Dong Jinwei, Xiao Xiangming, Sheldon Sage, Biradar Chandrashekhar, Zhang Geli, Duong Nguyen Dinh, Hazarika Manzul, Wikantika Ketut, Takeuhci Wataru, Moore Berrien
Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, Norman, Oklahoma, United States of America.
International Center for Agricultural Research in Dry Areas, Amman, Jordan.
PLoS One. 2014 Jan 22;9(1):e85801. doi: 10.1371/journal.pone.0085801. eCollection 2014.
Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.
20世纪90年代，东南亚的森林砍伐率高于其他各大洲，到了21世纪，它仍是森林变化的热点地区。生物多样性保护规划以及对森林碳通量和碳库的准确估算需要有关森林面积、空间分布和破碎化的更精确信息。然而，东南亚最近的森林地图是根据空间分辨率为数百米的光学图像生成的，无法很好地反映东南亚异常复杂和动态的环境。这些地图估算的森林面积差异很大，2009年从173万平方千米（全球土地覆盖图）到269万平方千米（MCD12Q1）不等；其不确定性受到频繁云层覆盖和粗略空间分辨率的限制。最近，搭载在先进陆地观测卫星（ALOS）上的相控阵L波段合成孔径雷达（PALSAR）获取了无云图像。我们利用2009年PALSAR的50米正射校正镶嵌图像生成了东南亚50米空间分辨率的森林覆盖图。通过使用从地理参考野外照片库收集的地面参考数据和谷歌地球中的高分辨率图像进行验证，结果表明我们的森林地图具有相当高的准确性（生产者精度为86%，用户精度为93%）。2009年基于PALSAR的森林面积估算在国家和次国家尺度上与全球土地覆盖图和MCD12Q1的估算显著相关，但由于空间分辨率、森林定义和算法不同，在像素尺度上的某些区域存在差异。由此生成的50米森林地图被用于量化森林破碎化，并揭示了森林破碎化的大量细节。这张新的50米热带森林地图可作为森林资源清查、森林砍伐监测、减少毁林和森林退化所致排放量（REDD+）实施以及生物多样性的基础地图。
