Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province, South Africa using remotely sensed data

Odebiri, Omosalewa; Mutanga, Onisimo; Odindi, John; Peerbhay, Kabir; Dovey, Steven

doi:10.1080/15481603.2020.1731108

Cited by 29 publications

(20 citation statements)

References 61 publications

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“…Furthermore, random forest permits model optimization for better results using two parameters, namely ntree, based on large sets of decision trees and bootstrap training sample, and mtry, based on the individual predictor variables selected from each tree node [25,40]. Normally, the standard value of ntree is set at 500, while mtry takes the square-root of the total number of an input predictor variable on a normal classification; on the regression, it divides all predictor variables by a default factor of three [9,56]. The optimal ntree and mtry values for best prediction performance are determined based on the smallest out-of-bag error [56].…”

Section: Discussionmentioning

confidence: 99%

“…Commonly, urban vegetation (especially forest ecosystems) sequestrate the emitted carbon and regulate climate systems within urban landscapes. However, deforestation and forest degradation that typifies urbanization processes reduces urban areas' carbon sequestration potential and increases greenhouse gas accumulations [7][8][9][10]. In sub-Saharan Africa for instance, studies show that urbanization exert enormous pressure on the spatial distribution of urban forest ecosystems, hence decreasing substantial amount of sequestrated carbon and accelerate potential risks and impacts of climate change [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Dube et al [22] used random forest ensemble to estimate above ground biomass of Eucalyptus and pine species in a commercial forest. Similarly, Odebiri et al [9] adopted ensemble random forest model to predict soil organic carbon stock in plantation forests, while Mutanga et al [25] demonstrated that random forest model is critical in predicting biomass in a wetland. Furthermore, it has been widely proven that the integration of Sentinel-2's spectral bands and vegetation indices in a robust machine learning algorithm facilitates accurate determination of aboveground vegetation carbon stocks [26,[38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

2021

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The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological processes and ecosystem services. Continuous forest degradation and deforestation due to urbanization have led to an increase in atmospheric carbon emissions, risks, and impacts associated with climate change within urban landscapes and beyond them. Hence, urban reforestation has become a reliable long-term alternative for carbon sink and climate change mitigation. However, there is an urgent need for spatially accurate and concise quantification of these forest carbon stocks in order to understand and effectively monitor the accumulation and progress on such ecosystem services. Hence, this study sought to examine the prospect of Sentinel-2 spectral data in quantifying carbon stock in a reforested urban landscape using the random forest ensemble. Results show that Sentinel-2 spectral data estimated reforested forest carbon stock to an RMSE between 0.378 and 0.466 t·ha−1 and R2 of 79.82 and 77.96% using calibration and validation datasets. Based on random forest variable selection and backward elimination approaches, the red-edge normalized difference vegetation index, enhanced vegetation index, modified simple ratio index, and normalized difference vegetation index were the best subset of predictor variables of carbon stock. These findings demonstrate the value and prospects of Sentinel-2 spectral data for predicting carbon stock in reforested urban landscapes. This information is critical for adopting informed management policies and plans for optimizing urban reforested landscapes carbon sequestration capacity and improving their climate change mitigation potential.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

2021

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“…In this study, we used the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at-Remote Sens. 2021, 13, 4106 5 of 15 mospheric correction method [34] to calibrate the atmospheric in ENVI 5.1 software; thirdly, due to the height angle of the sun, some remote sensing images seem to have mountain shadow, so we used the ratio method [35] in ENVI 5.1 software to eliminate it. In addition, since the study area was plain, we did not carry out topographic correction.…”

Section: Remote Sensing Related Variablesmentioning

confidence: 99%

Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

et al. 2021

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Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

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“…Therefore, it was not necessary to use the ground control points or digital elevation model (DEM) data to do geometric precision correction again. In addition, many previous studies have shown that the Bottom-Of-Atmosphere (BOA) reflectance is the most appropriate remote sensing variable to predict the spatial distribution of SOC and STN [28]. However, to obtain the BOA reflectance, atmospheric correction shall be performed for the remote sensing data.…”

Section: Remote Sensing Related Datamentioning

confidence: 99%

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

et al. 2020

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Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.

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Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province, South Africa using remotely sensed data

Cited by 29 publications

References 61 publications

The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

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