Annual Report (1 st Report) NASA Grant NNX14AD78G (4/1/2014 3/31/2017)

Transcription

1 Annual Report (1 st Report) NASA Grant NNX14AD78G (4/1/2014 3/31/2017) Mapping industrial forest plantations in tropical monsoon Asia through integration of Landsat and PALSAR imagery Principal Investigator Jinwei Dong University of Oklahoma, Norman, Oklahoma & Co-Principal Investigator Williams Salas Applied Geosolutions, Inc., Newmarket, New Hampshire January 31, 2015 (This report reflects activities and results over the period of 4/1/2014 1/31/2015) 1

2 Project Summary (The text from the original proposal submission) The areas of industrial forest plantations such as rubber, oil palm, teak, eucalyptus, acacia and bamboo have expanded enormously in recent years across the tropical regions, in particular tropical monsoon Asia. Conversion of natural forests with monoculture forest plantations has profound impacts on the water resources, carbon cycle, and biodiversity. The information on the area, spatial distribution and temporal dynamics of industrial forest plantations in tropical monsoon Asia is incomplete and outdated. This proposed project combines optical (Landsat) images and L-band synthetic aperture radar (PALSAR and JERS-1) images to identify and map six major industrial forest plantations (rubber, oil palm, teak, acacia, eucalyptus, bamboo) in tropical monsoon Asia. It focuses on four periods: circa 2015 ( ), 2010 ( ), 2005 ( ), and 2000 ( ). The overall goal of this proposed project is to quantify the changes of major industrial forest plantations in tropical monsoon Asia from 2000 to 2015 at 30-m spatial resolution and 5-year interval. The specific objectives are the following: 1. Map the area and spatial distribution of industrial forest plantations in tropical monsoon Asia in 2015, using Landsat 8 (OLI, TIR) and PALSAR-2 images from , 2. Map the area and spatial distribution of industrial forest plantations in tropical monsoon Asia in 2010, using Landsat 5/7 (TM/ETM+) and PALSAR images in , 3. Map the area and spatial distribution of industrial forest plantations in tropical monsoon Asia in 2005, using Landsat 5/7 (TM/ETM+) images in , and 4. Map the area and spatial distribution of industrial forest plantations in tropical monsoon Asia in 2000, using Landsat 5/7 (TM/ETM+) and JERS-1 images in This proposal will primarily focus on industrial forests component of this NASA NRA and will identify and map six industrial forest plantations (rubber, oil palm, teak, Eucalyptus, Acacia, bamboo) in tropical monsoon Asia. The in-situ data and resultant maps from this NASA project will provide timely information to support the FAO FRA 2015, REDD+, and food and water security projects. 2

3 Project Highlights in Year 1 During Year 1 we have made significant progress in several areas of this project. The major highlights are the followings: 1. We have developed new and novel algorithms for mapping deciduous rubber plantation, and the algorithms were evaluated in Southern China and have been published in multiple peerreviewed papers (Kou et al., 2015, RS; Chen et al. In press, ISPRS P&RS; Dong et al., 2012, ISPRS P&RS; Dong et al., 2013, RSE). We are extending the algorithms to other regions in monsoon Asia now, and that will be a major task in Year 2 and We have developed a new algorithm for mapping oil palm plantation, and a paper is recently published (Li et al., 2015, RS). The algorithm developments of other four plantation species (teak, eucalyptus, acacia and bamboo) are under way, by integrating time series Landsat data and cloud-free PALSAR data. 3. We have been organizing a Special Issue titled Mapping the Dynamics of Forest Plantations in Tropical and Subtropical Regions from Multi-Source Remote Sensing in the journal of Remote Sensing-Basal with guest editors of Dr. Xiangming Xiao and Dr. Jinwei Dong, which has already received more than ten submissions about the industrial plantation mapping and biomass estimation. 4. Based on the updated PALSAR 50-m mosaic datasets from JAXA in 2010, we have generated a global forest baseline map at 50-m resolution, which will greatly contribute to the identifications of our targeted plantations. We plan to publish the maps at the scales of country (China: Qin et al., In review, RSE), Monsoon Asia (Qin et al., in preparation) and globe (Xiao et al., in preparation). 5. MODIS Land surface temperature based plant growing season (starting and ending dates above 0 o C, 5 o C and 10 o C in spring and fall) have been developed for the masking of the continental industrial plantation mapping, given the sensitivity of plantations to temperature (e.g., rubber trees). 6. A robust Landsat data processing platform has been developed, which aimed to build time series Landsat datasets for pixel- and phenology-based plantation mapping and change detections. The platform has been tested by using a case study in tracking paddy rice expansion in high latitude area of monsoon Asia (Dong et al., 2015, RSE; Qin et al., In review, ISPRS P&RS) and rubber plantation expansion in Xishuangbanna (Kou et al., In preparation). 7. Thousands of field photos have been collected by the field trips and the contributions of our local collaborators, which have been archived in the Global Geo-Referenced Field Photo Library ( The field data provides a solid foundation for the algorithm training and validations of individual types of plantation mapping. 8. Strategic partnerships have been formed with international space agencies, local NGOs, and Ministries in regions where we are carrying out research and mapping at pilots sites. These include JAXA, LAPAN, CESBIO, ISRO, WWF, GIZ, IAE, AIT, VNU, and USAID among others. 3

4 4

5 A. Rubber plantation algorithm and continental rubber plantation mapping According to the FAO FRA 2010 report, the global rubber plantation area has steadily increased by 25% during the past two decades while around 97% of global natural rubber supply is from Southeast Asia. The dynamic rubber plantation maps would be vital for understanding of the global rubber plantation distribution. Due to the similar spectral features between natural forests and deciduous rubber plantations in plant growing season, it is very hard to identify rubber plantations via traditional image statistic-based approaches. Our previous studies proposed a phenology-based method by using two unique growth phases of rubber plantations: defoliation and foliation phases (Dong et al. 2013; Dong et al. 2012). The two key phases are shown in Figure 1 through in-situ observations. This phenology-based approach has been widely used in current literature with 18 citations since 2013 (according to Google Scholar). Furthermore, we improved the algorithm from three aspects: 1) given the complexity of phenology feature of large scale rubber plantations, high greenness in peak growing season and geographical suitability factors (e.g., elevation and slope) in addition to its defoliation phenology information were used for the rubber plantation mapping (Chen et al. In preparation); 2) a rubber plantation stand age retrieval algorithm was developed by defining the disturbance points (Kou et al. 2015) and the new algorithm has been up-scaled to the regional scale in Xishuangbanna (Kou et al. In preparation); 3) the object-based strategy was used to improve the noises in mountainous regions by using the phenology-based information and texture information (Zhai et al. In preparation). 5

6 Figure 1. Seasonal variation of the canopy and landscape of rubber plantations in Xishuangbanna during (from Zhai et al., In preparation). 6

7 B. Algorithm development for other plantations (oil palm, teak, eucalyptus, acacia and bamboo). We have also explored the oil palm mapping algorithms. For example, a comparison of three algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) has been investigated for oil palm plantation mapping (Li et al. 2015). Our results showed that the advantage of SVM was more obvious when the training sample size was smaller. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K- Means in terms of speed and performance (Figure 2). In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations. This conclusion agree with our previous study in Southeast Asia (Dong et al. 2014) that oil palm can be identified by using the backscatter of HV polarization. The PALSAR and Landsat integrated oil palm mapping approach is being operated in the selected hotspot regions in monsoon Asia such as Borneo Island (Dong et al., In preparation). Figure 2. The overall accuracies and Kappa coefficients from SVM, Decision Tree and K-Means classification methods. We have started the exploration of mapping methods for teak, eucalyptus, acacia and bamboo, specifically, a series of ground truth data has been collected for eucalyptus and we are working on the spectral and temporal profile analysis of the eucalyptus plantations in Thailand, Indonesia and China. 7

8 C. New progress in capacity building in community remote sensing and citizen science C.1. Increasing application of Smartphone app Field Photo We released smartphone app Field Photo iphone version in October 2013 and Android version in February 2014, which is freely available to the public in Apple Store and Google Play Store (Figure 3) and now it has been operational well. The Field Photo app and the web portal of the Global Geo-Referenced Field Photo Library share one database at the University of Oklahoma. Figure 3. Information on the smartphone app Field Photo C.2. Increasing field photos in the Global Geo-Referenced Field Photo Library The Field Photo Library (Xiao et al. 2011) has been well received by the research communities and citizens. It now hosts 130,000+ field photos in the world. With the efforts of the investigators and our international collaborators as well as the volunteered public users, we have an increasing numbers of field photos in the study area (Figure 4). Lots of them are about industrial plantations and that provides a rich data source to support the large scale mapping of this project. 8

9 Figure 4. The field photo distribution in Southeast Asia and bottom is the case photos for rubber trees, eucalyptus, and oil palm respectively. C.3. Improvement in the functions related to the field photos in the EOMF data portal Besides the existing functions in the EOMF data portal (e.g., the integration between field photos and MODIS time series data, multiple pane display of vegetation index data), a new function is being developed for the citizen-based sample production. The geo-referenced field photos are now used in conjunction with Google Earth to digitalize and delineate the Regions of Interest (ROIs) for various land cover types, and the resultant ROIs are then used for supervised classification, including the use of decision tree method. We have completed initial evaluation of the workflow from geo-referenced field photos to ROIs and that has been used in our recently publications (Kou et al., 2015; Dong et al., 2014, 2015; Qin et al., In review). 9

10 D. The 25-m and 50-m forest maps based on PALSAR data The forest cover map is an important base map for the industrial plantation mapping as all the six plantations studied in this project belong to the general forest type. For example, rubber plantations were identified by using the defoliation/foliation feature on the forest base maps. Following our previous efforts in the forest mapping in Southeast Asia (Dong et al. 2014), our most recent efforts have covered China (Qin et al. under review) and monsoon Asia with both evergreen forest and deciduous forest maps were generated (Qin et al. In preparation). Ground truth based validation and the comparison with existing products showed our products have a high quality (see Figure 5 for the details). All these studies are based on the PALSAR 50- m orthorectified mosaic images from JAXA. As new 25-m PALSAR data is available since the end of 2014, we have been working on the 25-m forest map which would provide more reliable convergence with Landsat imagery. Figure 5. A comparison of the spatial distributions of four forest maps from different sources: A. the PALSAR based forest map based on the algorithm at the University of Oklahoma, B. JAXA 10

11 forest map based on the PALSAR data by using different thresholds, C. GlobCover-based forest map form MERIS data, and D. MCD12Q1-based forest map from MODIS data. E. Maps of plant growing season from MODIS land surface temperature (LST) data Thermal conditions are important variables for forest plantations. For example, rubber plantation is usually planted in the area with annual average temperature over 20 o C. We used MODIS Land Surface Temperature data (8-day composite, MOD11A2 and MYD11A2) to map the starting dates and ending date of LST > 0 o C, 5 o C and 10 o C in spring, and fall seasons, respectively. That was used as an important physical suitability factor for forest plantation mapping. As part of effort to develop independent plantation mapping approaches for different types, we need to determine phenology and multi-temporal profile analysis for different plantations that can be used as input data for Landsat based mapping. We are developing the following products by using the NDVI, EVI and LSWI datasets: 1) maps of plant growing seasons (start, end and length), 2) maps of evergreen and deciduous vegetation. F. Time series Landsat data process and analysis Our previous studies have showed the roles of phenology features in rubber plantation and paddy rice mapping. Therefore, we need to process and organize time series Landsat images for an operational plantation mapping. By integrating such Landsat data processing packages as LEDAPS, AROP, and Fmask, we have developed a Landsat time series data platform in the OU EOMF testbed, and it produces the ready-to-use time series vegetation index datasets, including several modules (Figure 6): 1) calculate vegetation indices and label the data quality information into the vegetation index products, 2) subset and stack time series images within multi-year epochs, 3) extract time series data of individual pixels, 4) analyze Landsat data availability or good-quality observation statistics, and 5) map individual land cover masks through phenologybased algorithms (Dong et al., 2015, RSE). The platform has been tested by using a case study in tracking paddy rice expansion in high latitude area of monsoon Asia (Dong et al., 2015, RSE; Qin et al., In review, ISPRS P&RS) and rubber plantation expansion in Xishuangbanna (Kou et al., In preparation) 11

12 Figure 6. The workflow of the Landsat processing package taking paddy rice mapping as an example, developed at the EOMF/OU. G. Project Partnerships During year 1 we solidified new international partnerships that will be leveraged and utilized to help develop plantations maps. The first is extensive field datasets, LiDAR, and PALSAR for Kalimantan working with LAPAN. As part of a NASA CMS lead by AGS PI Stephen Hagen, extensive networking with LAPAN and Ministries in Indonesia have fostered field data that will be used to ground truth LCLUC plantations maps and (as part of the CMS project) support mapping for carbon, deforestation and activity data, and REDD+ research and development in collaboration with JPL, Winrock International, and Wageningen University. The second partnership is working with GIZ and a funded project in the Philippines. As part of a REDD+ effort to pilot MRV tools, the group is leading mapping of forest cover classification and change detection on Leyte Island using PALSAR. A quality field dataset was constructed during this project and will be shared with our LCLUC project. A third is working with WWF Indonesia who are active in mapping land cover and carbon stock in Sumatra and Borneo. The forth is working with the Institute of Environment in Vietnam. Field ground truth including plantation type was collected across Thank Hoa, Nghe An, and Thai Binh provinces in north central Vietnam. 12

13 H. PALSAR-2 expansion While optical data are useful for phenology these data are limited in SE Asia where clouds and seasonality are substantial and limit the ability to scale Landsat. We have been building an extensive archive of ALOS-1 PALSAR-1 and ALOS-2 PALSAR-2. Recently, AGS entered into a new agreement with JAXA under the Kyoto and Carbon Initiative (K&CI) Phase 4. This will provide Science Team products for this LCLUC project including data not available to the public. ALOS-2 PALSAR-2 Fine Beam (HH+HV) mosaic products (2015+) ALOS-2 PALSAR-2 ScanSAR (HH+HV) mosaic products (every cycle, non-gap filled. Foreseen over selected forest and wetlands regions up to 9 times/year 2014+) ALOS-2 PALSAR-2 Quad polarimetric baseline for pilot sites ALOS PALSAR FBD (HH+HV) mosaic products (2007, 2008, 2009, 2010) ALOS PALSAR ScanSAR (HH) mosaic products (every cycle, non-gap filled, Insular SE- Asia, ) JERS-1 SAR (HH) mosaic products (mid 1990's) I. SAR data handling We have expanded our SAR data handling under a BigData framework (Figure 7). We have developed a library for performing large scale data processing on imagery based on open source and custom programming. The library is made up of a component written in C++ called the Geospatial Image Processing (GIP) library, and a Python component called GIPPY (GIP for PYthon) that utilizes GIP and handles inventory management and file system operations (untarring, string processing, moving files, calculating vector intersections). GIPPY uses Python, C++ Boost libraries, GDAL, and Ubuntu LTS. GIPPY can be used in a flexible manner, either processing all products at once, or only processing specific products. The original raw file is used to generate raw products so there are no products that rely on other products being processed first. If multiple products are specified, they are processed at once in order to minimize the amount of disk I/O required. Commands execute operations such as backscatter stacking, inventory queries, link tiles based on vectors, and resample and mosaic to desired specifications. The backend processing, in C++, is performed in chunks. This minimizes the amount of memory that is required at a given time, but the chunk size is configurable. Included with GIPPY is a small batch script to perform test processing using multiple chunk sizes that can be used to determine the optimal chunk size for the architecture. Automation of processing can be done either by setting up the command line utility as a cron job to run on any newly acquired tiles or through a Python script. SAR Single Look Complex and Slant Range geometry is also automated. Our automated approach imports SLC and co-registers using a cubic convolution cross-correlation routine considering shifts and range and azimuth dependency. A multitemporal de Grandi speckle filter is applied to remove multiplicative noise. Images are terrain geocoded using the best available digital elevation model (DEM) and follow the range-doppler approach. Images are radiometrically calibrated and normalized by eliminating local incident angle effects to provide fully preprocessed (sigma nought σ db) following lineage production (Torbick et al. 2014; Torbick et al. 2012; Torbick et al. 2011) 13

14 Figure 7. Original ALOS-1 and ALOS-2 data plan being pursued under LCLUC Plantations. J. Developing Interferometric Synthetic Aperture Radar (InSAR) metrics for mapping plantations As part of this effort we are evaluating the use of InSAR techniques for improving the mapping of industrial plantations. This includes the use of phase, magnitude, coherence, and the full interferometic signature. The fundamental signature that InSAR relies on is what is known as the interferometric correlation, a complex number that is described by its magnitude and phase. With knowledge of the viewing geometry, the phase of this correlation is what is used for determining topography. The magnitude, also known as the coherence, is related to the volume scattering within a resolution element and therefore can be used for determining metrics such as height using simple volume-scattering models (Treuhaft and Siqueira 2004), or models that take in to account the motion of the scatterers between observing passes of the interferometric instrument (Lavalle et al. 2012). A more recent effort by Lei and Siqueira (2013), has used repeat-pass InSAR measures in conjunction with a temporal decorrelation model for mapping Forest Stand Height (FSH) over a large region from repeat-pass InSAR data. We are testing these approaches at pilot sites where we have quality field data and evaluating approaches to scaling these to large areas in an effort to improve mapping unique characteristics of plantations compared to natural forest area. K. Piloting PALSAR Landsat CART techniques for mapping plantations We developed initial SAR-Landsat fusion products in previous and tested the transferability of that approach for mapping plantations across Thanh Hoa and Nghe An 14

15 Vietnam. The fractions of clear observations were examined first (Figure 8a). A decision tree algorithm that ingests remote sensing indices, threshold values, and expert knowledge was used for the approach. Multitemporal ScanSAR imagery were collected at multiscales and formats that correspond to spatial resolution, temporal overpass frequency, and time line. SAR imagery included PALSAR and JERS-1. Geocoded products were radiometrically calibrated and normalized by eliminating local incident angle effects and antenna gain and spread loss patterns. We employed a Classification And Regression Tree (CART) algorithm to classify the remote sensing imagery into initial broad land use land cover classes (Figure 8b). The CART algorithm uses rules for categorizing the imagery into land types based on the training data we collected in the field. Georefenced field data were used to create polygon vectors (training data) to teach (train) the imagery in a supervised approach. The algorithm determines what class a pixel belongs to by statistically matching the most similar spectral signature based off the training samples. A set of indices, backscatter, and surface reflectance were used as CART input. The second stage of the classifier applies a thresholding and logic approach to refine the initial statistical classification (Figure 9). Figure 8. Areas in the north (Thanh Hoa) have a higher fraction of clear observations than the frequently cloudy areas in the south (Nghe An) where mountain shadows also reduce the data availability. Upland land cover in both provinces is still comprised of a significant amount of long-term forest (64% of analyzed area above 100 m). Shifting cultivation (16%) and plantations (10%) make up the largest uses of upland lands. 15

16 Figure 9. Preliminary land use and plantations map from fused ScanSAR and Landsat for lowland areas of Thanh Hoa and Nghe An Vietnam. L. Work plan in year 2 We will follow the work plan as described in the proposal. The major outcomes include (1) regional maps of rubber plantations in the region, (2) new and novel algorithms for oil palm and eucalyptus plantations. 16

17 Publications The following publications are funded or partly funded by this NASA grant. Under Review or In Preparation Qin, Y., Xiao, X., Dong, J., Zhang, G., Shimada, M., Liu, J., Li, C., Kou, W., & Moore, B. Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI. Remote Sensing of Environment. (Under review) Zhai, D.-L., Dong, J., Wang, M.-C., Cadish, G., Kou, W.-L., Xu, J.-C., & Xiao, X. (In preparation). Comparison of pixel- and object-based approaches in phenology-based rubber plantation mapping in fragmented hillside landscapes, In preparation Chen, B., Xiao, X., Zhao, B., Dong, J., Li, X., Kou, W., Yang, C., Wu, Z., Xie, G., & Lan, G Mapping tropical forests and deciduous rubber plantations by integrating PALSAR 25-m mosaic imagery and Landsat TM/ETM+ images. Remote Sensing of Environment (under review) Qin, Y., Xiao, X., Dong, J., Zhang, G., Shimada, M., Kou, W., Roy, P.S., Joshi, P., Gilani, H., & Moore, B. (). Evergreen and deciduous forest cover maps in Monsoon Asia through the integration of ALOS PALSAR and MODIS imagery. Remote Sensing of Environment. In preparation Kou, W., Xiao, X., Dong, J., Qin, Y., Zhang, G., Gan, S., & Chen, B. Area and stand age of deciduous rubber plantations in Xishuangbanna, China through analysis of time series Landsat, PALSAR and MODIS LST. In preparation Published or Accepted Dong, J., Xiao, X., Sheldon, S., Biradar, C., Zhang, G., Dinh Duong, N., Hazarika, M., Wikantika, K., Takeuhci, W., & Moore, B., III (2014). A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment. PLoS One, 9, e85801 Kou, W., Xiao, X., Dong, J., Gan, S., Zhai, D., Zhang, G., Qin, Y., & Li, L. (2015). Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images. Remote Sensing, 7, Li, L., Dong, J., Njeudeng Tenku, S., & Xiao, X. (2015). Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images. Remote Sensing, 7, Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., Jin, C., Zhou, Y., Wang, J., Biradar, C., et al. (2015). Tracking the dynamics of paddy rice planting area in through time series Landsat images and phenology-based algorithms. Remote Sensing of Environment, doi: /j.rse

18 References Dong, J., Xiao, X., Chen, B., Torbick, N., Jin, C., Zhang, G., & Biradar, C. (2013). Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sensing of Environment, 134, Dong, J., Xiao, X., Sheldon, S., Biradar, C., & Xie, G. (2012). Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery. Isprs Journal of Photogrammetry and Remote Sensing, 74, Torbick, N., Persson, A., Olefeldt, D., Frolking, S., Salas, W., Hagen, S., Crill, P., & Li, C.S. (2012). High Resolution Mapping of Peatland Hydroperiod at a High-Latitude Swedish Mire. Remote Sensing, 4, Torbick, N., Salas, W., Xiao, X.M., Ingraham, P., Fearon, M.G., Biradar, C., Zhao, D.L., Liu, Y., Li, P., & Zhao, Y.L. (2011). Integrating SAR and optical imagery for regional mapping of paddy rice attributes in the Poyang Lake Watershed, China. Canadian Journal of Remote Sensing, 37, Xiao, X., Dorovskoy, P., Biradar, C., & Bridge, E. (2011). A library of georeferenced photos from the field. Eos Trans. AGU, 92 18