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

Scatter plot of Landsat TM band 3 and NDVI vs. ln(SOC).It is based on 105 soil samples, with empirical smoothed line.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139042.g006: Scatter plot of Landsat TM band 3 and NDVI vs. ln(SOC).It is based on 105 soil samples, with empirical smoothed line.

Mentions: A surprising result revealed in Fig 4 is that Landsat TM band 3 (red visible) is the most important predictor in the BRT model, much better than NDVI. A single band has no correction for shadow effects nor for non-vegetation (i.e., red colour but not from red phytopigments); indeed this is why ratios such as NDVI were developed. The explanation is shown in Fig 5: the B3 feature-space distribution of the calibration samples is not representative of the full image (prediction area); specifically, there are fewer low values at the profile locations. The points of B4 are slight biased towards higher values than pixels. Thus, NDVI biases in the higher values. Partly this is because no dark-colored bare rock areas (low reflectance) were sampled for SOC; further, no soils were sampled in areas covered by water. On the other hand, there is a saturation effect in detecting SOC from NDVI and B3 at highly-vegetated areas (Fig 6). Even though the Pearson correlation is almost as high for NDVI and SOC (r = 0.79) as for B3 and SOC (r = -0.82), NDVI shows a lower sensitivity than B3 when they are applied to predict high SOC content. Therefore, B3 is preferred to NDVI in the BRT model.


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 plot of Landsat TM band 3 and NDVI vs. ln(SOC).It is based on 105 soil samples, with empirical smoothed line.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139042.g006: Scatter plot of Landsat TM band 3 and NDVI vs. ln(SOC).It is based on 105 soil samples, with empirical smoothed line.
Mentions: A surprising result revealed in Fig 4 is that Landsat TM band 3 (red visible) is the most important predictor in the BRT model, much better than NDVI. A single band has no correction for shadow effects nor for non-vegetation (i.e., red colour but not from red phytopigments); indeed this is why ratios such as NDVI were developed. The explanation is shown in Fig 5: the B3 feature-space distribution of the calibration samples is not representative of the full image (prediction area); specifically, there are fewer low values at the profile locations. The points of B4 are slight biased towards higher values than pixels. Thus, NDVI biases in the higher values. Partly this is because no dark-colored bare rock areas (low reflectance) were sampled for SOC; further, no soils were sampled in areas covered by water. On the other hand, there is a saturation effect in detecting SOC from NDVI and B3 at highly-vegetated areas (Fig 6). Even though the Pearson correlation is almost as high for NDVI and SOC (r = 0.79) as for B3 and SOC (r = -0.82), NDVI shows a lower sensitivity than B3 when they are applied to predict high SOC content. Therefore, B3 is preferred to NDVI in the BRT model.

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