基于遥感光谱的干旱区土地退化评价体系构建
|
摘要
干旱区土地退化(荒漠化)作为全球面临生态环境挑战之一,对粮食安全、环境质量和区域自然资源管理至关重要。土地退化本质是人与自然因素协同作用下土地利用/覆被类型、数量、结构以及功能的改变而引起的生态服务价值降低,核心是土壤和植被的退化。一方面,人与自然共同作用下的土地利用覆被可以表征土地退化状态,另一方面植被-土壤生境时间序列相互作用过程进一步辅助土地退化过程诊断。因此,该文首先从覆被结构、退化类型和退化程度3个层次建立干旱区土地退化状态评价体系。其次,采用GF-1/WFV时间序列遥感影像,基于多端元光谱混合分解模型建立土地利用/覆被精细分类量化表征下垫面质量属性,并进一步利用植被-生境组分互动特征参数进行功能量化,综合评价民勤2015年退化类型和退化程度。最后,结合地面立地景观照片以及采样点实测数据,对土地退化状态评价结果进行绝对定标和交叉验证。结果表明:遥感评价识别土地退化类型和程度的能力分别为87.5%和78.7%。对于民勤旱地系统,沙化过程、沙-盐化过程是主要的土地退化过程,轻度沙化、中度沙化为主导退化程度。该方法为宽波段遥感国产高分1号卫星在旱地系统土地退化状态信息提取和深入应用提供科学依据和实证研究。
Land degradation(desertification) in arid region, as one of the global ecological challenges, is crucial to food security, environmental quality and regional natural resource management. Fortunately, the development of remote sensing technology greatly improves the ability of land degradation information extraction and assessment, which can effectively reveal conditions of land degradation in arid region and provide the scientific basis for land degradation trend prediction and formulating the corresponding preventing measures. In fact, the essence of land degradation is the decrease of ecological service following the change of type, quantity, structure and function of land use/cover which emerges from interactions of the coupled human-environment(H-E) factors at multiple spatio-temporal scales, leading the difficulty of classification and assessment. This prompts an attempt to reduce the complexity of the assessment by identifying a limited suite number of processes and variables which makes the problem tractable at particular scale like the interaction process of soil and vegetation, the core of the degradation. Based the standard spectral endmember space of GF-1 satellite, this paper innovatively established three levels evaluation system of land degradation state in arid areas, including land use/cover structure, degradation type and degradation degree. Secondly, we realized dryland multi-temporary linear spectral mixture analysis. The remote sensing images of GF-1/WFV endmember fractions time series were applied to characterize the quality attributes of the underlying surface and organize the classification knowledge, and then to complete the fine land cover/use classification of Minqin county in 2015. Based on land use/cover mapping in arid region, the time series EMs including vegetation(GV), sand land(SL), saline land(SA), dark surface(DA) were combined to obtain and express the variables reflecting the ecosystem function, and then, were organized with decision tree(DT) for degradation state mapping. Finally, the results of land degradation state were assessed using photos of the site landscape and the measured data of the sampling sites. The results showed that: 1) The MESMA method could decompose four stable EMs types, which were consistent with the physical components of the surface cover in study area. The model could effectively simulate the spectral information of study area. More importantly, it solved the problem that the bands number of GF-1 WFV was few and the information mining was insufficient. And which provides a basis for establishing knowledge for decision tree classification and assessment. 2) The time series EMs in standard spectral endmember space could perfectly highlight the temporal interaction characteristics details between vegetation types and habitat and the ability of remote sensing to identify land degradation types and degree, with corresponding accuracy of 87.5% and 78.7%, respectively. 3) For Minqin, a typical dryland system, the sandification process and the sand-salinization process were the dominant land degradation processes. Light sandification and moderate sandification were the dominant degradation degrees. Through the relative evaluation results of land degradation types and degree, it helps divide the key control areas, ecological conservation areas and the use of decompression areas. The research result contributes to the sustainable development of regional management and control of ecological fragile areas, land space development and ecological environment protection. General remote-sensing technical framework for land degradation assessment was confirmed that it have the potential to be applied to the study of land degradation assessment in arid regions across time and regions.
Land degradation(desertification) in arid region, as one of the global ecological challenges, is crucial to food security, environmental quality and regional natural resource management. Fortunately, the development of remote sensing technology greatly improves the ability of land degradation information extraction and assessment, which can effectively reveal conditions of land degradation in arid region and provide the scientific basis for land degradation trend prediction and formulating the corresponding preventing measures. In fact, the essence of land degradation is the decrease of ecological service following the change of type, quantity, structure and function of land use/cover which emerges from interactions of the coupled human-environment(H-E) factors at multiple spatio-temporal scales, leading the difficulty of classification and assessment. This prompts an attempt to reduce the complexity of the assessment by identifying a limited suite number of processes and variables which makes the problem tractable at particular scale like the interaction process of soil and vegetation, the core of the degradation. Based the standard spectral endmember space of GF-1 satellite, this paper innovatively established three levels evaluation system of land degradation state in arid areas, including land use/cover structure, degradation type and degradation degree. Secondly, we realized dryland multi-temporary linear spectral mixture analysis. The remote sensing images of GF-1/WFV endmember fractions time series were applied to characterize the quality attributes of the underlying surface and organize the classification knowledge, and then to complete the fine land cover/use classification of Minqin county in 2015. Based on land use/cover mapping in arid region, the time series EMs including vegetation(GV), sand land(SL), saline land(SA), dark surface(DA) were combined to obtain and express the variables reflecting the ecosystem function, and then, were organized with decision tree(DT) for degradation state mapping. Finally, the results of land degradation state were assessed using photos of the site landscape and the measured data of the sampling sites. The results showed that: 1) The MESMA method could decompose four stable EMs types, which were consistent with the physical components of the surface cover in study area. The model could effectively simulate the spectral information of study area. More importantly, it solved the problem that the bands number of GF-1 WFV was few and the information mining was insufficient. And which provides a basis for establishing knowledge for decision tree classification and assessment. 2) The time series EMs in standard spectral endmember space could perfectly highlight the temporal interaction characteristics details between vegetation types and habitat and the ability of remote sensing to identify land degradation types and degree, with corresponding accuracy of 87.5% and 78.7%, respectively. 3) For Minqin, a typical dryland system, the sandification process and the sand-salinization process were the dominant land degradation processes. Light sandification and moderate sandification were the dominant degradation degrees. Through the relative evaluation results of land degradation types and degree, it helps divide the key control areas, ecological conservation areas and the use of decompression areas. The research result contributes to the sustainable development of regional management and control of ecological fragile areas, land space development and ecological environment protection. General remote-sensing technical framework for land degradation assessment was confirmed that it have the potential to be applied to the study of land degradation assessment in arid regions across time and regions.
引文
[1]MEA.Ecosystems and Human Well-Being:Desertification Synthesis[R].Washington,DC,2005.
[2]UNCCD.Report of the conference of the parties on its thirteenth session,held in Ordos,China,from 6 to 16September 2017[R].China:United Nations Convention to Combat Desertification,2017.
[3]国家林业局.中国荒漠化和沙化状况公报[R].北京,2015.
[4]UNCCD.A sustainable development goal for Rio+20[R].Brazi:United Nations Convention to Combat Desertification,2012.
[5]Hill J,Stellmes M,Udelhoven T,R?der A,et al.Mediterranean desertification and land degradation:Mapping related land use change syndromes based on satellite observations[J].Global&Planetary Change,2008,64(3/4):146-157.
[6]乔平林,张继贤,林宗坚.基于神经网络的土地荒漠化信息提取方法研究[J].测绘学报,2004,33(1):58-62.Qiao Pinglin,Zhang Jixian,Lin Zongjian.An artificial neural network method for the information of desertification extraction[J].Acta Geodaetica et Cartographica Sinica,2004,33(1):58-62.(in Chinese with English abstract)
[7]刘爱霞,王长耀,王静,等.基于MODIS和NOAA/AVHRR的荒漠化遥感监测方法[J].农业工程学报,2007,23(10):145-150.Liu Aixia,Wang Changyao,Wang Jing,et al.Method for remote sensing monitoring of desertification based on MODIS and NOAA/AVHRR data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(10):145-150.(in Chinese with English abstract)
[8]王大鹏,王周龙,李德一,等.综合非光谱信息的荒漠化土地CART分类[J].遥感学报,2007,11(4):487-492.Wang Dapeng,Wang Zhoulong,Li Deyi,et al.The extraction of desertification information using cart under knowledge guide[J].Journal of Remote Sensing,2007,11(4):487-492.(in Chinese with English abstract)
[9]Snyman H A.Dynamics and sustainable utilization of rangeland ecosystems in arid and semi-arid climates of southern Africa[J].Journal of Arid Environments,1998,39(4):645-666.
[10]Illius A W,O'Connor T G.On the relevance of nonequilibrium concepts to aridand semiarid grazing systems[J].Ecological Applications,1999,9(3):798-813.
[11]O'Connor T G,Haines L M,Snyman H A.Influence of precipitation and species composition on phytomass of a semi-arid African grassland[J].Journal of Ecology,2001,89(5):850-860.
[12]Chikhaoui M,Bonn F,Bokoye A I,A Merzouk.A spectral index for land degradation mapping using ASTER data:Application to a semi-arid Mediterranean catchment[J].International Journal of Applied Earth Observations&Geoinformation,2005,7(2):140-153.
[13]王莉雯,牛铮,卫亚星.基于MODIS NDVI的新疆土地覆盖类型划分与潜在荒漠化区域探测[J].干旱区地理,2007,30(4):573-578.Wang Liwen,Niu Zheng,Wei Yaxing.Land cover type classification and potential desertification area detecting in Xinjiang based on MODIS NDVI imagery[J].Arid Land Geography,2007,30(4):573-578.(in Chinese with English abstract)
[14]许宁,郭旭东,田淑芳,等.基于遥感和GIS的土地利用分类方法及其在土地退化程度分析中的应用:以陕西横山雷龙湾地区为例[J].生态学报,2008,28(11):5410-5417.Xu Ning,Guo Xudong,Tian Shufang,et al.Remote sensing and GIS based comparison of land-use classification methods and data mining for degree of land degradation:A case in Leilongwan Area,Hengshan County,Shaanxi province[J].Acta Ecological Sinica,2008,28(11):5410-5417.(in Chinese with English abstract)
[15]Sun D,Dawson R,Li H,et al.A landscape connectivity index for assessing desertification:A case study of Minqin County,China[J].Landscape Ecology,2007,22(4):531-543.
[16]Stafford Smith D M,Reynolds J F.Desertification:A new paradigm for an old problem[C]//Reynolds J F,Stafford Smith D M.Global Desertification:Do Humans Cause Deserts?Berlin:Dahlem University Press,2002:403-424.
[17]Geist H J,Lambin E F.Dynamic causal patterns of desertification[J].Bioscience 2004,54:817-829.
[18]Reynolds J F,Grainger A,Stafford Smith M,et al.Scientific concepts for anintegrated analysis of desertification[J].Land Degradation and Development,2011,22(2):166-183.
[19]Lambin E,Geist H.Land Use and Land Cover Change:Local Processes,Global Impacts[M].New York:Springer,2006.
[20]Sun D,Liu N.Coupling spectral unmixing and multiseasonal remote sensing for temperate dryland land-use/land-cover mapping in Minqin County,China[J].International Journal of Remote Sensing,2015,36(14):3636-3658.
[21]张熙川,赵英时.应用线性光谱混合模型快速评价土地退化的方法研究[J].中国科学院大学学报,1999(2):169-176.Zhang Xichuan,Zhao Yingshi.Application of liner spectral mixture model to rapid assessment of land degradation in semiarid area[J].Journal of Graduate School,Academia Sinica,1999(2):169-176.(in Chinese with English abstract)
[22]卢远,林年丰,汤洁,等.松嫩平原西部土地退化的遥感动态监测研究:以吉林省通榆县为例[J].地理与地理信息科学,2003,19(2):24-27.Lu Yuan,Lin Nianfeng,Tang Jie,et al.Remote sensing dynamic monitoring and research of land degradation in the West of Songnen Plain:Taking Tongyu County in Jilin Province as an example[J].Geography and Geo-information Science,2003,19(2):24-27.(in Chinese with English abstract)
[23]Sun D F.Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County,China[J].International Journal of Applied Earth Observation and Geoinformation,2015,41:34-45.
[24]姜宛贝,孙丹峰,孙强强.基于光谱混合分解的民勤生态治理效果评价[J].中国沙漠,2018(1):210-218.Jiang Wanbei,Sun Danfeng,Sun Qiangqiang.Ecological restoration assessment in Minqin based on spectral mixture analysis[J].Journal of Desert Research,2018(1):210-218.(in Chinese with English abstract)
[25]Small C,Milesi C.Multi-scale standardized spectral mixture models[J].Remote Sensing of Environment,2013,136(5):442-454.
[26]Smith M O,Ustin S L,Adams J B,et al.Vegetation in deserts:I.A regional measure of abundance from multispectral images[J].Remote Sensing of Environment,1990,31(1):1-26.
[27]Adams J B,Sabol D E,Kapos V,et al.Classification of multispectral images based on fractions of endmembers:Application to land-cover change in the Brazilian Amazon[J].Remote Sensing of Environment,1995,52:137-154.
[28]Roberts D A,Smith M O,Adams J B.Green vegetation nonphotosynthetic vegetation,and soils in AVIRIS data[J].Remote Sensing of Environment,1993,44:255-269.
[29]刘娜,李红,孙丹峰,等.绿洲景观多季相特征的线性光谱混合分解方法研究[J].农业工程学报,2010,26(8):174-182.Liu Na,Li Hong,Sun Danfeng,et al.Extracting oasis landscape multi-seasonal characteristics with linear spectral mixture analysis[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(8):174-182.(in Chinese with English abstract)
[30]姜宛贝,孙强强,曲葳,等.基于多季相光谱混合分解和决策树的干旱区土地利用分类[J].农业工程学报,2016,32(19):1-8.Jiang Wanbei,Sun Qiangqiang,Qu Wei,et al.Land use classification in arid region based on multi-seasonal linear spectral mixture analysis and decision tree method[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(19):1-8.(in Chinese with English abstract)
[31]Sun D,Zhang P,Sun Q,et al.A dryland cover state mapping using catastrophe model in a spectral endmember space of OLI:A case study in Minqin,China[J].International Journal of Remote Sensing,2019,14(40):5673-5694.
[32]Sun D,Jiang W.Agricultural soil alkalinity and salinity modeling in the cropping season in a spectral endmember space of TM in temperate drylands,Minqin,China[J].Remote Sensing,2016,8(9):714-731.
[33]Sun Q,Zhang P,Sun D,et al.Desert vegetation-habitat complexes mapping using Gaofen-1 WFV(wide field of view)time series images in Minqin County,China[J].International Journal of Applied Earth Observation and Geoinformation,2018,73:522-534.
[2]UNCCD.Report of the conference of the parties on its thirteenth session,held in Ordos,China,from 6 to 16September 2017[R].China:United Nations Convention to Combat Desertification,2017.
[3]国家林业局.中国荒漠化和沙化状况公报[R].北京,2015.
[4]UNCCD.A sustainable development goal for Rio+20[R].Brazi:United Nations Convention to Combat Desertification,2012.
[5]Hill J,Stellmes M,Udelhoven T,R?der A,et al.Mediterranean desertification and land degradation:Mapping related land use change syndromes based on satellite observations[J].Global&Planetary Change,2008,64(3/4):146-157.
[6]乔平林,张继贤,林宗坚.基于神经网络的土地荒漠化信息提取方法研究[J].测绘学报,2004,33(1):58-62.Qiao Pinglin,Zhang Jixian,Lin Zongjian.An artificial neural network method for the information of desertification extraction[J].Acta Geodaetica et Cartographica Sinica,2004,33(1):58-62.(in Chinese with English abstract)
[7]刘爱霞,王长耀,王静,等.基于MODIS和NOAA/AVHRR的荒漠化遥感监测方法[J].农业工程学报,2007,23(10):145-150.Liu Aixia,Wang Changyao,Wang Jing,et al.Method for remote sensing monitoring of desertification based on MODIS and NOAA/AVHRR data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(10):145-150.(in Chinese with English abstract)
[8]王大鹏,王周龙,李德一,等.综合非光谱信息的荒漠化土地CART分类[J].遥感学报,2007,11(4):487-492.Wang Dapeng,Wang Zhoulong,Li Deyi,et al.The extraction of desertification information using cart under knowledge guide[J].Journal of Remote Sensing,2007,11(4):487-492.(in Chinese with English abstract)
[9]Snyman H A.Dynamics and sustainable utilization of rangeland ecosystems in arid and semi-arid climates of southern Africa[J].Journal of Arid Environments,1998,39(4):645-666.
[10]Illius A W,O'Connor T G.On the relevance of nonequilibrium concepts to aridand semiarid grazing systems[J].Ecological Applications,1999,9(3):798-813.
[11]O'Connor T G,Haines L M,Snyman H A.Influence of precipitation and species composition on phytomass of a semi-arid African grassland[J].Journal of Ecology,2001,89(5):850-860.
[12]Chikhaoui M,Bonn F,Bokoye A I,A Merzouk.A spectral index for land degradation mapping using ASTER data:Application to a semi-arid Mediterranean catchment[J].International Journal of Applied Earth Observations&Geoinformation,2005,7(2):140-153.
[13]王莉雯,牛铮,卫亚星.基于MODIS NDVI的新疆土地覆盖类型划分与潜在荒漠化区域探测[J].干旱区地理,2007,30(4):573-578.Wang Liwen,Niu Zheng,Wei Yaxing.Land cover type classification and potential desertification area detecting in Xinjiang based on MODIS NDVI imagery[J].Arid Land Geography,2007,30(4):573-578.(in Chinese with English abstract)
[14]许宁,郭旭东,田淑芳,等.基于遥感和GIS的土地利用分类方法及其在土地退化程度分析中的应用:以陕西横山雷龙湾地区为例[J].生态学报,2008,28(11):5410-5417.Xu Ning,Guo Xudong,Tian Shufang,et al.Remote sensing and GIS based comparison of land-use classification methods and data mining for degree of land degradation:A case in Leilongwan Area,Hengshan County,Shaanxi province[J].Acta Ecological Sinica,2008,28(11):5410-5417.(in Chinese with English abstract)
[15]Sun D,Dawson R,Li H,et al.A landscape connectivity index for assessing desertification:A case study of Minqin County,China[J].Landscape Ecology,2007,22(4):531-543.
[16]Stafford Smith D M,Reynolds J F.Desertification:A new paradigm for an old problem[C]//Reynolds J F,Stafford Smith D M.Global Desertification:Do Humans Cause Deserts?Berlin:Dahlem University Press,2002:403-424.
[17]Geist H J,Lambin E F.Dynamic causal patterns of desertification[J].Bioscience 2004,54:817-829.
[18]Reynolds J F,Grainger A,Stafford Smith M,et al.Scientific concepts for anintegrated analysis of desertification[J].Land Degradation and Development,2011,22(2):166-183.
[19]Lambin E,Geist H.Land Use and Land Cover Change:Local Processes,Global Impacts[M].New York:Springer,2006.
[20]Sun D,Liu N.Coupling spectral unmixing and multiseasonal remote sensing for temperate dryland land-use/land-cover mapping in Minqin County,China[J].International Journal of Remote Sensing,2015,36(14):3636-3658.
[21]张熙川,赵英时.应用线性光谱混合模型快速评价土地退化的方法研究[J].中国科学院大学学报,1999(2):169-176.Zhang Xichuan,Zhao Yingshi.Application of liner spectral mixture model to rapid assessment of land degradation in semiarid area[J].Journal of Graduate School,Academia Sinica,1999(2):169-176.(in Chinese with English abstract)
[22]卢远,林年丰,汤洁,等.松嫩平原西部土地退化的遥感动态监测研究:以吉林省通榆县为例[J].地理与地理信息科学,2003,19(2):24-27.Lu Yuan,Lin Nianfeng,Tang Jie,et al.Remote sensing dynamic monitoring and research of land degradation in the West of Songnen Plain:Taking Tongyu County in Jilin Province as an example[J].Geography and Geo-information Science,2003,19(2):24-27.(in Chinese with English abstract)
[23]Sun D F.Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County,China[J].International Journal of Applied Earth Observation and Geoinformation,2015,41:34-45.
[24]姜宛贝,孙丹峰,孙强强.基于光谱混合分解的民勤生态治理效果评价[J].中国沙漠,2018(1):210-218.Jiang Wanbei,Sun Danfeng,Sun Qiangqiang.Ecological restoration assessment in Minqin based on spectral mixture analysis[J].Journal of Desert Research,2018(1):210-218.(in Chinese with English abstract)
[25]Small C,Milesi C.Multi-scale standardized spectral mixture models[J].Remote Sensing of Environment,2013,136(5):442-454.
[26]Smith M O,Ustin S L,Adams J B,et al.Vegetation in deserts:I.A regional measure of abundance from multispectral images[J].Remote Sensing of Environment,1990,31(1):1-26.
[27]Adams J B,Sabol D E,Kapos V,et al.Classification of multispectral images based on fractions of endmembers:Application to land-cover change in the Brazilian Amazon[J].Remote Sensing of Environment,1995,52:137-154.
[28]Roberts D A,Smith M O,Adams J B.Green vegetation nonphotosynthetic vegetation,and soils in AVIRIS data[J].Remote Sensing of Environment,1993,44:255-269.
[29]刘娜,李红,孙丹峰,等.绿洲景观多季相特征的线性光谱混合分解方法研究[J].农业工程学报,2010,26(8):174-182.Liu Na,Li Hong,Sun Danfeng,et al.Extracting oasis landscape multi-seasonal characteristics with linear spectral mixture analysis[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(8):174-182.(in Chinese with English abstract)
[30]姜宛贝,孙强强,曲葳,等.基于多季相光谱混合分解和决策树的干旱区土地利用分类[J].农业工程学报,2016,32(19):1-8.Jiang Wanbei,Sun Qiangqiang,Qu Wei,et al.Land use classification in arid region based on multi-seasonal linear spectral mixture analysis and decision tree method[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(19):1-8.(in Chinese with English abstract)
[31]Sun D,Zhang P,Sun Q,et al.A dryland cover state mapping using catastrophe model in a spectral endmember space of OLI:A case study in Minqin,China[J].International Journal of Remote Sensing,2019,14(40):5673-5694.
[32]Sun D,Jiang W.Agricultural soil alkalinity and salinity modeling in the cropping season in a spectral endmember space of TM in temperate drylands,Minqin,China[J].Remote Sensing,2016,8(9):714-731.
[33]Sun Q,Zhang P,Sun D,et al.Desert vegetation-habitat complexes mapping using Gaofen-1 WFV(wide field of view)time series images in Minqin County,China[J].International Journal of Applied Earth Observation and Geoinformation,2018,73:522-534.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |