Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, 1015, Lausanne, Switzerland.
International Union for Conservation of Nature (IUCN), East and Southern Africa Region, KN 16 Ave, Kigali, Rwanda.
Environ Monit Assess. 2024 Aug 27;196(9):852. doi: 10.1007/s10661-024-12986-4.
The African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990-2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda's sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment's ecological integrity, in alignment with national and global goals.
过去几十年来，由于各种因素的复杂相互作用，非洲大湖地区经历了大规模的土地利用和土地覆被变化（LULCC）。然而，对于 LULCC 与其解释变量之间的关系的综合分析仍有待探索。本研究以卢旺达基伍湖流域为重点，分析了 1990 年至 2020 年期间的土地利用土地覆被变化，确定了主要变量，并根据不同的发展轨迹预测了未来的土地利用情景。在 Google Earth Engine 中，通过使用随机森林分类器，结合季节性复合 Landsat 图像、光谱指数和地形特征进行图像分类，以提高区分度并捕捉季节性变化。结果表明，整体精度超过 83%。历史分析显示，在 1990-2000 十年间，森林损失（26.6%降至 18.7%）和农业用地扩张（27.7%增至 43%），这归因于政治冲突和人口流动。随后几十年观察到森林恢复（2020 年增长 24.8%），这是卢旺达可持续发展倡议推动的结果。来自 Land Change Modeler 的多层感知机神经网络根据自然、社会经济变量和历史变化预测了 2030 年和 2050 年的不同土地利用情景。对解释变量的分析强调了靠近城市中心、人口密度和地形在土地利用变化中的重要作用。预测表明，不同的轨迹受到人口和社会经济趋势的影响。该研究建议采用与正在进行的保护措施相一致的绿色增长经济情景。研究结果有助于确定土地恢复和保护工作的机会，促进基伍湖流域生态完整性的保护，符合国家和全球目标。
