Remote sensing insights into land cover dynamics and socio-economic Drivers: The case of Mtendeli refugee camp, Tanzania (2016–2022)

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Applications-Society and Environment Pub Date : 2024-09-10 DOI:10.1016/j.rsase.2024.101334
Ewa Gromny , Małgorzata Jenerowicz-Sanikowska , Jörg Haarpaintner , Sebastian Aleksandrowicz , Edyta Woźniak , Lluís Pesquer Mayos , Magdalena Chułek , Karolina Sobczak-Szelc , Anna Wawrzaszek , Szymon Sala , Astrid Espegren , Daniel Starczewski , Zofia Pawlak
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Abstract

The purpose of this article is to present the scope and the dynamics of the environmental changes unfolded in the vicinity of Mtendeli refugee camp. It presents a new method, which combines geospatial analysis of high-resolution Earth observation data (Sentinel-1&2) with ground-based observations and input from local experts. Time series classifications of annual land use/land cover in the surroundings of the camp is developed from remote data. Subsequently main transitions and trends are quantitatively achieved. This is a first study which, not only treats the land transition process in a comprehensive manner, but also tracks the changes and their main drivers on an annual scale over the lifetime of the camp (2016–2021) and the post-closure situation in 2022. Most importantly, thanks to the involvement of social studies, it unfolds the socio-economical drivers of those changes. Drawing upon a random forest algorithm and available databases, we achieve overall classification accuracies of 83.5% (2020) and 82.0% (2022). Our findings indicate an ongoing expansion of cropland between 2016 and 2021, to the detriment of natural vegetation classes. The impact of environmental restoration programs implemented in the former camp area is visible by 2022. The proposed method can be used to identify areas of environmental risk and thus support decisions linked with sustainable development and land management.

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遥感洞察土地覆被动态和社会经济驱动因素:坦桑尼亚 Mtendeli 难民营案例(2016-2022 年)
本文旨在介绍 Mtendeli 难民营附近环境变化的范围和动态。文章介绍了一种新方法,该方法结合了对高分辨率地球观测数据(哨兵-1&2)的地理空间分析、地面观测以及当地专家的意见。根据遥感数据对难民营周边地区每年的土地利用/土地覆盖情况进行时间序列分类。随后,对主要的变化和趋势进行了定量分析。这是第一项研究,不仅以全面的方式处理了土地过渡过程,而且还跟踪了营地使用期(2016-2021 年)内每年的变化及其主要驱动因素,以及 2022 年关闭后的情况。最重要的是,由于社会研究的参与,它揭示了这些变化的社会经济驱动因素。利用随机森林算法和现有数据库,我们的总体分类准确率达到 83.5%(2020 年)和 82.0%(2022 年)。我们的研究结果表明,在 2016 年至 2021 年期间,耕地面积不断扩大,损害了自然植被等级。到 2022 年,前营地地区实施的环境恢复计划的影响将显现出来。所提出的方法可用于识别环境风险区域,从而支持与可持续发展和土地管理相关的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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