基于空间密度函数的哈尼梯田世界遗产地滑坡时空格局变化

为探究哈尼梯田世界文化景观遗产地核心区滑坡灾害时空分布规律,以Google Earth 0.55 m分辨率的2005、2009、2015年3期遥感影像为基础,结合实地走访调查,建立滑坡数据库,在ArcGIS 10.2平台上计算滑坡点的最邻近指数、K函数曲线及密度分布。结果显示:1)哈尼梯田遗产核心区2005、2009、2015年的滑坡数量分别为184、337和285个,对应最邻近指数为0.556、0.603、0.628;最显著聚集的空间尺度为1 000 m,从聚集向离散分布转变的空间尺度阈值分别为2.9、3.9、3.6 km。2) 3个年份滑坡点高密度区占比逐渐增加(2.3%→5.8%→8.3%),中密度区占比亦逐渐增大(15.7%→21.8%→27.9%),低密度区占比逐渐减小(82.0%→72.5%→66.8%)。3)需要重点防范滑坡灾害风险的区域为森林区的西段和东段,村寨区的多依树、硐浦、勐品、水卜龙等地,以及阿勐控河和碧猛河流域内的梯田区。综上,研究区2005-2015年滑坡空间格局发生了显著变化,随着人类活动对地表景观干预程度不断加大,滑坡灾害风险增加了更多的不确定性。

Spatio-Temporal Patterns of Landslides in Hani Rice Terraces World Heritage Site Based on Spatial Density Function

Landslides are the fourth leading natural hazard that threaten human survival after floods, earthquakes, and drought. The frequency and density of landslides are increasing with the intensification of climate change and human activities. For World Heritage Sites around the world, landslide risk affects their integrity, aesthetic value, and sustainable development. The study of the spatial and temporal distribution patterns of landslides could aid in understanding the factors influencing landslide and provide guidance for disaster prevention and mitigation and heritage conservation. The Honghe Hani Rice Terraces World Heritage is an important cultural landscape located in the mountainous area of Yunnan Province, China, where landslides frequently occur during the annual rainy season. To investigate the spatiotemporal pattern of landslides in the heritage core area, a landslide database was established based on the remote sensing images of Google Earth at a resolution of 0.55 m in 2005, 2009, and 2015, combined with a field survey. The nearest neighbor index, K function, and kernel density function of the landslides are calculated and analyzed with ArcGIS 10.2. The results are as follows: 1) The number of landslides of study area in 2005, 2009, and 2015 are 184, 337, and 285 respectively. The nearest neighbor index indicates that the landslides are spatially clustered, and their aggregation decreased over time. The K-function analysis shows that the most significant spatial scale of landslide aggregation is 1 km. The thresholds from aggregation to discrete distribution are 2.9, 3.9, and 3.6 km in 2005, 2009, and 2015, respectively. 2) In terms of spatial distribution, the analysis of the kernel density function shows that the high-density area of the landslides in the heritage core area contains multiple centers, such as forest areas on the western and eastern sides of the study area; the villages of Duoyishu, Dongpu, Mengpin, and Shuibulong; and terraced fields in the Amengkong and Bimeng river basins. 3) During the period studied, the proportion of high-density landslide areas increased from 2005 to 2015 (2.3%→5.8%→8.3%). The proportion of medium-density areas also increased (15.7%→21.8%→27.9%), while the proportion of low-density areas decreased (82.0%→72.5%→66.8%). On the scale of administrative villages, the proportion of high, medium, and low-density landslides in each village varied over time. The villages with increased high-density areas over time are Dongpu, Shuibulong, Duoyishu, Baoshan, and Xinjie, which need to reduce landslide risks significantly. The detailed analysis of the occurrence mechanism, influencing factors, monitoring, and early warning of landslides is beyond the scope of the present study. In the future, we will explore the potential interactions between geographical conditions and landslides. In particular, we will quantify the contribution of human activities to landslide risks to improve our understanding of the mechanisms of landslide occurrence in similar regions as well as the accuracy of landslide risk prediction. In summary, the spatial pattern of landslides in the study area changed significantly from 2005 to 2015. The risk of landslides has become more unpredictable as human interventions increase on the surface landscapes.