A GIS-based methodology for sustainable farming planning: Assessment of land use/cover changes and carbon dynamics at farm level

Dargains, Alexandre; Cabral, Pedro

doi:10.1016/j.landusepol.2021.105788

Cited by 5 publications

(5 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The different transfer probabilities largely reflect differences in policies and other factors acting on future development. Four future development scenarios are constructed in this paper [54,55,71]: (1) natural trend scenario (NT), which assumes that future LULC will continue the conversion pattern of the previous period; (2) built-up land priority scenario (BP), in which economic development is accelerated and the proportion of other LULC types converted to built-up land areas is increased; (3) cropland priority scenario (CP), strengthening the protection and cultivation of cropland and strictly restricting the conversion of cropland to other LULC types; and (4) ecological priority scenario (EP), strengthening the protection of LULC types with outstanding ecological functions and restricting the conversion of forest and grassland to other LULC types. The above process was programmed in MathWorks MATLAB R2021a for implementation.…”

Section: Lulc Simulation With Markov-plus Model (1) Multi-scenario Lu...mentioning

confidence: 99%

Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China

Liu

Yang

Cao

et al. 2022

Land

View full text Add to dashboard Cite

Land use and land cover (LULC) change in tropical regions can cause huge amounts of carbon loss and storage, thus significantly affecting the global climate. Due to the differences in natural and social conditions between regions, it is necessary to explore the correlation mechanism between LULC and carbon storage changes in tropical regions from a broader geographical perspective. This paper takes Hainan Island as the research object, through the integration of the CA-Markov and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models, based on multi-source data, analyses the dynamics of LULC and carbon storage from 1992 to 2019 and the relationship between the two, and predicts future LULC and carbon storage under different scenarios. The results show that (1) the built-up land area of Hainan Island expanded from 103.59 km2 to 574.83 km2 from 1992 to 2019, an increase of 454.91%; the area of cropland and shrubland decreased; and the area of forest increased. (2) Carbon storage showed an upward trend during 1992–2000, and a downward trend during 2000–2019. Overall, LULC changes during 1992–2019 reduced carbon storage by about 1.50 Tg. (3) The encroachment of cropland in built-up land areas is the main reason for the reduction of carbon storage. The conversion of shrubland to forest is the main driving force for increasing carbon storage. The increase and decrease of carbon storage have obvious spatial clustering characteristics. (4) In the simulation prediction, the natural trend scenario (NT), built-up land priority scenario (BP) and ecological priority scenario (EP) reduce the carbon storage of Hainan Island, and the rate of decrease is BP> NT > EP. The cropland priority scenario (CP) can increase the LULC carbon storage, and the maximum increase in 2050 can reach 0.79 Tg. This paper supplements and improves the understanding of the correlation between LULC and carbon storage changes in tropical regions, and can provide guidance for the optimization of LULC structure in tropical regions with high economic development from a low-carbon perspective.

show abstract

Section: Lulc Simulation With Markov-plus Model (1) Multi-scenario Lu...mentioning

confidence: 99%

Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China

Liu

Yang

Cao

et al. 2022

Land

View full text Add to dashboard Cite

show abstract

“…The Carbon module of the InVEST model divides the carbon storage of ecosystems into four basic carbon pools [35,61]: above-ground biomass carbon (AGC: carbon in all surviving plants above the soil), below-ground biomass carbon (BGC: carbon present in living root system of plants), soil organic carbon (SOC: organic carbon distributed in organic and mineral soils), and dead organic matter carbon (DOC: carbon in dead plants and fallen matter) [62]. The carbon storage of the cell (i, j) can be calculated by its corresponding land-use carbon density [22].…”

Section: Invest Modelmentioning

confidence: 99%

“…Leh et al [21] used the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to quantify the land-use change effects on CS in west Africa. Dargains and Cabral [22] evaluated the CS on a cattle ranch from 2007 to 2017 and then created four alternative land-use scenarios with the InVEST Scenario Generator for 2027 to simulate LUCC and forecast future CS dynamics. Wang et al [10] constructed an urban-level framework of future LUCC and CS simulation under the context of CMIP6 climate change scenarios, integrating system dynamics (SD) model, patch-generating land use simulation (PLUS) model, and the InVEST model.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Land-Use Change on the Spatio-Temporal Patterns of Terrestrial Ecosystem Carbon Storage in the Gansu Province, Northwest China

et al. 2022

View full text Add to dashboard Cite

Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to determine the role of land-use change in affecting ecosystem carbon storage from 1980 to 2050 in the Gansu province of China, using the MCE-CA-Markov and InVEST models. In addition, to quantify the relative size of the land-use change effect in comparison with other environmental drivers, we also considered the effects of climate change, CO2 enrichment, and cropland and forest managements in the models. The results show that the ecosystem carbon storage in the Gansu province increased by 208.9 ± 99.85 Tg C from 1980 to 2020, 12.87% of which was caused by land-use change, and the rest was caused by climate change, CO2 enrichment, and ecosystem managements. The land-use change-induced carbon sequestration was mainly associated with the land-use category conversion from farmland to grassland as well as from saline land and desert to farmland, driven by the grain-for-green projects in the Loess Plateau and oasis cultivation in the Hexi Corridor. Furthermore, it was projected that ecosystem carbon storage in the Gansu province from 2020 to 2050 will change from −14.69 ± 12.28 Tg C to 57.83 ± 53.42 Tg C (from 105.62 ± 51.83 Tg C to 177.03 ± 94.1 Tg C) for the natural development (ecological protection) scenario. By contrast, the land-use change was supposed to individually increase the carbon storage by 56.46 ± 9.82 (165.84 ± 40.06 Tg C) under the natural development (ecological protection) scenario, respectively. Our results highlight the importance of ecological protection and restoration in enhancing ecosystem carbon storage for arid regions, especially under future climate change conditions.

show abstract

“…Research on carbon storage is an important issue [15]. Focusing on global warming, researchers have conducted research on carbon storage [16][17][18][19]. First, field sampling and physicochemical analysis have been used to explore the impact of different land-use patterns or certain land-use types on soil carbon storage [17].…”

Section: Introductionmentioning

confidence: 99%

“…First, field sampling and physicochemical analysis have been used to explore the impact of different land-use patterns or certain land-use types on soil carbon storage [17]. Second, the effect of land-use changes on different types of carbon storage has been revealed by remote sensing and geographic information system software [18,19]. Third, the influence of social economy and population development level on carbon storage was analyzed using an econometric model and carbon storage module [20,21].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Differentiation and Driving Factors of Carbon Storage in Cultivated Land-Use Transition

Gai

2023

Sustainability

View full text Add to dashboard Cite

Revealing the response of carbon storage to cultivated land-use transition (CLUT) and identifying its driving factors are of great significance for maintaining ecosystem stability and promoting regional carbon peak and carbon neutrality goals. Given the knowledge gap regarding the driving factors of carbon storage in CLUT, this study takes the Songhua River Basin in the black soil region of China as the case study area. The study aimed to reveal the spatial–temporal heterogeneity of carbon storage in CLUT based on the grid element method and carbon storage density. In addition, the driving factors were demonstrated using the geodetector model. The results show that the cultivated land area gradually decreased from 1990 to 2020, and the transition between cultivated and construction land was the most significant. The carbon storage in cultivated land-use transitions showed a substantial decreasing trend. The conversion of cultivated land to construction land resulted in the loss of 130,443,200 tons of carbon reserves. Moreover, the transformation from unused land to cultivated land led the highest increase in carbon storage, which increased by 29,334,600 tons. The gravity center of carbon storage was stable, moving 28.77 km to the northeast between 1990 and 2020. Conversely, the spatial structure of carbon storage showed a transformation trend from multicore fragmentation to mononuclear agglomeration, with obvious regional accumulation, a weakened degree of fragmentation, and uniform distribution. Carbon reserves increased by 388,600 tons from 1990 to 2000, and carbon reserves lost 60,121,700 tons from 2010 to 2020, nearly 155.700 times. The mean annual rainfall was the main carbon storage factor. The interaction between mean annual rainfall and land-use intensity had strong explanatory power, and the spatial heterogeneity of carbon storage resulted from multiple factors.

show abstract

A GIS-based methodology for sustainable farming planning: Assessment of land use/cover changes and carbon dynamics at farm level

Cited by 5 publications

References 54 publications

Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China

Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China

Impacts of Land-Use Change on the Spatio-Temporal Patterns of Terrestrial Ecosystem Carbon Storage in the Gansu Province, Northwest China

Spatio-Temporal Differentiation and Driving Factors of Carbon Storage in Cultivated Land-Use Transition

Contact Info

Product

Resources

About