Limits...
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

Difference map of soil organic carbon (g kg-1) derived from MA and MB models (overlaid hillshading).MA model included all predictors (topography, climate and Landsat TM imagery); MB model included only topography and climate variables.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4608698&req=5

pone.0139042.g009: Difference map of soil organic carbon (g kg-1) derived from MA and MB models (overlaid hillshading).MA model included all predictors (topography, climate and Landsat TM imagery); MB model included only topography and climate variables.

Mentions: Fig 9 shows the difference in predicted SOC content based on the MA (full) and MB (topography and climate only) models. It is clear that adding multispectral Landsat TM imagery (model MA) provides more detail especially in the high SOC areas of model MB. By adding Landsat TM imagery, SOC in areas covered by glaciers and bare rocks dramatically decreases, with a corresponding increase in areas with high vegetation cover. The maps from the MA (full) and MC (imagery only) models are similar. Though SOC is well-correlated with precipitation and air temperature (Table 2), these climate features operate over wide areas and thus are too coarse to explain local SOC variability. This is where fine resolution remote sensing data can improve prediction (as shown in the visualization) due to its high resolution and relation to vegetation cover.


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)

Difference map of soil organic carbon (g kg-1) derived from MA and MB models (overlaid hillshading).MA model included all predictors (topography, climate and Landsat TM imagery); MB model included only topography and climate variables.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139042.g009: Difference map of soil organic carbon (g kg-1) derived from MA and MB models (overlaid hillshading).MA model included all predictors (topography, climate and Landsat TM imagery); MB model included only topography and climate variables.
Mentions: Fig 9 shows the difference in predicted SOC content based on the MA (full) and MB (topography and climate only) models. It is clear that adding multispectral Landsat TM imagery (model MA) provides more detail especially in the high SOC areas of model MB. By adding Landsat TM imagery, SOC in areas covered by glaciers and bare rocks dramatically decreases, with a corresponding increase in areas with high vegetation cover. The maps from the MA (full) and MC (imagery only) models are similar. Though SOC is well-correlated with precipitation and air temperature (Table 2), these climate features operate over wide areas and thus are too coarse to explain local SOC variability. This is where fine resolution remote sensing data can improve prediction (as shown in the visualization) due to its high resolution and relation to vegetation cover.

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