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A satellite-based disturbance index algorithm for monitoring mitigation strategies effects on desertification change in an arid environment

机译:基于卫星的干扰指数算法,用于监测缓解策略对干旱环境中荒漠化变化的影响

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This research focuses on monitoring the desertification change as a result of mitigation and adaptation strategies in arid environmental condition. Exploring environmental hazards, specifically desertification development, is important for understanding loss of productivity in dry lands. Developing a new satellite-based algorithm for monitoring desertification in an arid environment delivers information useful in protecting the environment and mitigating natural hazards. A multi-temporal remote sensing data of MODerate resolution Imaging Spectroradiometer (MODIS) were used for estimating the Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST), based on monthly data during the years 2002, 2005, 2008 and 2011. The MODIS-based disturbance index (MBDI) was improved by estimating the long-term variation in the ratio of annual maximum composite LST and SAVI on a pixel-by-pixel basis. A significant correlation (r = -0.88; P 0.001) was found between the mean-maximum SAVI and mean-maximum LST in the dry season. The response of the MBDI to land degradation was assessed by comparing the obtained soil salinity data to the algorithm outcomes. The results showed that the proposed new satellite-based algorithm has a high potential to detect the spatial extent of prime land degradation in an arid environment. Also, this algorithm was able to recognize the difference between the natural variability and instantaneouson-instantaneous desertification symptoms in an arid environment. The mitigation strategies in the case study decreased the desertification development and combat the land degradation in the last decade.
机译:这项研究的重点是监测干旱环境下的缓解和适应策略导致的荒漠化变化。探索环境危害,特别是荒漠化发展,对于了解干旱地区的生产力损失非常重要。开发一种新的基于卫星的算法以监测干旱环境中的荒漠化,可提供有益于保护环境和减轻自然灾害的信息。基于2002年,2005年,2008年和2011年的月度数据,使用MODerate分辨率成像光谱仪(MODIS)的多时相遥感数据估算土壤调整的植被指数(SAVI)和地表温度(LST)。通过逐像素估算年最大复合LST和SAVI之比的长期变化,改进了基于MODIS的干扰指数(MBDI)。在旱季,平均最大SAVI和平均最大LST之间发现了显着的相关性(r = -0.88; P <0.001)。通过将获得的土壤盐分数据与算法结果进行比较,评估了MBDI对土地退化的响应。结果表明,提出的新的基于卫星的算法在干旱环境中检测原始土地退化的空间范围上具有很高的潜力。而且,该算法能够识别干旱环境中自然变异性与瞬时/非瞬时荒漠化症状之间的差异。案例研究中的缓解策略减少了荒漠化的发展,并在过去十年中防治了土地退化。

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