Remote Sensing Monitoring the Health of Artificial Robinia Pseudoacacia Forest

Some of Robinia Pseudoacacia Forest have been dead or dieback in the Yellow River Delta since 1990s.The area of Robinia was extracted using Landsat TM images with supervised classification.NDVI,NDWI,SAVI,MSAVI and TCT transforms were used,and then cluster analysis was used to separate the Robinias into four classes of tree vigor.We evaluated 376 trees at 40 sites across the study area using the USFS crown condition rating guide,and separate Robinia Pseudoacacia into four levels of tree vigor.The field data were used to measure the accuracy of various Vegetation Index classification techniques.The normalized difference water index(NDWI) transform provided the best overall accuracy,82.5%,for classifying Robinia Pseudoacacia according to tree vigor.Health detection result and relative environment factor analysis showed that high soil salinity and low groundwater depth were found to be significantly correlative with the health of Robinia Pseudoacacia.