Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang, Meng; Du, Huaqiang; Zhou, Guomo; Li, Xuejian; Mao, Fangjie; Dong, Long; Zheng, Junlong; Liu, Hua; Huang, Zihao; He, Shaobai

doi:10.3390/f10111004

Cited by 26 publications

(19 citation statements)

References 68 publications

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“…The accuracy of land use maps is especially important for land use change analysis. The temporal mismatch between remote sensing and field survey data due to the lack of cloud-free Landsat scenes corresponding to the exact times of ground observation can lead to accuracy error in the verification results [19]. We used images from 2009 as a substitute for 2010, which caused error in the verification process.…”

Section: Discussionmentioning

confidence: 99%

“…Maps of LUCC can quantify a wide range of processes such as forest harvesting, forest disturbances, land use pressures and urban expansion, which are all important for rational use and scientific management of land resources [17,18]. Traditional field surveys can accurately evaluate the development trends and characteristics of LUCC, but field surveys require a large amount of manpower, material resources, financial resources, and time, which is impractical for large-scale monitoring of dynamic changes in land use [19]. With the development of remote sensing technology, satellite remote sensing has been widely used to detect LUCC [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Zhang

Mao

et al. 2020

IJGI

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Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Zhang

Mao

et al. 2020

IJGI

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“…Gray-scale co-occurrence matrix (GLCM) has been widely used to extract texture information of remote sensing images [ 50 ]. In order to reduce the effect of different texture windows on texture feature values, eight texture features of red-edge bands were extracted through five texture windows (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) [ 51 , 52 , 53 , 54 ]. Details of the extracted texture features are shown in Table 6 .…”

Section: Methodsmentioning

confidence: 99%

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

Jiang

Kutia²,

Sarkissian³

et al. 2020

Sensors

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Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

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“…Forests are good criteria for controlling the carbon value of the atmosphere because they are the most important carbon pools for carbon sequestration (Wegiel and Polowy 2020). Forests reserve more than twice the value of carbon in the atmosphere (Zhang et al 2019;Pan et al 2011), about 70% of global soil organic carbon and approximately 80 % of aboveground carbon (Santini et al 2019;Lin et al 2019). Therefore, these worth ecosystems are the most important carbon pools among terrestrial ecosystems and play a sustainable and long-term role in reducing climate change (Labrecque et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Sacred Groves: A Pattern of Zagros Forests for Carbon Sequestration and Climate Change Reduction

Moradi

Shabanian

2021

Preprint

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Background Rising atmospheric carbon dioxide has led to the global consequences of climate change. Biological carbon sequestration through vegetation and soils is one of the cost-effective ways to reduce this gas. Forests ecosystems are the most important carbon pools among terrestrial ecosystems and play a sustainable and long-term role in reducing climate change. Among forest ecosystems, sacred groves are less-disturbed and they can be a pattern of successful forest management for carbon sequestration and climate change reduction. In the present study, for the first time, the amount of carbon content in sacred grove and silvopastoral lands were investigated to determine the capacity of Zagros oak forests in carbon sequestration and climate change reduction. The aim of this study was to estimate the amount of carbon reserves in mentioned land-uses in order to obtain a systematic attitude towards management of these different land-use types and attain a suitable solution to counter the climate change crisis and ultimately sustainable environmental development. Results The results showed that each of the studied variables in the two studied land use is significantly different from each other. The mean of each of these biomass or carbon pools in silvopastoral is significantly lower than sacred groves. The results indicate that the common utilizations in the forests of the study area cause a significant reduction (P ≤ 0.01) in the forest biomass value and respective carbon content. Sacred grove currently absorbs 826.96 tons of carbon dioxide per hectare more than silvopastoral lands and this is a sign of high degradation in the forests of the study area. Conclusions According to the results obtained in this study, forest ecosystems that are protected against human intervention play a significant role in long-term carbon storage. Any interference with the natural conditions of the ecosystem has a significant negative impact on carbon reserves. Therefore, by selecting appropriate measures, local communities should be empowered to reduce their dependence on low incomes obtained from deforestation and conversion.

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Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Cited by 26 publications

References 68 publications

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

Sacred Groves: A Pattern of Zagros Forests for Carbon Sequestration and Climate Change Reduction

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