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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

Rose diagram of the aspect of 105 sample sites.The proportion of samples facing specific aspect was shown as the length of green bar.
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pone.0139042.g010: Rose diagram of the aspect of 105 sample sites.The proportion of samples facing specific aspect was shown as the length of green bar.

Mentions: Despite the success of Landsat TM imagery in this study, it is important to note that using only imagery for prediction has some drawbacks. In high-relief areas reflectance is influenced by shadow caused by high relief and clouds [53], leading to confusion for image classification and land cover recognition [53–55]. In our study, SOC on north-facing slopes are predicted to be somewhat higher than on the south-facing slopes (Fig 7); this is consistent with field observations and the low but significant correlation between SOC and aspect (r = -0.22, Table 2). However, the SOC content distribution map from the MC (imagery only) model seems to be influenced by mountain shadows, so that very high SOC contents are predicted in the N aspect positions (Fig 7). Note that the Landsat 5 overfly time is nominally 0945, i.e., mid-morning. The mosaic is from July to September, i.e., mid to late summer; in mid-August the Sun at 0945 has azimuth of 100° (i.e., ESE) and elevation of 36° (http://www.esrl.noaa.gov/gmd/grad/solcalc/azel.html), meaning that steep slopes facing WNW will be in shadow and have low reflectance, thus exaggerating the actual effect of aspect. NDVI is expected to correct for shadow effects, since it is normalized by the same bands as used in the difference. However, the samples are not evenly distributed as shown in the rose diagram of the aspect of the sample sites (Fig 10). They are mostly NNW to NE facing, and there are few samples facing the sun at the time of acquisition. Thus, the shadow correction is not so important in this study. Highly variable topographical attributes of plateau terrain cause difficulties in mapping SOC based only on remote sensing imagery. Topography is proved to be a valuable predictor for improving prediction accuracy from remote sensing data and resulting in more reliable predictions in such areas. Thus, topographical attributes are recommended in addition to remote sensing data for accurate SOC mapping in Alpine environments.


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)

Rose diagram of the aspect of 105 sample sites.The proportion of samples facing specific aspect was shown as the length of green bar.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139042.g010: Rose diagram of the aspect of 105 sample sites.The proportion of samples facing specific aspect was shown as the length of green bar.
Mentions: Despite the success of Landsat TM imagery in this study, it is important to note that using only imagery for prediction has some drawbacks. In high-relief areas reflectance is influenced by shadow caused by high relief and clouds [53], leading to confusion for image classification and land cover recognition [53–55]. In our study, SOC on north-facing slopes are predicted to be somewhat higher than on the south-facing slopes (Fig 7); this is consistent with field observations and the low but significant correlation between SOC and aspect (r = -0.22, Table 2). However, the SOC content distribution map from the MC (imagery only) model seems to be influenced by mountain shadows, so that very high SOC contents are predicted in the N aspect positions (Fig 7). Note that the Landsat 5 overfly time is nominally 0945, i.e., mid-morning. The mosaic is from July to September, i.e., mid to late summer; in mid-August the Sun at 0945 has azimuth of 100° (i.e., ESE) and elevation of 36° (http://www.esrl.noaa.gov/gmd/grad/solcalc/azel.html), meaning that steep slopes facing WNW will be in shadow and have low reflectance, thus exaggerating the actual effect of aspect. NDVI is expected to correct for shadow effects, since it is normalized by the same bands as used in the difference. However, the samples are not evenly distributed as shown in the rose diagram of the aspect of the sample sites (Fig 10). They are mostly NNW to NE facing, and there are few samples facing the sun at the time of acquisition. Thus, the shadow correction is not so important in this study. Highly variable topographical attributes of plateau terrain cause difficulties in mapping SOC based only on remote sensing imagery. Topography is proved to be a valuable predictor for improving prediction accuracy from remote sensing data and resulting in more reliable predictions in such areas. Thus, topographical attributes are recommended in addition to remote sensing data for accurate SOC mapping in Alpine environments.

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