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While remote sensing and geographic information systems have been used successfully to classify forest health using recent image, applying this process to older images is problematic because contemporaneous field data are not available to measure the accuracy of the classification of historical images. Data ranges of normalized difference water index(NDWI) were established for each Robinia Pseudoacacia health class using a contemporary image and field data by sequential cluster analysis. These ranges were used to separate Landsat Thematic Mapper (TM) images acquired from 1999 to 2007 into a series of health-class maps, By applying cross-tabulation procedures to pairs of classified images, we can see how the Robinia Pseudoacacia health class of each pixel in the images of the study area had changed over time. The resulting maps provide a look back at forest conditions of the past and can be used to identify areas of special interest. Further analysis carried out between environmental factors(soil salt, soil texture, soil type, DEM, groundwater depth and groundwater salinity)and the Robinia Pseudoacacia health led to identify the likely causes of these negative trends. © Springer-Verlag Berlin Heidelberg 2016.

Monitoring Health of Artificial Robinia Pseudoacacia Changes in the Yellow River Delta by the Analysis of Multiyear NDWI Data