基于SPCA和遥感指数的干旱内陆河流域生态脆弱性时空演变及动因分析——以石羊河流域为例
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摘要
以石羊河流域为研究区,基于干旱内陆河流域生态特征和遥感数据快速、客观、大面积观测的特点,采用遥感模型计算湿度、绿度、干度和热度等指标,并构建石羊河流域生态脆弱性评价指标体系,在此基础上运用空间主成分分析法(SPCA)对石羊河流域2000和2016年生态脆弱性时空演变及动因进行了分析。研究结果表明:(1)从各遥感指数空间分布来看,湿度和绿度指标均值在17年间呈增长趋势,证明该流域水源涵养能力变好,植被覆盖率变大;干度指标均值有所下降,表明该流域地表裸露程度有所降低;而与植被和水资源关系密切的地表温度均值呈逐年上升趋势,说明该流域水热平衡差异进一步增加,对未来生态脆弱性影响显著;(2)从全流域生态脆弱性时空演变特征来看,该流域主要以强度和中度脆弱为主,17年间生态脆弱性整体上呈缓慢降低趋势;(3)从不同的海拔生态脆弱性分布来看,中山区(1000—2000m)最高,高中山区(2000—3000m)次之,高山区(>3000m)最低,17年间中山区生态脆弱性有所下降,而高中山区与高山区却呈上升的趋势;(4)从不同的行政区划生态脆弱性来看,金川区、凉州区、永昌县、民勤县和古浪县整体上处于中度和强度脆弱水平,而天祝县和肃南县处于轻度和微度脆弱水平;(5)从生态脆弱性的演变动因来看,4个指标对石羊河流域生态脆弱性影响均为显著。2000年生态脆弱性的主导影响因子依次为热度>湿度>绿度>干度,而2016年为热度>干度>绿度>湿度。总的来看,石羊河流域生态脆弱程度近年来有所降低,但综合治理工作仍任重道远。本文的遥感方法和分析思路对该流域生态脆弱性保护及治理提供一定的理论基础和决策依据。
With Shiyang River Basin as the study area, this paper used a remote sensing model to calculate indicators such as greenness, dryness, wetness and heat to construct ecological vulnerability assessment indicators based on the ecological characteristics of Arid Inland River Basin and the characteristics of rapid, objective, and large-area observational remote sensing, which was analyzed the spatio-temporal evolution and motivation of ecological vulnerability by Spatial Principal Component Analysis(SPCA) in 2000 and 2016 in Shiyang River Basin. The research showed that(1) From the spatial distribution of each remote sensing index, the mean of wetness and greenness showed an increasing trend in 17 years, which proved that the water conservation capacity was better and the vegetation coverage was larger in the basin. The mean of dryness had decreased, indicating that the degree of surface exposure of the basin had reduced. The mean of surface temperature closely related to vegetation and water resources was increasing year by year, which indicated that the differences of water and heat balance were further increasing in the basin, which had a significant effect on ecological vulnerability in the future.(2) From the spatio-temporal evolution of ecological vulnerability in the whole basin, the basin was mainly dominated by strong vulnerability and moderate vulnerability, and the ecological vulnerability had been slowly decreasing in 17 years.(3) From the ecological vulnerability distribution at different elevations, the highest in medium mountain area(1000—2000 m), the medium in high-medium mountain area(2000—3000 m), and the lowest in high mountain area(>3000 m). During 17 years of change, the ecological vulnerability of medium mountain area had decreased, while the high-medium mountain area and high mountain area were on the rise.(4) From the ecological vulnerability of different administrative divisions, Jinchuan, Liangzhou, Yongchang, Minqin and Gulang as a whole were at the level of moderate vulnerability and strong vulnerability, while Tianzhu and Sunan were at the level of slight vulnerability and light vulnerability.(5) From the motivation of ecological vulnerability, the four indicators had significant effects on the ecological vulnerability of Shiyang River Basin. The main influencing factors of ecological vulnerability were heat > wetness > greenness > dryness in 2000, and the main influencing factors of ecological vulnerability were heat > dryness > greenness > wetness in 2016. Overall, the ecological vulnerability of Shiyang River Basin had decreased in recent years, however the comprehensive management still has a long way to go. The remote sensing methods and analysis ideas of this paper provided theoretical basis and decision-making foundation for the ecological vulnerability protection and management in this basin.
With Shiyang River Basin as the study area, this paper used a remote sensing model to calculate indicators such as greenness, dryness, wetness and heat to construct ecological vulnerability assessment indicators based on the ecological characteristics of Arid Inland River Basin and the characteristics of rapid, objective, and large-area observational remote sensing, which was analyzed the spatio-temporal evolution and motivation of ecological vulnerability by Spatial Principal Component Analysis(SPCA) in 2000 and 2016 in Shiyang River Basin. The research showed that(1) From the spatial distribution of each remote sensing index, the mean of wetness and greenness showed an increasing trend in 17 years, which proved that the water conservation capacity was better and the vegetation coverage was larger in the basin. The mean of dryness had decreased, indicating that the degree of surface exposure of the basin had reduced. The mean of surface temperature closely related to vegetation and water resources was increasing year by year, which indicated that the differences of water and heat balance were further increasing in the basin, which had a significant effect on ecological vulnerability in the future.(2) From the spatio-temporal evolution of ecological vulnerability in the whole basin, the basin was mainly dominated by strong vulnerability and moderate vulnerability, and the ecological vulnerability had been slowly decreasing in 17 years.(3) From the ecological vulnerability distribution at different elevations, the highest in medium mountain area(1000—2000 m), the medium in high-medium mountain area(2000—3000 m), and the lowest in high mountain area(>3000 m). During 17 years of change, the ecological vulnerability of medium mountain area had decreased, while the high-medium mountain area and high mountain area were on the rise.(4) From the ecological vulnerability of different administrative divisions, Jinchuan, Liangzhou, Yongchang, Minqin and Gulang as a whole were at the level of moderate vulnerability and strong vulnerability, while Tianzhu and Sunan were at the level of slight vulnerability and light vulnerability.(5) From the motivation of ecological vulnerability, the four indicators had significant effects on the ecological vulnerability of Shiyang River Basin. The main influencing factors of ecological vulnerability were heat > wetness > greenness > dryness in 2000, and the main influencing factors of ecological vulnerability were heat > dryness > greenness > wetness in 2016. Overall, the ecological vulnerability of Shiyang River Basin had decreased in recent years, however the comprehensive management still has a long way to go. The remote sensing methods and analysis ideas of this paper provided theoretical basis and decision-making foundation for the ecological vulnerability protection and management in this basin.
引文
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[24] Zou T H,Kunihiko Yoshino.Environment vulnerability evaluation using a spatial components Approach in the Daxing′anling region,China.Ecological Indicators,2017,78:405- 415.
[25] 邓华,邵景安,王金亮,高明,魏朝富.多因素耦合下三峡库区土地利用未来情景模拟.地理学报,2016,71(11):1979- 1997.
[26] 陈志明.论中国地貌图的研制原则、内容与方法——以1:4000000全国地貌图为例.地理学报,1993,48(2):105- 113.
[27] 石三娥,魏伟,杨东,胡鑫,周俊菊,张强.基于RSEDI的石羊河流域绿洲区生态环境质量时空演变.生态学杂志,2018,37(4):1152- 1163.
[28] Sun W,Zhang L,Zhang L,Lai Y M.A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification.IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2016,9(9):4374- 4388.
[29] 王士远,张学霞,朱彤,杨维,赵静瑶.长白山自然保护区生态环境质量的遥感评价.地理科学进展,2016,35(10):1269- 1278.
[30] 潘竟虎,刘晓.基于空间主成分和最小累积阻力模型的内陆河景观生态安全评价与格局优化——以张掖市甘州区为例.应用生态学报,2015,26(10):3126- 3136.
[31] 王劲峰,徐成东.地理探测器:原理与展望.地理学报,2017,72(1):116- 134.
[32] 武鹏,李同昇,李卫民.县域农村贫困化空间分异及其影响因素——以陕西山阳县为例.地理研究,2018,37(3):593- 606.
[33] 叶妍君,齐清文,姜莉莉,张岸.基于地理探测器的黑龙江垦区农场粮食产量影响因素分析.地理研究,2018,37(1):171- 182.
[2] 孟岩,赵庚星.基于卫星遥感数据的河口区生态环境状况评价——以黄河三角洲垦利县为例.中国环境科学,2009,29(2):163- 167.
[3] Song G,Li Z,Yang Y,Semakula HM,Zhang S.Assessment of ecological vulnerability and decision-making application for prioritizing roadside ecological restoration:A method combining geographic information system,Delphi survey and Monte Carlo simulation.Ecological Indicators,2015,52:57- 65.
[4] 徐广才,康慕谊,贺丽娜,李亚飞,陈雅如.生态脆弱性及其研究进展.生态学报,2009,29(5):2578- 2588.
[5] 陈群利,左太安,孟天友,张凤太.基于SPA的毕节水土流失区生态脆弱性评价.中国水土保持,2010,12:53- 56.
[6] 徐庆勇,黄玫,李雷,唐磊,王军邦.晋北地区生态环境脆弱性的GIS综合评价.地球信息科学学报,2013,15(5):705- 711.
[7] 王志杰,苏嫄.南水北调中线汉中市水源地生态脆弱性评价与特征分析.生态学报,2018,38(2):432- 442.
[8] 姚雄,余坤勇,刘健,杨素萍,何平,邓洋波,俞欣妍,陈樟昊.南方水土流失严重区的生态脆弱性时空演变.应用生态学报,2016,27(3):735- 745.
[9] 常兆丰,韩富贵.石羊河下游沙漠化的自然因素和人为因素及其位移.干旱区地理,2005,28(2):150- 155.
[10] 肖笃宁,李小玉,宋冬梅.石羊河尾闾绿洲的景观变化与生态恢复对策.生态学报,2005,25(10):2477- 2483.
[11] 徐涵秋.城市生态指数的创建及其应用.生态学报,2013,33(24):7853- 7862.
[12] 魏伟,石培基,冯海春,王旭峰.干旱内陆河流域人居环境适宜性评价——以石羊河流域为例.自然资源学报,2012,27(11):1940- 1950.
[13] 李芳芳,贾永红.一种基于TM影像的湿地信息提取方法及其变化检测.测绘科学,2008,33(2):147- 149.
[14] 汪燕,董张玉.基于缨帽变换影像融合的遥感影像植被信息提取.地理空间信息,2013,11(4):85- 86.
[15] Baig M H A,Zhang L F,Shuai T,Tong Q.Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance.Remote Sensing of Environment,2014,5(5):423- 431.
[16] Crist E P.A TM tasseled cap equivalent transformation for reflectance factor data.Remote Sensing of Environment,17(3):301- 306
[17] Goward S N,Xue Y K,Czajkowski K P.Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements:An exploration with the simplified simple biosphere model.Remote Sensing Letters,2002,79(2):225- 242.
[18] Xu H Q.A new index for delineating built-up land features in satellite imagery.International Journal of Remote Sensing,2008,29(14):4269- 4276.
[19] Rikimaru A,Roy P S,Miyatake S.Tropical forest cover density mapping.Tropical Ecology,2002,43(1):39- 47.
[20] NASA.Landsat 7 Science Data Users Handbook:General Interest Publication.[2018-01-06].http://landsat.usgs.gov.
[21] NASA.Landsat 8 Data Users Handbook.[2018-01-06].http://landsathandbook.gsfc.nasa.gov.
[22] 覃志豪,李文娟,徐斌,陈仲新,刘佳.陆地卫星TM6波段范围内地表比辐射率的估计.国土资源遥感,2004,16(3):28- 32.
[23] 徐涵秋.新型Landsat8卫星影像的反射率和地表温度反演.地球物理学报,2015,58(3):741- 747.
[24] Zou T H,Kunihiko Yoshino.Environment vulnerability evaluation using a spatial components Approach in the Daxing′anling region,China.Ecological Indicators,2017,78:405- 415.
[25] 邓华,邵景安,王金亮,高明,魏朝富.多因素耦合下三峡库区土地利用未来情景模拟.地理学报,2016,71(11):1979- 1997.
[26] 陈志明.论中国地貌图的研制原则、内容与方法——以1:4000000全国地貌图为例.地理学报,1993,48(2):105- 113.
[27] 石三娥,魏伟,杨东,胡鑫,周俊菊,张强.基于RSEDI的石羊河流域绿洲区生态环境质量时空演变.生态学杂志,2018,37(4):1152- 1163.
[28] Sun W,Zhang L,Zhang L,Lai Y M.A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification.IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2016,9(9):4374- 4388.
[29] 王士远,张学霞,朱彤,杨维,赵静瑶.长白山自然保护区生态环境质量的遥感评价.地理科学进展,2016,35(10):1269- 1278.
[30] 潘竟虎,刘晓.基于空间主成分和最小累积阻力模型的内陆河景观生态安全评价与格局优化——以张掖市甘州区为例.应用生态学报,2015,26(10):3126- 3136.
[31] 王劲峰,徐成东.地理探测器:原理与展望.地理学报,2017,72(1):116- 134.
[32] 武鹏,李同昇,李卫民.县域农村贫困化空间分异及其影响因素——以陕西山阳县为例.地理研究,2018,37(3):593- 606.
[33] 叶妍君,齐清文,姜莉莉,张岸.基于地理探测器的黑龙江垦区农场粮食产量影响因素分析.地理研究,2018,37(1):171- 182.