Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Wang, Shuai; Wang, Qiubing; Adhikari, Kabindra; Jia, Shuhai; Jin, Xinxin; Liu, Hongbin

doi:10.3390/su8111154

Cited by 45 publications

(49 citation statements)

References 63 publications

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“…However, in the central plain areas where human activities interfered greatly, the SOC stocks tended to decrease. In 1990, we found that there was a high correlation between regional SOC stocks and vegetation variables, especially NDVI, which was confirmed by previous studies [22,[35][36][37]. In Seoul Forest Park, Seoul, Republic of Korea, Bae et al [37] found that the SOC stock in topsoil in 2013 was about three times that in 2003.…”

Section: Estimates Of Soc Stockssupporting

confidence: 82%

“…The data of forest land soil properties in 1990 were obtained from the second National Soil Survey database of Liaoning Province [22]. A total of 367 forest soil data covering different soil types and parent material types were obtained.…”

Section: Soil Survey Data For 1990mentioning

confidence: 99%

“…Except for vegetation-related variables, SOC stocks also showed a close relationship with topographic variables, especially elevation variables, in 2015. Recent studies have pointed out that topographic variables, especially elevation variables, could be used as key environmental variables to effectively predict SOC stocks [11,22,[36][37][38][39][40]. Tsui et al [41] considered the effects of soil grade, vegetation type, and elevation on SOC stocks in subtropical wet volcanic ash in Yangmingshan National Park, Taipei City, Taiwan Province, China.…”

Section: Estimates Of Soc Stocksmentioning

confidence: 99%

“…In this process, the model improves its performance through iteration and enhancement technology to reduce its fitting errors [19]. The BRT model can deal with imperfect data, such as outliers, missing values, and data interaction [21,22]. Thus, the BRT model is widely used in many fields, such as remote sensing [23], ecology [24], environmental science, epidemiology [25], fisheries science [26], and soil science [19,22].…”

mentioning

confidence: 99%

“…The BRT model can deal with imperfect data, such as outliers, missing values, and data interaction [21,22]. Thus, the BRT model is widely used in many fields, such as remote sensing [23], ecology [24], environmental science, epidemiology [25], fisheries science [26], and soil science [19,22]. However, the BRT model is rarely selected for the study of temporal and spatial changes of forest organic carbon stocks at regional scales.In this study, the BRT model was used to estimate the temporal and spatial changes of SOC stocks in forest topsoil in Liaoning Province, China.…”

mentioning

confidence: 99%

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Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m −2 . In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region. soil properties are affected by many environmental factors, it is difficult to predict soil properties accurately and efficiently at the regional scale [8]. As an efficient and low-cost method, digital soil mapping (DSM) technology is able to accurately describe the spatial variability of soil attributes in a region by using a limited amount of sampling data and environmental variables [9]. In order to accurately predict the SOC stocks in a region and analyze its key environmental factors, scholars have carried out a lot of researches [10][11][12]. In fact, the direct relationship between soil properties and environmental factors is non-linear and complex [13,14]. Therefore, machine learning algorithms which can effectively avoid this kind of problem are widely used in DSM mapping [11,15]. For example, on Barro Colorado Island, Panama, Grimm et al. [15] used a random forest model to simulate SOC stocks in 0-10 cm, 10-20 cm, 20-30 cm, and 30-50 cm layers, respectively. Giasson et al. [16] applied a multiple logistic regression model to predict the occurrence of soil types in southern Brazil. Dorji et al. [17] selected regression kriging and equal-area spline profile function to predict the SOC stocks at depths 0-5, 5-15, 15-30, 30-60, and 60-100 cm in montane ecosystems, Eastern Himalayas. In Denmark, Adhikari et al. [18] used regression kriging and 12 environmental variables to simulate the spatial distribution of SOC stocks at five soil layers. Were et al. [19] compared the performance of support vector regression, artificial neural network, and random forest models in predicti...

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Section: Estimates Of Soc Stockssupporting

confidence: 82%

Section: Soil Survey Data For 1990mentioning

confidence: 99%

Section: Estimates Of Soc Stocksmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Yang

Xiao-min

Guo

et al. 2022

Soil Science Soc of Amer J

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Soil organic C (SOC) plays an important role in soil quality. Thus, it is of great significance to know the spatial distribution characteristics of SOC. Environmental variables, such as natural predictors, remote sensing (RS) variables, and digital soil mapping approaches have been widely used in SOC prediction. However, it is still challenging to determine which methods and variables are effective to map SOC in farmland. In this study, we compare the performance of three machine learning models, including random forest, Cubist, and the support vector machine model for the prediction of SOC contents using RS variables, topographic variables, climate variables, soil property variables, and spatial position variables as environmental covariates. The recursive feature elimination (RFE) technique was used to select optimal variables. Of the 130 sampling sites of the cultivated layer that were collected in Shanggao county, 70% of the data were used for training and 30% for validation. Prediction accuracy and uncertainty was quantified using the bootstrap approach with 100 iterations. The performance of the model was verified by the mean absolute error, RMSE, R2, and Lin's concordance correlation coefficient (CCC); the SD was used as a measure of prediction uncertainty. Results showed that the Cubist model combined with the RFE method (Cubist_RFE) had a mean R2, RMSE, and SD of .84, 2.39 g kg–1, and 0.73 g kg–1, respectively, which showed the highest prediction accuracy and the lowest uncertainty. The soil property variables (soil alkaline hydrolysis N and pH) and RS variables (soil adjusted vegetation index, enhanced vegetation index, and band7 of Sentinel‐2) were the controlling factors of SOC prediction. Thus, the results indicate the potential of the Cubist model in SOC prediction, and the combination of the optical and radar RS data is a viable way for predicting SOC.

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Scenario modeling for spatial-temporal change detection of carbon storage and sequestration in a forested landscape in Northern Iran

Zarandian¹,

Badamfirouz²,

Musazadeh³

et al. 2018

Environ Monit Assess

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The present study was conducted, based on scenario modeling approach, in the Do-hezar and Se-hezar forested landscape in the Mazandaran Province in Northern Iran in order to detect spatial-temporal changes of carbon storage and sequestration in four different carbon pools, i.e., aboveground and belowground biomasses, dead organic matter, and organic soils. For this purpose, firstly, the changing trend of land use/land cover (LULC) was detected by analyzing and comparing remotely sensed data of the landscape during the period of 1984-2016. Then, the impacts of future LULC changes on carbon storage and sequestration were predicted and valued using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model under two future plausible scenarios of business as usual (BAU) and balanced development (BD). According to the results of BAU scenario, continuation of the current trend will lead to a significant reduction in the carbon sequestration and a huge amount of social cost due to the loss of carbon stored in the landscape and its release to the atmosphere. The BD scenario which refers to the principled and under control development of human settlements simultaneously with forest conservational and restoration activities, could potentially reverse the downtrend of carbon sequestration service and avoid future socioeconomic costs, hence add to the economic value of the forest landscape in terms of providing a better sink for carbon storage. The results of this research can facilitate the quantitative and accurate assessment of carbon storage and sequestration relying on more precise biophysical and economic data as well as provide insight for effective land-use planning.

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Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Cited by 45 publications

References 63 publications

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Scenario modeling for spatial-temporal change detection of carbon storage and sequestration in a forested landscape in Northern Iran

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