Carbon storage and sequestration in a eucalyptus productive zone in the Brazilian Cerrado, using the Ca-Markov/Random Forest and InVEST models

Bacani, Vitor Matheus; Machado da Silva, Bruno Henrique; Ayumi de Souza Amede Sato, Amanda; Souza Sampaio, Bruna Dienifer; Rodrigues da Cunha, Elias; Pereira Vick, Erivelton; Ribeiro de Oliveira, Víncler Fernandes; Decco, Hermiliano Felipe

doi:10.1016/j.jclepro.2024.141291

Cited by 6 publications

(1 citation statement)

References 59 publications

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“…Additionally, research on soil erosion changes should not be limited to the past; studying soil erosion under future land use changes has more practical significance. The CA-Markov model is widely used in future simulations of land use, but it is limited in handling complex competition and interactions between different types of land use [32,33]. To overcome these deficiencies, the GeoSOS-FLUS model has been improved based on the CA-Markov model by incorporating artificial neural network algorithms, enhancing adaptive inertia and competition mechanisms, and significantly improving simulation accuracy and adaptability [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China

Lai,

Li,

Liu

2024

Forests

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Revealing the relationship between land use changes and soil erosion provides a reference for formulating future land use strategies. This study simulated historical and future soil erosion changes based on the RULSE and GeoSOS-FLUS models and used a random forest model to explain the relative importance of natural and anthropogenic factors on soil erosion. The main conclusions are as follows: (1) From 1990 to 2020, significant changes in land use occurred in Kunming, with a continuous reduction in woodland, grassland, and cropland, being converted into construction land, which grew by 195.18% compared with 1990. (2) During this period, the soil erosion modulus decreased from 133.85 t/(km²·a) in 1990 to 130.32 t/(km²·a) in 2020, with a reduction in soil loss by 74,485.46 t/a, mainly due to the conversion of cropland to construction and ecological lands (woodland, grassland). (3) The expansion of construction land will continue, and it is expected that by 2050, the soil erosion modulus will decrease by 3.77 t/(km²·a), 4.27 t/(km²·a), and 3.27 t/(km²·a) under natural development, rapid development, and ecological protection scenarios, respectively. However, under the cropland protection scenario, the soil erosion modulus increased by 0.26 t/(km²·a) compared with 2020. (4) The spatial pattern of soil erosion is influenced by both natural and anthropogenic factors, and as human activities intensify in the future, the influence of anthropogenic factors will further increase. Traditionally, the expansion of construction land is thought to increase soil loss. Our study may offer a new perspective and provide a reference for future land use planning and soil loss management in Kunming.

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Section: Introductionmentioning

confidence: 99%

Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China

Lai,

Li,

Liu

2024

Forests

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Spatiotemporal variations of ecosystem services and driving factors in the Tianchi Bogda Peak Natural Reserve of Xinjiang, China

Zhu,

Wang,

Tang

et al. 2024

J. Arid Land

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Identifying the Coupling Coordination Relationship between Urbanization and Ecosystem Services Supply–Demand and Its Driving Forces: Case Study in Shaanxi Province, China

Liu,

Wang,

Hui

et al. 2024

Remote Sensing

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Exploring the relationship and driving forces between supply–demand of ecosystem services (ESs) and urbanization can help solve the environmental problems and promote regional sustainable development. This study analyzed the spatio-temporal distribution characteristics of supply–demand of ESs and comprehensive urbanization level (CUL) in Shaanxi Province from 2010 to 2019 and assessed the coupling relationship between ecosystem service supply–demand ratio (ESSDR) and CUL using the coupling coordination degree (CCD) model. Random forests and geographically weighted regression methods were utilized to characterize the contribution and spatial distribution of the drivers of CCD. The results showed that: (1) except for habitat quality, the ESSDR of the other three types of services as well as the comprehensive services showed a decreasing trend, CUL exhibited increasing trend; (2) Although CCD was generally increasing, a significant portion (78.51%) of regions still remained uncoordinated, with relatively better coordination shown around the Guanzhong urban agglomeration, which has a higher urbanization level; (3) The CCD in Shaanxi Province was primarily influenced by local financial income, the secondary industry, and temperature forces. In regions with high and increasing CCD, the tertiary industry was the decisive force. In other areas, there were significant spatial variations in the driving forces. These findings provide a coupled and coordinated perspective for urban ecological management, which can provide scientific reference and practical guidance for cities with different development modes.

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Carbon storage and sequestration in a eucalyptus productive zone in the Brazilian Cerrado, using the Ca-Markov/Random Forest and InVEST models

Cited by 6 publications

References 59 publications

Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China

Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China

Spatiotemporal variations of ecosystem services and driving factors in the Tianchi Bogda Peak Natural Reserve of Xinjiang, China

Identifying the Coupling Coordination Relationship between Urbanization and Ecosystem Services Supply–Demand and Its Driving Forces: Case Study in Shaanxi Province, China

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