DERIVATION OF TREE CANOPY COVER BY MULTISCALE REMOTE SENSING APPROACH

摘要

In forestry, treecanopy cover (CC) is an important biophysical indicator for characterizing terrestrial ecosystemsand modeling global biogeochemical cycles, e.g., woody biomass estimation, carbon balance analysis (sink/emission). However, currently available CC product cannot fully meet what we need while conducting woody biomass estimation in tropical savannas. It is thus necessary to develop an approach to estimate more reliable CC. Based on the acquisition of multisensor and multiresolution dataset, this study introduces an innovative multiscalemethod for this purpose taking the multiple savannas country Sudan as an example. The procedure includes: (1)Measurement of CC using Google Earth Pro in which very high resolution images such as QuickBirdand GeoEye images are available, and then the measured CC was coupled with atmospherically corrected and reflectance-based 16 frames of Landsat ETM+ vegetation indices (EVI, SARVI and NDVI)dated Nov 1999-2002 to establish the CC-VIs models; it was noted that among these indices NDVI indicates the best correlation with CC (CC = 153.09NDVI- 10.12, R~(2) = 0.91);(2) The NDVI of Landsat ETM+ was calibrated against MODIS NDVI of the same time period (Nov 2002)to make sure that model developed from Landsat ETM+ data can be applied to MODIS data for upscalingto regional scale study; (3)Time-series MODIS NDVI data of the period Jan 2002-Dec 2009 (MODIS13Q1, 250m, 186 acquisitions) were acquired and used to decompose the woody component(NDVI) from seasonal changeand herbaceous component by time-series analysis;(4) The equation obtained in step 1 was applied to the decomposed MODIS woody NDVI images to derive country scale CC data. The produced CC was checked against the 287 ground measured CC obtained in step 1 and a good agreement (R~(2) = 0.53-0.71) was found. It is hence concluded that the proposed multiscale approach is effective, operational and can be applied for reliable estimation of regional and even continental scales CC data.