Remote sensing estimation of aquatic vegetation biomass in Ulansuhai Lake

doi:10.13474/j.cnki.11-2246.2025.0909

Abstract

Abstract: Aquatic vegetation plays an irreplaceable role in the ecological balance and water purification of rivers and lakes.Biomass,as an important biophysicochemical indicator of aquatic vegetation,not only reflects primary productivity but also plays a key role in assessing nitrogen and phosphorus reserves and carbon sequestration in water bodies.To address the challenge of comprehensively surveying aquatic vegetation biomass in the complex lake vegetation environment of Ulansuhai Lake and to account for the spectral response differences between emergent and submerged plants,this study proposes a biomass estimation model combining remote sensing data with field survey data.Using Landsat 8 imagery and data from 32 field sampling points,and based on ENVI and Matlab software,a partial least squares regression model for the retrieval of aquatic vegetation biomass in Ulansuhai Lake is developed and its accuracy assessed.The results showed that the estimation accuracies of the biomass retrieval models for emergent plants and submerged plants reached 92.93%and 79.80%,respectively.This indicates that the partial least squares regression model is suitable for retrieving aquatic vegetation biomass under small sample conditions and can achieve high accuracy in complex lake vegetation environments.The total biomass of aquatic vegetation in Ulansuhai Lake in June and September 2023 was 781.6×10⁴ and 791.9×10⁴ t,respectively,with emergent plants accounting for 78.03%and 76.82%.Compared to June 2023,the area of emergent plants in Ulansuhai Lake decreased in September,the average biomass per unit area increased,and there was no significant change in spatial distribution; The biomass of submerged plants show a notable increase,with the area expanding,approaching the historical high from 1988(102.06 km²).This research provides scientific evidence for subsequent lake ecological management,health assessment,and water quality restoration in Ulansuhai Lake.

Key words: Ulansuhai, aquatic vegetation, biomass inversion modeling, partial least squares regression, vegetation index

CLC Number:

P237

GONG Liangchen, XU Dongpo, KUANG Zhen, FAN Yingchun, DONG Jiahui. Remote sensing estimation of aquatic vegetation biomass in Ulansuhai Lake[J]. Bulletin of Surveying and Mapping, 2025, 0(9): 51-58.

References

[1] 沈赫.大庆龙凤湿地水生植物多样性及生态功能探究[J].现代园艺, 2024, 47(6):48-50.
[2] ELLIS J B, SHUTES R B, REVITT D M, et al.Use of macrophytes for pollution treatment in urban wetlands[J].Resources, Conservation and Recycling, 1994, 11(1-4):1-12.
[3] 杨清心.东太湖水生植被的生态功能及调节机制[J].湖泊科学, 1998, 10(1):67-72.
[4] JENSEN J R, CHRISTENSEN E, RAMSEY E W, et al.Remote sensing inland wetlands:a multispectral approach[J].Photogrammetric Engineering and Remote Sensing, 1985, 52:87-100.
[5] 刘侠, 张树林, 苏文盛.陆地卫星图象在洞庭湖芦苇资源调查中的应用[J].地理科学, 1981, 1(1):52-57.
[6] BYRD K B, O'CONNELL J L, DI TOMMASO S, et al.Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation[J].Remote Sensing of Environment, 2014, 149:166-180.
[7] LUO J H, MA R H, FENG H H, et al.Estimating the total nitrogen concentration of reed canopy with hyperspectral measurements considering a non-uniform vertical nitrogen distribution[J].Remote Sensing, 2016, 8(10):789.
[8] GE C T, ZHANG C, ZHANG Y, et al.Synergy of UAV-LiDAR data and multispectral remote sensing images for allometric estimation of phragmites australis aboveground biomass in coastal wetland[J].Remote Sensing, 2024, 16(16):3073.
[9] ZOU W N, YUAN L, ZHANG L Q.Analyzing the spectral response of submerged aquatic vegetation in a eutrophic lake, Shanghai, China[J].Ecological Engineering, 2013, 57:65-71.
[10] YADAV S, YONEDA M, SUSAKI J, et al.A satellite-based assessment of the distribution and biomass of submerged aquatic vegetation in the optically shallow basin of Lake Biwa[J].Remote Sensing, 2017, 9(9):966.
[11] LU L R, LUO J H, XIN Y H, et al.A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data[J].Science of The Total Environment, 2024, 912:169404.
[12] 毛旭锋, 魏晓燕.富营养化湖泊叶绿素a时空变化特征及其影响因素分析[J].中国环境监测, 2015, 31(6):65-70.
[13] 芦津, 杨瑾晟, 罗菊花, 等.环境因子及生态补水对乌梁素海“黄苔”年际暴发面积的影响(1986—2021年)[J].湖泊科学, 2023, 35(6):1881-1890.
[14] 高闻一.乌梁素海水生生物多样性调查分析及健康评估[D].呼和浩特:内蒙古农业大学, 2021.
[15] 梁齐.基于Landsat数据对乌梁素海水生植物时空分布特征分析[D].呼和浩特:内蒙古农业大学, 2023.
[16] QING Song, A Runa, SHUN Buri, et al.Distinguishing and mapping of aquatic vegetations and yellow algae bloom with Landsat satellite data in a complex shallow Lake, China during 1986—2018[J].Ecological Indicators, 2020, 112:106073.
[17] 贾明明, 王宗明, 张柏, 等.综合环境卫星与MODIS数据的面向对象土地覆盖分类方法[J].武汉大学学报(信息科学版), 2014, 39(3):305-310.
[18] 徐婷, 曹林, 佘光辉.基于Landsat 8 OLI的特征变量优化提取及森林生物量反演[J].遥感技术与应用, 2015, 30(2):226-234.
[19] 严红萍, 俞兵.主成分分析在遥感图像处理中的应用[J].资源环境与工程, 2006(2):168-170.
[20] 罗批, 郭继昌, 李锵, 等.基于偏最小二乘回归建模的探讨[J].天津大学学报(自然科学与工程技术版), 2002, 35(6):783-786.
[21] 李世波, 林辉, 王光明, 等.基于GF-1的森林蓄积量遥感估测[J].中南林业科技大学学报, 2019, 39(8):70-75.
[22] 刘琼阁, 彭道黎, 涂云燕.基于偏最小二乘回归的森林蓄积量遥感估测[J].中南林业科技大学学报, 2014, 34(2):81-84.
[23] 王淼, 储学远, 钱家炜, 等.区域水稻田土壤-作物系统重金属污染高光谱遥感定量估测[J].江苏农业科学, 2023, 51(12):172-180.
[24] 夏媛媛.基于无人机高光谱遥感的静宁县苹果园水肥估测研究[D].兰州:甘肃农业大学, 2023.
[25] 邓晓红, 张巧丽, 刘文强, 等.乌梁素海水生生物多样性调查及分析[J].内蒙古林业, 2020(10):37-38.
[26] 张春媛, 于长水, 潘高娃, 等.湖泊生态系统健康评估初探——以乌梁素海为例[J].北方环境, 2011, 23(8):38-39.
[27] 杨志岩.大型挺水植物对乌梁素海营养元素去除能力研究[D].呼和浩特:内蒙古农业大学, 2009.
[28] 包菡, 卓义, 刘华民, 等.乌梁素海芦苇湿地遥感生物量估算研究[J].干旱区研究, 2016, 33(5):1028-1035.
[29] CHEN T Q, GUESTRIN C.XGBoost:a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco, California: ACM Press, 2016:785-794.
[30] 张艺伟, 郭焱培, 唐荣, 等.高光谱遥感在植物多样性研究中的应用进展与趋势[J].遥感学报, 2025, 27(11):2467-2483.
[31] 李少波, 王晓强, 郭利彪, 等.草类植物无人机遥感图像中深度学习应用综述[J].计算机科学与探索, 2025, 19(5):1157-1176.
[32] 高越, 布仁仓, 熊在平, 等.基于气候时滞效应和空间异质性对中国植被总初级生产力的模拟[J].生态学报, 2024, 44(17):7615-7630.
[33] 王俊迪, 赵慧, 罗耀华, 等.深度学习在植被高光谱遥感中的研究综述[J].遥感信息, 2024, 39(4):11-22.
[34] 陈永贵, 朱玉香.黄河流域典型内陆湖泊水生植被类群与藻华遥感监测[J].灌溉排水学报, 2022, 41(12):81-88.
[35] 巴达日夫.乌梁素海水环境因子时空分布特征及富营养化评价[J].海洋湖沼通报, 2019(4):108-114.
[36] 范元元, 李兴, 春喜.乌梁素海水体富营养化研究进展[J].环境保护科学, 2018, 44(4):83-88.
[37] 周茜.2013—2020年乌梁素海水环境指标变化特征及趋势分析[D].呼和浩特:内蒙古农业大学, 2021.
[38] 张启荧.生态补水背景下乌梁素海水体交换能力与水质改善模拟与评价[D].呼和浩特:内蒙古农业大学, 2022.
[39] 田伟东.内蒙古乌梁素海湖泊健康评估[D].呼和浩特:内蒙古农业大学, 2016.