Imperial College of London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK.
Glob Chang Biol. 2014 Jun;20(6):1707-22. doi: 10.1111/gcb.12523. Epub 2014 Mar 28.
Land-use and land-cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South-East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio-temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land-use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline.
土地利用和土地覆盖（LULC）变化是全球生物多样性丧失和碳排放的最大驱动因素之一。我们以亚马逊、刚果盆地和东南亚的热带雨林为例，研究 LULC 变化的空间预测模型。目前的预测在建模方法、高度变异性和验证方法方面存在差异。我们对 48 种建模方法进行了定量回顾，考虑了模型的时空尺度、输入、校准和验证方法。此外，我们还向每个审查的模型请求了模型输出，并对巴西亚马逊地区的热带 LULC 预测进行了模型性能的定量评估。我们强调了该学科存在的不足，并揭示了需要解决的三个关键点，以提高热带 LULC 变化模型的透明度、可靠性和实用性：（1）在描述和提供模型输入和模型代码方面缺乏开放性；（2）适当模型验证的困难；（3）热带 LULC 模型用户在获取模型预测方面面临的困难，以帮助他们自己的分析和政策决策。我们还比较了热带地区的 LULC 变化模型与其他学科的建模方法和范式，并认为气候变化和物种分布建模领域的最新变化可能为热带 LULC 变化建模者提供一条途径，以进一步改进该学科。气候变化模型对公众对气候变化的看法产生了相当大的影响，现在对各级政治的政策决策产生影响。我们认为，热带 LULC 变化模型同样具有很高的潜力来影响公众舆论，并根据合理的未来情景影响土地利用政策的制定，但要做到这一点，可能需要在该学科进一步改进。
