratio to obtain an optimal ground resolution and signal to noise ratio. Both de grandi and an anisotropic non-linear

4 diffusion filter that adapts to target features were applied to reduce speckle. Terrain geocoding used SRTM digital elevation model (DEM) data and followed the range-doppler approach as polynomials are inappropriate for SAR systems as nonlinear compression occurs (Aspert et al 2007). FB data were further radiometrically calibrated and normalized by eliminating local incident angle effects and antenna gain and spread loss patterns (Low and Mauser 2003, Holecz et al 2005). Resulting preprocessed data will provide fully calibrated sigma nought σº products for classifications. We used approximately 48 fine-beam scenes that were projected into UTM. The dates of the imagery varied depending on location. Image footprints cover approximately 75km east-to-west and repeat intervals limit the frequency of observations for any location. In this application we targeted four temporal windows for the wall-to-wall mapping for 6Ci. It took 12 scenes (footprints) to completely cover the 6Ci wall-to-wall with ascending imagery four times meaning we used 48 images. The four target windows included: period 1:11/28/10-12/15/10; period 2: 8/11/10-9/14/10; period 3: 2/14/10-3/14/10; and period 4: 6/20/10 7/31/10. Mosaics Mosaics are preprocessed by a JAXA and NASA JPL collaboration. These images are collected in single (HH) and dual pole (HH/HV) during dry and wet seasons or summer and winter seasons for observations of key phenological patterns. The product resolution is 50m and are provide in a geographic project in WGS84. Alos PALSAR Mosaic at 50m resolution for Java in Slant Range K&C Strips The Alos PALSAR instrument features a wide-swath ScanSAR mode with single polarization. The center frequency is 1270 Mhz (23.6 cm), with 14 MHz bandwidth in ScanSAR mode. In 5-beam ScanSAR mode, the incidence angle range varies from 18.0 to Ground resolutions depend on mode and include square 100m spatial resolution pixels for ScanSAR stamps (small footprints) and ~50 by 70m pixels for ScanSAR strips (longer footprints) in the azimuth and range direction, respectively. Absolute radiometric accuracy is <1.5dB between orbits. Adjacent ScanSAR acquisitions overlap 50%, so effectively there are 2 acquisitions every 46 days contiguously starting from October Therefore, due to the observation strategy these strip products are ideal for assessing phenological information such as rice paddy

5 hydrioperiod. The SLT ScanSAR imagery was preprocessed from native resolutions to radiometrically normalized and calibrated terrain geocoded strips. Raw SLT data was imported using orbit, doppler, and parameter files. Strips that observed similar geographic area underwent coregistration using a 4 th order cubic convolution algorithm and SRTM DEM as input. Cross correlation windows had a range and azimuth size of 1024 and 4096, respectively, and a cross correlation grid threshold of Fine shift parameters had range and azimuth sized windows of 32. These parameters were used due to the large coverage of the long ScanSAR strips. All coregistered images underwent multitemporal filtering to reduce speckle or random multiplicative noise. An optimum weighting factor was applied to balance difference in signal across time. The data next was terrain geocoded following the range-doppler approach as polynomials are inappropriate for SAR systems as nonlinear compression occurs. Imagery was radiometrically calibrated and normalized by eliminating local incident angle effects and antenna gain and spread loss patterns using an optimal resolution resampling approach. Local incidence angle and layover/shadow maps were generated for post classification processing to adjust for poor data pixels. Geofield photos GPS field photos were collected across West Java as part of a field campaign in Geophotos (georeferenced digital photos) were collected using a stratified random sampling scheme at a suite of strategic locations across major routes travelled in West Java. A set of stratified clusters along primary road networks were used to guide the geophotos collection campaign. KML (keyhole markup language) files were created to store and display points and photos on Google Earth. KML files use a tag based structure with attributes that allow display. These photos are available for viewing and sharing in Google Earth at server. At this website users can search and share a library of global georeferenced field photos for product validation. Photos and land covers were interpreted at thousands of unique points with a focus on land covers of interest.

6 3.0 Classification Approach We employed a classification tree approach with bagging using rpart and randomforest libraries (R software) that has been shown to be a powerful classifier. This classification tree approach is non-parametric and falls under the broader category of classification and regression trees (CART) (Lawrence et al 2004, Lawrence et al 2006). CART can produce either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively. Decision trees have substantial advantages for remote sensing classifications problems because of their flexibility, intuitive simplicity, and computational efficiency (Friedl et al. 1997; Defries et al. 1998). Decision trees predict class membership by recursively partitioning data into more homogenous subsets (Defries and Chan 2000). In this study within scene training areas were developed for each class using geofield photos to create polygon training areas and visual interpretation of imagery. We randomize the calibration/validation data and use these data subsets to create many trees, the number of trees is depends on the ratio of observations between classes. These trees are not pruned. Each tree is made up of a series of rules (i.e. nodes) created during calibration. The rules are designed to split the training data into sub-groups maximizing differentiation. The splitting stops when no further gain in differentiation can be made. In the end, every new pixel for prediction is run through all trees and each tree casts votes to develop a suite of likelihood or probabilities scenarios. Typically, the majority class is selected for the class in the final map; however, an advantage of this approach is the ability to compare likelihoods against others classes to construct logic statements and rules to obtain the best map for a particular application (ie, water resource or urban mapping). We then applied operational thresholding to further constrain output and identify phenological information following methods detailed in Torbick et al 2010.

7 4.0 Results. We present results as graphics of map products and short descriptions to reduce redundant discussion. All products have been delivered to Demis for modeling activities. Multitemporal K&C strips were used to map rice extent across Java with the operational approach. This was a version 1.0 product that was updated using the CART approach and mosaics products. Fine-beam ascending mosaic for the 6Ci river basin study region. Four time periods were used as input into the CART to generate a fine scale land use land cover product. The colors visible highlight areas of dynamics changes including rice agriculture and hydroperiod status for paddies at ~15m scale. The green and blue colors illustrate different stages of rice cropping (ie, planting, haulm heading, flooding). The image is displayed as a stacked Red, Green, and Blue with finebeam HH from November, August, and February, respectively.

8 Map product from 6Ci study region for nine primary classes. CART product used four time periods of FBS/D PALSAR to characterize landscape.

9 Hydroperiod Intensity Top: Map product from Mosaic CART classification for Java with Forested and Bush classes combined to improve accuracy. Bottom: Map product from SLT strips from operational hydroperiod approach to map paddy dynamics and pre-stage rice flood frequency.

10 References Aspert F., M. Bach Cuadra, J.P. Thiran, A. Cantone, and F. Holecz, Time-varying segmentation for mapping of land cover changes, Proceeding of ESA Symposium, Montreux, Defries, R., Hansen, M., Townsend, J., Sohlberg, R Global land cover classification at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing 19: Defries, R., Chan, J Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment 74: Friedl, M., Brodley, C Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 61: Holecz F., Freeman A., van Zyl, J. Topographic effects on the antenna gain pattern correction, Proc. of IEEE-IGARSS 95, Florence, Italy. Lawrence, R. L., A. Bunn, S. Powell, and M. Zambon, Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing of Environment 90: Lawrence, R.L., S. Wood, and R. Sheley, Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomforest). Remote Sensing of Environment 100: Low, A., Mauser, W Generation of geometrically and radiometrically terrain corrected ScanSAR images. Geoscience and Remote Sensing Symposium, IGARSS apos;03. Proceedings IEEE International Volume 6, Issue, July 2003 Page(s): vol.6. Rosenqvist, A., Shimada, S., Watanbe, M ALOS PALSAR: Technical outline and mission concepts. 4th International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications. Torbick, N. et al Integrating SAR and optical imagery for regional mapping of paddy rice attributes in the Poyang Lake Watershed, China. Canadian Journal of Remote Sensing (under press). Torbick, N., Salas, W., Hagen, S., Xiao, X Mapping rice agriculture in the Sacramento Valley, USA with multitemporal PALSAR and MODIS imagery. IEEE J. Selected Topics in Remote Sensing. DOI /JSTARS