基于高分五号高光谱数据的石漠化调查应用研究

doi:10.6046/zrzyyg.2022129

摘要
图/表
参考文献
相关文章
Metrics

全文: PDF(4480 KB)

HTML
输出: BibTeX | EndNote (RIS)

摘要

石漠化是我国西南岩溶山区的首要生态环境问题,必须制定科学的石漠化防治措施,全面推进石漠化防治进程。遥感技术快速定位、覆盖区域广以及经济高效的特点使其成为调查区域石漠化空间分布情况的重要技术方法。为此,采用基于高分五号(GF-5)高光谱数据的混合像元分解法和基于Landsat8多光谱数据的光谱指数法,分别对研究区植被覆盖率、基岩裸露率和土被覆盖率3个表征石漠化信息的关键指标进行提取。结果表明,2种卫星遥感数据对于植被覆盖信息都能进行准确提取; 但是Landsat8的波段设置和光谱分辨率很难区分裸露基岩和裸露土壤; 而GF-5高光谱数据不仅能直接有效提取基岩裸露率和土被覆盖率,并能精确识别裸露基岩中方解石和白云石等矿物成分。研究结果可为石漠化的评价和分级以及综合治理提供更科学有效的技术基础和理论依据。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李娜
	甘甫平
	董新丰
	李娟
	张世凡
	李彤彤

关键词 ：石漠化, 高分五号, 混合像元分解, Landsat8

Abstract：

Rocky desertification is the primary eco-environmental problem in Karst mountainous areas in southwestern China. Scientific measures must be formulated to comprehensively promote the prevention and control of rocky desertification. Remote sensing technology, which enjoys the advantages of rapid positioning, wide coverage, and economic efficiency, has become an important technical method for investigating the spatial distribution of regional rocky desertification. Therefore, this study extracted three key indices used to characterize rocky desertification information (i.e., vegetation coverage, bedrock exposure rate, and soil coverage) of the study area using the pixel unmixing method based on GF-5 hyperspectral data and the spectral index method based on Landsat8 multispectral data. The results show that information on vegetation coverage can be accurately extracted from the two types of satellite remote sensing data. However, Landsat8 multispectral data are difficult to distinguish information about exposed bedrocks from that of bare soil due to their band setting and spectral resolution. By contrast, GF-5 hyperspectral data enable the direct and effective extraction of bedrock exposure rate and soil coverage, as well as the accurate identification of mineral components such as calcite and dolomite in exposed bedrocks. The results of this study can provide a scientific and effective technical and theoretical basis for the evaluation, classification, and comprehensive control of rocky desertification.

Key words： rocky desertification GF-5 pixel unmixing Landsat8

收稿日期: 2022-04-06 出版日期: 2023-07-07

ZTFLH:	TP79
	P627
	P623

基金资助:国家重点研发计划项目“中空间分辨率光谱地球研发与应用技术研究”(2019YFE0127300);民用航天项目(D040102);中国地质调查局项目“全国遥感地质调查与监测”(DD20221642)

通讯作者: 甘甫平(1971-),男,博士,研究员,研究方向为遥感地质应用。Email: fpgan@aliyun.com。

作者简介: 李娜(1989-),女,硕士,工程师,研究方向为高光谱遥感。Email: 942750607@qq.com。

引用本文:

李娜, 甘甫平, 董新丰, 李娟, 张世凡, 李彤彤. 基于高分五号高光谱数据的石漠化调查应用研究[J]. 自然资源遥感, 2023, 35(2): 230-235.
LI Na, GAN Fuping, DONG Xinfeng, LI Juan, ZHANG Shifan, LI Tongtong. Investigation and applications of rocky desertification based on GF-5 hyperspectral data. Remote Sensing for Natural Resources, 2023, 35(2): 230-235.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022129 或 https://www.gtzyyg.com/CN/Y2023/V35/I2/230

Fig.1 研究区位置

Tab.1 GF-5高光谱相机主要技术指标

Fig.2 GF-5数据植被覆盖率、土被覆盖率和基岩裸露率

Fig.3 方解石和白云石分布

Fig.4 验证点影像光谱曲线、野外样品ASD曲线及野外照片

Fig.5 Landsat8数据植被覆盖率、土被覆盖率和基岩裸露率

[1]	冯淑娜. 基于多端元混合像元分解的喀斯特石漠化表征因子提取研究[D]. 北京: 中国矿业大学(北京), 2015.
	Feng S N. Extraction of characterization factors of Karst rocky desertification based on multi terminal mixed pixel decomposition[D]. Beijing: China University of Mining and Technology (Beijing), 2015.
[2]	鞠建华, 戴传固. 岩溶石漠化遥感监测与防护规划[M]. 北京: 地质出版社, 2006.
	Ju J H, Dai C G. Remote sensing monitoring and protection planning of Karst rocky desertification[M]. Beijing: Geological Publishing House, 2006.
[3]	Xie L W, Zhong J, Chen F F, et al. Evaluation of soil fertility in the succession of Karst rocky desertification using principal component analysis[J]. Solid Earth, 2015, 6(2):515-524. doi: 10.5194/se-6-515-2015
[4]	Lian Y Q, You J Y, Lin K R, et al. Characteristics of climate change in southwest China Karst region and their potential environmental impacts[J]. Environmental Earth Sciences, 2015, 74(2):937-944. doi: 10.1007/s12665-014-3847-8
[5]	Cao J H, Yuan D X, Tong L Q, et al. An overview of Karst ecosystem in Southwest China:Current state and future management[J]. Journal of Resource sand Ecology, 2015, 6 (4):247-256.
[6]	陈起伟, 兰安军, 熊康宁, 等. 基于遥感光谱特征的喀斯特石漠化信息提取[J]. 贵州师范大学学报(自然科学版), 2003(4):82-87.
	Chen Q W, Lan A J, Xiong K N, et al. Extraction of Karst rocky desertification information based on remote sensing spectral features[J]. Journal of Guizhou Normal University (Natural Science Edition), 2003(4):82-87.
[7]	陈飞, 周德全, 白晓永, 等. 典型喀斯特槽谷区石漠化时空演变及未来情景模拟[J]. 农业资源与环境学报, 2018, 35(2):174-180.
	Chen F, Zhou D Q, Bai X Y, et al. Temporal and spatial evolution and future scenario simulation of rocky desertification in typical Karst valley area[J]. Journal of Agricultural Resources and Environment, 2018, 35(2):174-180.
[8]	Benz U, Baatz M, Schreier G. OSCAR - object oriented segmentation and classification of advanced Radar allow automated information extraction[C]// 2001 International Geoscience and Remote Sensing Symposium (IGARSS), 2001:1913-1915.
[9]	岳跃民, 张兵, 王克林, 等. 石漠化遥感评价因子提取研究[J]. 遥感学报, 2011, 15(4):722-736.
	Yue Y M, Zhang B, Wang K L, et al. Study on extraction of remote sensing evaluation factors of rocky desertification[J]. Journal of Remote Sensing, 2011, 15(4):722-736.
[10]	杨苏新, 张霞, 帅通, 等. 基于混合像元分解的喀斯特石漠化地物丰度估测[J]. 遥感技术与应用, 2014, 29(5):823-832.
	Yang S X, Zhang X, Shuai T, et al. Abundance estimation of Karst rocky desertification based on mixed pixel decomposition[J]. Remote Sensing Technology and Application, 2014, 29(5):823-832.
[11]	Nouri T, Oskouei M M. Processing of Hyperion data set for detection of indicative minerals using a hybrid method in Dost-Bayli,Iran[J]. International Journal of Remote Sensing, 2016, 37(19/20):4923-4947. doi: 10.1080/01431161.2016.1225176
[12]	董新丰, 甘甫平, 李娜, 等. 高分五号高光谱影像矿物精细识别[J]. 遥感学报, 2020, 24(4):454-464.
	Dong X F, Gan F P, Li N, et al. Fine mineral identification of GF-5 hyperspectral image[J]. Journal of Remote Sensing, 2020, 24(4):454-464.
[13]	李娜, 董新丰, 甘甫平, 等. 高光谱遥感技术在基岩区区域地质调查填图中的应用[J]. 地质通报, 2021, 40(1):13-21.
	Li N, Dong X F, Gan F P, et al. Application of hyperspectral remote sensing technology in regional geological survey and mapping of bedrock area[J]. Geological Bulletin of China, 2021, 40(1):13-21.
[14]	张贵, 何绕生, 王波, 等. 云南华宁县盘溪大龙潭水文地质特征[J]. 贵州大学学报(自然科学版), 2020, 37(5):6.
	Zhang G, He R S, Wang B, et al. Hydrogeological characteristics of Panxi Dalongtan in Huaning County,Yunnan Province[J]. Journal of Guizhou University(Natural Science Edition), 2020, 37(5):6.
[15]	刘鹏, 王妍, 刘宗滨, 等. 云南省县域尺度的石漠化分布与区划[J]. 浙江农林大学学报, 2019, 36(5):965-973.
	Liu P, Wang Y, Liu Z B, et al. Distribution and regionalization of rocky desertification at county scale in Yunnan Province[J]. Journal of Zhejiang Agriculture and Forestry University, 2019, 36(5):965-973.
[16]	Dobigeon N, Tourneret J, Chang C. Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery[J]. IEEE Transactions on Signal Processing, 2008, 56(7):2684-2695. doi: 10.1109/TSP.2008.917851
[17]	Keshava N, Mustard J F. Spectral unmixing[J]. IEEE Signal Processing Magazine, 2002, 19(1):44-57. doi: 10.1109/79.974727
[18]	金鑫, 柯长青. 基于混合像元分解的天山典型地区冰雪变化监测[J]. 国土资源遥感, 2012, 24(4):146-151.doi:10.6046/gtzyyg.2012.04.24. doi: 10.6046/gtzyyg.2012.04.24
	Jin X, Ke C Q. Monitoring of snow cover changes in Tianshan mountains based on mixed pixel decomposition[J]. Remote Sensing for Land and Resources, 2012, 24(4):146-151.doi:10.6046/gtzyyg.2012.04.24. doi: 10.6046/gtzyyg.2012.04.24
[19]	Nalawade D B, Solankar M M, Surase R R, et al. Hyperspectral remote sensing image analysis with SMACC and PPI algorithms for endmember extraction[J]. Communications in Computer and Information Science, 2019, 1035:319-328
[20]	王润生, 杨苏明, 闫柏琨. 成像光谱矿物识别方法与识别模型评述[J]. 国土资源遥感, 2007, 19(1):1-9.doi:10.6046/gtzyyg.2007.01.01. doi: 10.6046/gtzyyg.2007.01.01
	Wang R S, Yang S M, Yan B K. A review of mineral spectral identification methods and models with imaging spectrometer[J]. Remote Sensing for Land and Resources, 2007, 19(1):1-9.doi:10.6046/gtzyyg.2007.01.01. doi: 10.6046/gtzyyg.2007.01.01
[21]	Rikmaru A. Landsat TM data processing guide for forest canopy density mapping and monitoring model[C]// ITTO Workshop on Utilization of Remote Sensing in Site Assessment and Planning for Rehabilitation of Logged-over Forest, 1996:1-8.
[22]	皮茂强, 潘祖高. 影响贵州石漠化形成和演化的地质因素[J]. 科海故事博览(科技探索), 2014(4):84-85.
	Pi M Q, Pan Z G. Geological factors affecting the formation and evolution of rocky desertification in Guizhou[J]. Broad Review of Scientific Stories (Scientific and Technological Exploration), 2014(4):84-85.

[1]	张士博, 胡文敏, 韩祯颖, 李果, 王忠诚, 高志海. GF-6与Landsat8混合像元分解的石漠化信息提取差异研究——以普定县为例[J]. 自然资源遥感, 2023, 35(3): 274-283.
[2]	盛德志, 邢前国, 刘海龙, 郑向阳. 基于混合像元分解的池塘养殖动态遥感监测[J]. 自然资源遥感, 2022, 34(4): 53-59.
[3]	陈慧欣, 陈超, 张自力, 汪李彦, 梁锦涛. 一种基于Google Earth Engine云平台的潮间带遥感信息提取方法[J]. 自然资源遥感, 2022, 34(4): 60-67.
[4]	董双发, 范晓, 石海岗, 许莉萍, 章新益. 基于Landsat8和无人机的福清核电温排水分布研究[J]. 自然资源遥感, 2022, 34(3): 112-120.
[5]	仇一帆, 柴登峰. 无人工标注数据的Landsat影像云检测深度学习方法[J]. 国土资源遥感, 2021, 33(1): 102-107.
[6]	王琳, 谢洪波, 文广超, 杨运航. 基于Landsat8的含蓝藻湖泊水体信息提取方法研究[J]. 国土资源遥感, 2020, 32(4): 130-136.
[7]	张红利, 罗蔚然, 李艳. 基于粒子群优化和像元分解模型的遥感影像时空融合[J]. 国土资源遥感, 2020, 32(4): 33-40.
[8]	秦其明, 陈晋, 张永光, 任华忠, 吴自华, 张赤山, 吴霖升, 刘见礼. 定量遥感若干前沿方向探讨[J]. 国土资源遥感, 2020, 32(4): 8-15.
[9]	蔡耀通, 刘书彤, 林辉, 张猛. 基于多源遥感数据的CNN水稻提取研究[J]. 国土资源遥感, 2020, 32(4): 97-104.
[10]	王玲玉, 陈全, 吴跃, 周忠发, 但雨生. 基于地块级时序NDVI的喀斯特山区撂荒地特征精准识别[J]. 国土资源遥感, 2020, 32(3): 23-31.
[11]	石海岗, 梁春利, 张建永, 张春雷, 程旭. 岸线变迁对田湾核电站温排水影响遥感调查[J]. 国土资源遥感, 2020, 32(2): 196-203.
[12]	朱爽, 张锦水. 时间序列低分影像修正中分遥感冬小麦分布[J]. 国土资源遥感, 2020, 32(1): 19-26.
[13]	吴林霖, 官云兰, 李嘉伟, 袁晨鑫, 李睿. 基于MODIS影像喀斯特石漠化状况研究——以贵州省为例[J]. 国土资源遥感, 2019, 31(4): 235-242.
[14]	刘畅, 杨康, 程亮, 李满春, 郭紫燕. Landsat8不透水面遥感信息提取方法对比[J]. 国土资源遥感, 2019, 31(3): 148-156.
[15]	王大钊, 王思梦, 黄昌. Sentinel-2和Landsat8影像的四种常用水体指数地表水体提取对比[J]. 国土资源遥感, 2019, 31(3): 157-165.

Viewed

Full text

Abstract

Cited

Shared

Discussed