Integrated high-resolution, continental-scale land change forecasting

被引:3
|
作者
Calderon-Loor, Marco [1 ,2 ]
Hadjikakou, Michalis [1 ]
Hewitt, Richard [3 ]
Marcos-Martinez, Raymundo [4 ]
Bryan, Brett A. [1 ]
机构
[1] Deakin Univ, Sch Life & Environm Sci, Melbourne, Australia
[2] Univ Amer UDLA, Grp Invest Biodivers Medio Ambiente & Salud BIOMAS, Quito, Ecuador
[3] Univ Complutense Madrid UCM, Fac Geog & Hist, Geog Dept, Transport Infrastruct & Terr Res Grp t GIS, C Prof Aranguren S-N, Madrid 28040, Spain
[4] Commonwealth Sci & Ind Res Org CSIRO, Canberra, Australia
基金
澳大利亚研究理事会;
关键词
Land-use change; Integrated model; Forecast; Random forest; Google earth engine; COVER CHANGE; CELLULAR-AUTOMATA; ERROR PROPAGATION; NEURAL-NETWORK; USE SCENARIOS; MODELS; POLICY; SUSTAINABILITY; CLASSIFICATION; BIODIVERSITY;
D O I
10.1016/j.envsoft.2023.105749
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Predicting future land change is crucial in anticipating societal and environmental impacts and informing responses at different scales. We designed an integrated, high-resolution, land-change model and forecasted Australia's land change for the years 2020, 2025 and 2030 for Cropland, Forest, Grassland, and Built-up landuses using cloud-based and high-performance computing. A spatially explicit set of drivers was fed into a random forest classifier to generate 30-m per-class suitability layers for the country, which were then used for allocating land-use. The model was validated against 2015 data, then land-use was projected until 2030. Accuracy at the national level was -94%. Forecasts showed increases in Grassland and Built-up areas and decreases in Forest and Cropland. Our modelling framework expands the current capabilities of large-scale land-change models and provides a first-of-its-kind multiclass land forecast for Australia that can inform land policy at multiple scales in Australia.
引用
收藏
页数:13
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