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2021年第12期
2019年第02期
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利用无人机和激光扫描仪调查会宁县黄土落水洞形态结果数据集(2021)


胡胜1,2姜宗达3王宁练*1,2张帆宇*4陈一先1,2吴松柏1,2汪霖3李思思3
1 陕西省地表系统与环境承载力重点实验室,西安7101272 西北大学城市与环境学院,西安7101273 西北大学信息科学与技术学院,西安7101274 兰州大学土木工程与力学学院,兰州730000

DOI:10.3974/geodb.2024.05.05.V1

出版时间:2024年5月

网页浏览次数:606       数据下载次数:11      
数据下载量:6696.37 MB      数据DOI引用次数:

关键词:

黄土落水洞,形态学,土壤侵蚀,无人机调查,激光雷达

摘要:

2021年作者利用无人机系统(UAS)和手持激光扫描仪(HLS)对甘肃省会宁县涝子沟河流阶地上的黄土落水洞进行了精细化调查,并获取了研究区无人机正射影像图和每个落水洞三维激光点云数据。后期在ArcGIS 10.5中完成了落水洞解译和数据库构建,在CloudCompare、Geomagic Wrap 2021、Python中完成了点云数据处理、建模和体积计算。该数据集内容包括:(1)基础地理数据,含研究区范围、落水洞的分布(点、面)、无人机正射影像图、山体阴影图;(2)142个落水洞形态学特征参数和土壤流失量计算属性表,包括编号、主轴长、次轴长、延长率、偏心率、周长、面积、体积、土壤流失量等17个参数;(3)考察照片和视频;(4)每个落水洞精细化的LiDAR点云数据;(5)典型落水洞建模数据;(6)基于Python语言自主开发的落水洞体积计算切片方案(体积计算结果存在属性表里面);(7)由Geomagic Wrap 2021软件构建落水洞点云封装模型(体积计算结果存在属性表里面)。该数据集以.JPG、.mp4、.bin、.txt、.wrp、.pdf、.xlsx和.shp格式存储,由712个数据文件组成,数据量为1.59 GB(压缩为2个文件,1.16 GB)。.bin和.txt格式的点云数据可以使用开源软件CloudCompare打开,.wrp格式的模型数据可以用Geomagic Wrap 2021打开。

基金项目:

国家自然科学基金(42371009,42001006);西北大学2023年本科人才培养建设项目(2023-38);西北大学研究生科研创新项目(2024-212)

数据引用方式:

胡胜, 姜宗达, 王宁练*, 张帆宇*, 陈一先, 吴松柏, 汪霖, 李思思. 利用无人机和激光扫描仪调查会宁县黄土落水洞形态结果数据集(2021)[J/DB/OL]. 全球变化数据仓储电子杂志(中英文), 2024. https://doi.org/10.3974/geodb.2024.05.05.V1.

参考文献:


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     [5] de Carvalho Júnior, O. A., Guimarães, R. F., Montgomery, D. R., et al. Karst depression detection using ASTER, ALOS/PRISM and SRTM-derived digital elevation models in the Bambuí Group, Brazil [J]. Remote Sensing, 2013, 6(1): 330-351.
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     [9] Liu, H., Wang, L. Mapping detention basins and deriving their spatial attributes from airborne LiDAR data for hydrological applications [J]. Hydrological Processes: An International Journal, 2008, 22(13): 2358-2369.
     [10] Öztürk, M. Z., Şener, M. F., Şener, M., et al. Structural controls on distribution of dolines on Mount Anamas (Taurus Mountains, Turkey) [J]. Geomorphology, 2018, 317: 107-116.
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     [12] Zhu, J., Pierskalla Jr, W. P. Applying a weighted random forests method to extract karst sinkholes from LiDAR data [J]. Journal of Hydrology, 2016, 533: 343-352.
     [13] Zumpano, V., Pisano, L., Parise, M. An integrated framework to identify and analyze karst sinkholes [J]. Geomorphology, 2019, 332: 213-225.
     

数据下载:

序号 数据名 数据大小 操作
1 SinkholesHuining2021_1.rar 637065.88KB
2 SinkholesHuining2021_2.rar 586851.36KB
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