Kleemann Janina, Baysal Gülendam, Bulley Henry N N, Fürst Christine
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany; University of Bonn, Center for Development Research (ZEF), Walter-Flex-Str. 3, 53113 Bonn, Germany.
University of Bonn, Center for Development Research (ZEF), Walter-Flex-Str. 3, 53113 Bonn, Germany.
J Environ Manage. 2017 Jul 1;196:411-442. doi: 10.1016/j.jenvman.2017.01.053. Epub 2017 Mar 21.
Land use and land cover change (LULCC) is the result of complex human-environmental interactions. The high interdependencies in social-ecological systems make it difficult to identify the main drivers. However, knowledge of key drivers of LULCC, including indirect (underlying) drivers which cannot be easily determined by spatial or economic analyses, is essential for land use planning and especially important in developing countries. We used a mixed-method approach in order to detect drivers of LULCC in the Upper East Region of northern Ghana by different qualitative and quantitative methods which were compared in a confidence level analysis. Viewpoints from experts help to answer why the land use is changing, since many triggering effects, especially non-spatial and indirect drivers of LULCC, are not measurable by other methodological approaches. Geo-statistical or economic analyses add to validate the relevance of the expert-based results. First, we conducted in-depth interviews and developed a list of 34 direct and indirect drivers of LULCC. Subsequently, a group of experts was asked in a questionnaire to select the most important drivers by using a Likert scale. This information was complemented by remote sensing analysis. Finally, the driver analysis was compared to information from literature. Based on these analyses there is a very high confidence that population growth, especially in rural areas, is a major driver of LULCC. Further, current farming practice, bush fires, livestock, the road network and climate variability were the main direct drivers while the financial capital of farmers and customary norms regarding land tenure were listed as important indirect drivers with high confidence. Many of these driving forces, such as labour shortage and migration, are furthermore interdependent. Governmental laws, credits, the service by extension officers, conservational agriculture and foreign agricultural medium-scale investments are currently not driving land use changes. We conclude that the mixed-method approach improves the confidence of findings and the selection of most important drivers for modelling LULCC, especially in developing countries.
土地利用和土地覆盖变化（LULCC）是复杂的人类与环境相互作用的结果。社会生态系统中的高度相互依存关系使得难以确定主要驱动因素。然而，了解LULCC的关键驱动因素，包括那些难以通过空间或经济分析轻易确定的间接（潜在）驱动因素，对于土地利用规划至关重要，在发展中国家尤为重要。我们采用了混合方法，通过不同的定性和定量方法来检测加纳北部上东部地区LULCC的驱动因素，并在置信水平分析中对这些方法进行了比较。专家的观点有助于回答土地利用为何发生变化，因为许多触发效应，特别是LULCC的非空间和间接驱动因素，无法通过其他方法进行测量。地理统计或经济分析有助于验证基于专家的结果的相关性。首先，我们进行了深入访谈，并列出了34个LULCC的直接和间接驱动因素。随后，我们通过问卷调查让一组专家使用李克特量表选出最重要的驱动因素。这些信息通过遥感分析得到补充。最后，将驱动因素分析与文献中的信息进行了比较。基于这些分析，我们非常确信人口增长，尤其是农村地区的人口增长，是LULCC的主要驱动因素。此外，当前的农业实践、丛林火灾、牲畜、道路网络和气候变异性是主要的直接驱动因素，而农民的金融资本和关于土地保有权的习惯规范被列为具有高度置信度的重要间接驱动因素。此外，许多这些驱动力，如劳动力短缺和移民，是相互依存的。政府法律、信贷、推广人员的服务、保护性农业和外国农业中等规模投资目前并未推动土地利用变化。我们得出结论，混合方法提高了研究结果的可信度，并改善了对LULCC建模最重要驱动因素的选择，特别是在发展中国家。
