Understanding vegetation disturbance around protected areas (PAs) is critical as it significantly affects the sustainable conservation of wildlife. However, there is a lack of analyses of consistent long-term data on vegetation disturbance. In this study, the LandTrendr algorithm and Google Earth Engine were used to access satellite data and explore the vegetation dynamics history across the Ruaha–Rungwa landscape, Tanzania. We characterized vegetation disturbance patterns and change attributes, including disturbance occurrence trends, rate, and severity, by using each pixel’s normalized burn ratio index time series. Between 2000 and 2019, 36% of the vegetation was significantly disturbed by anthropogenic activities. The results of this study show that the disturbance trends, severity, and patterns are highly variable and strongly depend on the management approaches implemented in the heterogeneous landscape: Ruaha National Park (RNP), Rungwa–Kizigo–Muhesi Game Reserves (RKMGR), and the surrounding zones. The disturbance rates and severity were pronounced and increased toward the edges of the western RKMGR. However, the disturbance in the areas surrounding the RNP was lower. The characterization of the vegetation disturbance over time provides spatial information that is necessary for policy makers, managers, and conservationists to understand the ongoing long-term changes in large PAs.

中文翻译：

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利用Landsat时间序列数据监测坦桑尼亚中部保护区周围的植被扰动

了解保护区（PA）周围的植被扰动至关重要，因为它会严重影响野生动植物的可持续保护。但是，缺乏对植被扰动长期数据的一致分析。在这项研究中，LandTrendr算法和Google Earth Engine用于访问卫星数据，并探索坦桑尼亚Ruaha-Rungwa风景中的植被动力学历史。我们通过使用每个像素的归一化燃烧率指数时间序列来表征植被扰动模式和变化属性，包括扰动发生趋势，发生率和严重性。在2000年至2019年之间，人为活动严重干扰了36％的植被。这项研究的结果表明，干扰趋势，严重程度，其模式是高度可变的，并且在很大程度上取决于在异质景观中实施的管理方法：鲁阿哈国家公园（RNP），Rungwa-Kizigo-Muhesi禁猎区（RKMGR）以及周边地区。扰动率和严重性明显，并朝着西部RKMGR的边缘增加。但是，RNP周围区域的干扰较小。随着时间的流逝，植被扰动的特征为决策者，管理者和保护主义者提供了必要的空间信息，以了解大型PA的长期长期变化。扰动率和严重性明显，并朝着西部RKMGR的边缘增加。但是，RNP周围区域的干扰较小。随着时间的流逝，植被扰动的特征为决策者，管理者和保护主义者提供了必要的空间信息，以了解大型PA的长期长期变化。扰动率和严重性明显，并朝着西部RKMGR的边缘增加。但是，RNP周围区域的干扰较小。随着时间的流逝，植被扰动的特征为决策者，管理者和保护主义者提供了必要的空间信息，以了解大型PA的长期长期变化。