基于多端元解混模型的博斯腾湖区域植被和水域时空变化特征及趋势分析

doi:10.12118/j.issn.1000-6060.2023.024

摘要/Abstract

摘要：

湿地高精度动态变化遥感监测，对于湿地保护和恢复具有重要的实践意义。以新疆博斯腾湖湿地为研究对象，运用多端元混合像元分解（Multiple endmember spectral mixture analysis，MESMA）模型提取Landsat影像中植被、水体和裸地面积，通过无人机影像验证精度后，结合趋势分析法探明2000—2022年博斯腾湖湿地时空变化特征及趋势。结果表明：（1）通过无人机影像重采样精度验证的MESMA分类结果中植被像元拟合优度（R²）为0.75，水体像元R²为0.84，表明分类结果符合实际地物情况。（2） 2000—2022年博斯腾湖湿地植被面积共增加536.65 km²，增加了183.14%；水域面积则减少595.76 km²，减少了37.07%；裸地面积共增加99.12 km²，增加了25.42%。（3）博斯腾湖湿地植被面积呈增加趋势的区域占总面积的30.6%，位于大湖区西北部和小湖区北部；反之水域面积呈减少趋势的区域占总面积的34.6%，位于大湖北岸、东岸及小湖湿地。准确掌握博斯腾湖湿地时空变化情况及其趋势，可对干旱区内陆湿地监测与保护提供参考依据。

关键词: MESMA, 混合像元, 湿地, 时空变化, 博斯腾湖

Abstract:

High-precision remote sensing monitoring of dynamic changes in wetlands is of great practical significance for wetland conservation and restoration. Taking the wetland of Bosten Lake in Xinjiang as the research object, we use the multiple endmember spectral mixture analysis (MESMA) method to extract the vegetation, water body, and bare land area from Landsat images, verify the accuracy by UAV images, and then combine with the trend analysis method to explore the spatial and temporal change characteristics and trends of Bosten Lake wetland from 2000 to 2022. The results show that: (1) The MESMA classification results verified by the resampling accuracy of UAV images showed that the goodness of fit (R²) of vegetation image element is 0.75 and the R² of water body image element R² is 0.84, indicating that the classification results were consistent with the actual feature conditions. (2) From 2000 to 2022, the vegetation area of Bosten Lake increased by 536.65 km², an increase of 183.14%; the water area decreased by 595.76 km², a decrease of 37.07%; the bare land area increased by 99.12 km², an increase of 25.42%. (3) The area of vegetation in the wetlands of Bosten Lake with an increasing trend accounts for 30.6% of the total area, which is located in the northwestern part of the Great Lake and the northern part of the Small Lake; on the contrary, the area of water with a decreasing trend accounts for 34.6% of the total area, which is located in the northern and eastern shores of the Great Lake and the wetlands of the Small Lake. To accurately grasp the spatial and temporal changes of Bosten Lake wetlands and their trends, it provides a reference basis for monitoring and protecting inland wetlands in the arid zone.

Key words: MESMA, mixed pixel, wetland, spatiotemporal variation, Bosten Lake

亚夏尔·艾斯克尔, 玉素甫江·如素力. 基于多端元解混模型的博斯腾湖区域植被和水域时空变化特征及趋势分析[J]. 干旱区地理, 2023, 46(10): 1622-1631.

Yaxiaer AISIKEER, Yusufjiang RUSULI. Spatiotemporal variation characteristics and trend analysis of vegetation and water area in the Bosten Lake based on multiple endmember spectral mixture analysis model[J]. Arid Land Geography, 2023, 46(10): 1622-1631.

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参考文献 34

[1]	陈宜瑜, 吕宪国. 湿地功能与湿地科学的研究方向[J]. 湿地科学, 2003(1): 7-11.
	[Chen Yiyu, Lü Xianguo. The wetland function and research tendency of wetland science[J]. Wetland Science, 2003(1): 7-11. ]
[2]	谭志强, 李云良, 张奇, 等. 湖泊湿地水文过程研究进展[J]. 湖泊科学, 2022, 34(1): 18-37. doi: 10.18307/2022.0104
	[Tan Zhiqiang, Li Yunliang, Zhang Qi, et al. Progress of hydrological process researches in lake wetland: A review[J]. Journal of Lake Sciences, 2022, 34(1): 18-37. ] doi: 10.18307/2022.0104
[3]	马炜, 周天元, 蒋亚芳, 等. 中国湿地保护状况和未来湿地保护的目标和重点[J]. 湿地科学, 2021, 19(4): 435-441.
	[Ma Wei, Zhou Tianyuan, Jiang Yafang, et al. Protection status and future protection objectives of the wetlands in China[J]. Wetland Science, 2021, 19(4): 435-441. ]
[4]	游晓斌, 游先祥, 相莹莹. 混合像元及混合像元分析[J]. 北京林业大学学报, 2003(12): 28-32.
	[You Xiaobin, You Xianxiang, Xiang Yingying. Mixed pixel and mixed pixel analysis[J]. Journal of Beijing Forestry University, 2003(12): 28-32. ]
[5]	张皓楠, 温兴平, 徐俊龙, 等. 影响混合像元分解精度的因子研究[J]. 遥感信息, 2019, 34(3): 48-53.
	[Zhang Haonan, Wen Xingping, Xu Junlong, et al. Influence factors of decomposition precision of mixed-pixels based on CLSMM[J]. Remote Sensing Information, 2019, 34(3): 48-53. ]
[6]	杨超, 王金亮, 渠立权, 等. 最小二乘混合像元分解的端元丰度信息提取研究[J]. 测绘科学, 2017, 42(9): 143-150, 157.
	[Yang Chao, Wang Jinliang, Qu Liquan, et al. Research on the extraction of surface feature abundance based on the least square mixed pixel decomposition[J]. Science of Surveying and Mapping, 2017, 42(9): 143-150, 157. ]
[7]	惠巍巍, 衣德萍, 廖彩霞, 等. 混合像元分解研究综述[J]. 林业科技情报, 2007, 39(1): 2-3.
	[Hui Weiwei, Yi Deping, Liao Caixia, et al. The study of decomposing mix element[J]. Forestry Science and Technology Information, 2007, 39(1): 2-3. ]
[8]	吴见, 彭道黎. 改进线性光谱混合分解模型湿地信息提取[J]. 中国农业大学学报, 2011, 16(3): 140-144.
	[Wu Jian, Peng Daoli. Wetland information extraction based on improved linear spectral mixture model[J]. Journal of China Agricultural University, 2011, 16(3): 140-144. ]
[9]	李微, 刘伟男, 陈官滨, 等. 基于混合像元分解的辽河口滨海湿地分类[J]. 湿地科学与管理, 2017, 13(1): 25-28.
	[Li Wei, Liu Weinan, Chen Guanbin, et al. Classification of Liaohe Estuary Coastal Wetlands based on image analysis with decomposition of mixed pixels[J]. Wetland Science & Management, 2017, 13(1): 25-28. ]
[10]	李哲, 宫兆宁, 刘先林, 等. 基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究[J]. 遥感技术与应用, 2018, 33(6): 1149-1158.
	[Li Zhe, Gong Zhaoning, Liu Xianlin, et al. Vegetation coverage retrieval and spatio-temporal distribution based on object-oriented multi-terminal mixed model[J]. Remote Sensing Technology and Application, 2018, 33(6): 1149-1158. ]
[11]	曾光, 高会军, 朱刚, 等. 近32年新疆博斯腾湖湿地动态变化及机制分析[J]. 国土资源遥感, 2010, 86(增刊1): 213-218.
	[Zeng Guang, Gao Huijun, Zhu Gang, et al. A remote sensing analysis of wetlands dynamic changes and mechanism in the past 32 years in Bosten Lake, Xinjiang[J]. Remote Sensing for Land & Resources, 2010, 86 (Suppl. 1): 213-218. ]
[12]	王涛, 陶辉, 雷刚, 等. 博斯腾湖流域植被覆盖变化及驱动因素分析[J]. 中国农学通报, 2015, 31(4): 228-236. doi: 10.11924/j.issn.1000-6850.2014-2461
	[Wang Tao, Tao Hui, Lei Gang, et al. Analysis of vegetation cover changes and its driving forces in Bosten Lake Basin[J]. Chinese Agricultural Science Bulletin, 2015, 31(4): 228-236. ] doi: 10.11924/j.issn.1000-6850.2014-2461
[13]	禹泽龙, 徐昔保, 李景宜. 1955—2019年博斯腾湖生态系统服务价值及其影响因素研究[J]. 湿地科学, 2022, 20(3): 395-403.
	[Yu Zelong, Xu Xibao, Li Jingyi. Ecosystem service values of Bosten Lake from 1955 to 2019 and their influence factors[J]. Wetland Science, 2022, 20(3): 395-403. ]
[14]	Franke J, Roberts D A, Halligan K, et al. Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments[J]. Remote Sensing of Environment, 2009, 113(8): 1712-1723. doi: 10.1016/j.rse.2009.03.018
[15]	马孟莉, 朱艳, 李文龙, 等. 基于分层多端元混合像元分解的水稻面积信息提取[J]. 农业工程学报, 2012, 28(2): 154-159.
	[Ma Mengli, Zhu Yan, Li Wenlong, et al. Extracting area information of paddy rice based on stratified multiple endmember spectral mixture analysis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(2): 154-159. ]
[16]	王浩, 吴炳方, 李晓松, 等. 流域尺度的不透水面遥感提取[J]. 遥感学报, 2011, 15(2): 388-400.
	[Wang Hao, Wu Bingfang, Li Xiaosong, et al. Extraction of impervious surface in Hai Basin using remote sensing[J]. Journal of Remote Sensing, 2011, 15(2): 388-400. ]
[17]	廖春华, 张显峰, 刘羽. 基于多端元光谱分解的干旱区植被覆盖度遥感反演[J]. 应用生态学报, 2012, 23(12): 3243-3249. pmid: 23479862
	[Liao Chunhua, Zhang Xianfeng, Liu Yu. Remote sensing retrieval of vegetation coverage in arid areas based on multiple endmember spectral unmixing[J]. Chinese Journal of Applied Ecology, 2012, 23(12): 3243-3249. ] pmid: 23479862
[18]	刘英, 钟瑞森, 段永超, 等. 博斯腾湖小湖区湿地生态需水量阈值研究[J]. 干旱区地理, 2021, 44(6): 1525-1533.
	[Liu Ying, Zhong Ruisen, Duan Yongchao, et al. Threshold of ecological water demands for the small lake wetland of Bosten Lake[J]. Arid Land Geography, 2021, 44(6): 1525-1533. ]
[19]	买尔哈巴·买买提汗. 博斯腾湖芦苇湿地的动态监测及驱动因素分析[D]. 乌鲁木齐: 新疆师范大学, 2017.
	[Maimaitihan Marerhaba. Dynamic monitoring and driving factors of Phragmites australis wetland in Bosten Lake[D]. Urumqi: Xinjiang Normal University, 2017. ]
[20]	姜磊鹏, 丁建丽, 包青岭, 等. 低空遥感结合卫星影像的河道流量反演[J]. 干旱区地理, 2023, 46(3): 385-396.
	[Jiang Leipeng, Ding Jianli, Bao Qingling, et al. Runoff estimation with low altitude remote sensing and satellite images[J]. Arid Land Geography, 2023, 46(3): 385-396. ]
[21]	张瑞杰, 李俐俐, 李礼, 等. 利用无人机影像数据进行油菜长势监测[J]. 测绘地理信息, 2021, 46(增刊1): 227-231.
	[Zhang Ruijie, Li Lili, Li Li, et al. Rapeseed growth monitoring using UAV imagery[J]. Journal of Geomatics, 2021, 46(Suppl. 1): 227-231. ]
[22]	Quintano C, Fernandez-Manso A, Roberts D A. Burn severity mapping from Landsat MESMA fraction images and land surface temperature[J]. Remote Sensing of Environment: An Interdisciplinary Journal, 2017, 190: 83-95.
[23]	王俊奇, 王广军, 梁四海, 等. 1996—2015年黄河源区植被覆盖度提取和时空变化分析[J]. 冰川冻土, 2021, 43(2): 662-674. doi: 10.7522/j.issn.1000-0240.2021.0046
	[Wang Junqi, Wang Guangjun, Liang Sihai, et al. Extraction and spatio-temporal analysis of vegetation coverage from 1996 to 2015 in the source region of the Yellow River[J]. Journal of Glaciology and Geocryology, 2021, 43(2): 662-674. ] doi: 10.7522/j.issn.1000-0240.2021.0046
[24]	陈元鹏, 郧文聚, 周旭, 等. 基于MESMA和RF的山丘区土地利用信息分类提取[J]. 农业机械学报, 2017, 48(7): 136-144.
	[Chen Yuanpeng, Yun Wenju, Zhou Xu, et al. Classification and extraction of land use information in hilly area based on MESMA and RF classifier[J]. Transactions of the Chinese Society of Agricultural Machinery, 2017, 48(7): 136-144. ]
[25]	Roberts D A, Gardner M, Church R, et al. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models[J]. Remote Sensing of Environment, 1998, 65(3): 267-279. doi: 10.1016/S0034-4257(98)00037-6
[26]	丁建丽, 姚远. 干旱区绿洲典型地物MESMA模拟分解与验证[J]. 地球信息科学学报, 2013, 15(3): 452-460. doi: 10.3724/SP.J.1047.2013.00452
	[Ding Jianli, Yao Yuan. Research on pixel unmixing of typical surface features in oasis based on the MESMA model[J]. Journal of Geo-information Science, 2013, 15(3): 452-460. ] doi: 10.3724/SP.J.1047.2013.00452
[27]	闫萧萧, 李晶, 杨震. 2000—2016年陈巴尔虎旗植被覆盖度时空变化遥感动态监测[J]. 中国农业大学学报, 2018, 23(6): 121-129.
	[Yan Xiaoxiao, Li Jing, Yang Zhen. Dynamic remote sensing monitoring on the temporal-spatial changes of vegetation coverage in Chen Barag Banner from 2000 to 2016[J]. Journal of China Agricultural University, 2018, 23(6): 121-129. ]
[28]	耿仁方, 付波霖, 金双根, 等. 面向对象的无人机遥感影像岩溶湿地植被遥感识别[J]. 测绘通报, 2020(11): 13-18. doi: 10.13474/j.cnki.11-2246.2020.0346
	[Geng Renfang, Fu Bolin, Jin Shuanggen, et al. Object-based karst wetland vegetation classification using UAV images[J]. Bulletin of Surveying and Mapping, 2020(11): 13-18. ] doi: 10.13474/j.cnki.11-2246.2020.0346
[29]	刘耀辉, 于祥惠, 范洁洁, 等. 基于无人机影像和面向对象的中国西部地区农村宅基地面积快速估算[J]. 测绘通报, 2022(6): 125-129. doi: 10.13474/j.cnki.11-2246.2022.0184.
	[Liu Yaohui, Yu Xianghui, Fan Jiejie, et al. Rapid estimation of rural homestead area in western China based on UAV imagery and object-oriented method[J]. Bulletin of Surveying and Mapping, 2022(6): 125-129. ] doi: 10.13474/j.cnki.11-2246.2022.0184.
[30]	Kattenborn T, Javier F, Michael B, et al. UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data[J]. Remote Sensing of Environment: An Interdisciplinary Journal, 2019, 227: 61-73.
[31]	陈昂. 基于Google Earth Engine与无人机影像的沙漠化信息提取——以内蒙古正蓝旗为例[D]. 北京: 中国农业科学院, 2020.
	[Chen Ang. Desertification information extraction based on Google Earth Engine and UAV images: A case study of Zhenglan Banner, Inner Mongolia[D]. Beijing: Chinese Academy of Agricultural, 2020. ]
[32]	买尔哈巴·买买提汗, 玉素甫江·如素力, 安尼瓦尔·阿布都热依木, 等. 近26 a博斯腾湖芦苇湿地的动态监测及其驱动因素[J]. 干旱区研究, 2016, 33(4): 797-804.
	[Maimaitihan Maierhaba, Rusuli Yusufujiang, Abudureyimu Anniwaer, et al. Dynamic variation of Phragmites australis wetland in the Bosten Lake Basin and its driving factors in recent 26 years[J]. Arid Zone Research, 2016, 33(4): 797-804. ]
[33]	彭妍菲, 李忠勤, 姚晓军, 等. 基于多源遥感数据和GEE平台的博斯腾湖面积变化及影响因素分析[J]. 地球信息科学学报, 2021, 23(6): 1131-1153. doi: 10.12082/dqxxkx.2021.200361
	[Peng Yanfei, Li Zhongqin, Yao Xiaojun, et al. Area change and cause analysis of Bosten Lake based on multi-source remote sensing data and GEE platform[J]. Journal of Geo-information Science, 2021, 23(6): 1131-1153. ] doi: 10.12082/dqxxkx.2021.200361
[34]	李玉焦, 陈亚宁, 张齐飞, 等. 1960—2018年博斯腾湖水位变化特征及其影响因素分析[J]. 干旱区研究, 2021, 38(1): 48-58.
	[Li Yujiao, Chen Yaning, Zhang Qifei, et al. Analysis of the change in water level and its influencing factors on Bosten Lake from 1960 to 2018[J] Arid Zone Research, 2021, 38(1): 48-58. ]