Forest ecosystem is an important component of terrestrial ecosystem and plays an important role in global changes. Aboveground biomass (AGB) of forest ecosystem is an important factor in global carbon cycle studies. The purpose of this study was to retrieve the yearly Net Primary Productivity (NPP) of forest from the 8-days-interval MODIS-LAI images of a year and produce a yearly NPP distribution map. The LAI, DBH (diameter at breast height), tree height, and tree age field were measured in different 80 plots for Chinese fir, Masson pine, bamboo, broadleaf, mix forest in Liping County. Based on the DEM image and Landsat TM images acquired on May 14th, 2000, the geometric correction and terrain correction were taken. In addition, the "6S"model was used to gain the surface reflectance image. Then the correlation between Leaf Area Index (LAI) and Reduced Simple Ratio (RSR) was built. Combined with the Landcover map, forest stand map, the LAI, aboveground biomass, tree age map were produced respectively. After that, the 8-days- interval LAI images of a year, meteorology data, soil data, forest stand image and Landcover image were inputted into the BEPS model to get the NPP spatial distribution. At last, the yearly NPP spatial distribution map with 30m spatial resolution was produced. The values in those forest ecological parameters distribution maps were quite consistent with those of field measurements. So it's possible, feasible and time-saving to estimate forest ecological parameters at a large scale by using remote sensing.
In this study, investigation was designed to find an effective method for estimating chlorophyll and nitrogen
concentration in the canopies of rice from hyperspectral EO-1 Hyperion image. Continuum-removal analysis enables the
isolation of absorption features and minimizes the background influence, thus absorption features stand out. We applied
stepwise regression analysis and absorption feature analysis to the field measured foliage and canopy
continuum-removed spectra. The results showed that the continuum-removed spectra from the whole range could be
broke down into four isolated wavelength ranges and the first wavelength range was centered at 670nm. The area of the
wavelength range centered at 670nm based on the BNC spectra was strongly correlated with the chlorophyll and nitrogen
concentration. It was validated by EO-1 Hyperion image data, the results showed that the multiple correlation
coefficients (R2) between the area of the wavelength range centered at 670nm based on the BNC image spectra and
chlorophyll and nitrogen concentration were 0.485 and 0.783 separately. Then the estimation equations were applied to
the rice pixels of image which were recognized through Normalized Difference Vegetation Index (NDVI), Land Surface
Water Index (LSWI) and Enhanced Vegetation Index (EVI). Thus the chlorophyll and nitrogen concentration
distribution maps were obtained. The values in the maps were quite consistent with those of field measurements.
Based on a SPOT-5 image, this study built knowledge pool of vegetation spectral information, adopted classification algorithm of decision tree, proposed a vegetation classification model based on their spectral information and classified the vegetation of Nanjing. The results showed that the overall accuracy was 86.95% and Kappa coefficient was 0.8287. Then the classification model was validated by using an IKONOS image of Yuhuatai region and was improved through combining the textural information. The classification overall accuracy was increased to 92.70% and Kappa coefficient was increased to 0.8648.
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