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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

Standard deviation maps of predicted topsoil organic carbon (g kg-1).a) MA model included all predictors (topography, climate and Landsat TM imagery); b) MB model included only topography and climate variables; and c) MC model included only Landsat TM imagery (B3, B4, B5 and NDVI); d), e) and f) small areas outlined with red color in left large areas for showing detail information.
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pone.0139042.g008: Standard deviation maps of predicted topsoil organic carbon (g kg-1).a) MA model included all predictors (topography, climate and Landsat TM imagery); b) MB model included only topography and climate variables; and c) MC model included only Landsat TM imagery (B3, B4, B5 and NDVI); d), e) and f) small areas outlined with red color in left large areas for showing detail information.

Mentions: The predicted distributions of SOC content and standard deviation from three BRT models are shown in Fig 7 and Fig 8. Areas of glaciers and bare rocks are figured out from Landsat TM imagery using a supervised classification method and masked out in Fig 7 and assigned zero SOC values. Spatial patterns of SOC are obviously closely related to vegetation (compare Fig 1). High SOC contents are found in the south-eastern mountains, which have the densest vegetation cover, according to Jin et al. [52] who quantified vegetation distribution in the Qilian Mountains and found the densest vegetation cover between 3200 and 3600 m elevation. Low SOC contents were in the northern and north-western parts, which are dominated by low productivity plants such as desert-grassland and dry shrub-grassland [52].


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)

Standard deviation maps of predicted topsoil organic carbon (g kg-1).a) MA model included all predictors (topography, climate and Landsat TM imagery); b) MB model included only topography and climate variables; and c) MC model included only Landsat TM imagery (B3, B4, B5 and NDVI); d), e) and f) small areas outlined with red color in left large areas for showing detail information.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139042.g008: Standard deviation maps of predicted topsoil organic carbon (g kg-1).a) MA model included all predictors (topography, climate and Landsat TM imagery); b) MB model included only topography and climate variables; and c) MC model included only Landsat TM imagery (B3, B4, B5 and NDVI); d), e) and f) small areas outlined with red color in left large areas for showing detail information.
Mentions: The predicted distributions of SOC content and standard deviation from three BRT models are shown in Fig 7 and Fig 8. Areas of glaciers and bare rocks are figured out from Landsat TM imagery using a supervised classification method and masked out in Fig 7 and assigned zero SOC values. Spatial patterns of SOC are obviously closely related to vegetation (compare Fig 1). High SOC contents are found in the south-eastern mountains, which have the densest vegetation cover, according to Jin et al. [52] who quantified vegetation distribution in the Qilian Mountains and found the densest vegetation cover between 3200 and 3600 m elevation. Low SOC contents were in the northern and north-western parts, which are dominated by low productivity plants such as desert-grassland and dry shrub-grassland [52].

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