基于DEM的喀斯特峰丛洼地地貌信息提取及形态特征分析

何佶泳; 田义超; 张强; 王栋华; 张亚丽; 周慧娟

doi:10.11932/karst20240306

基于DEM的喀斯特峰丛洼地地貌信息提取及形态特征分析

doi: 10.11932/karst20240306

何佶泳^1,,
田义超^{1, 2, 3, ,},
张强¹,
王栋华³,
张亚丽¹,
周慧娟¹

1.
北部湾大学资源与环境学院/北部湾海洋发展研究中心, 广西钦州 535000
2.
北部湾大学广西北部湾海洋环境变化与灾害研究重点实验室/海洋地理信息资源开发利用重点实验室, 广西钦州 535000
3.
桂林理工大学环境科学与工程学院, 广西桂林 541004

基金项目: 国家自然科学基金项目（42061020）；广西基地和人才项目（2019AC20088）；广西自然科学基金联合培育项目（2018JJA150135）；广西创新驱动发展专项（AA18118038）；北部湾大学高层次人才引进项目（2019KYQD28）；2021年度广西高校中青年教师科研基础能力提升项目(2021KY0431)

详细信息

作者简介:
何佶泳（2000－），女，本科，主要从事喀斯特地区生态保护方向的研究。E-mail：2663639691@qq.com

通讯作者:
田义超（1986－），男，博士，教授，主要从事喀斯特与海岸带生态水文过程方面的研究。E-mail：tianyichao1314@yeah.net。

中图分类号: P931.5;P208
计量
- 文章访问数: 79
- HTML浏览量: 15
- PDF下载量: 21
- 被引次数: 0
出版历程
- 收稿日期: 2023-06-13
- 网络出版日期: 2024-08-15
- 刊出日期: 2024-06-25

Geomorphic information extraction and morphological characteristics analysis of karst peak-cluster depressions based on DEM

1.
Beibu Gulf Marine Development Research Center/College of Resources and Environment, Beibu Gulf University, Qinzhou, Guangxi 535000, China
2.
Guangxi Key Laboratory of Marine Environmental Change and Disaster Research/Key Laboratory of Development and Utilization of Marine Geographic Information Resources, Beibu Gulf University, Qinzhou, Guangxi 535000, China
3.
College of Environmental Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China

摘要

摘要: 文章以桂西南典型喀斯特地貌——峰丛洼地为研究对象，基于DEM数据采用水文法、鞍座法提取出峰丛洼地，在此基础上借助于空间分析方法、分形理论等对研究区的峰丛洼地结构形态特征、空间分布进行定量分析和研究。结果表明：（1）运用水文分析方法能有效提取鞍部点，有效识别出洼地凹陷，提取鞍部点精度为50.00%，而鞍座法提取鞍部点的精度为79.80%；（2）94%的峰丛洼地形态为盆形，小部分为深锥形和碟状形。研究区洼地斑块周长－面积的关系为y =0.5772x+0.2674，二者的相关系数R²=0.9462，周长—面积的分维数 D =1.15，洼地图斑镶嵌结构较稳定；（3）80%的峰丛洼地分布于研究区南部、北部的石灰岩与白云岩互层地区和连续性石灰岩上，中部碎屑岩岩层上峰丛洼地发育不明显。
- DEM /
- 水文分析 /
- 喀斯特 /
- 峰丛洼地 /
- 形态特征 /
- 桂西南
Abstract: The largest area of karst in the world is distributed in China. In the karst area, the peak-cluster depression is regarded as one of the typical landforms. The positive and negative landforms in peak-cluster depressions control the spatial distribution pattern of soil and water resources, and significantly affect regional landforms, soil erosion and quality of regional ecological environment.In the southwest of Guangxi Zhuang Autonomous Region (hereinafter referred to as southwest Guangxi), the distribution of karst landforms is relatively concentrated, and their unique and steep terrain greatly restricts the development of regional economy. Consequently, this remote and border region became a poverty-stricken area in which are located old revolutionary bases and is inhabited by minority nationalities. The landform of karst peak-cluster depression together with urban architecture formed a unique mosaic landscape in southwest Guangxi, which is vulnerable in ecological environment. Although the traditional method of artificial vectorization can accurately obtain the positive and negative topographic features of peak-cluster depressions, this method has its disadvantages such as high working intensity, low working efficiency and long working period, which may pose challenges to the information extraction of peak-cluster depressions on a large regional scale, and which may also limit the detailed study on the ecosystem of peak-cluster depression basins. Therefore, it is of great significance for us to explore the formation mechanism of peak-cluster depressions and the evolution of regional geographic environment so that we can provide a scientific basis for the protection and sustainable development of regional ecological environment.In this study, with the use of software such as ArcGIS and Google Earth, information of peak-cluster depressions was extracted based on Digital Elevation Model (DEM) and satellite remote sensing images by two methods: hydrology method and saddle method. By means of spatial analysis and fractal theory, the structural morphological characteristics and spatial distribution of peak-cluster depressions were quantitatively analyzed. The results show as follows. (1) In terms of extraction methods, the saddle method can extract the depression boundary based on the features of the saddle, while the hydrology method can effectively identify the depression by simulating the convergence process of water flow. Compared with the hydrologic method, the saddle method can improve the accuracy of extracting saddle points by 29.80%. (2) In the morphological analysis of peak-cluster depressions, 94% of the depressions in the study area are basin-shaped, and the rest are deep cone-shaped and dish-shaped. The main morphology of peak-cluster depressions in the study area is basin-shaped with a shallow depth and a small area. In addition, the analysis of the relationship between the circumference and the area of the depression patch finds that there is a high correlation between the two, and its fractal dimension is 1.15. The mosaic structure of the depression patch is relatively stable. (3) In terms of spatial distribution, 80% of the peak-cluster depressions in the study area are concentrated in the interbedding areas of limestone and dolomite and in the contiguous areas of limestone in the south and north of the study area, while the development of peak-cluster depressions in the central clastic rock strata is not obvious.
- digital elevation model /
- hydrologic analysis /
- karst /
- peak-cluster depression /
- morphological characteristics /
- southwest Guangxi

HTML

图 1 研究区概况

Figure 1. Overview of the study area

下载: 全尺寸图片幻灯片

图 2 研究区峰丛洼地提取与分析技术路线

Figure 2. Technical route for extraction and analysis of peak-cluster depressions in the study area

下载: 全尺寸图片幻灯片

图 3 水文提取鞍点高程直方图

Figure 3. Elevation histogram of saddle points extracted from hydrology method

下载: 全尺寸图片幻灯片

图 5 研究区洼地分布图

Figure 5. Distribution of depressions in the study area

下载: 全尺寸图片幻灯片

图 4 a鞍点局部图，b样区高程空间格局

Figure 4. a. Local map of saddle points，b. Spatial pattern of elevation of sample area

下载: 全尺寸图片幻灯片

图 6 洼地周长—面积双对数散点图

Figure 6. Double logarithmic scatter plot of the perimeter and area of the depressions in the study area

下载: 全尺寸图片幻灯片

图 7 洼地形态分析图

Figure 7. Morphological analysis of the depressions in the study area

下载: 全尺寸图片幻灯片

图 8 洼地在不同岩性上的分布

Figure 8. Distribution of depressions in different types of lithology

下载: 全尺寸图片幻灯片

图 9 研究样区洼地提取图

Figure 9. Extraction map of depressions in the study area

下载: 全尺寸图片幻灯片

图 10 桂西南峰丛洼地与洼地激光点云数据^[38]

Figure 10. Cloud data of laser points of peak-cluster depressions and depressions in southwest Guangxi^[38]

下载: 全尺寸图片幻灯片

表 1 洼地提取方法结果分析表

Table 1. Analysis of depression extraction methods

方法数量/个最大面积/km² 最小面积/km² 平均面积/km² 总面积/km²

水文法 57 1.36 0.01 0.27 15.25
鞍座法 91 2.52 0.01 0.36 33.17

下载: 导出CSV

参考文献(38)

[1]	杨树文, 谢飞, 冯光胜, 刘涛. 基于SPOT 5图像的岩溶地貌单元自动提取方法[J]. 国土资源遥感, 2012, 25(2):56-60. YANG Shuwen, XIE Fei, FENG Guangsheng, LIU Tao. Automatic extraction of karst landscape elements based on SPOT 5 image[J]. Remote Sensing for Land and Resources, 2012, 25(2): 56-60.
[2]	闫利会, 周忠发, 黄登红, 但雨生. 基于Landsat 8的喀斯特峰丛洼地地貌信息提取[J]. 科学技术与工程, 2018, 18(28):182-188. doi: 10.3969/j.issn.1671-1815.2018.28.025 YAN Lihui, ZHOU Zhongfa, HUANG Denghong, DAN Yusheng. Geomorphic information extraction of the karst peak cluster-depression based on Landsat 8[J]. Science Technology and Engineering, 2018, 18(28): 182-188. doi: 10.3969/j.issn.1671-1815.2018.28.025
[3]	杨先武. 基于DEM的喀斯特峰林峰丛地形特征与空间分异研究[D]. 南京:南京师范大学, 2019. YANG Xianwu. DEM based research on the topographic characteristics and spatial variation of fenglin and fengcong karst landforms[D]. Nanjing: Nanjing Normal University, 2019.
[4]	蒋忠信. 黔西南高原区峰丛洼地的形态特征与发育规律[J]. 热带地理, 1997, 17(3):267-274. JIANG Zhongxin. Morphological characteristics and development regularity of peak-cluster and depression in the plateau region of southwestern Guizhou Province[J]. Tropical Geography, 1997, 17(3): 267-274.
[5]	宋林华. 喀斯特洼地的发育机理及其水文地质意义[J]. 地理学报, 1986, 53(1):41-50. SONG Linhua. Mechanism of karst depression evolution and its hydrogeological significance[J]. Acta Geographica Sinica, 1986, 53(1): 41-50.
[6]	蒋忠诚. 论中国岩溶峰丛洼地的形成(英文)[J]. 中国岩溶, 1996, 15(Suppl.1):89-90. JIANG Zhongcheng. An analysis on formation of the karst fengcong depressions in China[J]. Carsologica Sinica, 1996, 15(Suppl.1): 89-90.
[7]	孟欣. 基于DEM的峰丛区岩溶洼地提取与形态特征分析[D]. 南京:南京师范大学, 2019. MENG Xin. Extraction and morphological characteristics analysis of karst depressions in Fengcong area based on DEMs[D]. Nanjing: Nanjing Normal University, 2019.
[8]	邱从毫. 峰丛洼地形态数量特征及空间格局研究:以贵州南部斜坡地带为例[D]. 贵阳:贵州师范大学, 2016. QIU Conghao. The quantitative characteristics and spatial pattern study of peak-cluster depression: A case study of the southern slope of Guizhou[D]. Guiyang: Guizhou Normal University, 2016.
[9]	王迪, 许模, 漆继红, 张强. 滇东南丘北区峰丛-洼地地貌形态特征分析[J]. 中国岩溶, 2010, 29(3):239-245. doi: 10.3969/j.issn.1001-4810.2010.03.004 WANG Di, XU Mo, QI Jihong, ZHANG Qiang. Analysis on morphologic features of the peak-cluster depression in Qiubei, southeast Yunnan[J]. Carsologica Sinica, 2010, 29(3): 239-245. doi: 10.3969/j.issn.1001-4810.2010.03.004
[10]	刘爱利, 汤国安. 中国地貌基本形态DEM的自动划分研究[J]. 地球信息科学, 2006, 8(4): 8-14, 5. LIU Aili, TANG Guoan. DEM based auto-classification of Chinese landform[J]. Geo-information Science, 2006, 8(4): 8-14, 5.
[11]	王恒松, 熊康宁, 张芳美, 罗鼎, 廖钟方. 广西环江锥状峰丛喀斯特景观演化机制[J]. 热带地理, 2014, 34(5):672-680. WANG Hengsong, XIONG Kangning, ZHANG Fangmei, LUO Ding, LIAO Zhongfang. Evolution mechanism of dolomite karst peak cluster-canyon landform: A genesis view on Huanjiang, Guangxi[J]. Tropical Geography, 2014, 34(5): 672-680.
[12]	宋林华, 房金福, 邓自民, 刘宏. 喀斯特洼地的分形特性研究[J]. 地理研究, 1995, 14(1):8-16. doi: 10.3321/j.issn:1000-0585.1995.01.002 SONG Linhua, FANG Jinfu, DENG Zimin, LIU Hong. Fractal and geometry of karst depressions in South China[J]. Geographical Research, 1995, 14(1): 8-16. doi: 10.3321/j.issn:1000-0585.1995.01.002
[13]	Huang Shufen, Lan Anjun, Ma Jiaqiong, Guo Haixiang. Information extraction of typical karst landform based on RS[C]// Society of Photo-optical Instrumentation Engineers. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2014.
[14]	程晓鑫. 基于高分影像的黄土高原沟谷地自动提取及地貌形态差异分析[D]. 北京:北京林业大学, 2019. CHENG Xiaoxin. Automatic extraction of valley area and geomorphological analysis on the Loess Plateau based on high spatial resolution images[D]. Beijing: Beijing Forestry University, 2019.
[15]	Paul W Williams. Morphometric analysis of polygonal karst in New Guinea[J]. Geological Society of America Bulletin, 1972, 83(3): 761-796.
[16]	朱德浩. 桂林地区峰丛洼地的形态量计及其演化[J]. 中国岩溶, 1982, 1(2):127-134. ZHU Dehao. Evolution of peak cluster-depression in Guilin area and morphometric measurement[J]. Carsologica Sinica, 1982, 1(2): 127-134.
[17]	丁智强, 高璇, 李玉辉, 俞筱押. 石林县域洼地特征与资源环境效应[J]. 山地学报, 2019, 37(3):316-327. DING Zhiqiang, GAO Xuan, LI Yuhui, YU Xiaoya. Regional characteristics and resource-environmental effects of karst depressions in Shilin county, China[J]. Mountain Research, 2019, 37(3): 316-327.
[18]	G A Brook, D C Ford. The origin of labyrinth and tower karst and the climatic conditions necessary for their development[J]. Nature, 1978, 275(5680): 493-496.
[19]	Waltham T. Fengcong, fenglin, cone karst and tower karst[J]. Cave and Karst Science, 2008, 35(3): 77-88.
[20]	刘京涛. 桂西南岩溶生态系统健康及其评价研究[D]. 南宁:广西大学, 2003. LIU Jingtao. Karst ecosystem health and assessment of southwest Guangxi[D]. Nanning: Guangxi University, 2003.
[21]	邹陈杰. 南盘江天生桥地区区域喀斯特地貌研究[M]. 喀斯特地貌与洞穴:科学出版社, 1985:77-96. ZOU Chenjie. Regional karst geomorphology in Tianshengqiao area of Nanpanjiang river[M]. Karst Geomorphology and Caves: Science Press, 1985: 77-96.
[22]	Barbara Theilen Willige, Ralf Löwner, F El Bchari, H Ait Malek, M Chaibi, A Charif, C Nakhcha, M Ait Ougougdal, M Ridaoui, E Boumaggard. Remote sensing and GIS contribution to the detection of areas susceptible to natural hazards in the Safi area, W-Morocco[C]//International Conference on Information & Communication Technologies for Disaster Management. IEEE, 2014.
[23]	阿如旱, 杨持, 同丽嘎. 基于分形理论的沙漠化土地空间结构:以内蒙古多伦县为例[J]. 地理研究, 2010, 29(2):283-290. Aruhan, YANG Chi, Tongliga. Spatial structure of desertified land based on fractal theory: Taking Duolun county, Inner Mongolia as an example[J]. Geographical Research, 2010, 29(2): 283-290.
[24]	朱晓华, 色布力马. 中国沙漠化土地类型的分形研究[J]. 中国沙漠, 2006, 26(1):35-39. ZHU Xiaohua, SEBU Lima. Fractal analysis applied to fractal character of China desertification land[J]. Journal of Desert Research, 2006, 26(1): 35-39.
[25]	YANG Xianwu, TANG Guoan, MENG Xin, XIONG Liyang . Saddle position-based method for extraction of depressions in fengcong areas by using digital elevation models[J]. International Journal of Geo-information, 2018, 7(4): 136.
[26]	邓自强, 林玉石, 张美良, 刘功余, 魏志民. 桂林地质构造与岩溶地貌发育的时序关系[J]. 中国岩溶, 1986, 5(4):289-296. DENG Ziqiang, LIN Yushi, ZHANG Meiliang, LIU Gongyu, WEI Zhimin. Time-sequence relationship between geological structures and development of karst features in Guilin area[J]. Carsologica Sinica, 1986, 5(4): 289-296.
[27]	李成芳, 王忠诚, 李振炜, 徐宪立. 西南喀斯特区土壤侵蚀研究进展[J]. 中国岩溶, 2022, 41(6):962-974. LI Chengfang, WANG Zhongcheng, LI Zhenwei, XU Xianli. Research progress of soil erosion in karst areas of Southwest China[J]. Carsologica Sinica, 202, 41(6): 962-974.
[28]	武健强, 顾春生, 许书刚, 赵秀峰, 黄光明. 苏南地区碳酸盐岩的溶蚀性分析[J]. 中国岩溶, 2021, 40(4):565-571. WU Jianqiang, GU Chunsheng, XU Shugang, ZHAO Xiufeng, HUANG Guangming. Corrosion analysis of carbonate rocks in southern Jiangsu Province[J]. Carsologica Sinica, 2021, 40(4): 565-571.
[29]	王建. 现代自然地理学[M]. 北京:高等教育出版社, 2010:47-51. WANG Jian. Modern Physical Geography[M]. Beijing: Higher Education Press, 2010:47-51.
[30]	丁智强, 俞筱押, 高璇, 李玉辉. 云南石林县域喀斯特洼地空间分布特征及影响因素研究[J]. 中国岩溶, 2019, 38(3):325-335. doi: 10.11932/karst20190305 DING Zhiqiang, YU Xiaoya, GAO Xuan, LI Yuhui. Study on spatial distribution characteristics and influencing factors of karst depression in Shilin county, Yunnan Province[J]. Carsologica Sinica, 2019, 38(3): 325-335. doi: 10.11932/karst20190305
[31]	汤国安. 我国数字高程模型与数字地形分析研究进展[J]. 地理学报, 2014, 69(9): 1305-1325. TANG Guoan. Progress of DEM and digital terrain analysis in China[J]. Acta Geographica Sinica, 2014, 69(9): 1305-1325.
[32]	仲腾, 汤国安, 周毅, 李若殷, 张维. 基于反地形DEM的山顶点自动提取[J]. 测绘通报, 2009, 55(4):35-37. ZHONG Teng, TANG Guoan, ZHOU Yi, LI Ruoyin, ZHANG Wei. Method of extracting surface peaks based on reverse DEMs[J]. Bulletin of Surveying and Mapping, 2009, 55(4): 35-37.
[33]	张维, 汤国安, 陶旸, 罗明良. 基于DEM汇流模拟的鞍部点提取改进方法[J]. 测绘科学, 2011, 36(1):158-163. ZHANG Wei, TANG Guoan, TAO Yang, LUO Mingliang. An improved method to saddles extraction based on runoff concentration simulation in DEM[J]. Science of Surveying and Mapping, 2011, 36(1): 158-163.
[34]	李玉美, 郭庆华, 万波, 秦宏楠, 王德智. 基于激光雷达的自然资源三维动态监测现状与展望[J]. 遥感学报, 2021, 25(1):381-402. LI Yumei, GUO Qinghua, WAN Bo, QIN Hongnan, WANG Dezhi. Current status and prospect of three-dimensional dynamic monitoring of natural resources based on LiDAR[J]. National Remote Sensing Bulletin, 2021, 25(1): 381-402.
[35]	于嫒平. 基于正射影像和激光雷达数据相结合的土地覆盖信息提取方法研究[D]. 昆明:云南大学, 2021. YU Yuanping. Extracting land cover types from orthophotos and LiDAR data[D]. Kunming: Yunnan University, 2021.
[36]	刘刚, 金鼎坚, 吴芳, 于坤, 李奇, 张文凯, 王建超. 机载激光雷达在水下地貌识别与断裂构造精细解译中的应用[J]. 海洋地质与第四纪地质, 2022, 42(2):190-199. LIU Gang, JIN Dingjian, WU Fang, YU Kun, LI Qi, ZHANG Wenkai, WANG Jianchao. Application of airborne LiDAR to identification of underwater geomorphology and fine interpretation of faults[J]. Marine Geology & Quaternary Geology, 2022, 42(2): 190-199.
[37]	Asselen S V, Seijmonsbergen A C. Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM[J]. Geomorphology, 2006, 78(3-4): 309-320.
[38]	田义超, 黄远林, 张强, 陶进, 张亚丽, 杨永伟, 林俊良. 地方应用型本科高校无人机教学基地建设与实践[J]. 教育现代化, 2022, 9(4):81-86. doi: 10.12365/j.issn.2095-8420.2022.04.6492 TIAN Yichao, HUANG Yuanlin, ZHANG Qiang, TAO Jin, ZHANG Yali, YANG Yongwei, LIN Junliang. Construction and practice of UAV teaching base in local application-oriented universities[J]. Education Modernization, 2022, 9(4): 81-86. doi: 10.12365/j.issn.2095-8420.2022.04.6492