Global Maps of Agricultural Expansion Potential at a 300 m Resolution

Čengić, Mirza; Steinmann, Zoran J. N.; Defourny, Pierre; Doelman, Jonathan C.; Lamarche, Céline; Stehfest, Elke; Schipper, Aafke M.; Huijbregts, Mark A. J.

doi:10.3390/land12030579

Cited by 4 publications

(4 citation statements)

References 42 publications

(58 reference statements)

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“…The AUC value obtained for the global spherical geographic deforestation model was 0.9 in the calibration phase and 0.87 in the validation phase. Other global geosimulation model applications in the scientific literature report AUC values ranging between 0.72 and 0.93 [92][93][94]. For the FoM metric, the value obtained was 36.5 for the model calibration and 29.9% during model validation.…”

Section: Model Implementation and Evaluationmentioning

confidence: 91%

Modelling Global Deforestation Using Spherical Geographic Automata Approach

Addae,

Dragićević

2023

IJGI

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Deforestation as a land-cover change process is linked to several environmental problems including desertification, biodiversity loss, and ultimately climate change. Understanding the land-cover change process and its relation to human–environment interactions is important for supporting spatial decisions and policy making at the global level. However, current geosimulation model applications mainly focus on characterizing urbanization and agriculture expansion. Existing modelling approaches are also unsuitable for simulating land-cover change processes covering large spatial extents. Thus, the objective of this research is to develop and implement a spherical geographic automata model to simulate deforestation at the global level under different scenarios designed to represent diverse future conditions. Simulation results from the deforestation model indicate the global forest size would decrease by 10.5% under the “business-as-usual” scenario through 2100. The global forest extent would also decline by 15.3% under the accelerated deforestation scenario and 3.7% under the sustainable deforestation scenario by the end of the 21st century. The obtained simulation outputs also revealed the rate of deforestation in protected areas to be considerably lower than the overall forest-cover change rate under all scenarios. The proposed model can be utilized by stakeholders to examine forest conservation programs and support sustainable policy making and implementation.

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Section: Model Implementation and Evaluationmentioning

confidence: 91%

Modelling Global Deforestation Using Spherical Geographic Automata Approach

Addae,

Dragićević

2023

IJGI

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“…IMAGE uses food system data from MAGNET, such as demand for crop and livestock production and trends in intensification or extensification to project gridded land use in the future. Expansion of agricultural land is allocated at the grid level using empirically based statistical suitability layers derived from remote-sensing based land-use change data (Cengic et al 2023). Gridded land use and climate change are implemented in the dynamic global vegetation model LPJmL which represents the carbon and hydrological cycles as well as crop growth for rainfed and irrigated agriculture (Müller et al 2016, Schaphoff et al 2018.…”

Section: The Image 32 Model Frameworkmentioning

confidence: 99%

“…The data that support the findings of this study are openly available at the following URL/DOI: https://doi. org/10.5281/zenodo.7681342 (Doelman et al 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios

Doelman

Verhagen

Stehfest

et al. 2023

Environ. Res.: Climate

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Peatlands only cover a small fraction of the global land surface (∼3%) but store large amounts of carbon (∼600 GtC). Drainage of peatlands for agriculture results in the decomposition of organic matter, leading to greenhouse gas (GHG) emissions. As a result, degraded peatlands are currently responsible for 2%–3% of global anthropogenic emissions. Preventing further degradation of peatlands and restoration (i.e. rewetting) are therefore important for climate change mitigation. In this study, we show that land-use change in three SSP scenarios with optimistic, recent trends, and pessimistic assumptions leads to peatland degradation between 2020 and 2100 ranging from −7 to +10 Mha (−23% to +32%), and a continuation or even an increase in annual GHG emissions (−0.1 to +0.4 GtCO2-eq yr−1). In default mitigation scenarios without a specific focus on peatlands, peatland degradation is reduced due to synergies with forest protection and afforestation policies. However, this still leaves large amounts of GHG emissions from degraded peatlands unabated, causing cumulative CO2 emissions from 2020 to 2100 in an SSP2-1.5 °C scenario of 73 GtCO2. In a mitigation scenario with dedicated peatland restoration policy, GHG emissions from degraded peatlands can be reduced to nearly zero without major effects on projected land-use dynamics. This underlines the opportunity of peatland protection and restoration for climate change mitigation and the need to synergistically combine different land-based mitigation measures. Peatland location and extent estimates vary widely in the literature; a sensitivity analysis implementing various spatial estimates shows that especially in tropical regions degraded peatland area and peatland emissions are highly uncertain. The required protection and mitigation efforts are geographically unequally distributed, with large concentrations of peatlands in Russia, Europe, North America and Indonesia (33% of emission reductions are located in Indonesia). This indicates an important role for only a few countries that have the opportunity to protect and restore peatlands with global benefits for climate change mitigation.

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“…We used relatively simple suitability maps largely based on expert judgment. For future work, we recommend establishing more refined suitability maps that better capture the dependency of suitability on local environmental conditions [57]. We adopted land-use projections from global integrated assessment models, which are not necessarily representative of a single (small) country.…”

Section: Implications and Outlookmentioning

confidence: 99%

Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium

Rashidi,

Patil,

Schipper

et al. 2023

Land

Self Cite

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Land use change scenarios, and their projected impacts on biodiversity, are highly relevant at local scales but not adequately captured by the coarse spatial resolutions of global land use models. In this study, we used the land use allocation tool of the GLOBIO 4 model to downscale the Land Use Harmonization v2 (LUH2) data from their original spatial resolution (0.25°) to 100 m and 10 m resolutions, using the country of Belgium as an example. Inputs to the tool included: (1) a reference present-day land cover map at the high spatial resolution, (2) regional land demand projections for three future scenarios, Sustainability (SSP1xRCP2.6), Regional Rivalry (SSP3xRCP6.0), and Fossil-fuelled Development (SSP5xRCP8.5), and (3) raster layers representing the suitability of the grid cells for different land use types. We further investigated the impact of using different reference land cover maps (CORINE at 100 m resolution and ESA WorldCover at 100 m and 10 m resolutions) on the downscaling outcomes. Comparison of downscaled current and future land use maps with the original LUH2 dataset showed that the use of ESA WorldCover as a reference map provides better agreement (RSR: 0.11–0.24, overall accuracy: 0.94–0.98, Kappa: 0.91–0.97) than CORINE (RSR: 0.28–0.33, overall accuracy: 0.90–0.93, Kappa: 0.90–0.91). Additionally, the validation of the present-day downscaled maps showed a good agreement with the independent Copernicus Global Land Service dataset. Our findings suggest that the choice of reference land cover map influences the degree of agreement between the downscaled and the original coarse-grain land-use maps. Moreover, the land use maps produced using our downscaling approach can provide valuable insights into the potential impacts of land use change on biodiversity and can guide local decision-making processes for sustainable land management and conservation efforts.

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Global Maps of Agricultural Expansion Potential at a 300 m Resolution

Cited by 4 publications

References 42 publications

Modelling Global Deforestation Using Spherical Geographic Automata Approach

Modelling Global Deforestation Using Spherical Geographic Automata Approach

The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios

Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium

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