利用遥感信息监测宁南县县域石漠化及石漠化发展趋势分析
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摘要
研究县域石漠化的空间分布特征,分析其发展趋势。以宁南县为例,首先运用遥感(remote sensing,RS)和地理信息系统(geographic information system,GIS)技术提取2005年和2015年两期的石漠化范围并分级;其次,将提取结果分别与坡度、坡向、河流、人口数据叠加,分析石漠化空间分布特征;最后利用马尔科夫模型对宁南县未来10年石漠化的发展趋势进行预测。研究表明:宁南县2015年重度、中度石漠化面积较2005年有所减少;潜在、轻度石漠化面积有所增加,分别增加3. 9%和1. 7%;重度、中度石漠化主要分布在黑水河、金沙江流域,人口密度大于120人/km~2的区域,且坡向偏南、偏东,坡度为缓坡、缓陡坡。根据预测结果,2025年,重度、中度、轻度、无石漠化面积减小,潜在石漠化面积增加。研究为西南地区石漠化治理与重点防治以及其他地区石漠化监测及预测提供了参考。
In order to study the spatial distribution characteristics of rocky desertification and analyze its development trend in counties,take Ningnan County as an example,firstly,remote sensing( RS) and geographic information system( GIS) were used to extract and classify the rocky desertification areas in 2005 and 2015. Secondly,the spatial distribution characteristics of rocky desertification were analyzed by combining the extracted results with slope,slope direction,river and population data. Finally,the Markoff model is used to predict the development trend of rocky desertification in the next ten years. The research shows that,the heavy and moderate areas of Ningnan County decreased in 2015 compared with 2005,and the area of potential and mild rocky desertification increased by 3. 9% and 1. 7% respectively. Moderate and severe rocky desertification is mainly distributed in the Heishui River and Jinsha River Basin. The population density is greater than 120/km~2,and the slope is south and east,and the slope is gentle slope and gentle slope is also the main distribution area. According to the prediction results,in 2025,the area of severe,moderate,mild and non-rocky desertification decreased,and the potential stony desertification area increased. A reference for rocky desertification control and key prevention and control is provided as well as monitoring and prediction of rocky desertification in other areas of Southwest China.
In order to study the spatial distribution characteristics of rocky desertification and analyze its development trend in counties,take Ningnan County as an example,firstly,remote sensing( RS) and geographic information system( GIS) were used to extract and classify the rocky desertification areas in 2005 and 2015. Secondly,the spatial distribution characteristics of rocky desertification were analyzed by combining the extracted results with slope,slope direction,river and population data. Finally,the Markoff model is used to predict the development trend of rocky desertification in the next ten years. The research shows that,the heavy and moderate areas of Ningnan County decreased in 2015 compared with 2005,and the area of potential and mild rocky desertification increased by 3. 9% and 1. 7% respectively. Moderate and severe rocky desertification is mainly distributed in the Heishui River and Jinsha River Basin. The population density is greater than 120/km~2,and the slope is south and east,and the slope is gentle slope and gentle slope is also the main distribution area. According to the prediction results,in 2025,the area of severe,moderate,mild and non-rocky desertification decreased,and the potential stony desertification area increased. A reference for rocky desertification control and key prevention and control is provided as well as monitoring and prediction of rocky desertification in other areas of Southwest China.
引文
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12刘芳.基于热红外遥感的广西平果县石漠化初步研究[J].长江流域资源与环境,2016,25(6):952-956Liu Fang.Evaluation of karst rocky desertification based on thermal infrared remote sensing in Pingguo Country,Guangxi[J].Resources and Environment in the Yangtze Basin,2016,25(6):952-956
13李灿刚,张信宝.实现水土保持产业化的途径---以四川省宁南县为例[J].水土保持研究,2001,8(2):124-126Li Cangang,Zhang Xinbao.Approaches to realizing industrialization of soil and water conservation:taking Ningnan County of Sichuan for an example[J].Research of Soil and Water Conservation,2001,8(2):124-126
14地理空间数据云官网[EB/OL].[2017-01-01]http://www.gscloud.cn/Geospitial data cloud[EB/OL].[2017-01-01]http://www.gscloud.cn/
15孙小涛,周忠发,陈全.重点生态功能区水土流失敏感性评价与分布研究---以贵州省雷山县为例[J].水土保持学报,2016,30(6):73-78Sun Xiaotao,Zhou Zhongfa,Chen Quan,et al.Sensitivity evaluation and the spatial distribution of soil erosion in key ecological function areas:a case of Leishan City of Guizhou[J].Research of Soil and Water Conservation,2016,30(6):73-78
16李丽,童立强,李小慧.基于植被覆盖度的石漠化遥感信息提取方法研究[J].国土资源遥感,2010(2):59-62Li Li,Tong Liqiang,Li Xiaohui.The remote sensing information extraction method based on vegetation coverage[J].Remote Sensing for Land&Resources,2010(2):59-62
17张盼盼,胡远满,肖笃宁,等.一种基于多光谱遥感影像的喀斯特地区裸岩率的计算方法初探[J].遥感技术与应用,2010,25(4):510-514Zhang Panpan,Hu Yuanman,Xiao Duning,et al.A method of the percentage of bare rock calculation in Karst areas based on multispectrum remote sensing image[J].Remote Sensing Technology and Application,2010,25(4):510-514
18熊康宁,李晋,龙明忠.典型喀斯特石漠化治理区水土流失特征与关键问题[J].地理学报,2012,67(7):878-888Xiong Kangning,Li Jin,Long Mingzhong.Features of soil and water loss and key issues in demonstration areas for combating Karst rocky desertification[J].Acta Geographica Sinica,2012,67(7):878-888
19潘真真,苏维词,王建伟.基于可拓-马尔科夫模型的贵州省生态安全预警[J].山地学报,2016,34(5):580-590Pan Zhenzhen,Su Weici,Wang Jianwei.Early-warning model based on extension and markov for ecological security in Guizhou[J].Mountain Research,2016,34(5):580-590
20张勇荣,周忠发,马士彬,等.基于Markov的石漠化景观演变特征分析与预测[J].长江科学院报,2015,32(1):52-56,69Zhang Yongrong,Zhou Zhongfa,Ma Shibin,et al.Analysis and forecast of the evolution features of rocky desertification landscape by Markov model[J].Journal of Yangtze River Scientific Research Institute,2015,32(1):52-56,69
21徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002Xu Jianhua.Mathematical methods in contemporary geography[M].Beijing:Higher Education Press,2002
22中华人民共和国水利部.土壤侵蚀分级分类标准:SL 190-2007[S].北京:中国水利水电出版社,2008Ministry of Water Resources of the People's Republic of China.Classification standard of soil erosion:SL 190-2007[S].Beijing:China Water&Power Press,2008
23中华人民共和国水利部.岩溶地区水土流失综合治理技术标准:SL 461-2009[S].北京:中国水利水电出版社,2009Ministry of Water Resources of the People's Republic of China.Technical standard for comprehensive control of soil erosion and water loss in Karst region:SL 461-2009[S].Beijing:China Water&Power Press,2009
24袁先强,卫亚星.基于马尔科夫模型的盖州市土地利用动态变化研究[J].国土与自然资源研究,2017(1):19-22Yuan Xianqiang,Wei Yaxing.Study on the dynamic change of land use in Gaizhou City based on Markov model[J].Territory&Natural Resources Study,2017(1):19-22
2张恒,刘宗祥,钱江澎,等.四川省岩溶石山地区石漠化分布特征及综合治理建议[J].四川地质学报,2011,31(1):43-46Zhang Heng,Liu Zongxiang,Qian Jiangpeng,et al.Distribution and control of hamada in karstification region,Sichuan[J].Acta Geologica Sichuan,2011,31(1):43-46
3苏旺德,史正涛,刘钢.基于RS和GIS的南汀河流域石漠化评价[J].中国岩溶,2016,35(5):594-601Su Wangde,Shi Zhengtao,Liu Gang.Rocky desertification evaluation based on RS and GIS in Nanting river basin[J].Karst in China,2016,35(5):594-601
4崔舜铫,姚佛军,连琛芹.多源遥感数据在植被覆盖区的水体信息提取[J].科学技术与工程,2018,18(28):116-122Cui Shunyao,Yao Fojun,Lian Chenqin.Water body information extraction based on multisource remote sensing data in vegetation coverage area[J].Science Technology and Engineering,2018,18(28):116-122
5王秋燕,陈仁喜,徐佳,等.环境一号卫星影像中水体信息提取方法研究[J].科学技术与工程,2012,12(13):3051-3056Wang Qiuyan,Chen Renxi,Xu Jia,et al.Research on methods for extracting water body information from HJ-1A/B data[J].Science Technology and Engineering,2012,12(13):3051-3056
6毕硕本,钱育君,王启富,等.基于TM影像的水体信息提取算法研究[J].科学技术与工程,2014,14(3):222-226Bi Shuoben,Qian Yujun,Wang Qifu,et al.Research on water information extraction algorithm based on TM images[J].Science Technology and Engineering,2014,14(3):222-226
7李水明,舒宁,王国聪,等.广西石漠化的成因分析和发展趋势预测[J].广西科学院学报,2006,22(3):193-196Li Shuiming,Shu Ning,Wang Guocong,et al.The origination analysis and progress of the rocky desert of land in Guangxi[J].Guangxi Academy of Sciences,2006,22(3):193-196
8陈起伟,熊康宁,兰安军.基于3S的贵州喀斯特石漠化遥感监测研究[J].干旱区资源与环境,2014(3):62-67Chen Qiwei,Xiong Kangning,Lan Anjun.Monitoring studies on Karst rocky desertification in Guizhou based on 3S[J].Journal of Arid Land Resources and Environment,2014(3):62-67
9熊康宁,黎平,周忠发,等.喀斯特石漠化的遥感---GIS典型研究[M].北京:地质出版社,2002Xiong Kangning,Li Ping,Zhou Zhongfa,et al.Typical research on remote sensing:GIS of rocky desertification in Karst[M].Beijing:Geological Publishing House,2002
10陈起伟,兰安军,熊康宁,等.基于遥感光谱特征的碳酸盐岩石漠化信息提取[J].贵州师范大学学报(自然科学版),2003,21(4):82-87Chen Qiwei,Lan Anjun,Xiong Kangning,et al.Spectral feature based model for extracting Karst rock desertification from remote sensing image[J].Journal of Guizhou Normal University(Natural Sciences),2003,21(4):82-87
11史迎春,舒英格.喀斯特石漠化时空变化特征及驱动因子分析---以贵州晴隆县为例[J].林业资源管理,2017(1):135-143Shi Yingchun,Shu Yingge.Analysis on karst rocky desertification temporal and spatial variation characteristics and driving factors:a case study of Qinglong County of Guizhou Province[J].Forest Resources Management,2017(1):135-143
12刘芳.基于热红外遥感的广西平果县石漠化初步研究[J].长江流域资源与环境,2016,25(6):952-956Liu Fang.Evaluation of karst rocky desertification based on thermal infrared remote sensing in Pingguo Country,Guangxi[J].Resources and Environment in the Yangtze Basin,2016,25(6):952-956
13李灿刚,张信宝.实现水土保持产业化的途径---以四川省宁南县为例[J].水土保持研究,2001,8(2):124-126Li Cangang,Zhang Xinbao.Approaches to realizing industrialization of soil and water conservation:taking Ningnan County of Sichuan for an example[J].Research of Soil and Water Conservation,2001,8(2):124-126
14地理空间数据云官网[EB/OL].[2017-01-01]http://www.gscloud.cn/Geospitial data cloud[EB/OL].[2017-01-01]http://www.gscloud.cn/
15孙小涛,周忠发,陈全.重点生态功能区水土流失敏感性评价与分布研究---以贵州省雷山县为例[J].水土保持学报,2016,30(6):73-78Sun Xiaotao,Zhou Zhongfa,Chen Quan,et al.Sensitivity evaluation and the spatial distribution of soil erosion in key ecological function areas:a case of Leishan City of Guizhou[J].Research of Soil and Water Conservation,2016,30(6):73-78
16李丽,童立强,李小慧.基于植被覆盖度的石漠化遥感信息提取方法研究[J].国土资源遥感,2010(2):59-62Li Li,Tong Liqiang,Li Xiaohui.The remote sensing information extraction method based on vegetation coverage[J].Remote Sensing for Land&Resources,2010(2):59-62
17张盼盼,胡远满,肖笃宁,等.一种基于多光谱遥感影像的喀斯特地区裸岩率的计算方法初探[J].遥感技术与应用,2010,25(4):510-514Zhang Panpan,Hu Yuanman,Xiao Duning,et al.A method of the percentage of bare rock calculation in Karst areas based on multispectrum remote sensing image[J].Remote Sensing Technology and Application,2010,25(4):510-514
18熊康宁,李晋,龙明忠.典型喀斯特石漠化治理区水土流失特征与关键问题[J].地理学报,2012,67(7):878-888Xiong Kangning,Li Jin,Long Mingzhong.Features of soil and water loss and key issues in demonstration areas for combating Karst rocky desertification[J].Acta Geographica Sinica,2012,67(7):878-888
19潘真真,苏维词,王建伟.基于可拓-马尔科夫模型的贵州省生态安全预警[J].山地学报,2016,34(5):580-590Pan Zhenzhen,Su Weici,Wang Jianwei.Early-warning model based on extension and markov for ecological security in Guizhou[J].Mountain Research,2016,34(5):580-590
20张勇荣,周忠发,马士彬,等.基于Markov的石漠化景观演变特征分析与预测[J].长江科学院报,2015,32(1):52-56,69Zhang Yongrong,Zhou Zhongfa,Ma Shibin,et al.Analysis and forecast of the evolution features of rocky desertification landscape by Markov model[J].Journal of Yangtze River Scientific Research Institute,2015,32(1):52-56,69
21徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002Xu Jianhua.Mathematical methods in contemporary geography[M].Beijing:Higher Education Press,2002
22中华人民共和国水利部.土壤侵蚀分级分类标准:SL 190-2007[S].北京:中国水利水电出版社,2008Ministry of Water Resources of the People's Republic of China.Classification standard of soil erosion:SL 190-2007[S].Beijing:China Water&Power Press,2008
23中华人民共和国水利部.岩溶地区水土流失综合治理技术标准:SL 461-2009[S].北京:中国水利水电出版社,2009Ministry of Water Resources of the People's Republic of China.Technical standard for comprehensive control of soil erosion and water loss in Karst region:SL 461-2009[S].Beijing:China Water&Power Press,2009
24袁先强,卫亚星.基于马尔科夫模型的盖州市土地利用动态变化研究[J].国土与自然资源研究,2017(1):19-22Yuan Xianqiang,Wei Yaxing.Study on the dynamic change of land use in Gaizhou City based on Markov model[J].Territory&Natural Resources Study,2017(1):19-22
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