The Experts below are selected from a list of 210 Experts worldwide ranked by ideXlab platform
Forrest G Hall - One of the best experts on this subject based on the ideXlab platform.
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boreas tgb 12 soil carbon and flux data of nsa msa in Raster Format
ORNL DAAC, 2000Co-Authors: Forrest G Hall, David E Knapp, Gloria Rapalee, Eric A Davidson, Jennifer W Harden, Susan E Trumbore, Hugo VeldhuisAbstract:The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drain-age, and soil carbon inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; and (4) a Raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated. This data set was created from Raster files, soil polygon data files, and detailed lab analysis of soils data that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. Also used were soils data from Susan Trumbore and Jennifer Harden (BOREAS TGB-12). The binary Raster file covers a 733-km 2 area within the NSA-MSA.
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boreas te 1 soils data over the ssa tower sites in Raster Format
ORNL DAAC, 2000Co-Authors: Forrest G Hall, Darwin Anderson, David E KnappAbstract:The BOREAS TE-1 team collected various data to characterize the soil-plant systems in the BOREAS SSA. This data set was gridded from vector layers of soil maps that were received from Dr. Darwin Anderson (TE-1), who did the original soil mapping in the field during 1994. The vector layers were gridded into Raster files that cover approximately 1 square kilometer over each of the tower sites in the SSA. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
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boreas te 20 soils data over the nsa msa and tower sites in Raster Format
ORNL DAAC, 2000Co-Authors: Forrest G Hall, Hugo Veldhuis, David E KnappAbstract:The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set was gridded from vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. The vector layers were gridded into Raster files that cover the NSA-MSA and tower sites. The data are stored in binary, image Format files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).
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boreas regional dem in Raster Format and aeac projection
ORNL DAAC, 2000Co-Authors: David E Knapp, Kristine Verdin, Forrest G HallAbstract:This data set is based on the GTOPO30 Digital Elevation Model (DEM) produced by the United States Geological Survey EROS Data Center (USGS EDC). The BOReal Ecosystem-Atmosphere Study (BOREAS) region (1,000 km x 1000 km) was extracted from the GTOPO30 data and reprojected by BOREAS staff into the Albers Equal-Area Conic (AEAC) projection. The pixel size of these data is 1 km. The data are stored in binary, image Format files.
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boreas regional soils data in Raster Format and aeac projection
ORNL DAAC, 2000Co-Authors: Bryan Monette, David E Knapp, Forrest G Hall, J E NickesonAbstract:This data set was gridded by BOREAS InFormation System (BORIS) Staff from a vector data set received from the Canadian Soil InFormation System (CanSIS). The original data came in two parts that covered Saskatchewan and Manitoba. The data were gridded and merged into one data set of 84 files covering the BOREAS region. The data were gridded into the AEAC projection. Because the mapping of the two provinces was done separately in the original vector data, there may be discontinuities in some of the soil layers because of different interpretations of certain soil properties. The data are stored in binary, image Format files.
David E Knapp - One of the best experts on this subject based on the ideXlab platform.
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boreas tgb 12 soil carbon and flux data of nsa msa in Raster Format
ORNL DAAC, 2000Co-Authors: Forrest G Hall, David E Knapp, Gloria Rapalee, Eric A Davidson, Jennifer W Harden, Susan E Trumbore, Hugo VeldhuisAbstract:The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drain-age, and soil carbon inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; and (4) a Raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated. This data set was created from Raster files, soil polygon data files, and detailed lab analysis of soils data that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. Also used were soils data from Susan Trumbore and Jennifer Harden (BOREAS TGB-12). The binary Raster file covers a 733-km 2 area within the NSA-MSA.
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boreas te 1 soils data over the ssa tower sites in Raster Format
ORNL DAAC, 2000Co-Authors: Forrest G Hall, Darwin Anderson, David E KnappAbstract:The BOREAS TE-1 team collected various data to characterize the soil-plant systems in the BOREAS SSA. This data set was gridded from vector layers of soil maps that were received from Dr. Darwin Anderson (TE-1), who did the original soil mapping in the field during 1994. The vector layers were gridded into Raster files that cover approximately 1 square kilometer over each of the tower sites in the SSA. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
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boreas te 20 soils data over the nsa msa and tower sites in Raster Format
ORNL DAAC, 2000Co-Authors: Forrest G Hall, Hugo Veldhuis, David E KnappAbstract:The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set was gridded from vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. The vector layers were gridded into Raster files that cover the NSA-MSA and tower sites. The data are stored in binary, image Format files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).
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boreas regional dem in Raster Format and aeac projection
ORNL DAAC, 2000Co-Authors: David E Knapp, Kristine Verdin, Forrest G HallAbstract:This data set is based on the GTOPO30 Digital Elevation Model (DEM) produced by the United States Geological Survey EROS Data Center (USGS EDC). The BOReal Ecosystem-Atmosphere Study (BOREAS) region (1,000 km x 1000 km) was extracted from the GTOPO30 data and reprojected by BOREAS staff into the Albers Equal-Area Conic (AEAC) projection. The pixel size of these data is 1 km. The data are stored in binary, image Format files.
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boreas regional soils data in Raster Format and aeac projection
ORNL DAAC, 2000Co-Authors: Bryan Monette, David E Knapp, Forrest G Hall, J E NickesonAbstract:This data set was gridded by BOREAS InFormation System (BORIS) Staff from a vector data set received from the Canadian Soil InFormation System (CanSIS). The original data came in two parts that covered Saskatchewan and Manitoba. The data were gridded and merged into one data set of 84 files covering the BOREAS region. The data were gridded into the AEAC projection. Because the mapping of the two provinces was done separately in the original vector data, there may be discontinuities in some of the soil layers because of different interpretations of certain soil properties. The data are stored in binary, image Format files.
J M Mckimmey - One of the best experts on this subject based on the ideXlab platform.
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applications of fuzzy logic to the prediction of soil erosion in a large watershed
Geoderma, 1998Co-Authors: B Mitra, H D Scott, John C Dixon, J M MckimmeyAbstract:There is a need in many emerging nations to develop simple methods for predicting areas of extensive soil erosion using imprecise, but real-world, input data at low cost with considerable accuracy. The objectives of this study are to (1) develop fuzzy logic models that predict soil erosion in a relatively large watershed using a limited number of input variables, (2) determine the effects of scale and grid-size variations in input data on fuzzy logic model output, and (3) compare the predictions of soil erosion using fuzzy logic methodologies with those of the Universal Soil Loss Equation (USLE). Two fuzzy-logic rule bases were constructed: (1) a two-variable-based model which required inputs of slope angle and landuse ratio or landuse, and (2) a three-variable-based model which required inputs of slope length (FLS), soil erodibility (FK) and vegetative cover (FC). Reducing the grid size of input data resulted in a decrease in the areal extent of predicted high soil erosion. The more dispersed pattern of soil erosion, observed with a 4-mile grid at a scale of 1:250,000, became more clustered in the 2-mile grid and even more clustered in a 2-mile grid at a scale of 1:24,000. The trend of clustering towards smaller areas of high erosion potential as map scale increased was also found at the grid sizes 1 and 0.4 miles. Soil erosion, predicted with the two-variable fuzzy logic model using 30 m resolution GIS-based data sets of slope and landuse, was similarly distributed among the low, moderately low and moderate soil erosion categories. This was attributed to the lack of inherent fuzziness in the input data obtained from Raster Format. The areal extent and locations of soil erosion predicted by the USLE model and by the two-variable-fuzzy logic model were similar. Differences between the predictions of the USLE model and the three-variable fuzzy logic model were primarily in the moderately low and moderate soil erosion categories and were attributed to the effects of the two non-fuzzy variables used in the USLE model, annual rainfall erosivity and cropping practices, which were not used in the fuzzy logic model. Compared with the USLE model predictions, the fuzzy logic-soil erosion prediction models were successful at locating and differentiating areas of soil erosion with minimum input data.
Luu Thanh Binh - One of the best experts on this subject based on the ideXlab platform.
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landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods a case study in the upper lo river catchment vietnam
Landslides, 2016Co-Authors: Le Quoc Hung, Nguyen Thi Hai Van, Do Minh Duc, Pham Van Son, Nguyen Ho Khanh, Luu Thanh BinhAbstract:The purpose of this study is to carry out a regional landslide susceptibility mapping for the upper Lo River catchment (ULRC) in northern Vietnam, where data on spatial distribution of historic landslides and environmental factors are very limited. Two methods, analytical hierarchy process (AHP) and weighted linear combination (WLC), were combined to create a landslide susceptibility map for the ULRC study area. In the first step, 216 existing landslides that occurred in the study area were mapped in field surveys in 2010 and 2011. A spatial database including six landslide factor maps related to elevation, slope gradient, drainage density, fault density, types of weathering crust, and types of land cover was constructed from various sources. To determine the relative importance of the six landslide factors and their classes within the landslide susceptibility analysis, weights of each factor and each factor class were defined by expert knowledge using the AHP method. To compute the landslide susceptibility, defined weights were assigned to all factor maps in Raster Format using the WLC method. The result is a landslide susceptibility index that is reclassified into four susceptible zones to produce a landslide susceptibility map. Finally, the landslide susceptibility zonation map was overlaid with the observed landslides in the inventory map to validate the produced map as well as the overall methodology. The results are in accordance with the occurrences of the observed landslides, in which 47.69 % of observed landslides are located in the two most susceptible zones (very-high-susceptibility zone and high-susceptibility zone) that cover 40.96 % of the total area. As the approach is able to integrate expert knowledge in the weighting of the input factors, the actual study shows that the combination of AHP and WLC methods is suitable for landslide susceptibility mapping in large mountainous areas at medium scales, particularly for areas lacking detailed input data.
B Mitra - One of the best experts on this subject based on the ideXlab platform.
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applications of fuzzy logic to the prediction of soil erosion in a large watershed
Geoderma, 1998Co-Authors: B Mitra, H D Scott, John C Dixon, J M MckimmeyAbstract:There is a need in many emerging nations to develop simple methods for predicting areas of extensive soil erosion using imprecise, but real-world, input data at low cost with considerable accuracy. The objectives of this study are to (1) develop fuzzy logic models that predict soil erosion in a relatively large watershed using a limited number of input variables, (2) determine the effects of scale and grid-size variations in input data on fuzzy logic model output, and (3) compare the predictions of soil erosion using fuzzy logic methodologies with those of the Universal Soil Loss Equation (USLE). Two fuzzy-logic rule bases were constructed: (1) a two-variable-based model which required inputs of slope angle and landuse ratio or landuse, and (2) a three-variable-based model which required inputs of slope length (FLS), soil erodibility (FK) and vegetative cover (FC). Reducing the grid size of input data resulted in a decrease in the areal extent of predicted high soil erosion. The more dispersed pattern of soil erosion, observed with a 4-mile grid at a scale of 1:250,000, became more clustered in the 2-mile grid and even more clustered in a 2-mile grid at a scale of 1:24,000. The trend of clustering towards smaller areas of high erosion potential as map scale increased was also found at the grid sizes 1 and 0.4 miles. Soil erosion, predicted with the two-variable fuzzy logic model using 30 m resolution GIS-based data sets of slope and landuse, was similarly distributed among the low, moderately low and moderate soil erosion categories. This was attributed to the lack of inherent fuzziness in the input data obtained from Raster Format. The areal extent and locations of soil erosion predicted by the USLE model and by the two-variable-fuzzy logic model were similar. Differences between the predictions of the USLE model and the three-variable fuzzy logic model were primarily in the moderately low and moderate soil erosion categories and were attributed to the effects of the two non-fuzzy variables used in the USLE model, annual rainfall erosivity and cropping practices, which were not used in the fuzzy logic model. Compared with the USLE model predictions, the fuzzy logic-soil erosion prediction models were successful at locating and differentiating areas of soil erosion with minimum input data.
