Spatially-explicit modeling and intensity analysis of China's land use change 2000–2050

2020 ◽  
Vol 263 ◽  
pp. 110407 ◽  
Author(s):  
Yongjiu Feng ◽  
Zhenkun Lei ◽  
Xiaohua Tong ◽  
Chen Gao ◽  
Shurui Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatially Explicit ◽  
Intensity Analysis ◽  
Spatially Explicit Modeling

scholarly journals Assessment on the rates and potentials of soil organic carbon sequestration in agricultural lands in Japan using a process-based model and spatially explicit land-use change inventories – Part 1: Historical trend and validation based on nation-wide soil monitoring

2014 ◽  
Vol 11 (16) ◽  
pp. 4429-4442 ◽  
Author(s):  
Y. Yagasaki ◽  
Y. Shirato
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Land Use Change ◽  
Paddy Fields ◽  
Spatially Explicit ◽  
Crop Fields ◽  
Upland Crop ◽  
Soc Stock ◽  
Stock Change ◽  
Soc Stocks

Abstract. In order to estimate a country-scale soil organic carbon (SOC) stock change in agricultural lands in Japan, while taking into account the effect of land-use changes, climate, different agricultural activities and the nature of soils, a spatially explicit model simulation system was developed using Rothamsted Carbon Model (RothC) with an integration of spatial and temporal inventories. Simulation was run from 1970 to 2008 with historical inventories. Simulated SOC stock was compared with observations in a nation-wide stationary monitoring program conducted during 1979–1998. Historical land-use change, characterized by a large decline in the area of paddy fields as well as a small but continuous decline in the area of orchards, occurred along with a relatively large increase in upland crop fields, unmanaged grasslands, and settlements (i.e. conversion of agricultural fields due to urbanization or abandoning). Results of the simulation on SOC stock change under varying land-use change indicated that land-use conversion from agricultural fields to settlements or other lands, as well as that from paddy fields to croplands have likely been an increasing source of CO2 emission, due to the reduction of organic carbon input to soils and the enhancement of SOC decomposition through transition of soil environment from anaerobic to aerobic conditions. The area-weighted mean concentrations of the simulated SOC stocks calculated for major soil groups under paddy fields and upland crop fields were comparable to those observed in the monitoring. Whereas in orchards, the simulated SOC stocks were underestimated. As the results of simulation indicated that SOC stock change under managed grasslands and settlements has been likely a major sink and source of CO2 emission at country-scale, respectively, validation of SOC stock change under these land-use types, which could not have been accomplished due to limited availability or a lack of measurement, remains a forthcoming challenge.

scholarly journals Deriving spatially explicit water uses from land use change modelling results in four river basins across Europe

2018 ◽  
Vol 628-629 ◽  
pp. 1079-1097 ◽  
Author(s):  
Verena Huber García ◽  
Swen Meyer ◽  
Kasper Kok ◽  
Peter Verweij ◽  
Ralf Ludwig
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
River Basins ◽  
Spatially Explicit ◽  
Explicit Water ◽  
Water Uses

scholarly journals Land Use Change from Biofuels Derived from Forest Residue: A Case of Washington State

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Brent ◽  
Sergey Rabotyagov
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
The United States ◽  
Washington State ◽  
Spatially Explicit ◽  
Biofuel Policy ◽  
Indirect Land Use Change ◽  
Forest Residue ◽  
Net Returns ◽  
Processing Plants

Biofuel policy in the United States is transitioning away from corn towards second-generation biofuels in part because of the debate over environmental damages from indirect land use change. We combine a spatially explicit parcel level model for land use change in Washington State with simulations for biofuel policy aimed at utilizing forest residue as feedstock. Using a spatially explicit model provides greater precision in measuring net returns to forestland and development and indicates which areas will be most impacted by biofuel policy. The effect of policy is simulated via scenarios of increasing net returns to forestry and of siting feedstock-processing plants. Our results suggest that forestland will increase from such a policy, leading to a net reduction in atmospheric carbon from indirect land use change. This is in contrast to the experience of corn ethanol where the change in carbon emissions is potentially positive and large in magnitude.

scholarly journals An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)

2015 ◽  
Vol 8 (4) ◽  
pp. 3359-3402 ◽  
Author(s):  
S. Moulds ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Global Environmental Change ◽  
Model Development ◽  
Spatially Explicit ◽  
Change Models ◽  
R Programming Language ◽  
Global Environmental ◽  
R Programming ◽  
Scientific Results

Abstract. Land use change has important consequences for biodiversity and the sustainability of ecosystem services, as well as for global environmental change. Spatially explicit land use change models improve our understanding of the processes driving change and make predictions about the quantity and location of future and past change. Here we present the lulccR package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of different models; (3) different aspects of the modelling procedure must be performed in different environments because existing applications usually only perform the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the widely used CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a dataset included with the package. It is envisaged that lulccR will enable future model development and comparison within an open environment.

scholarly journals New land use change scenarios for Brazil: refining global SSPs with a regional spatially-explicit allocation model

2021 ◽  
Author(s):  
Francisco Gilney Silva Bezerra ◽  
Celso Von Randow ◽  
Talita Oliveira Assis ◽  
Karine Rocha Aguiar Bezerra ◽  
Graciela Tejada ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Forest Restoration ◽  
Global Climate ◽  
Global Analysis ◽  
Spatially Explicit ◽  
Surface Model ◽  
Modeling Framework ◽  
Allocation Model ◽  
The Future

The future of land use and cover change in Brazil, in particular due to deforestation and forest restoration processes, is critical for the future of global climate and biodiversity, given the richness of its five biomes. These changes in Brazil depend on the interlink between global factors, due to its role as one of the main exporters of commodities in the world, and the national to local institutional, socioeconomic and biophysical contexts. Aiming to develop scenarios that consider the balance between global and local factors, a new set of land use change scenarios for Brazil were developed, aligned with the global structure Shared Socio-Economic Pathways (SSPs) and Representative Concentration Pathway (RCPs) developed by the global change research community. The narratives of the new scenarios align with  SSP1/RCP 1.9, SSP2/RCP 4.5, and SSP3/RCP 7.0. The scenarios were developed combining the LuccME spatially explicit land change allocation modeling framework and the INLAND surface model to incorporate the climatic variables in water deficit.  Based on detailed biophysical, socio-economic and institutional factors for each biome in Brazil, we have created spatially-explicit scenarios  until 2050, considering the following classes: forest vegetation, grassland vegetation, planted pasture, agriculture, mosaic of small land uses, and forestry. The results aim at regionally detailing global models and could be used both regionally to support decision-making, but also to enrich global analysis.

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