Spatial autocorrelation region-based spatial-temporal difference detection method for vegetation net primary productivity

Abstract

The invention discloses a spatial autocorrelation region-based spatial-temporal difference detection method for vegetation net primary productivity. . The method comprises the following steps: S1, obtaining MOD17A3 data of research years of a research area; S2, extracting a vegetation NPP graph of a research time period of the research area; S3, calculating the vegetation NPP variable quantity inthe research time period of the research area; S4, calculating a global Moran index, a high / low value clustering index and related statistics of the vegetation NPP in the research period of the research area; S5, judging the overall clustering characteristics of the research area data samples; S6, calculating a hot spot analysis index of the vegetation NPP in the research time period of the research area; S7, judging hot spot distribution characteristics of the vegetation NPP in the research time period of the research area; S8, calculating a local Moran index of annual vegetation NPP variation in the research period of the research area; S9, judging spatial aggregation characteristics of annual vegetation NPP variations in the research period of the research area. The overall clustering characteristics of the vegetation NPP can be accurately expressed, and the local spatial characteristics can be detected.

Description

technical field [0001] The invention relates to the field of vegetation productivity detection, in particular to a space-time differentiation detection of vegetation net primary productivity in a space autocorrelation region, and in particular to a fast, efficient, low-cost and accurate detection of large-scale vegetation net primary productivity space-time using satellite remote sensing data method of differentiating features. Background technique [0002] Climate and human activities have a great influence on the regional distribution of vegetation productivity levels. Accurately detecting the spatial distribution of vegetation net primary productivity is of great significance for evaluating the structure and function of terrestrial ecosystems. Net primary productivity (NetPrimary Productivity) is referred to as NPP. The traditional monitoring of the spatial and temporal distribution of NPP mainly relies on the calculation and The comparison ignores the spatial correlatio...

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Problems solved by technology

Accurately detecting the spatial distribution of vegetation net primary productivity is of great significance for evaluating the structure and function of terrestrial ecosystems. Net primary productivity (NetPrimary Productivity) is referred to as NPP. The traditional monitoring of the spatial and temporal distribution of NPP mainly relies on the calculation and The comparison ignores the spatial correlation between various parts, and the research results represent the change of most points, which leads to differences in the description of the change in the same area at different research scales, or even the opposite

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