The PLUS model is a Cellular Automata(CA) for land use/land cover(LULC) change simulaiton. The PLUS model integrates a rule mining framework based on Land Expansion Analysis Strategy (LEAS) and a CA model based on multi-type Random Seeds (CARS), which was used to understand the drivers of land expansion and project patch-level landscape dynamics.
Run PLUS software by double-clicking the exe file 'PLUS V1.0_boxed.exe'. PLUS software can run independently on Windows Vista/7/8/X 64-bit environment, without any dependencies and setup process.
Please find the attached PDF file 'User Manual PLUS -20191206-Eng.pdf' in the Repository.
Please find the compressed file 'PLUS_test_data.zip' in the Repository.
CA model is developed to improve the representation of complex land-use and land-cover (LULC) systems. Previous studies focus on the improvement of technical modeling procedures, little researches have pay attention to promote understanding of the nonlinear relationships underlying LULC. A lack of model ability to reflect patch-level landscape evolution also limit the application potential of CAs for policy development. This study presents a patch-level land use simulation (PLUS) model that integrates a rule mining framework based on Land Expansion Analysis Strategy (LEAS) and a CA model based on multi-type Random Seeds (CARS), which was used to understand the drivers of land expansion and project landscape dynamics. The PLUS model can obtain higher simulation accuracy and more similar landscape pattern than other models. The LEAS can help researchers find some underlying transition rules. The proposed method combines simulation, knowledge discovery and policy-making, which can provide vital information for both researchers and policy-makers.
Land use/land cover change(LULC) simulaiton, policy making, knowlege discovery for LULC, urban planning, eco-security early-warning and etc.
PLUS was developed purely in the C++ language. The parallel technology of PLUS software is from High-performance Spatial Computational Intelligence Lab @ China University of Geosciences (Wuhan) (https://github.com/HPSCIL). The Random forest technique in our model is from a powerful open source library called Alglib 3.9.2 (http://www.alglib.net/). The linear regression algorithm is from (https://github.com/fengbingchun/NN_Test). The UI of the software is built using a famous open source library Qt 5 (https://www.qt.io/download/). This UI provides a real-time display of dynamic changes of land use in simulation process. Moreover, the using of open source library GDAL 2.0.2 (http://www.gdal.org/) allows our model to directly read and write raster data (.tif, .img, .txt files) that includes geographical coordinate information.
If you have technical questions regarding PLUS software, please contact Dr. Xun Liang (liangxun@cug.edu.cn)
High-performance Spatial Computational Intelligence Lab(HPSCIL) (https://github.com/HPSCIL) School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei 430078, China. For any possible research collaboration, please contact Prof. Qingfeng Guan (guanqf@cug.edu.cn)