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Predictive Mapping of Topsoil Organic Carbon in an Alpine Environment Aided by Landsat TM.

Yang R, Rossiter DG, Liu F, Lu Y, Yang F, Yang F, Zhao Y, Li D, Zhang G - PLoS ONE (2015)

Bottom Line: The best model, combining all covariates, was only marginally better than using only imagery.The good results from imagery are likely due to (1) the strong dependence of SOC on native vegetation intensity in this Alpine environment; (2) the strong correlation in this environment between imagery and environmental covariables, especially elevation (corresponding to temperature), precipitation, and slope aspect.We conclude that multispectral satellite data from Landsat TM images may be used to predict topsoil SOC with reasonable accuracy in Alpine regions, and perhaps other regions covered with natural vegetation, and that adding topography and climate covariables to the satellite data can improve the predictive accuracy.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of the Chinese Academy of Sciences, Beijing 100049, China.

ABSTRACT
The objective of this study was to examine the reflectance of Landsat TM imagery for mapping soil organic Carbon (SOC) content in an Alpine environment. The studied area (ca. 3*104 km2) is the upper reaches of the Heihe River at the northeast edge of the Tibetan plateau, China. A set (105) of topsoil samples were analyzed for SOC. Boosted regression tree (BRT) models using Landsat TM imagery were built to predict SOC content, alone or with topography and climate covariates (temperature and precipitation). The best model, combining all covariates, was only marginally better than using only imagery. Imagery alone was sufficient to build a reasonable model; this was a bit better than only using topography and climate covariates. The Lin's concordance correlation coefficient values of the imagery only model and the full model are very close, larger than the topography and climate variables based model. In the full model, SOC was mainly explained by Landsat TM imagery (65% relative importance), followed by climate variables (20%) and topography (15% of relative importance). The good results from imagery are likely due to (1) the strong dependence of SOC on native vegetation intensity in this Alpine environment; (2) the strong correlation in this environment between imagery and environmental covariables, especially elevation (corresponding to temperature), precipitation, and slope aspect. We conclude that multispectral satellite data from Landsat TM images may be used to predict topsoil SOC with reasonable accuracy in Alpine regions, and perhaps other regions covered with natural vegetation, and that adding topography and climate covariables to the satellite data can improve the predictive accuracy.

No MeSH data available.


Related in: MedlinePlus

Relative importance of each predictor in the full (MA) model.CA, catchment area; TWI, SAGA wetness index; MAP, mean annual precipitation; MAT, mean annual temperature; B3, Landsat TM band 3; B4, Landsat TM band 4; B5, Landsat TM band 5; NDVI, normalized difference vegetation index.
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pone.0139042.g004: Relative importance of each predictor in the full (MA) model.CA, catchment area; TWI, SAGA wetness index; MAP, mean annual precipitation; MAT, mean annual temperature; B3, Landsat TM band 3; B4, Landsat TM band 4; B5, Landsat TM band 5; NDVI, normalized difference vegetation index.

Mentions: The BRT model also reports the relative importance of each predictor variable. In the full model, the largest contributions were from B3, MAP, NDVI, aspect and elevation (Fig 4). SOC was mainly explained by Landsat TM imagery (65% relative importance), followed by climate variables (20%) and topography variables (15%). This shows that vegetation, as detected by the imagery, was the most influential factor in predicting SOC content, followed by climate and topography factors. This is expected, since vegetation has been proven to be well-correlated with the spatial patterns of topsoil C, especially in naturally vegetated areas [48]. Remotely-sensed images and derived vegetation indexes have been associated with vegetation cover, vegetation type, biomass and productivity [49–51]. In digital soil mapping procedures, remote sensing images have been used as a proxy for the biosphere as a soil forming factor [5].


Predictive Mapping of Topsoil Organic Carbon in an Alpine Environment Aided by Landsat TM.

Yang R, Rossiter DG, Liu F, Lu Y, Yang F, Yang F, Zhao Y, Li D, Zhang G - PLoS ONE (2015)

Relative importance of each predictor in the full (MA) model.CA, catchment area; TWI, SAGA wetness index; MAP, mean annual precipitation; MAT, mean annual temperature; B3, Landsat TM band 3; B4, Landsat TM band 4; B5, Landsat TM band 5; NDVI, normalized difference vegetation index.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4608698&req=5

pone.0139042.g004: Relative importance of each predictor in the full (MA) model.CA, catchment area; TWI, SAGA wetness index; MAP, mean annual precipitation; MAT, mean annual temperature; B3, Landsat TM band 3; B4, Landsat TM band 4; B5, Landsat TM band 5; NDVI, normalized difference vegetation index.
Mentions: The BRT model also reports the relative importance of each predictor variable. In the full model, the largest contributions were from B3, MAP, NDVI, aspect and elevation (Fig 4). SOC was mainly explained by Landsat TM imagery (65% relative importance), followed by climate variables (20%) and topography variables (15%). This shows that vegetation, as detected by the imagery, was the most influential factor in predicting SOC content, followed by climate and topography factors. This is expected, since vegetation has been proven to be well-correlated with the spatial patterns of topsoil C, especially in naturally vegetated areas [48]. Remotely-sensed images and derived vegetation indexes have been associated with vegetation cover, vegetation type, biomass and productivity [49–51]. In digital soil mapping procedures, remote sensing images have been used as a proxy for the biosphere as a soil forming factor [5].

Bottom Line: The best model, combining all covariates, was only marginally better than using only imagery.The good results from imagery are likely due to (1) the strong dependence of SOC on native vegetation intensity in this Alpine environment; (2) the strong correlation in this environment between imagery and environmental covariables, especially elevation (corresponding to temperature), precipitation, and slope aspect.We conclude that multispectral satellite data from Landsat TM images may be used to predict topsoil SOC with reasonable accuracy in Alpine regions, and perhaps other regions covered with natural vegetation, and that adding topography and climate covariables to the satellite data can improve the predictive accuracy.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of the Chinese Academy of Sciences, Beijing 100049, China.

ABSTRACT
The objective of this study was to examine the reflectance of Landsat TM imagery for mapping soil organic Carbon (SOC) content in an Alpine environment. The studied area (ca. 3*104 km2) is the upper reaches of the Heihe River at the northeast edge of the Tibetan plateau, China. A set (105) of topsoil samples were analyzed for SOC. Boosted regression tree (BRT) models using Landsat TM imagery were built to predict SOC content, alone or with topography and climate covariates (temperature and precipitation). The best model, combining all covariates, was only marginally better than using only imagery. Imagery alone was sufficient to build a reasonable model; this was a bit better than only using topography and climate covariates. The Lin's concordance correlation coefficient values of the imagery only model and the full model are very close, larger than the topography and climate variables based model. In the full model, SOC was mainly explained by Landsat TM imagery (65% relative importance), followed by climate variables (20%) and topography (15% of relative importance). The good results from imagery are likely due to (1) the strong dependence of SOC on native vegetation intensity in this Alpine environment; (2) the strong correlation in this environment between imagery and environmental covariables, especially elevation (corresponding to temperature), precipitation, and slope aspect. We conclude that multispectral satellite data from Landsat TM images may be used to predict topsoil SOC with reasonable accuracy in Alpine regions, and perhaps other regions covered with natural vegetation, and that adding topography and climate covariables to the satellite data can improve the predictive accuracy.

No MeSH data available.


Related in: MedlinePlus