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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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To identify the degree of hydrologic alteration of streams in the Mississippi Alluvial Plain (MAP), we used random forest (RF) regression methods (Breiman, 2001) to model the relation between six selected streamflow characteristics and explanatory variables (such as drainage area, precipitation, soils, and other watershed characteristics). RFs were chosen for this study because they have been proven to be more robust and accurate than traditional linear regression methods (Carlisle and others, 2010; Lawler and others, 2006; Prasad and others, 2006; Cutler and others, 2007). Estimated expected monthly mean streamflow from the RF models were compared to observed monthly mean streamflow at 68 sites located within the...
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This dataset represents the cumulative result of multi-season classification of land cover in the GCPO LCC geography to NatureServe Ecological Systems based on 2011 seasonal Landsat Satellite Imagery. The approach used a Random Forest algorithm and several dozen input data layers to classify land cover at a 30 m pixel resolution. The description below is taken directly from the report titled “Update of the Eastern GCPO Land Cover Database to 2011 Using a LS2SRC Approach”, by Dr. Qingmin Meng, Department of Geosciences, Mississippi State University.Random Forest classifier is based on the general decision tree approach, which has been a popular approach to multilevel and multistage decision making. Its basic idea...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...
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Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of Field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha-1 for a range of biomass between 0 and 454 Mg ha-1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate...


map background search result map search result map Zambia woody above-ground biomass (tonnes/hectare) Zimbabwe woody above-ground biomass (tonnes/hectare) Togo woody above-ground biomass (tonnes/hectare) Rwanda woody above-ground biomass (tonnes/hectare) Nigeria woody above-ground biomass (tonnes/hectare) Mozambique woody above-ground biomass (tonnes/hectare) Malawi woody above-ground biomass (tonnes/hectare) Liberia woody above-ground biomass (tonnes/hectare) Kenya woody above-ground biomass (tonnes/hectare) Guinea woody above-ground biomass (tonnes/hectare) Guinea-Bissau woody above-ground biomass (tonnes/hectare) Ghana woody above-ground biomass (tonnes/hectare) Gabon woody above-ground biomass (tonnes/hectare) Ethiopia woody above-ground biomass (tonnes/hectare) Democratic Republic of the Congo woody above-ground biomass (tonnes/hectare) Côte d'Ivoire woody above-ground biomass (tonnes/hectare) Central African Republic woody above-ground biomass (tonnes/hectare) Angola woody above ground biomass (tonnes/hectare) Ecological Systems Classification 2011 Update for the Eastern GCPO LCC Geography Basin Characteristics and Climate Data Used in Random Forest Models to Determine Hydrologic Alteration in the Mississippi Alluvial Plain Rwanda woody above-ground biomass (tonnes/hectare) Guinea-Bissau woody above-ground biomass (tonnes/hectare) Liberia woody above-ground biomass (tonnes/hectare) Basin Characteristics and Climate Data Used in Random Forest Models to Determine Hydrologic Alteration in the Mississippi Alluvial Plain Zimbabwe woody above-ground biomass (tonnes/hectare) Togo woody above-ground biomass (tonnes/hectare) Guinea woody above-ground biomass (tonnes/hectare) Gabon woody above-ground biomass (tonnes/hectare) Côte d'Ivoire woody above-ground biomass (tonnes/hectare) Ghana woody above-ground biomass (tonnes/hectare) Malawi woody above-ground biomass (tonnes/hectare) Ecological Systems Classification 2011 Update for the Eastern GCPO LCC Geography Central African Republic woody above-ground biomass (tonnes/hectare) Nigeria woody above-ground biomass (tonnes/hectare) Mozambique woody above-ground biomass (tonnes/hectare) Kenya woody above-ground biomass (tonnes/hectare) Zambia woody above-ground biomass (tonnes/hectare) Ethiopia woody above-ground biomass (tonnes/hectare) Angola woody above ground biomass (tonnes/hectare) Democratic Republic of the Congo woody above-ground biomass (tonnes/hectare)