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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

Scatter plots of observed vs. predicted ln(SOC) by three boosted regression tree (BRT) models.MA (topography, climate and Landsat TM imagery); MB (only topography and climate variables); and MC (only Landsat TM imagery).
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pone.0139042.g002: Scatter plots of observed vs. predicted ln(SOC) by three boosted regression tree (BRT) models.MA (topography, climate and Landsat TM imagery); MB (only topography and climate variables); and MC (only Landsat TM imagery).

Mentions: Fig 2 shows scatter plots of observed and predicted ln(SOC) obtained from three BRT models. These three models underestimated high and overestimated low SOC contents, i.e., the relation has a negative gain, typical result of model smoothing. The MC (imagery-only) model showed the least gain, whereas the MB (topography and climate only model) showed the most. This is consistent with Huang et al. [15] who estimated soil total carbon via 15 m Landsat ETM reflectance data with and without topography variables using multiple regression equations over bare soil. They found that the explained of the variations in total carbon increased from 43% to 60% by combining imagery with topographical data.


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)

Scatter plots of observed vs. predicted ln(SOC) by three boosted regression tree (BRT) models.MA (topography, climate and Landsat TM imagery); MB (only topography and climate variables); and MC (only Landsat TM imagery).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139042.g002: Scatter plots of observed vs. predicted ln(SOC) by three boosted regression tree (BRT) models.MA (topography, climate and Landsat TM imagery); MB (only topography and climate variables); and MC (only Landsat TM imagery).
Mentions: Fig 2 shows scatter plots of observed and predicted ln(SOC) obtained from three BRT models. These three models underestimated high and overestimated low SOC contents, i.e., the relation has a negative gain, typical result of model smoothing. The MC (imagery-only) model showed the least gain, whereas the MB (topography and climate only model) showed the most. This is consistent with Huang et al. [15] who estimated soil total carbon via 15 m Landsat ETM reflectance data with and without topography variables using multiple regression equations over bare soil. They found that the explained of the variations in total carbon increased from 43% to 60% by combining imagery with topographical data.

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