Estimation Of Forest Aboveground Biomass In Jiangxi Province Using Glas And Landsat Data

Using forest in Jiangxi province as the research area, estimate the forest canopy average height and biomass by multispectral Landsat TM data and GLAS waveform data. Extracted waveform extent, leading edge extent and trailing edge extent from the GLAS waveform data as the waveform characteristic parameters to represent waveform features, also extracted terrain index and terrain standard deviation from ASTER GDEM data to reduce the influence of terrain factors on the waveform data. Using waveform characteristic parameters(waveform extent, leading edge extent and trailing edge extent) and terrain feature parameters(terrain index and terrain standard deviation) build model to estimate tree height, completed the GLAS full waveform data processing algorithms, set up forest canopy height estimation model that can reduce the influence of terrain factors. Considering the large-footprint LiDAR data were spatially discrete and lack of imaging capability, the study brought forward the estimation model of regional forest canopy height by combine with optical remote sensing images. Finally estimated regional scale forest biomass of the study area by canopy height and field measured biomass data.The research results show: using GLAS data, ASTER GDEM data and measured data to estimate forest canopy height have high accuracy. The model which contain waveform extent, leading edge extent, trailing edge extent and terrain standard deviation has the highest correlation coefficient(0.6505). Build up the linear, power function, index and logarithmic 6 kinds of continuous forest canopy height models by large laser radar GLAS data and optical Landsat TM data, model results show that the linear correction the highest correlation coefficient(0.7219); build up the linear, power function, index and logarithmic 6 kinds of forest biomass estimate models by forest biomass measured data and tree height, the results show that the power function model has the highest correlation coefficient(0.6887) which can be a good characterization of the relationship between tree height and biomass.Some deficiencies stills exist at this research. Such as the acquisition time of the remote data: the acquisition time of the GLAS data is august 2008, the Landsat TM data is acquired in January and October in 2009. Field measured data and remote sensing data time is inconsistency, the acquisition time of field measured data is 2006, 2008 and 2012. The research don’t get enough field measured data, only have 128 tree height filed measured data, 31 biomass field measured data.In general, the large-footprint LiDAR GLAS data combined with optical Landsat TM remote sensing data to estimate the forest canopy average height and biomass can give full play to the advantage of multisource remote sensing and improve the accuracy of forest biomass inversion.