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1、森林覆盖率与高度的遥感定量反演森林覆盖率与高度的遥感定量反演 庞庞 勇勇1 ; 13521588630 李增元1, 陈尔学1, Guoqing Sun2, Chengquan Huang2, Michael Lefsky3 1 中国林科院资源信息研究所 2 美国马里兰大学地理系 3 美国科罗拉多州立大学林学系 遥感定量反演算法研讨会，遥感定量反演算法研讨会， 20102010年年7 7月月1111- -1212日北京日北京 报告大纲报告大纲 1. 世界森林资源概况世界森林资源概况 2. 森林覆盖率的遥感定量反演森林覆盖率的遥感定量反演 3. 森林高度的遥感定量反演森林高度的遥感定量反演 4.
2、发展趋势发展趋势 报告大纲报告大纲 1. 世界森林资源概况世界森林资源概况 2. 森林覆盖率的遥感定量反演森林覆盖率的遥感定量反演 3. 森林高度的遥感定量反演森林高度的遥感定量反演 4. 发展趋势发展趋势 The worlds forests 31% OF TOTAL LAND AREA 4,000,000,000 ha From UN FAO OF TOTAL LAND AREA 740,000,000 ha 26% The forests of Asia and the Pacific - 2010 From UN FAO 全球土地覆盖产品中森林的差异全球土地覆盖产品中森林的差异 For
3、est definitions: IGBP legend : percent tree cover 60% / tree height 2m GLC2000 legend : percent tree cover 15% / tree height 3m Credit: M. Herold / GOFC-GOLD 不同遥感手段的森林回波信号示意图不同遥感手段的森林回波信号示意图 （Lefsky et al., 2003） 报告大纲报告大纲 1. 世界森林资源概况世界森林资源概况 2. 森林覆盖率的遥感定量反演森林覆盖率的遥感定量反演 3. 森林高度的遥感定量反演森林高度的遥感定量反演 4. 发
4、展趋势发展趋势 Vegetation Continuous Fields Vegetation Continuous Fields Percent tree cover from MODIS year 2001. Slide from J. Townshend, UMD Leaf type % evergreen % deciduous % needleleaf % broadleaf Vegetation Continuous Fields Slide from J. Townshend, UMD GLOBCOVER (2005/6) Dataset release: September 2
5、008 Rainfed CroplandRainfed Cropland PostPost- -flooding or irrigated croplandsflooding or irrigated croplands Mosaic cropland (50Mosaic cropland (50- -70%) / vegetation 70%) / vegetation (grassland/shrubland/forest) (20(grassland/shrubland/forest) (20- -50%)50%) Mosaic vegetation (grassland/shrubla
6、nd/forest) Mosaic vegetation (grassland/shrubland/forest) (50(50- -70%) / cropland (2070%) / cropland (20- -50%)50%) Closed to open (15%) broadleaved Closed to open (15%) broadleaved evergreen and/or semievergreen and/or semi- -deciduous forest deciduous forest (5m)(5m) Closed (40%) broadleaved deci
7、duous Closed (40%) broadleaved deciduous forest (5m)forest (5m) Open (15Open (15- -40%) broadleaved deciduous 40%) broadleaved deciduous forest/woodland (5m)forest/woodland (5m) Closed (40%) needleClosed (40%) needle- -leaved evergreen leaved evergreen forest (5m)forest (5m) Open (15Open (15- -40%)
8、needle40%) needle- -leaved deciduous leaved deciduous or evergreen forest (5m)or evergreen forest (5m) Closed to open (15%) mixed broadleaved Closed to open (15%) mixed broadleaved and needleaved forestand needleaved forest Mosaic forest or shrubland (50Mosaic forest or shrubland (50- -70%) and 70%)
9、 and grassland (20grassland (20- -50%)50%) Mosaic grassland (50Mosaic grassland (50- -70%) and forest or 70%) and forest or shrubland (20shrubland (20- -50%) 50%) Closed to open (15%) shrubland (15%) shrubland (15%) grasslandClosed to open (15%) grassland Sparse (40%) broadleaved forest regularly Cl
10、osed (40%) broadleaved forest regularly flooded, fresh water flooded, fresh water * * Closed (40%) broadleaved semiClosed (40%) broadleaved semi- -deciduous deciduous and/or evergreen forest regularly flooded, and/or evergreen forest regularly flooded, saline watersaline water Closed to open (15%) g
11、rassland or Closed to open (15%) grassland or shrubland or woody vegetation on regularly shrubland or woody vegetation on regularly flooded or waterlogged soil, fresh, brackish flooded or waterlogged soil, fresh, brackish or saline wateror saline water Artificial surfaces and associated areas Artifi
12、cial surfaces and associated areas (Urban areas 50%)(Urban areas 50%) Bare AreasBare Areas Water BodiesWater Bodies Permanent Snow and IcePermanent Snow and Ice No DataNo Data GlobCover Legend 22 LCCS classes + 51 sub-classes at regional level * Resulting from the reference dataset* Resulting from t
13、he reference dataset Hansen M., Stehman S., Potapov P. (2010) Quantification of global gross forest cover loss. Hansen M., Stehman S., Potapov P. (2010) Quantification of global gross forest cover loss. Automated Forest Change Mapping Method SVM more accurate than other classifiers (Huang et al., 20
14、02; Pal Lefsky et al., 2007） 单木探测及参数估计单木探测及参数估计 机载激光雷达估计的单木树高估计机载激光雷达估计的单木树高估计 （黑河试验区）（黑河试验区） 激光雷达估计的单木树高（激光雷达估计的单木树高（m） 地面实测的单木树高（地面实测的单木树高（m） RSq=0.81 RMSE=1.36 82. 10.94HfieldLiDARHfield1.06HLiDARH单木树高估测精度平均值为单木树高估测精度平均值为92.7%。 基于机载激光雷达反演的森林平均高基于机载激光雷达反演的森林平均高 Legend (m): 051015202530Lorey Height
15、 from Field (m)0510152025303540 Lidar 95 percentile height (m)Regression PlotR2=0.85 RMSE=2.07 N = 73 2.533.544.555.566.57Vold Actual234567Vold Predicted P.0001 RSq=0.84 RMSE=0.313Actual by Predicted Plot基于机载激光雷达反演的森林蓄积量基于机载激光雷达反演的森林蓄积量 Log(Estimated volume density) (m3/ha) Log(Field volume density)
16、 (m3/ha) R2=0.84 RMSE=0.31 N = 73 Legend (m3/ha): Legend (m3/ha): Estimation of forest height from different observation periods of GLAS data using field measurements L3C and L3F L2A, L3A, L3D and L3G （Pang et al.，2008) Land use map from Landsat ETM+(2000) Vegetation height map by GLAS GLAS数据反演区域尺度森林高度数据反演区域尺度森林高度 （Pang et al.，2005) 基于基于GLAS数据估计的全球植被高度图数据估
