Extraction and Inversion of deciduous broad-leaved forest Based on HJ-CCD Remote Sensing Data

In this paper, Chuzhou area in Anhui province was taken as the study area, and deciduous broad-leaved forest as the study object. The NDVI was obtained based on HJ-CCD remote sensing images acquired on April 1,2012 and May 4,2012 when broad-leaved forest was respectively in leaf expansion and flowering. Next, combined with the position information collected in field, the recognition model for deciduous broad-leaved forest was proposed with NDVI difference rate between leaf expansion and flowering. And then deciduous broad-leaved forest in the study area was extracted effectively and the result was verified. Finally, poplar forest was taken as an example, and then the LAI inversion was carried out and the result was verified by using the LAI data obtained in field combined with poplar forest data information collected at the plot. The results show the validity of NDVI difference rate recognition method proposed in this paper and also verify the advantages of LAI inversion for poplar forest using HJ-CCD data.