基于RS/GIS的泉州湾红树林湿地时空动态变化分析
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摘要
红树林湿地具有重要的生态服务功能,明确其时空动态变化特征对于保护和管理红树林湿地具有重要意义。本研究在野外实地调查与不同季相红树林、滩涂和米草光谱特征对比分析的基础上,基于Landsat TM/OLI RS影像,利用面向对象分类方法得到1990、1997、2005、2010和2017年泉州湾河口湿地自然保护区土地覆盖空间分布数据,并结合动态度、空间叠置分析、质心迁移等方法分析红树林时空动态变化特征及其驱动力因素。结果表明:1990—2017年,红树林面积整体呈大幅度增加,仅在1997—2005年间呈现小面积减少,其中在2010—2017年间面积增加最为剧烈,动态度为32.78%。相对于其他土地覆盖类型,米草和滩涂与红树林的面积转化最大,其中1997—2005年共有4.70 hm2红树林转出为米草和滩涂,而2010—2017年间共有178.60 hm2滩涂和米草转入为红树林。1990—2017年,红树林与米草质心均向海迁移,且彼此间的距离逐渐增大。红树林面积时空变化的驱动力因素涉及多个方面,其中自然环境变化、外来植被入侵和养殖业发展对红树林的存在与繁衍具有一定的消极作用,而人工造林工程与入侵植被治理对其具有积极作用,且作用大于前者。继续实施和深入研究红树林人工造林工程、米草治理方法和建立健全红树林生态系统监测体系是保护和管理红树林湿地的重要手段和措施。
Mangrove swamps are ecologically important ecosystems. Discerning the spatiotemporal dynamics of mangrove swamps is of great significance to their protection and management. Considering the tidal information,suitable Landsat TM/OLI images were selected as the base remote sensing data. Based on field investigation and comparison analysis of the spectral characteristics of mangrove forests,mudflats,and Spartina in different seasons,the spatial distribution data of land cover type in the Quanzhou Bay Estuary Wetland Nature Reserve in 1990,1997,2005,2010,and 2017 were obtained by an object-oriented classification method.The spatial and temporal dynamic characteristics and driving factors of mangrove forests were analyzed through a combination of dynamic degree,spatial overlay analysis,and centroid migration methods. The results showed that the classification accuracy and efficiency of the mangrove forest information extraction were improved based on the method used in the study,which had certain advantages and potential in mangrove forest information extraction. The area of mangrove forests increased significantly from 1990 to2017 as a whole,despite a slight reduction from 1997 to 2005. During 2010—2017,the mangrove forest area increased the most,with a dynamic degree of 32.78%. Compared with other land cover types,the conversion area between Spartina as well as mudflat and mangrove forests was the largest,of which 4.70 hm2 mangrove forests was transferred to Spartina and mudflat from 1997 to2005,and 178.60 hm2 Spartina and mudflat were converted into mangrove forests from 2010 to 2017. From 1990 to 2017,the centroids of mangrove forests and Spartina migrated to the sea,and the distance between them increased. The threat of Spartina to mangrove forests gradually decreased. The driving factors of spatial and temporal variation in mangrove forest area were related to many aspects,among which natural environment change,exotic vegetation invasion,and development of the aquaculture industry negatively affected the existence and reproduction of mangrove forests,while the mangrove forest artificial afforestation project and invasive vegetation management positively affected it. The positive effects can neutralize or offset the negative effects to a large extent.Anthropogenic activities significantly affect mangrove swamp ecosystems via active protection. Thus,it is important to protect and manage mangrove swamps by continuing to carry out the mangrove forest artificial afforestation project,by controlling Spartina,and by establishing and perfecting the mangrove forest ecosystem monitoring system.
Mangrove swamps are ecologically important ecosystems. Discerning the spatiotemporal dynamics of mangrove swamps is of great significance to their protection and management. Considering the tidal information,suitable Landsat TM/OLI images were selected as the base remote sensing data. Based on field investigation and comparison analysis of the spectral characteristics of mangrove forests,mudflats,and Spartina in different seasons,the spatial distribution data of land cover type in the Quanzhou Bay Estuary Wetland Nature Reserve in 1990,1997,2005,2010,and 2017 were obtained by an object-oriented classification method.The spatial and temporal dynamic characteristics and driving factors of mangrove forests were analyzed through a combination of dynamic degree,spatial overlay analysis,and centroid migration methods. The results showed that the classification accuracy and efficiency of the mangrove forest information extraction were improved based on the method used in the study,which had certain advantages and potential in mangrove forest information extraction. The area of mangrove forests increased significantly from 1990 to2017 as a whole,despite a slight reduction from 1997 to 2005. During 2010—2017,the mangrove forest area increased the most,with a dynamic degree of 32.78%. Compared with other land cover types,the conversion area between Spartina as well as mudflat and mangrove forests was the largest,of which 4.70 hm2 mangrove forests was transferred to Spartina and mudflat from 1997 to2005,and 178.60 hm2 Spartina and mudflat were converted into mangrove forests from 2010 to 2017. From 1990 to 2017,the centroids of mangrove forests and Spartina migrated to the sea,and the distance between them increased. The threat of Spartina to mangrove forests gradually decreased. The driving factors of spatial and temporal variation in mangrove forest area were related to many aspects,among which natural environment change,exotic vegetation invasion,and development of the aquaculture industry negatively affected the existence and reproduction of mangrove forests,while the mangrove forest artificial afforestation project and invasive vegetation management positively affected it. The positive effects can neutralize or offset the negative effects to a large extent.Anthropogenic activities significantly affect mangrove swamp ecosystems via active protection. Thus,it is important to protect and manage mangrove swamps by continuing to carry out the mangrove forest artificial afforestation project,by controlling Spartina,and by establishing and perfecting the mangrove forest ecosystem monitoring system.
引文
[1]王文卿,王瑁.中国红树林[M].北京:科学出版社,2007:5.
[2]李屹,陈一宁,李炎.红树林与互花米草盐沼交界区空间格局变化规律的遥感分析[J].海洋通报,2017,36(3):348-360.
[3]丁冬静,廖宝文,管伟,等.东寨港红树林自然保护区滨海湿地生态系统服务价值评估[J].生态科学,2016,35(6):182-190.
[4]章海波,骆永明,刘兴华,等.海岸带蓝碳研究及其展望[J].中国科学:地球科学,2015,45(11):1 641-1 648.
[5]但新球,廖宝文,吴照柏,等.中国红树林湿地资源、保护现状和主要威胁[J].生态环境学报,2016,25(7):1 237-1 243.
[6] ALONGI D M. Present state and future of the world's mangrove forests[J]. Environmental Conservation,2002,29(3):331-349.
[7]贾明明. 1973—2013年中国红树林动态变化遥感分析[D].长春:中国科学院东北地理与农业生态研究所,2014.
[8] MURRAY N J,KEITH D A,BLAND L M,et al. The role of satellite remote sensing in structured ecosystem riskassessments[J]. Science of the Total Environment,2018,619-620:249-257.
[9] LIU K,LI X,SHI X,et al. Monitoring mangrove forest changes using remote sensing and GIS data with decision-treelearning[J]. Wetlands,2008,28(2):336-346.
[10] QUADER M A,AGRAWAL S,KERVYN M. Multi-decadal land cover evolution in the Sundarban,the largest mangroveforest in the world[J]. Ocean&Coastal Management,2017,139:113-124.
[11]王武霞,苏奋振,冯雪,等.中越北部湾红树林差异性研究[J].地球信息科学学报,2017,19(2):264-272.
[12]王俊杰,刘珏,石铁柱,等. 1990—2015年广西廉州湾红树林遥感动态监测[J].森林与环境学报,2016,36(4):455-460.
[13]李春干,代华兵.红树林空间分布信息遥感提取方法[J].湿地科学,2014,12(5):580-589.
[14]章恒,王世新,周艺,等.多源遥感影像红树林信息提取方法比较[J].湿地科学,2015,13(2):145-152.
[15]路春燕,王宗明,贾明明,等.基于ENVISAT ASAR、Landsat TM与DEM的泥炭沼泽信息提取方法[J].武汉大学学报(信息科学版),2017,42(2):185-192.
[16] VO T Q,KUENZER C,OPPELT N. How remote sensing supports mangrove ecosystem service valuation:a case study inCa Mau province,Vietnam[J]. Ecosystem Services,2015,14:67-75.
[17]刘凯,朱远辉,李骞,等.基于多源遥感的广东镇海湾红树林演变分析[J].热带地理,2016,36(5):850-859.
[18] JIA M M,ZHANG Y Z,WANG Z M,et al. Mapping the distribution of mangrove species in the Core Zone of Mai PoMarshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data[J]. International Journal ofApplied Earth Observation and Geoinformation,2014,33:226-231.
[19]黄慧萍.面向对象影像分析中的尺度问题研究[D].北京:中国科学院遥感应用研究所,2003.
[20]路春燕,王宗明,刘明月,等.松嫩平原西部湿地自然保护区保护有效性遥感分析[J].中国环境科学,2015,35(2):599-609.
[21] LIU M Y,LI H Y,LI L,et al. Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imageryin the Zhangjiang Estuary,China[J]. Remote Sensing,2017,9(6):539.
[22] LI H Y,MAN W D,LI X Y,et al. Remote sensing investigation of anthropogenic land cover expansion in the low-elevationcoastal zone of Liaoning Province,China[J]. Ocean&Coastal Management,2017,148:245-259.
[2]李屹,陈一宁,李炎.红树林与互花米草盐沼交界区空间格局变化规律的遥感分析[J].海洋通报,2017,36(3):348-360.
[3]丁冬静,廖宝文,管伟,等.东寨港红树林自然保护区滨海湿地生态系统服务价值评估[J].生态科学,2016,35(6):182-190.
[4]章海波,骆永明,刘兴华,等.海岸带蓝碳研究及其展望[J].中国科学:地球科学,2015,45(11):1 641-1 648.
[5]但新球,廖宝文,吴照柏,等.中国红树林湿地资源、保护现状和主要威胁[J].生态环境学报,2016,25(7):1 237-1 243.
[6] ALONGI D M. Present state and future of the world's mangrove forests[J]. Environmental Conservation,2002,29(3):331-349.
[7]贾明明. 1973—2013年中国红树林动态变化遥感分析[D].长春:中国科学院东北地理与农业生态研究所,2014.
[8] MURRAY N J,KEITH D A,BLAND L M,et al. The role of satellite remote sensing in structured ecosystem riskassessments[J]. Science of the Total Environment,2018,619-620:249-257.
[9] LIU K,LI X,SHI X,et al. Monitoring mangrove forest changes using remote sensing and GIS data with decision-treelearning[J]. Wetlands,2008,28(2):336-346.
[10] QUADER M A,AGRAWAL S,KERVYN M. Multi-decadal land cover evolution in the Sundarban,the largest mangroveforest in the world[J]. Ocean&Coastal Management,2017,139:113-124.
[11]王武霞,苏奋振,冯雪,等.中越北部湾红树林差异性研究[J].地球信息科学学报,2017,19(2):264-272.
[12]王俊杰,刘珏,石铁柱,等. 1990—2015年广西廉州湾红树林遥感动态监测[J].森林与环境学报,2016,36(4):455-460.
[13]李春干,代华兵.红树林空间分布信息遥感提取方法[J].湿地科学,2014,12(5):580-589.
[14]章恒,王世新,周艺,等.多源遥感影像红树林信息提取方法比较[J].湿地科学,2015,13(2):145-152.
[15]路春燕,王宗明,贾明明,等.基于ENVISAT ASAR、Landsat TM与DEM的泥炭沼泽信息提取方法[J].武汉大学学报(信息科学版),2017,42(2):185-192.
[16] VO T Q,KUENZER C,OPPELT N. How remote sensing supports mangrove ecosystem service valuation:a case study inCa Mau province,Vietnam[J]. Ecosystem Services,2015,14:67-75.
[17]刘凯,朱远辉,李骞,等.基于多源遥感的广东镇海湾红树林演变分析[J].热带地理,2016,36(5):850-859.
[18] JIA M M,ZHANG Y Z,WANG Z M,et al. Mapping the distribution of mangrove species in the Core Zone of Mai PoMarshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data[J]. International Journal ofApplied Earth Observation and Geoinformation,2014,33:226-231.
[19]黄慧萍.面向对象影像分析中的尺度问题研究[D].北京:中国科学院遥感应用研究所,2003.
[20]路春燕,王宗明,刘明月,等.松嫩平原西部湿地自然保护区保护有效性遥感分析[J].中国环境科学,2015,35(2):599-609.
[21] LIU M Y,LI H Y,LI L,et al. Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imageryin the Zhangjiang Estuary,China[J]. Remote Sensing,2017,9(6):539.
[22] LI H Y,MAN W D,LI X Y,et al. Remote sensing investigation of anthropogenic land cover expansion in the low-elevationcoastal zone of Liaoning Province,China[J]. Ocean&Coastal Management,2017,148:245-259.
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