In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensi...In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.展开更多
Climate change and human activities are increasingly linked with the extinction of species globally. In semi-arid regions, these pressures threaten the natural distribution and ecology of species. The threat that the ...Climate change and human activities are increasingly linked with the extinction of species globally. In semi-arid regions, these pressures threaten the natural distribution and ecology of species. The threat that the shea butter tree (<em>Vitellaria paradoxa</em> subsp. <em>nilotica</em>) faces from human activity is well researched yet the sensitivity of its distribution to climate change remains barely known. We set out to assess the potential distribution of <em>Vitellaria</em> under different climate change scenarios using a MaxEnt. A current distribution model was first developed using only biophysical variables of soil type, temperature, precipitation, land use type, and elevation. This model was then projected onto two global warming scenarios (RCP 4.5 & RCP 8.5) for 2050 and 2070 using multi-model averages (BCC-CSM, CSM4, and MIROC5) derived from three general circulation models. Reductions are seen in distribution area across the landscape with soil type being the most important variable. These results draw useful implications for conservation of <em>Vitellaria</em> in that they show how it is vulnerable is to a changing climate as its natural range is mostly reduced. Since climate change is important in the distribution of the shea butter tree, the areas with highest suitability in this study can be used in establishing the Shea butter tree sustainable use zones/area within the Kidepo Critical Landscape (KCL), Uganda.展开更多
基金Project(2007CB714407) supported by the Major State Basic Research and Development Program of ChinaProject(2004DFA06300) supported by Key International Collaboration Project in Science and TechnologyProjects(40571107, 40701102) supported by the National Natural Science Foundation of China
文摘In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.
文摘Climate change and human activities are increasingly linked with the extinction of species globally. In semi-arid regions, these pressures threaten the natural distribution and ecology of species. The threat that the shea butter tree (<em>Vitellaria paradoxa</em> subsp. <em>nilotica</em>) faces from human activity is well researched yet the sensitivity of its distribution to climate change remains barely known. We set out to assess the potential distribution of <em>Vitellaria</em> under different climate change scenarios using a MaxEnt. A current distribution model was first developed using only biophysical variables of soil type, temperature, precipitation, land use type, and elevation. This model was then projected onto two global warming scenarios (RCP 4.5 & RCP 8.5) for 2050 and 2070 using multi-model averages (BCC-CSM, CSM4, and MIROC5) derived from three general circulation models. Reductions are seen in distribution area across the landscape with soil type being the most important variable. These results draw useful implications for conservation of <em>Vitellaria</em> in that they show how it is vulnerable is to a changing climate as its natural range is mostly reduced. Since climate change is important in the distribution of the shea butter tree, the areas with highest suitability in this study can be used in establishing the Shea butter tree sustainable use zones/area within the Kidepo Critical Landscape (KCL), Uganda.
