Research progress of aquatic vegetation remote sensing in shallow lakes

Juhua LUO; Jingzhicheng YANG; Hongtao DUAN; Lirong LU; Zhe SUN; Yihao XIN

doi:10.11834/jrs.20221208

Review | Views : 0 下载量: 248 CSCD: 1

PDF
Export
Share
Collection
Album

Research progress of aquatic vegetation remote sensing in shallow lakes
Vol. 26, Issue 1, Pages: 68-76(2022)
DOI： 10.11834/jrs.20221208

扫描看全文

Juhua LUO, Jingzhicheng YANG, Hongtao DUAN, et al. Research progress of aquatic vegetation remote sensing in shallow lakes. [J]. National Remote Sensing Bulletin 26(1):68-76(2022)
DOI：

Juhua LUO, Jingzhicheng YANG, Hongtao DUAN, et al. Research progress of aquatic vegetation remote sensing in shallow lakes. [J]. National Remote Sensing Bulletin 26(1):68-76(2022) DOI： 10.11834/jrs.20221208.

摘要

在浅水湖泊中，水生植物具有净化水质、抑制藻类、提供鱼类食物和栖息环境等生态功能，同时，其过度扩张也会加速湖泊淤浅和沼泽化、引起湖泊二次污染等环境负效应。实时动态地掌握湖泊水生植被类群和种群的空间分布及其面积、生物量等指标信息，对湖泊生态修复和评估、水生植被恢复和管理等具有重要现实意义。遥感技术的大面积、实时、动态等特点，为水生植被的现状监测、历史追溯、变化规律揭示等提供了有效手段。本文围绕“水生植被遥感”研究主题，开展了国内外文献调研，梳理了水生植被遥感监测的主要研究内容，并重点阐述了各内容的研究进展和方法等。最后，探讨了水生植被遥感存在的主要问题，并结合当前的遥感大数据发展，对未来水生植被遥感研究重点和发展趋势进行了展望。

Abstract

In shallow lakes or reservior, aquatic vegetation plays an important role in purifying water, maintaining the balance of lake ecosystems, supporting socioeconomic functions and protecting lake ecological environment. However, an excessive amount of macrophytes, especially floating-leaved vegetation, can have some negative effects on lake ecology. For example, the addition of large amounts of plant material to the lake bottom can cause lake silting and accelerate lake swamping; the release of pollutants into the lake water when the plants die and decay can result in water pollution. Therefore, it is very important to map spatiotemporal distribution and their changes of aquatic vegetation and then to retrieve biochemical parameters such as coverage and biomass for ecological restoration and management of lakes. Remote sensing techniques have become powerful and effective tools for mapping aquatic vegetation types and their changes over a large area and a long period. In this paper, with the theme of aquatic vegetation remote sensing, we reviewed and summarized the major progresses and methods of remote sensing application in aquatic vegetation in shallow lakes by literature review. We found the research topics in aquatic vegetation remote sensing mainly included hyperspectral analyses, classification and mapping, parameter inversion, change detection, and so on. We also offered a literature statistical diagram of classification methods for mapping aquatic vegetation, and found decision tree was the most popular and machine learning was becoming more and more popular in all mapping methods. Finally, we discussed existing major challenges, potential solutions and future prospects in aquatic vegetation remote sensing, including developing a multi-parameter method for mapping different species of submerged vegetation, expanding the spatial-temporal scale of inversion models in parameters in application and making full use of the advantages of UAV (unmanned aerial vehicle) coupled with hyperspectral and multispectral sensors for mapping and parameter inversion in aquatic vegetation.

关键词

水生植被沉水植被遥感分类生物量浅水湖泊湖泊修复

Keywords

aquatic vegetationsubmerged aquatic vegetationbiomassremote sensingclassificationchange detectionshallow lakeslake restoration

references

Byrd K B, O’Connell J L, Di Tommaso S and Kelly M. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sensing of Environment, 149: 166-180 [DOI: 10.1016/j.rse.2014.04.003http://dx.doi.org/10.1016/j.rse.2014.04.003]

Chen S Q, Xu Q J, Li F S and Liu J. 2008. Influence of environmental factors on the distribution and growth of aquatic macrophytes. Journal of Biology, 25(2): 11-15

陈书琴, 许秋瑾, 李法松, 刘俊. 2008. 环境因素对湖泊高等水生植物生长及分布的影响. 生物学杂志, 25(2): 11-15 [DOI: 10.3969/j.issn.2095-1736.2008.02.003http://dx.doi.org/10.3969/j.issn.2095-1736.2008.02.003]

Chen Z Y, Lei Z X, Zhou J, Wen F C and Chen J K. 2000. Monthly quantitative and biomass dynamics of six submerged macrophytes populations in Liangzi Lake. Acta Hydrobiologica Sinica, 24(6): 582-588

陈中义, 雷泽湘, 周进, 文凤春, 陈家宽. 2000. 梁子湖六种沉水植物种群数量和生物量周年动态. 水生生物学报, 24(6): 582-588 [DOI: 10.3321/j.issn:1000-3207.2000.06.002http://dx.doi.org/10.3321/j.issn:1000-3207.2000.06.002]

Cui X H, Chen J K and Li W. 1999. Survey methods on aquatic macrophyte vegetation in lakes in the middle and lower reaches of Changjiang River. Journal of Wuhan Botanical Research, 17(4): 357-361

崔心红, 陈家宽, 李伟. 1999. 长江中下游湖泊水生植被调查方法. 武汉植物学研究, 17(4): 357-361 [DOI: 10.3969/j.issn.2095-0837.1999.04.013http://dx.doi.org/10.3969/j.issn.2095-0837.1999.04.013]

Corti Meneses N, Baier S, Geist J and Schneider T. 2017. Evaluation of green-lidar data for mapping extent, density and height of aquatic reed beds at lake chiemsee, bavaria—germany. Remote Sensing, 9(12): 1308. [DOI: 10.3390/rs9121308http://dx.doi.org/10.3390/rs9121308]

Costa M. 2005. Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS‐1. International Journal of Remote Sensing, 26(20): 4527-4536 [DOI: 10.1080/01431160500213433http://dx.doi.org/10.1080/01431160500213433]

Costa M P F, Niemann O, Novo E and Ahern F. 2002. Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat. International Journal of Remote Sensing, 23(7): 1401-1426 [DOI: 10.1080/01431160110092957http://dx.doi.org/10.1080/01431160110092957]

Dogan O K, Akyurek Z and Beklioglu M. 2009. Identification and mapping of submerged plants in a shallow lake using quickbird satellite data. Journal of Environmental Management, 90(7): 2138-2143 [DOI: 10.1016/j.jenvman.2007.06.022http://dx.doi.org/10.1016/j.jenvman.2007.06.022]

Gao Y N, Gao J F, Wang J, Wang S S, Li Q, Zhai S H and Zhou Y. 2017. Estimating the biomass of unevenly distributed aquatic vegetation in a lake using the normalized water-adjusted vegetation index and scale transformation method. Science of the Total Environment, 601-602: 998-1007 [DOI: 10.1016/j.scitotenv.2017.05.163http://dx.doi.org/10.1016/j.scitotenv.2017.05.163]

Giardino C, Bresciani M, Valentini E, Gasperini L, Bolpagni R and Brando V E. 2015. Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake. Remote Sensing of Environment, 157: 48-57 [DOI: 10.1016/j.rse.2014.04.034http://dx.doi.org/10.1016/j.rse.2014.04.034]

Han X X, Chen X L and Feng L. 2015. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sensing of Environment, 156: 426-437 [DOI: 10.1016/j.rse.2014.10.003http://dx.doi.org/10.1016/j.rse.2014.10.003]

Han X X, Feng L, Hu C M and Chen X L. 2018. Wetland changes of China’s largest freshwater lake and their linkage with the Three Gorges Dam. Remote Sensing of Environment, 204: 799-811 [DOI: 10.1016/j.rse.2017.09.023http://dx.doi.org/10.1016/j.rse.2017.09.023]

Held P and von Deimling J S. 2019. New feature classes for acoustic habitat mapping—A multibeam echosounder point cloud analysis for mapping submerged aquatic vegetation (SAV). Geosciences, 9(5): 235 [DOI: 10.3390/geosciences9050235http://dx.doi.org/10.3390/geosciences9050235]

Hestir E L, Khanna S, Andrew M E, Santos M J, Viers J H, Greenberg J A, Rajapakse S S and Ustin S L. 2008. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sensing of Environment, 112(11): 4034-4047 [DOI: 10.1016/j.rse.2008.01.022http://dx.doi.org/10.1016/j.rse.2008.01.022]

Hou X J, Feng L, Chen X L and Zhang Y L. 2018. Dynamics of the wetland vegetation in large lakes of the Yangtze Plain in response to both fertilizer consumption and climatic changes. ISPRS Journal of Photogrammetry and Remote Sensing, 141: 148-160 [DOI: 10.1016/j.isprsjprs.2018.04.015http://dx.doi.org/10.1016/j.isprsjprs.2018.04.015]

Husson E, Ecke F and Reese H. 2016. Comparison of manual mapping and automated object-based image analysis of non-submerged aquatic vegetation from very-high-resolution UAS images. Remote Sensing, 8(9): 724 [DOI: 10.3390/rs8090724http://dx.doi.org/10.3390/rs8090724]

Janssen A B G, Teurlincx S, An S Q, Janse J H, Paerl H W and Mooij W M . 2014. Alternative stable states in large shallow lakes? Journal of Great Lakes Research, 40(4): 813-826 [DOI: 10.1016/j.jglr.2014.09.019http://dx.doi.org/10.1016/j.jglr.2014.09.019]

Jiang H, Zhao D H, Cai Y and An A Q. 2012. A method for application of classification tree models to map aquatic vegetation using remotely sensed images from different sensors and dates. Sensors, 12(9): 12437-12454 [DOI: 10.3390/s120912437http://dx.doi.org/10.3390/s120912437]

Jing R, Deng L, Zhao W J and Gong Z N. 2016. Object-oriented aquatic vegetation extraction approach based on visible vegetation indices. Chinese Journal of Applied Ecology, 27(5): 1427-1436

井然, 邓磊, 赵文吉, 宫兆宁. 2016. 基于可见光植被指数的面向对象湿地水生植被提取方法. 应用生态学报, 27(5): 1427-1436 [DOI: 10.13287/j.1001-9332.201605.002http://dx.doi.org/10.13287/j.1001-9332.201605.002]

Jin B F and Guo Y H. 2001. Primary studies on the reproductive characteristics of potanogeton maackianus. Acta Hydrobiologica Sinica, 25(5): 439-448

靳宝锋, 郭友好. 2001. 微齿眼子菜繁殖生物学特性的初步研究. 水生生物学报, 25(5): 439-448 [DOI: 10.3321/j.issn:1000-3207.2001.05.002http://dx.doi.org/10.3321/j.issn:1000-3207.2001.05.002]

Kellndorfer J, Walker W, Pierce L, Dobson C, Fites J A, Hunsaker C, Vona J and Clutter M. 2004. Vegetation height estimation from shuttle radar topography mission and national elevation datasets. Remote Sensing of Environment, 93(3): 339-358 [DOI: 10.1016/j.rse.2004.07.017http://dx.doi.org/10.1016/j.rse.2004.07.017]

Liang Q C, Zhang Y C, Ma R H, Loiselle S, Li J and Hu M Q. 2017. A MODIS-based novel method to distinguish surface cyanobacterial scums and aquatic macrophytes in Lake Taihu. Remote Sensing, 9(2): 133 [DOI: 10.3390/rs9020133http://dx.doi.org/10.3390/rs9020133]

Liu X H, Zhang Y L, Shi K, Zhou Y Q, Tang X M, Zhu G W and Qin B Q. 2015. Mapping aquatic vegetation in a large, shallow eutrophic lake: a frequency-based approach using multiple years of MODIS data. Remote Sensing, 7(8): 10295-10320 [DOI: 10.3390/rs70810295http://dx.doi.org/10.3390/rs70810295]

Luo J H, Duan H T, Ma R H, Jin X L, Li F, Hu W P, Shi K and Huang W J. 2017. Mapping species of submerged aquatic vegetation with multi-seasonal satellite images and considering life history information. International Journal of Applied Earth Observation and Geoinformation, 57: 154-165 [DOI: 10.1016/j.jag.2016.11.007http://dx.doi.org/10.1016/j.jag.2016.11.007]

Luo J H, Li X C, Ma R H, Li F, Duan H T, Hu W P, Qin B Q and Huang W J. 2016a. Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China. Ecological Indicators, 60: 503-513 [DOI: 10.1016/j.ecolind.2015.07.029http://dx.doi.org/10.1016/j.ecolind.2015.07.029]

Luo J H, Ma R H, Duan H T, Hu W P, Zhu J G, Huang W J and Lin C. 2014. A new method for modifying thresholds in the classification of tree models for mapping aquatic vegetation in Taihu Lake with satellite images. Remote Sensing, 6(8): 7442-7462 [DOI: 10.3390/rs6087442http://dx.doi.org/10.3390/rs6087442]

Luo J H, Ma R H, Feng H H and Li X C. 2016b. Estimating the total nitrogen concentration of reed canopy with hyperspectral measurements considering a non-uniform vertical nitrogen distribution. Remote Sensing, 8(10): 789 [DOI: 10.3390/rs8100789http://dx.doi.org/10.3390/rs8100789]

Luo J H, Pu R L, Duan H T, Ma R H, Mao Z G, Zeng Y, Huang L S and Xiao Q T. 2020. Evaluating the influences of harvesting activity and eutrophication on loss of aquatic vegetations in Taihu Lake, China. International Journal of Applied Earth Observation and Geoinformation, 87: 102038 [DOI: 10.1016/j.jag.2019.102038http://dx.doi.org/10.1016/j.jag.2019.102038]

Luo S Z, Wang C, Xi X H, Pan F F, Qian M J, Peng D L, Nie S, Qin H M and Lin Y. 2017. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 58: 107-117 [DOI: 10.1016/j.jag.2017.01.016http://dx.doi.org/10.1016/j.jag.2017.01.016]

Ma R H, Duan H T, Gu X H and Zhang S X. 2008. Detecting aquatic vegetation changes in Taihu Lake, China using multi-temporal satellite imagery. Sensors, 8(6): 3988-4005 [DOI: 10.3390/s8063988http://dx.doi.org/10.3390/s8063988]

Massicotte P, Bertolo A, Brodeur P, Hudon C, Mingelbier M and Magnan P. 2015. Influence of the aquatic vegetation landscape on larval fish abundance. Journal of Great Lakes Research, 41(3): 873-880 [DOI: 10.1016/j.jglr.2015.05.010http://dx.doi.org/10.1016/j.jglr.2015.05.010]

Mobley C D. 2001. Radiative transfer in the ocean//Steele J H, ed. Encyclopedia of Ocean Sciences. San Diego: Academic Press, 2321-2330 [DOI: 10.1006/rwos.2001.0469http://dx.doi.org/10.1006/rwos.2001.0469]

Murphy F, Schmieder K, Baastrup-Spohr L, Pedersen O and Sand‐Jensen K. 2018. Five decades of dramatic changes in submerged vegetation in Lake constance. Aquatic Botany, 144: 31-37 [DOI: 10.1016/j.aquabot.2017.10.006http://dx.doi.org/10.1016/j.aquabot.2017.10.006]

Mutanga O, Adam E and Cho M A. 2012. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18: 399-406 [DOI: 10.1016/j.jag.2012.03.012http://dx.doi.org/10.1016/j.jag.2012.03.012]

Oyama Y, Matsushita B and Fukushima T. 2015. Distinguishing surface cyanobacterial blooms and aquatic macrophytes using Landsat/TM and ETM+ shortwave infrared bands. Remote Sensing of Environment, 157: 35-47 [DOI: 10.1016/j.rse.2014.04.031http://dx.doi.org/10.1016/j.rse.2014.04.031]

Pande-Chhetri R, Abd-Elrahman A and Jacoby C. 2014. Classification of submerged aquatic vegetation in black river using hyperspectral image analysis. Geomatica, 68(3): 169-182 [DOI: 10.5623/cig2014-302http://dx.doi.org/10.5623/cig2014-302]

Pang C C, Wu S Q, Lai X J and Wu D. 2014. Water flow with submerged vegetation and its effect on water turbidity. Research of Environmental Sciences, 27(5): 498-504

庞翠超, 吴时强, 赖锡军, 武迪. 2014. 沉水植被降低水体浊度的机理研究. 环境科学研究, 27(5): 498-504 [DOI: 10.13198/j.issn.1001-6929.2014.05.07http://dx.doi.org/10.13198/j.issn.1001-6929.2014.05.07]

Poerschmann J, Weiner B, Wedwitschka H, Zehnsdorf A, Koehler R and Kopinke F D. 2015. Characterization of biochars and dissolved organic matter phases obtained upon hydrothermal carbonization of Elodea nuttallii. Bioresource Technology, 189: 145-153 [DOI: 10.1016/j.biortech.2015.03.146http://dx.doi.org/10.1016/j.biortech.2015.03.146]

Pu R L, Bell S, Meyer C, Baggett L and Zhao Y C. 2012. Mapping and assessing seagrass along the western coast of Florida using Landsat TM and EO-1 ALI/Hyperion imagery. Estuarine, Coastal and Shelf Science, 115: 234-245 [DOI: 10.1016/j.ecss.2012.09.006http://dx.doi.org/10.1016/j.ecss.2012.09.006]

Qing S, A R N, Shun B, Zhao W J, Bao Y H and Hao Y L. 2020. Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986-2018. Ecological Indicators, 112: 106073 [DOI: 10.1016/j.ecolind.2020.106073http://dx.doi.org/10.1016/j.ecolind.2020.106073]

Rogers K H and Breen C M. 1983. An investigation of macrophyte, epiphyte and grazer interactions//Wetzel P G, ed. Periphyton of Freshwater Ecosystems. Dordrecht: Springer [DOI: 10.1007/978-94-009-7293-3_29http://dx.doi.org/10.1007/978-94-009-7293-3_29]

Sawaya K E, Olmanson L G, Heinert N J, Brezonik P L and Bauer W E. 2003. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88(1/2): 144-156 [DOI: 10.1016/j.rse.2003.04.006http://dx.doi.org/10.1016/j.rse.2003.04.006]

Shi H, Li X W, Niu Z C, Li J Y, Li Y and Li N. 2016. Remote sensing information extraction of aquatic vegetation in Lake Taihu based on random forest model. Journal of Lake Sciences, 28(3): 635-644

侍昊, 李旭文, 牛志春, 李继影, 李杨, 李宁. 2016. 基于随机森林模型的太湖水生植被遥感信息提取. 湖泊科学, 28(3): 635-644 [DOI: 10.18307/2016.0320http://dx.doi.org/10.18307/2016.0320]

Shi L L, Jia Y F, Zuo A J, Ma T H, Lei J L, Lei G C and Wen L. 2018. Dynamic change of vegetation cover and productivity of Poyang Lake wetland based on MODIS EVI time series. Biodiversity Science, 26(08): 828-837

史林鹭, 贾亦飞, 左奥杰, 马童慧, 雷佳琳, 雷光春, 文力. 2018. 基于MODIS EVI时间序列的鄱阳湖湿地植被覆盖和生产力的动态变化. 生物多样性: 26(08): 828-837 [DOI: 10.17520/biods.2018089http://dx.doi.org/10.17520/biods.2018089]

Singh G, Reynolds C, Byrne M and Rosman B. 2020. A remote sensing method to monitor water, aquatic vegetation, and invasive water hyacinth at national extents. Remote Sensing, 12(24): 4021 [DOI: 10.3390/rs12244021http://dx.doi.org/10.3390/rs12244021]

Soana E, Naldi M and Bartoli M. 2012. Effects of increasing organic matter loads on pore water features of vegetated (Vallisneria spiralis L.) and plant-free sediments. Ecological Engineering, 47: 141-145 [DOI: 10.1016/j.ecoleng.2012.06.016http://dx.doi.org/10.1016/j.ecoleng.2012.06.016]

Spears B M, Mackay E B, Yasseri S, Gunn I D M, Waters K E, Andrews C, Cole S, De Ville M, Kelly A, Meis S, Moore A L, Nürnberg G K, van Oosterhout F, Pitt J A, Madgwick G, Woods H J and Lürling M. 2016. A meta-analysis of water quality and aquatic macrophyte responses in 18 lakes treated with lanthanum modified bentonite (Phoslock®). Water Research, 97: 111-121 [DOI: 10.1016/j.watres.2015.08.020http://dx.doi.org/10.1016/j.watres.2015.08.020]

Tian Y Q, Yu Q, Zimmerman M J, Flint S and Waldron M C. 2010. Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing. Freshwater Biology, 55(8): 1658-1673 [DOI: 10.1111/j.1365-2427.2010.02400.xhttp://dx.doi.org/10.1111/j.1365-2427.2010.02400.x]

Underwood E C, Mulitsch M J, Greenberg J A, Whiting M L, Ustin S L and Kefauver S C. 2006. Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery. Environmental Monitoring and Assessment, 121(1/3): 47-64 [DOI: 10.1007/s10661-005-9106-4http://dx.doi.org/10.1007/s10661-005-9106-4]

Villa P, Bresciani M, Bolpagni R, Pinardi M and Giardino C. 2015. A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sensing of Environment, 171: 218-233 [DOI: 10.1016/j.rse.2015.10.020http://dx.doi.org/10.1016/j.rse.2015.10.020]

Villa P, Pinardi M, Bolpagni R, Gillier J M, Zinke P, Nedelcuţ F and Bresciani M. 2018. Assessing macrophyte seasonal dynamics using dense time series of medium resolution satellite data. Remote Sensing of Environment, 216: 230-244 [DOI: 10.1016/j.rse.2018.06.048http://dx.doi.org/10.1016/j.rse.2018.06.048]

Visser F, Buis K, Verschoren V and Schoelynck J. 2018. Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge. Hydrobiologia, 812(1): 157-175 [DOI: 10.1007/s10750-016-2928-yhttp://dx.doi.org/10.1007/s10750-016-2928-y]

Visser F, Wallis C and Sinnott A M. 2013. Optical remote sensing of submerged aquatic vegetation: opportunities for shallow clearwater streams. Limnologica, 43(5): 388-398 [DOI: 10.1016/j.limno.2013.05.005http://dx.doi.org/10.1016/j.limno.2013.05.005]

Wang Q, Zhou X D, Luo J H and Chen C. 2015. The remote sensing monitoring of dominant species of submerged vegetation of Lake Taihu with the consideration of their living histories. Journal of Lake Sciences, 27(5): 953-961

王琪, 周兴东, 罗菊花, 陈冲. 2015. 考虑生活史的太湖沉水植物优势种遥感监测. 湖泊科学, 27(5): 953-961 [DOI: 10.18307/2015.0523http://dx.doi.org/10.18307/2015.0523]

Wang Z H, Xin C L, Sun Z, Luo J H and Ma R H. 2019. Automatic extraction method of aquatic vegetation types in small shallow lakes based on sentinel-2 data: a case study of cuiping lake. Remote Sensing Information, 34(5): 132-141

汪政辉, 辛存林, 孙喆, 罗菊花, 马荣华. 2019. Sentinel-2数据的小型湖泊水生植被类群自动提取方法——以翠屏湖为例. 遥感信息, 34(5): 132-141 [DOI: 10.3969/j.issn.1000-3177.2019.05.022http://dx.doi.org/10.3969/j.issn.1000-3177.2019.05.022]

Whiteside T G and Bartolo R E. 2015. Mapping aquatic vegetation in a tropical wetland using high spatial resolution multispectral satellite imagery. Remote Sensing, 7(9): 11664-11694 [DOI: 10.3390/rs70911664http://dx.doi.org/10.3390/rs70911664]

Wiegleb G and Brux H. 1991. Comparison of life history characters of broad-leaved species of the genus Potamogeton L. I. General characterization of morphology and reproductive strategies. Aquatic Botany, 39(1/2): 131-146 [DOI: 10.1016/0304-3770(91)90028-4http://dx.doi.org/10.1016/0304-3770(91)90028-4]

Wolter Peter T, Carol A Johnston, and Gerald J Niemi. 2007. Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data. International Journal of Remote Sensing, 26(23): 5255-5274 [DOI: 10.1080/01431160500219208http://dx.doi.org/10.1080/01431160500219208]

Xiao C, Wang X F, Xia J and Liu G H. 2010. The effect of temperature, water level and burial depth on seed germination of Myriophyllum spicatum and Potamogeton malaianus. Aquatic Botany, 92(1): 28-32 [DOI: 10.1016/j.aquabot.2009.09.004http://dx.doi.org/10.1016/j.aquabot.2009.09.004]

Yadav S, Yoneda M, Susaki J, Tamura M, Ishikawa K and Yamashiki Y. 2017. A satellite-based assessment of the distribution and biomass of submerged aquatic vegetation in the optically shallow Basin of Lake Biwa. Remote Sensing, 9(9): 966 [DOI: 10.3390/rs9090966http://dx.doi.org/10.3390/rs9090966]

Yang J Z C, Luo J H, Lu L R, Sun Z, Cao Z G, Zeng Q F and Mao Z G. 2021. Changes in aquatic vegetation communities based on satellite images before and after pen aquaculture removal in East Lake Taihu. Journal of Lake Sciences, 33(2): 507-517

杨井志成, 罗菊花, 陆莉蓉, 孙喆, 曹志刚, 曾庆飞, 毛志刚. 2021. 东太湖围网拆除前后水生植被群落遥感监测及变化. 湖泊科学, 33(2): 507-517 [DOI: 10.18307/2021.0228http://dx.doi.org/10.18307/2021.0228]

Yang Q X. 1998. Ecological functions of aquatic vegetation in East Taihu Lake and its reasonable regulation. Journal of Lake Sciences, 10(1): 67-72

杨清心. 1998. 东太湖水生植被的生态功能及调节机制. 湖泊科学, 10(1): 67-72 [DOI: 10.18307/1998.0111http://dx.doi.org/10.18307/1998.0111]

Ye C, Li C H, Yu H C, Song X F, Zou G Y and Liu J. 2011. Study on ecological restoration in near-shore zone of a eutrophic lake, Wuli Bay, Taihu Lake. Ecological Engineering, 37(9): 1434-1437 [DOI: 10.1016/j.ecoleng.2011.03.028http://dx.doi.org/10.1016/j.ecoleng.2011.03.028]

Yuan L and Zhang L. 2006. Identification of the spectral characteristics of submerged plant vallisneria spiralis. Acta Ecologica Sinica, 26(4): 1005-1010 [DOI: 10.1016/S1872-2032(06)60019-Xhttp://dx.doi.org/10.1016/S1872-2032(06)60019-X]

Zhang S X, Duan H T and Gu X H. 2008, Remote sensing information extraction of hydrophytes based on the retrieval of water transparency in Lake Taihu, China. Journal of Lake Sciences, 20(2): 184-190

张寿选, 段洪涛, 谷孝鸿. 2008. 基于水体透明度反演的太湖水生植被遥感信息提取. 湖泊科学, 20(2): 184-190 [DOI: 10.18307/2008.0208http://dx.doi.org/10.18307/2008.0208]

Zhang Y L, Jeppesen E, Liu X H, Qin B Q, Shi K, Zhou Y Q, Thomaz S M and Deng J M. 2017. Global loss of aquatic vegetation in lakes. Earth-Science Reviews, 173: 259-265 [DOI: 10.1016/j.earscirev.2017.08.013http://dx.doi.org/10.1016/j.earscirev.2017.08.013]

Zhang Y L, Liu X H, Qin B Q, Shi K, Deng J M and Zhou Y Q. 2016. Aquatic vegetation in response to increased eutrophication and degraded light climate in Eastern Lake Taihu: implications for lake ecological restoration. Scientific Reports, 6: 23867 [DOI: 10.1038/srep23867http://dx.doi.org/10.1038/srep23867]

Zhao D H, Jiang H, Yang T W, Cai Y, Xu D L and An S Q. 2012. Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds. Journal of Environmental Management, 95(1): 98-107 [DOI: 10.1016/j.jenvman.2011.10.007http://dx.doi.org/10.1016/j.jenvman.2011.10.007]

Zhao D H, Lv M T, Jiang H, Cai Y, Xu D L and An S Q. 2013. Spatio-temporal variability of aquatic vegetation in taihu lake over the past 30 years. PLoS One, 8(6): e66365 [DOI: 10.1371/journal.pone.0066365http://dx.doi.org/10.1371/journal.pone.0066365]

Zhou G H, Ma Z Q, Sathyendranath S, Platt T, Jiang C and Sun K. 2018. Canopy reflectance modeling of aquatic vegetation for algorithm development: global sensitivity analysis. Remote Sensing, 10(6): 837 [DOI: 10.3390/rs10060837http://dx.doi.org/10.3390/rs10060837]

Zhou G H, Niu C Y, Xu W J, Yang W N, Wang J W and Zhao H J. 2015. Canopy modeling of aquatic vegetation: a radiative transfer approach. Remote Sensing of Environment, 163: 186-205 [DOI: 10.1016/j.rse.2015.03.015http://dx.doi.org/10.1016/j.rse.2015.03.015]

Zhou G H, Yang S, Sathyendranath S and Platt T. 2020. Canopy modeling of aquatic vegetation: a geometric optical approach (AVGO). Remote Sensing of Environment, 245: 111829 [DOI: 10.1016/j.rse.2020.111829http://dx.doi.org/10.1016/j.rse.2020.111829]

Alert me when the article has been cited

提交

暂无数据