典型小流域喀斯特石漠化演变特征及其关键表征因子与驱动因素
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摘要
中国西南喀斯特区域土地石漠化生态灾难已经严重制约了经济和社会的发展,而对石漠化进行治理,其空间分布信息的准确提取是必不可少的。针对现有石漠化信息提取技术存在不可能发生喀斯特石漠化(Impossible to develop karst rocky desertification,IKRD)范围提取不准确,关于石漠化的时空演变分析较少及其驱动因子、表征因子繁杂等问题,本文以典型喀斯特流域后寨河流域为研究对象,基于高精度影像识别提取IKRD范围,利用植被覆盖度、岩石裸露率对研究区域准确提取石漠化信息,揭示该区域不同石漠化等级的时空演变规律,同时探明石漠化分布与地表反照率、坡度的相关性。研究结果表明:(1)2005年至2010年后寨河流域石漠化程度整体在恶化,在后寨河流域西南部分和中部极少区域石漠化程度有所减轻,其余区域基本未发生变化,而在2010年至2015年后寨河流域石漠化程度大部分区域都呈减轻状态,而且石漠化程度减轻强度较大,在南侧大部分区域石漠化程度都呈减轻状态,且强度较大。(2)2005年至2010年后寨河流域石漠化程度减轻、未发生变化以及加重的面积分别为4.23 km~2、25.736 km~2、20.81 km~2,各部分面积占比分别为8.3%、 50.7%、41%。(3)2010年至2015年后寨河流域石漠化程度减轻、未发生变化以及加重的面积分别为31.87 km~2、16.57 km~2、6.85 km~2,各部分面积占比分别为57.6%、30%、 12.4%。(4)石漠化区域的坡度、地表反照率主要集中在2°—22°、0.12—0.21之间,而且随着坡度值的增加,潜在石漠化、重度石漠化面积占比增高,轻度石漠化和中度石漠化面积占比降低,随着地表反照率增加潜在石漠化、轻度石漠化面积占比增高,重度石漠化面积占比降低。综上所述,在2005年至2015年间,后寨河流域石漠化演变因贵州省2008年至2010年开始实施退耕还林还草等石漠化综合治理工程而呈现先恶化,后减轻的状态,同时通过对该区域石漠化分布与地表反照率、坡度的相关分析发现,地表反照率和坡度可以作为石漠化研究的辅助表征因子和驱动因子。
The ecological disaster of karst rocky desertification in the karst area of southwestern China has severely restricted the economic and social development. To control karst rocky desertification, accurate extraction of its spatial distribution information is indispensable. We consider the problems of inaccurate extraction of impossible to develop karst rocky desertification(IKRD) range, less analysis of the temporal and spatial evolution of karst rocky desertification, complicated driving factors and characterization factors, which exist in the existing technology of karst rocky desertification information extraction. This paper takes the typical karst basin-Houzhai River Basin as the research area. We extracted IKRD range based on high resolution image and using fractional vegetation coverage and rock exposure rate to accurately extract karst rocky desertification information from the study area. We want to reveal the evolution law of different karst rocky desertification levels in this area, and prove the correlation between the distribution of karst rocky desertification and the distribution of surface albedo and slope. The results showed that:(1) from 2005 to 2010, the karst rocky desertification of Houzhai River Basin as a whole has deteriorated. Few central parts of the southwestern has been alleviated, while the rest of the area has not changed. However, most areas of karst rocky desertification in the Houzhai River Basin have been alleviated from 2010 to 2015, and the degree of karst rocky desertification has been reduced significantly, which was more evident in most areas of the southern part in the Houzhai River, and the intensity was relatively high.(2) From 2005 to 2010, the area of karst rocky desertification with alleviated, unchanged, and aggravated were 4.23 km~2, 25.74 km~2 and 20.81 km~2, accounting for about 8.3%, 50.7%, and 41.0%, respectively.(3) From 2010 to 2015, the area of karst rocky desertification with alleviated, unchanged, and aggravated were 31.87 km~2, 16.57 km~2 and 6.85 km~2, accounting for about 57.6%, 30.0% and 12.4%, respectively.(4) The slope and surface albedo of karst rocky desertification area were mainly concentrated between 2°—22° and 0.12—0.21. With the increase of slope, the proportion of potential karst rocky desertification, and severe karst rocky desertification increases, the proportion of light karst rocky desertification and moderate karst rocky desertification decreased. With the increase of surface albedo, the proportion of potential karst rocky desertification and light karst rocky desertification increased, and the proportion of severe karst rocky desertification decreased. In summary, from 2005 to 2015, the evolution of karst rocky desertification in Houzhai River Basin was firstly deteriorated and then alleviated due to the implementation of comprehensive karst rocky desertification control projects such as returning farmland to forests and grasslands in Guizhou Province from 2008 to 2010. At the same time, the correlation analysis between the distribution of karst rocky desertification and the distribution of the surface albedo slope showed that the surface albedo and slope could be used as auxiliary characterization factors and driving factors to karst rocky desertification research.
The ecological disaster of karst rocky desertification in the karst area of southwestern China has severely restricted the economic and social development. To control karst rocky desertification, accurate extraction of its spatial distribution information is indispensable. We consider the problems of inaccurate extraction of impossible to develop karst rocky desertification(IKRD) range, less analysis of the temporal and spatial evolution of karst rocky desertification, complicated driving factors and characterization factors, which exist in the existing technology of karst rocky desertification information extraction. This paper takes the typical karst basin-Houzhai River Basin as the research area. We extracted IKRD range based on high resolution image and using fractional vegetation coverage and rock exposure rate to accurately extract karst rocky desertification information from the study area. We want to reveal the evolution law of different karst rocky desertification levels in this area, and prove the correlation between the distribution of karst rocky desertification and the distribution of surface albedo and slope. The results showed that:(1) from 2005 to 2010, the karst rocky desertification of Houzhai River Basin as a whole has deteriorated. Few central parts of the southwestern has been alleviated, while the rest of the area has not changed. However, most areas of karst rocky desertification in the Houzhai River Basin have been alleviated from 2010 to 2015, and the degree of karst rocky desertification has been reduced significantly, which was more evident in most areas of the southern part in the Houzhai River, and the intensity was relatively high.(2) From 2005 to 2010, the area of karst rocky desertification with alleviated, unchanged, and aggravated were 4.23 km~2, 25.74 km~2 and 20.81 km~2, accounting for about 8.3%, 50.7%, and 41.0%, respectively.(3) From 2010 to 2015, the area of karst rocky desertification with alleviated, unchanged, and aggravated were 31.87 km~2, 16.57 km~2 and 6.85 km~2, accounting for about 57.6%, 30.0% and 12.4%, respectively.(4) The slope and surface albedo of karst rocky desertification area were mainly concentrated between 2°—22° and 0.12—0.21. With the increase of slope, the proportion of potential karst rocky desertification, and severe karst rocky desertification increases, the proportion of light karst rocky desertification and moderate karst rocky desertification decreased. With the increase of surface albedo, the proportion of potential karst rocky desertification and light karst rocky desertification increased, and the proportion of severe karst rocky desertification decreased. In summary, from 2005 to 2015, the evolution of karst rocky desertification in Houzhai River Basin was firstly deteriorated and then alleviated due to the implementation of comprehensive karst rocky desertification control projects such as returning farmland to forests and grasslands in Guizhou Province from 2008 to 2010. At the same time, the correlation analysis between the distribution of karst rocky desertification and the distribution of the surface albedo slope showed that the surface albedo and slope could be used as auxiliary characterization factors and driving factors to karst rocky desertification research.
引文
[1] Yuan D X.Rock desertification in the subtropical Karst of South China.Zeitschrift für Geomorphologie N.F.,1997,108:81- 90.
[2] 王世杰.喀斯特石漠化概念演绎及其科学内涵的探讨.中国岩溶,2002,21(2):101- 105.
[3] 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.地理学报,2012,67(7):878- 888.
[4] 李阳兵,白晓永,邱兴春,周国富,兰安军,周绪,熊康宁.喀斯特石漠化与土地利用相关性研究.资源科学,2006,28(2):67- 73.
[5] 姚远.喀斯特石漠化遥感信息提取方法综述.测绘与空间地理信息,2016,39(12):64- 67.
[6] 陈起伟,兰安军,熊康宁,肖时珍,王俊,熊娟.基于遥感光谱特征的喀斯特石漠化信息提取.贵州师范大学学报:自然科学版,2003,21(4):82- 87.
[7] 吴风志,白开梅.基于OLI数据的云南禄劝县石漠化敏感性评价.测绘与空间地理信息,2017,40(11):83- 86,89- 89.
[8] 夏学齐,田庆久,杜凤兰.石漠化程度遥感信息提取方法研究.遥感学报,2006,10(4):469- 474.
[9] Huang Q H,Cai Y L.Mapping karst rock in Southwest China.Mountain Research and Development,2009,29(1):14- 20.
[10] 张晓伦,甘淑.基于NDRI像元二分模型的石漠化信息提取研究.新技术新工艺,2014,(1):72- 75.
[11] 岳跃民,张兵,王克林,李儒,刘波,张明阳.石漠化遥感评价因子提取研究.遥感学报,2011,15(04):722- 736.
[12] 涂杰楠,杨亮,梁丽新,童立强,郭兆成,刘婷,贺鹏.基于RapidEye遥感影像的比值密度分割法在岩溶石漠化调查中的应用——以云南鹤庆县为例.中国岩溶,2015,34(3):298- 307.
[13] 朱大运,熊康宁,肖华,蓝家程.基于植被指数的GF- 1与Landsat-OLI石漠化识别能力对比评价.自然资源学报,2016,31(11):1949- 1957.
[14] 岳跃民,王克林,张兵,刘波,陈洪松,张明阳.喀斯特石漠化信息遥感提取的不确定性.地球科学进展,2011,26(3):266- 274.
[15] 童立强.西南岩溶石山地区石漠化信息自动提取技术研究.国土资源遥感,2003,(4):35- 38.
[16] Li Y B,Xie J,Luo G J,Yang H,Wang S J.The evolution of a karst rocky desertification land ecosystem and its driving forces in the Houzhaihe Area,China.Open Journal of Ecology,2015,5(10):501- 512.
[17] Xu E Q,Zhang H Q.A spatial simulation model for karst rocky desertification combining top-down and bottom-up approaches.Land Degradation & Development,2018,29(10):3390- 3404.
[18] 涂杰楠,童立强,王珊珊,郭兆成,贺鹏,马祖陆.南洞地下河流域南部岩溶石漠化空间分布特征分析.中国岩溶,2016,35(5):566- 573.
[19] 杨奇勇,蒋忠诚,马祖陆,罗为群,尹辉,喻琦雯,李文军.基于地统计学和遥感的岩溶区石漠化空间变异特征.农业工程学报,2012,28(4):243- 247.
[20] Sheng M Y,Xiong K N,Wang L J,Li X N,Li R,Tian X J.Response of soil physical and chemical properties to Rocky desertification succession in South China Karst.Carbonates and Evaporites,2018,33(1):15- 28.
[21] 薛显武,陈喜,张志才,秦年秀.基于地形因子特征值的喀斯特流域地貌类型判别.中国岩溶,2009,28(2):175- 180.
[22] 朱钟正,陈玉福,朱文泉,郑周涛.适用于多目标遥感自动解译的最佳专题指数筛选.遥感技术与应用,2017,32(3):564- 574.
[23] 查勇,倪绍祥,杨山.一种利用TM图像自动提取城镇用地信息的有效方法.遥感学报,2003,7(1):37- 40.
[24] Xu H.A new index for delineating built-up land features in satellite imagery.International Journal of Remote Sensing,2008,29(14):4269- 4276.
[25] 李丹,梁欣,孙丽娜,刘洋,宋以健.基于多时相遥感影像的耕地利用信息提取研究.现代测绘,2018,41(4):21- 24.
[26] 毕海芸,王思远,曾江源,赵岩,王辉,殷慧.基于TM影像的几种常用水体提取方法的比较和分析.遥感信息,2012,27(5):77- 82.
[27] 丁凤.基于新型水体指数(NWI)进行水体信息提取的实验研究.测绘科学,2009,34(4):155- 157.
[28] 吕丹红,姜琦刚,王德军,葛锦涛.Landsat数据的植被覆盖估算和景观格局分析.测绘科学,2018,43(11):157- 164.
[29] Bai X Y,Wang S J,Xiong K N.Assessing spatial-temporal evolution processes of Karst rocky desertification land:indications for restoration strategies.Land Degradation & Development,2013,24(1):47- 56.
[30] Liang S L.Narrowband to broadband conversions of land surface albedo I:algorithms.Remote Sensing of Environment,2001,76(2):213- 238.
[2] 王世杰.喀斯特石漠化概念演绎及其科学内涵的探讨.中国岩溶,2002,21(2):101- 105.
[3] 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.地理学报,2012,67(7):878- 888.
[4] 李阳兵,白晓永,邱兴春,周国富,兰安军,周绪,熊康宁.喀斯特石漠化与土地利用相关性研究.资源科学,2006,28(2):67- 73.
[5] 姚远.喀斯特石漠化遥感信息提取方法综述.测绘与空间地理信息,2016,39(12):64- 67.
[6] 陈起伟,兰安军,熊康宁,肖时珍,王俊,熊娟.基于遥感光谱特征的喀斯特石漠化信息提取.贵州师范大学学报:自然科学版,2003,21(4):82- 87.
[7] 吴风志,白开梅.基于OLI数据的云南禄劝县石漠化敏感性评价.测绘与空间地理信息,2017,40(11):83- 86,89- 89.
[8] 夏学齐,田庆久,杜凤兰.石漠化程度遥感信息提取方法研究.遥感学报,2006,10(4):469- 474.
[9] Huang Q H,Cai Y L.Mapping karst rock in Southwest China.Mountain Research and Development,2009,29(1):14- 20.
[10] 张晓伦,甘淑.基于NDRI像元二分模型的石漠化信息提取研究.新技术新工艺,2014,(1):72- 75.
[11] 岳跃民,张兵,王克林,李儒,刘波,张明阳.石漠化遥感评价因子提取研究.遥感学报,2011,15(04):722- 736.
[12] 涂杰楠,杨亮,梁丽新,童立强,郭兆成,刘婷,贺鹏.基于RapidEye遥感影像的比值密度分割法在岩溶石漠化调查中的应用——以云南鹤庆县为例.中国岩溶,2015,34(3):298- 307.
[13] 朱大运,熊康宁,肖华,蓝家程.基于植被指数的GF- 1与Landsat-OLI石漠化识别能力对比评价.自然资源学报,2016,31(11):1949- 1957.
[14] 岳跃民,王克林,张兵,刘波,陈洪松,张明阳.喀斯特石漠化信息遥感提取的不确定性.地球科学进展,2011,26(3):266- 274.
[15] 童立强.西南岩溶石山地区石漠化信息自动提取技术研究.国土资源遥感,2003,(4):35- 38.
[16] Li Y B,Xie J,Luo G J,Yang H,Wang S J.The evolution of a karst rocky desertification land ecosystem and its driving forces in the Houzhaihe Area,China.Open Journal of Ecology,2015,5(10):501- 512.
[17] Xu E Q,Zhang H Q.A spatial simulation model for karst rocky desertification combining top-down and bottom-up approaches.Land Degradation & Development,2018,29(10):3390- 3404.
[18] 涂杰楠,童立强,王珊珊,郭兆成,贺鹏,马祖陆.南洞地下河流域南部岩溶石漠化空间分布特征分析.中国岩溶,2016,35(5):566- 573.
[19] 杨奇勇,蒋忠诚,马祖陆,罗为群,尹辉,喻琦雯,李文军.基于地统计学和遥感的岩溶区石漠化空间变异特征.农业工程学报,2012,28(4):243- 247.
[20] Sheng M Y,Xiong K N,Wang L J,Li X N,Li R,Tian X J.Response of soil physical and chemical properties to Rocky desertification succession in South China Karst.Carbonates and Evaporites,2018,33(1):15- 28.
[21] 薛显武,陈喜,张志才,秦年秀.基于地形因子特征值的喀斯特流域地貌类型判别.中国岩溶,2009,28(2):175- 180.
[22] 朱钟正,陈玉福,朱文泉,郑周涛.适用于多目标遥感自动解译的最佳专题指数筛选.遥感技术与应用,2017,32(3):564- 574.
[23] 查勇,倪绍祥,杨山.一种利用TM图像自动提取城镇用地信息的有效方法.遥感学报,2003,7(1):37- 40.
[24] Xu H.A new index for delineating built-up land features in satellite imagery.International Journal of Remote Sensing,2008,29(14):4269- 4276.
[25] 李丹,梁欣,孙丽娜,刘洋,宋以健.基于多时相遥感影像的耕地利用信息提取研究.现代测绘,2018,41(4):21- 24.
[26] 毕海芸,王思远,曾江源,赵岩,王辉,殷慧.基于TM影像的几种常用水体提取方法的比较和分析.遥感信息,2012,27(5):77- 82.
[27] 丁凤.基于新型水体指数(NWI)进行水体信息提取的实验研究.测绘科学,2009,34(4):155- 157.
[28] 吕丹红,姜琦刚,王德军,葛锦涛.Landsat数据的植被覆盖估算和景观格局分析.测绘科学,2018,43(11):157- 164.
[29] Bai X Y,Wang S J,Xiong K N.Assessing spatial-temporal evolution processes of Karst rocky desertification land:indications for restoration strategies.Land Degradation & Development,2013,24(1):47- 56.
[30] Liang S L.Narrowband to broadband conversions of land surface albedo I:algorithms.Remote Sensing of Environment,2001,76(2):213- 238.
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