Citation: | LUO Qi-Bin, HE Liang, GUO Shi-Yuan, XIE Zhong-Shu, WANG Zhao-Ying, GE Gang, LI Shu. INVERSION OF FLOATING LEAF VEGETATION COVERAGE BASED ON SATELLITE IMAGES AND DRONE IMAGES[J]. ACTA HYDROBIOLOGICA SINICA, 2025, 49(4): 042512. DOI: 10.7541/2025.2024.0160 |
In order to quickly and accurately monitor the coverage of floating leaf vegetation, this article focuses on the sub-lakes and isolated lakes of Poyang Lake which with widespread floating leaf vegetation. First, the normalized difference vegetation index (NDVI) of the pixels were calculated based on Sentinel-2 satellite imagery. Then, corresponding field coverage of floating leaf vegetation (FCFLV) in the pixels were assessed using drone aerial imagery, and a regression model between NDVI and FCFLV is established. Finally, using Dawu Lake as an example, floating leaf vegetation’s coverage (FLVC) of Dawu Lake was inverted using both the method presented in this paper and the traditional pixel dichotomy method, and the inversion accuracy of the two methods was compared. The results indicate: (1) The regression model based on the NDVI and FCFLV demonstrates a very good fit, with a coefficient of determination (R²) reaching 0.9; the model’s root mean square error (RMSE) is 5.75%, and the mean relative error (MRE) is only 9.0%; (2) Using the traditional threshold dichotomy method to invert FLVC yields a minimum RMSE of 20.25% and a minimum MRE of 53.68% when the NDVI threshold is 0.081, both of which are significantly higher than the RMSE and MRE obtained from the method in this paper; (3) Compared to the traditional pixel dichotomy method, this model can more accurately invert FLVC, especially in areas where the vegetation is sparse. The method presented in this paper, which constructs a regression model based on drone aerial data and satellite NDVI, can provide a reference for the rapid monitoring and quantitative inversion of aquatic plant communities.
[1] |
Karus K, Zagars M, Agasild H, et al. The influence of macrophyte ecological groups on food web components of temperate freshwater lakes [J]. Aquatic Botany, 2022(183): 103571. doi: 10.1016/j.aquabot.2022.103571
|
[2] |
Kolada A. The use of aquatic vegetation in lake assessment: testing the sensitivity of macrophyte metrics to anthropogenic pressures and water quality [J]. Hydrobiologia, 2010, 656(1): 133-147. doi: 10.1007/s10750-010-0428-z
|
[3] |
Seto M, Takamura N, Iwasa Y. Individual and combined suppressive effects of submerged and floating-leaved macrophytes on algal blooms [J]. Journal of Theoretical Biology, 2013(319): 122-133.
|
[4] |
Kato Y, Nishihiro J, Yoshida T. Floating-leaved macrophyte (Trapa japonica) drastically changes seasonal dynamics of a temperate lake ecosystem [J]. Ecological Research, 2016, 31(5): 695-707. doi: 10.1007/s11284-016-1378-3
|
[5] |
Wang C, Zheng S S, Wang P F, et al. Effects of vegetations on the removal of contaminants in aquatic environments: a review [J]. Journal of Hydrodynamics, 2014, 26(4): 497-511. doi: 10.1016/S1001-6058(14)60057-3
|
[6] |
Yasuno N, Shimada T, Fujimoto Y, et al. Semiaquatic spiders alopecosa cinnameopilosa rely on prey derived from macrophyte-based food web: evidence from Lake Izunuma, Japan [J]. Wetlands Ecology and Management, 2021, 29(4): 507-517. doi: 10.1007/s11273-021-09797-6
|
[7] |
Kawatsu M, Morimoto G, Kagami M. Seasonal changes in the water bird community in Lake Inba: influence of floating-leaved macrophytes on habitat selection [J]. Aquatic Botany, 2015(126): 32-37. doi: 10.1016/j.aquabot.2015.06.003
|
[8] |
Yi C, Li J, Zhang C, et al. In situ monitoring of a eutrophicated pond revealed complex dynamics of nitrogen and phosphorus triggered by decomposition of floating-leaved macrophytes [J]. Water, 2021, 13(13): 1751.
|
[9] |
Free G, Bresciani M, Trodd W, et al. Estimation of lake ecological quality from Sentinel-2 remote sensing imagery [J]. Hydrobiologia, 2020, 847(6): 1423-1438. doi: 10.1007/s10750-020-04197-y
|
[10] |
罗菊花, 杨井志成, 段洪涛, 等. 浅水湖泊水生植被遥感监测研究进展 [J]. 遥感学报, 2022, 26(1): 68-76.] doi: 10.11834/jrs.20221208
Luo J H, Yang J Z C, Duan H T, et al. Research progress of aquatic vegetation remote sensing in shallow lakes [J]. National Remote Sensing Bulletin, 2022, 26(1): 68-76. [ doi: 10.11834/jrs.20221208
|
[11] |
Dai Y, Feng L, Hou X, et al. An automatic classification algorithm for submerged aquatic vegetation in shallow lakes using Landsat imagery [J]. Remote Sensing of Environment, 2021(260): 112459.
|
[12] |
Luo J, Ni G, Zhang Y, et al. A new technique for quantifying algal bloom, floating/emergent and submerged vegetation in eutrophic shallow lakes using Landsat imagery [J]. Remote Sensing of Environment, 2023(287): 113480. doi: 10.1016/j.rse.2023.113480
|
[13] |
张达, 郑玉权. 高光谱遥感的发展与应用 [J]. 光学与光电技术, 2013, 11(3): 67-73.]
Zhang D, Zheng Y Q. Hyperspectral remote sensing and its development and application review [J]. Optics & Optoelectronic Technology, 2013, 11(3): 67-73. [
|
[14] |
Pu R, Bell S. A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI/Hyperion images [J]. Isprs Journal of Photogrammetry and Remote Sensing, 2013(83): 116-129. doi: 10.1016/j.isprsjprs.2013.06.008
|
[15] |
Zou W, Yuan L, Zhang L. Analyzing the spectral response of submerged aquatic vegetation in a eutrophic lake, Shanghai, China [J]. Ecological Engineering, 2013(57): 65-71. doi: 10.1016/j.ecoleng.2013.04.008
|
[16] |
杜雨春子, 青松, 包玉海, 等. 乌梁素海沉水植物群落光谱特征及其受覆盖度的影响分析 [J]. 海洋与湖沼, 2022, 53(1): 74-83.]
Du Y C Z, Qing S, Bao Y H, et al. Spectral features of submerged aquatic vegetation under coverage impact in the Ulansuhai Lake [J]. Oceanologia et Limnologia Sinica, 2022, 53(1): 74-83. [
|
[17] |
王宁, 周明通, 魏宣, 等. 沙漠腹地绿洲植被覆盖度提取及植被指数优选 [J]. 水土保持通报, 2022, 42(6): 197-205,213.] doi: 10.3969/j.issn.1000-288X.2022.6.stbctb202206025
Wang N, Zhou M T, Wei X, et al. Extraction of vegetation cover and optimization of vegetation indices in a desert hinterland oasis [J]. Bulletin of Soil and Water Conservation, 2022, 42(6): 197-205,213. [ doi: 10.3969/j.issn.1000-288X.2022.6.stbctb202206025
|
[18] |
齐述华, 张秀秀, 江丰, 等. 鄱阳湖水文干旱化发生的机制研究 [J]. 自然资源学报, 2019, 34(1): 168-178.]
Qi S H, Zhang X X, Jiang F, et al. Research on the causes for hydrological drought trend in Poyang Lake [J]. Journal of Natural Resources, 2019, 34(1): 168-178. [
|
[19] |
Wu G, Liu Y. Combining multispectral imagery with in situ topographic data reveals complex water level variation in china’s largest freshwater lake [J]. Remote Sensing, 2015, 7(10): 13466-13484.
|
[20] |
Li K, Liu X, Zhou Y, et al. Temporal and spatial changes in macrozoobenthos diversity in Poyang Lake Basin, China [J]. Ecology and Evolution, 2019, 9(11): 6353-6365. doi: 10.1002/ece3.5207
|
[21] |
胡振鹏, 葛刚, 刘成林. 鄱阳湖湿地植被退化原因分析及其预警 [J]. 长江流域资源与环境, 2015, 24(3): 381-386.] doi: 10.11870/cjlyzyyhj201503005
Hu Z P, Ge G, Liu C L. Cause analysis and early warning for wetland vegetation degradation in Poyang Lake [J]. Resources and Environment in the Yangtze Basin, 2015, 24(3): 381-386. [ doi: 10.11870/cjlyzyyhj201503005
|
[22] |
胡振鹏, 林玉茹. 鄱阳湖水生植被30年演变及其驱动因素分析 [J]. 长江流域资源与环境, 2019, 28(8): 1947-1955.]
Hu Z P, Lin Y R. Analysis of evolution process and driving factors for aquatic vegetations of Poyang Lake in 30 years [J]. Resources and Environment in the Yangtze Basin, 2019, 28(8): 1947-1955. [
|
[23] |
谭志强, 张奇, 李云良, 等. 鄱阳湖湿地典型植物群落沿高程分布特征 [J]. 湿地科学, 2016, 14(4): 506-515.]
Tan Z Q, Zhang Q, Li Y L, et al. Distribution of typical vegetation communities along elevation in Poyang Lake wetlands [J]. Wetland Science, 2016, 14(4): 506-515. [
|
[24] |
葛刚, 吴兰. 南矶山自然保护区种子植物区系 [J]. 南昌大学学报(理科版), 2006, 30(1): 52-55.]
Ge G, Wu L. Analysis on the flora of seed plants in nanjishan nature reserve, Jiangxi [J]. Journal of Nanchang University (Natural Science), 2006, 30(1): 52-55. [
|
[25] |
Drusch M, Del Bello U, Carlier S, et al. Sentinel-2: ESA’s optical high-resolution mission for gmes operational services [J]. Remote Sensing of Environment, 2012(120): 25-36.
|
[26] |
Liang S, Gong Z, Wang Y, et al. Accurate monitoring of submerged aquatic vegetation in a macrophytic lake using time-series sentinel-2 images [J]. Remote Sensing, 2022, 14(3): 640. doi: 10.3390/rs14030640
|
[27] |
岳丹, 刘东伟, 王立新, 等. 基于NDVI的乌梁素海湿地植被变化 [J]. 干旱区研究, 2015, 32(2): 266-271.]
Yue D, Liu D W, Wang L X, et al. Change of vegetation cover based on NDVI at Wuliangsu Lake wetland [J]. Arid Zone Research, 2015, 32(2): 266-271. [
|
[28] |
Ke Y, Im J, Lee J, et al. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations [J]. Remote Sensing of Environment, 2015(164): 298-313. doi: 10.1016/j.rse.2015.04.004
|
[29] |
Du M, Noguchi N. Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system [J]. Remote Sensing, 2017, 9(3): 289.
|
[30] |
周在明, 杨燕明, 陈本清. 基于可见光波段无人机影像的入侵物种互花米草提取研究 [J]. 亚热带资源与环境学报, 2017, 12(2): 90-95.] doi: 10.3969/j.issn.1673-7105.2017.02.013
Zhou Z M, Yang Y M, Chen B Q. Study on the extraction of exotic species Spartina alterniflora from UAV visible images [J]. Journal of Subtropical Resources and Environment, 2017, 12(2): 90-95. [ doi: 10.3969/j.issn.1673-7105.2017.02.013
|
[31] |
高永平, 康茂东, 何明珠, 等. 基于无人机可见光波段对荒漠植被覆盖度提取的研究——以沙坡头地区为例 [J]. 兰州大学学报(自然科学版), 2018, 54 (6): 770-775.]
Gao Y P, Kang M D, He M Z, et al. Extraction of desert vegetation coverage based on visible light band information of unmanned aerial vehicle: A case study of Shapotou region [J]. Journal of Lanzhou University: Natural Sciences, 2018, 54 (6): 770-775. [
|
[32] |
Vahtmäe E, Kutser T, Paavel B. Performance and applicability of water column correction models in optically complex coastal waters [J]. Remote Sensing, 2020, 12(11): 1861.
|
[33] |
任超, 邓诗琴, 高懋芳. 一种提取北部湾沿海地区水体信息的动态阈值方法 [J]. 测绘通报, 2022(5): 14-19.] doi: 10.3969/j.issn.0494-0911.2022.5.chtb202205003
Ren C, Deng S Q, Gao M F. A method for extracting dynamic threshold value of water body information in Beibu Gulf coastal area [J]. Bulletin of Surveying and Mapping, 2022(5): 14-19. [ doi: 10.3969/j.issn.0494-0911.2022.5.chtb202205003
|
[34] |
白燕英, 高聚林, 张宝林. 基于NDVI与EVI的作物长势监测研究 [J]. 农业机械学报, 2019, 50(9): 153-161.] doi: 10.6041/j.issn.1000-1298.2019.09.017
Bai Y Y, Gao J L, Zhang B L. Monitoring of crops growth based on NDVI and EVI [J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(9): 153-161. [ doi: 10.6041/j.issn.1000-1298.2019.09.017
|
[35] |
Khadka K, Burt A J, Earl H J, et al. Does leaf waxiness confound the use of NDVI in the assessment of chlorophyll when evaluating genetic diversity panels of wheat [J]? Agronomy, 2021, 11(3): 486. doi: 10.3390/agronomy11030486
|
[36] |
Li C, Zhu X, Wei Y, et al. Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging [J]. Scientific Reports, 2018, 8(1): 3756. doi: 10.1038/s41598-018-21963-0
|
[37] |
Wang H, Shi R H, Liu P D. Theoretical simulation and feasibility analysis of the estimation of crop leaf chlorophyll using narrow band NDVI [J]. Applied Mechanics and Materials, 2014(651-653): 317-322.
|
1. |
何文佳,曾本和,朱成科,周朝伟. 茜素红S对异齿裂腹鱼稚鱼标记效果研究. 水产科学. 2024(01): 88-96 .
![]() |