Using landscape spatial relationships to improve estimates of land-cover area from coarse resolution remote sensing

被引:31
作者
Moody, A [1 ]
机构
[1] Univ N Carolina, Dept Geog, Chapel Hill, NC 27599 USA
基金
美国国家航空航天局;
关键词
D O I
10.1016/S0034-4257(98)00003-0
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A two-stage modeling strategy significantly improves land-cover area estimates from low spatial resolution remote sensing by correcting measurements of class proportions within large blocks of pixels. Vegetation class-type information is developed through supervised classification of Thematic Mapper spectral data at both fine (30 m) and coarse (1020 m) resolutions. Stage 1 models use measurements of landscape spatial properties to estimate the slopes nad intercepts of proportion transition relationships between fine- and coarse-resolution classes within randomly located pixel blocks. Following this step, a Stage II model uses a linear estimator to predict true class proportions based on measured coarse-scale proportions and the slope and intercept estimates from the Stage I models. Model development and testing on a training site is followed by testing and inversion for a validation site. Model inversion involves using spatial variables measured only at the coarse resolution as input to the Stage I models. For both the training and the validation data, the procedure results in a statistically significant reduction in error when estimating land-cover area by class type within the sampling blocks. (C) Elsevier Science Inc., 1998.
引用
收藏
页码:202 / 220
页数:19
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