Subpixel forest cover in central Africa from multisensor, multitemporal data
Subpixel forest cover in central Africa from multisensor, multitemporal data
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DOI：
10.1016/s0034-4257(96)00119-8
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发表时间：
1997-06-01
影响因子：
13.5
通讯作者：
Townshend, J
中科院分区：
文献类型：
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作者：
DeFries, R;Hansen, M;Townshend, J
非洲中部的7个Landsat多光谱扫描仪（MSS）场景与1987年AVHRR探路者陆地数据集的8公里分辨率数据共同注册。每8 km网格单元的森林覆盖率百分比来源于MSS分类场景。森林覆盖率百分比与AVHRR所有光学和热通道的30个多时段指标之间存在线性关系，其中年平均归一化植被指数（NDVI）和年平均亮度温度（AVHRR通道3）的相关性最强，近红外反射率（AVHRR通道2）的相关性最弱。利用这些关系，利用多元线性回归和回归树估算了研究区不同地点的森林覆盖率。总体而言，多元线性回归的结果更为准确。对于大约90%的网格单元，预测的森林覆盖率估计在“实际”森林覆盖率（从MSS数据中导出）的20%以内。预测的均方根误差为12%的森林覆盖率。当使用单个月的AVHRR数据来推导预测关系时，RMS误差大于18%。结果表明，反映植被物候的多时相数据可用于估算粗空间分辨率下的亚像元森林覆盖。(C) Elsevier Science Inc., 1997。
Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the ''actual'' percent forest cover (derived front the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions. (C) Elsevier Science Inc., 1997.