摘要
Background: Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. However, optical images of land surfaces are generally contaminated when clouds are present or rain occurs.Methods: To estimate the actual reflectance of land surfaces masked by clouds and potential rain, 3D simulations by the RAPID radiative transfer model were proposed and conducted on a forest farm dominated by birch and larch in Genhe City, Da Xing’An Ling Mountain in Inner Mongolia, China. The canopy height model(CHM) from lidar data were used to extract individual tree structures(location, height, crown width). Field measurements related tree height to diameter of breast height(DBH), lowest branch height and leaf area index(LAI). Series of Landsat images were used to classify tree species and land cover. MODIS LAI products were used to estimate the LAI of individual trees. Combining all these input variables to drive RAPID, high-resolution optical remote sensing images were simulated and validated with available satellite images.Results: Evaluations on spatial texture, spectral values and directional reflectance were conducted to show comparable results.Conclusions: The study provides a proof-of-concept approach to link lidar and MODIS data in the parameterization of RAPID models for high temporal and spatial resolutions of image reconstruction in forest dominated areas.
Background: Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. However, optical images of land surfaces are generally contaminated when clouds are present or rain occurs.Methods: To estimate the actual reflectance of land surfaces masked by clouds and potential rain, 3D simulations by the RAPID radiative transfer model were proposed and conducted on a forest farm dominated by birch and larch in Genhe City, Da Xing’An Ling Mountain in Inner Mongolia, China. The canopy height model(CHM) from lidar data were used to extract individual tree structures(location, height, crown width). Field measurements related tree height to diameter of breast height(DBH), lowest branch height and leaf area index(LAI). Series of Landsat images were used to classify tree species and land cover. MODIS LAI products were used to estimate the LAI of individual trees. Combining all these input variables to drive RAPID, high-resolution optical remote sensing images were simulated and validated with available satellite images.Results: Evaluations on spatial texture, spectral values and directional reflectance were conducted to show comparable results.Conclusions: The study provides a proof-of-concept approach to link lidar and MODIS data in the parameterization of RAPID models for high temporal and spatial resolutions of image reconstruction in forest dominated areas.
基金
the Chinese National Basic Research Program (2013CB733401)
the Chinese Natural Science Foundation Project (41171278)
