本文以宁夏彭阳县红河镇为例，通过无人机遥感技术获取了研究区的正射影像并生成相应的数据图层，识别滑坡和提取承灾体信息，实现了研究区滑坡易发性、危险性和风险评估。本文取得主要成果如下：

（1）使用大疆精灵4 RTK和纵横大鹏CW-30构建了无人机航摄系统并制定了面向宁南黄土丘陵区的无人机遥感空间信息采集方案，生成了红河镇0.13m的无人机遥感正射影像（DOM），面积为164km²，为后续黄土滑坡识别及发育特征分析、滑坡承灾体信息提取及区域滑坡风险评估研究提供了数据支撑。

（2）基于黄土滑坡识别标志和现场验证，确定了180个滑坡，红河镇的滑坡具有独特的形态、色调、水系、纹理、表面特征的无人机遥感识别标志。红河镇的黄土滑坡整体上呈南多北少具群发性；以半圆型和不规则型的滑坡较多；滑坡长度在30-300m上居多，长宽具有一定的相关性，长宽比值在0-1之间的滑坡数量较多；间歇性的水流对冲沟的作用为红河镇黄土丘陵边坡的滑坡发育创造了条件；滑向以北向、西北向居多；滑坡后缘一般清晰可辨多发于对滑式滑坡；滑动面总体上较为平直，处于阴坡的滑坡滑动面更容易生长树木；滑体地貌形态多样，有双沟同源现象，还可见滑坡阶地、鼓丘等地貌。

（3）分析其滑坡承灾体影像特征后，设定实验区并采用多尺度分割和单一尺度分割两种面向对象的滑坡承灾体信息提取方法，比较了基于两种尺度分割的滑坡承灾体信息提取精度和时间效率，通过方案优化实现了研究区滑坡承灾体信息提取，最终确定研究区单一分割尺度优化方案为采用100（形状因子0.2，紧致度0.5）和259（形状因子0.1，紧致度0.5）两个单一分割尺度快速提取滑坡承灾体信息。

（4）采用无人机遥感获取的土地利用类型数据、道路数据、河流数据、VDVI数据、承灾体数据、滑坡数据与非无人机遥感数据源获取的数据进行了红河镇镇域的滑坡风险评估，其中土地利用类型数据、道路数据、河流数据、VDVI数据、滑坡数据参与了滑坡易发性评价，最终预测结果的ROC值为0.814；土地利用类型数据、道路数据、承灾体数据、滑坡数据参与了易损性评价和风险评估。红河镇的中高风险区主要以点状或片状集中在红河河谷阶地以及南部塬区。实践结果表明，基于无人机遥感获取的数据能够参与滑坡风险评估。

Taking Honghe Town, Pengyang County, Ningxia as an example, this paper obtains the orthophoto image of the study area through UAV remote sensing technology, generates the corresponding data layer, identifies the landslide and extracts the information of the landslide elements at risk, and realizes the landslide susceptibility, hazard and risk assessment in the study area. The main achievements of this paper are as follows:

(1) Using DJI Phantom 4 RTK and JOUAV CW-30, the UAV aerial photography system is constructed, and the UAV remote sensing spatial information acquisition scheme for the loess hilly area of Southern Ningxia is formulated. The 0.13m UAV remote sensing Orthophoto Image (DOM) of Honghe town has been generated, covering an area of 164km², which provides data support for subsequent loess landslide identification and development feature analysis, landslide elements at risk information extraction and regional landslide risk assessment.

(2) Based on the identification marks of loess landslides and field verification, 180 landslides are determined. The landslides in Honghe town are UAV remote sensing identification marks with unique shape, color, water system, texture and surface characteristics. On the whole, the Loess Landslide in Honghe town is more in the South and less in the north; There are more semi-circular and irregular landslides; The length of landslide is mostly 30-300m, and the length and width have a certain correlation. There are many landslides with the length width ratio between 0-1; The effect of intermittent water flow against the ditch creates conditions for the development of landslide on the loess hilly slope in Honghe town; Most of them slide to the north and northwest; The trailing edge of landslide is generally clear and recognizable, and it mostly occurs in opposite sliding landslide; The sliding surface is generally straight, and the sliding surface of the landslide on the shady slope is easier to grow trees; The landforms of the sliding body are diverse, with the phenomenon of double ditch homology. Landslide terraces, drum mounds and other landforms can also be seen.

(3) After analyzing the image characteristics of landslide elements at risk, the experimental area is set, and two object-oriented landslide elements at risk information extraction methods of multi-scale segmentation and single-scale segmentation are adopted. The accuracy and time efficiency of landslide elements at risk information extraction based on the two scale segmentation are compared, and the landslide elements at risk information extraction in the study area is realized through scheme optimization, Finally, the optimization scheme of single segmentation scale in the study area is determined to quickly extract the information of landslide landslide elements at risk by using two single segmentation scales: 100 (shape factor 0.2, compactness 0.5) and 259 (shape factor 0.1, compactness 0.5).

(4) The landuse data, road data, river data, VDVI data, landslide elements at risk data, landslide data obtained by UAV remote sensing and the data obtained by non UAV remote sensing data sources are used to evaluate the landslide risk in Honghe town. Among them, the landuse data, road data, river data, VDVI data and landslide data participate in the landslide susceptibility assessment, and the ROC value of the final prediction result is 0.814; Landuse data, road data, landslide elements at risk data and landslide data participated in vulnerability and risk assessment. The medium and high risk zone in Honghe town are mainly concentrated in the terraces of Honghe River valley and the southern tableland in the form of dots or slices. The practice results show that the data obtained by UAV remote sensing can participate in landslide risk assessment.

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