Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data

Wang Xinmiao; Zhao Feng; Liu Hua; Ling Chengxing; Liu Xia; Zeng Haowei

doi:10.12171/j.1000-1522.20240185

Journal of Beijing Forestry University > 2025 > 47(2): 143-151. > DOI: 10.12171/j.1000-1522.20240185

Wang Xinmiao, Zhao Feng, Liu Hua, Ling Chengxing, Liu Xia, Zeng Haowei. Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data[J]. Journal of Beijing Forestry University, 2025, 47(2): 143-151. DOI: 10.12171/j.1000-1522.20240185

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Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data

Wang Xinmiao^{1, 2, 3,},
Zhao Feng^{1, 2, 3, ,},
Liu Hua^{1, 2, 3},
Ling Chengxing^{1, 2, 3},
Liu Xia^{1, 2},
Zeng Haowei^{1, 2}

1.
Institute of Resource Information, Chinese Academy of Forestry, Beijing 100091, China
2.
Key Laboratory of Forestry Remote Sensing and Information Technology, National Forestry and Grassland Administration, Beijing 100091, China
3.
Zhanjiang National Research Station for Mangrove Wetland Ecosystem, Zhanjiang 524448, Guangdong, China

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Received Date: June 06, 2024
Revised Date: August 28, 2024
Available Online: February 16, 2025

Graphical Abstract

Abstract

Abstract

Objective
This paper aims to quickly and accurately obtain mangrove fractional canopy coverage based on unmanned aerial vehicle (UAV) LiDAR data, which would provide important reference for evaluating the effectiveness of mangrove ecological restoration.
Method
The location of this research is on Lingtou Island in Taiping Town, Zhanjiang City of Guangdong Province, southern China. Based on ULS data and ground field data of 40 sample plots, linear regression was used to fit the measured and estimated fractional coverage of mangrove, and the determination coefficient (R²), root mean square error (RMSE) and estimation accuracy (E_A) were calculated. The estimation accuracies of mangrove fractional coverage through four different algorithms based on the first return proportionality model (FRRM), the all return proportionality model (ARRM), pulse return intensity proportionality model (PRIRM), and canopy height model (CHM) were compared. The correlations between sample site fractional coverage, sample site laser point cloud density, sample site LiDAR height characteristic variable and fractional coverage estimation error were analyzed. Finally, the optimal coverage estimation model with the best accuracy was selected to estimate mangrove coverage in the study area, and mapping was carried out.
Result
(1) The estimation accuracy of mangrove fractional coverage based on FRRM model was the highest (R² = 0.970 1, RMSE = 0.032 5, E_A = 93.01%), and the estimation error was the lowest with an average underestimation of 1.04%. The second was the algorithm based on ARRM model (R² = 0.977 4, RMSE= 0.033 6, E_A = 92.58%), the third was the algorithm based on CHM model (R² = 0.945 0, RMSE = 0.044 0, E_A = 90.54%), and the accuracy of PRIRM model was the lowest (R² = 0.9509, RMSE = 0.0610, E_A = 88.17%). (2) The estimation errors of PRIRM model were significantly negatively correlated with both coverage and height characterization variables, and were generally overestimated. Meanwhile, there was no significant correlation between sample site fractional coverage and estimation error of algorithm based on FRRM, ARRM and CHM models. (3) The results of LiDAR sampling sensitivity analysis showed that a raster size of 3 m was most suitable as a raster unit for mapping the coverage of the study area.
Conclusion
The estimation accuracy of mangrove fractional coverage based on the four models is high, and the estimation accuracy of fractional coverage based on FRRM model could be the highest, the estimation results are reliable, which can provide support for the scientific management and ecological restoration of mangrove forests on Lingtou Island, Guangdong Province of southern China.
- unmanned aerial vehicle (UAV) LiDAR,
- point cloud,
- first return,
- mangrove forest,
- coverage

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References (35)

References

[1]	Chen L, Wang W, Zhang Y, et al. Recent progresses in mangrove conservation, restoration and research in China[J]. Journal of Plant Ecology, 2009, 2(2): 45−54. doi: 10.1093/jpe/rtp009
[2]	Ma M, Wang Q, Liu R, et al. Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and accumulation effects[J]. Science of the Total Environment, 2023, 860: 160527. doi: 10.1016/j.scitotenv.2022.160527
[3]	Zhou M, Huang Y, Li G. Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage[J]. Environmental Science and Pollution Research, 2021, 28(18): 23405−23419. doi: 10.1007/s11356-020-12164-2
[4]	Zuo Y, Li Y, He K, et al. Temporal and spatial variation characteristics of vegetation coverage and quantitative analysis of its potential driving forces in the Qilian Mountains, China, 2000−2020[J]. Ecological Indicators, 2022, 143: 409−429.
[5]	朱林富. 基于MODIS数据的四川植被覆盖度景观格局特征分析 [J]. 乐山师范学院学报, 2022, 37(4): 45−51, 83. Zhu L F. Analysis on the landscape pattern characteristics of fractional vegetation coverage based on MODlS data in Sichuan [J]. Journal of Leshan Normal University, 2022, 37(4): 45−51, 83.
[6]	Piao S, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming[J]. Nature, 2008, 451: 49−52. doi: 10.1038/nature06444
[7]	李钰溦, 贾坤, 魏香琴, 等. 中国北方地区植被覆盖度遥感估算及其变化分析[J]. 国土资源遥感, 2015, 27(2): 112−117. Li Y W, Jia K, Wei X Q, et al. Fractional vegetation cover estimation in northern China and its change analysis[J]. Remote Sensing of Land and Resources, 2015, 27(2): 112−117.
[8]	刘二华, 周广胜, 周莉. 基于文献整合的中国不同下垫面植被覆盖度遥感估算模型数据集[J]. 中国科学数据(中英文网络版), 2019, 4(4): 176−184. Liu E H, Zhou G S, Zhou L. A literature-based dataset of fractional vegetation cover remote sensince stimation models for different underlying surfaces in China[J]. Scientific Data in China, 2019, 4(4): 176−184.
[9]	Yan G, Li L, Coy A, et al. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158: 23−34. doi: 10.1016/j.isprsjprs.2019.09.017
[10]	刘琳, 郑兴明, 姜涛, 等. 无人机遥感植被覆盖度提取方法研究综述[J]. 东北师大学报(自然科学版), 2021, 53(4): 151−160. Liu L, Zheng X M, Jiang T, et al. Extraction method of fractional vegetation cover from unmanned aerial vehicle remote sensing: a review[J]. Journal of Northeast Normal University (Natural Science Edition), 2021, 53(4): 151−160.
[11]	Coops N C, Hilker T, Wulder M A, et al. Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR[J]. Trees-Structure and Function, 2007, 21(3): 295−310. doi: 10.1007/s00468-006-0119-6
[12]	Falkowski M J, Smith A M S, Gessler P E, et al. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data[J]. Canadian Journal of Remote Sensing, 2008, 34: S338−S350. doi: 10.5589/m08-055
[13]	Ma Q, Su Y, Guo Q. Comparison of canopy cover estimations from airborne LiDAR, aerial imagery, and satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017: 1−12. doi: 10.1109/JSTARS.2017.2711482.
[14]	Wasser L, Day R, Chasmer L, et al. Influence of vegetation structure on Lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions[J]. PLoS One, 2013, 8(1): e54776. doi: 10.1371/journal.pone.0054776
[15]	Cui Y, Zhao K, Fan W, et al. Retrieving crop fractional cover and LAI based on airborne Lidar data[J]. Journal of Remote Sensing, 2011, 15(6): 1276−1288.
[16]	Riano D, Meier E, Allgöwer B, et al. Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling[J]. Remote Sensing of Environment, 2003, 86(2): 177−186. doi: 10.1016/S0034-4257(03)00098-1
[17]	Hopkinson C, Chasmer L. Testing LiDAR models of fractional cover across multiple forest ecozones[J]. Remote Sensing of Environment, 2009, 113(1): 275−288. doi: 10.1016/j.rse.2008.09.012
[18]	Kato A, Moskal L M, Schiess P, et al. Capturing tree crown formation through implicit surface reconstruction using airborne lidar data[J]. Remote Sensing of Environment, 2009, 113(6): 1148−1162. doi: 10.1016/j.rse.2009.02.010
[19]	Li C, Zheng Y, Zhang X, et al. Comparison of canopy cover and leaf area index estimation from airborne LiDAR and digital aerial potogrammetry in tropical forests[J]. Applied Sciences, 2022, 12(19): 9882. doi: 10.3390/app12199882
[20]	Angiulli F, Basta S, Pizzuti C. Distance-based detection and prediction of outliers[J]. IEEEE Transactions on Knowledge and Data Engineering, 2006, 18(2): 145−160. doi: 10.1109/TKDE.2006.29
[21]	张昌赛, 刘正军, 杨树文, 等. 基于LiDAR数据的布料模拟滤波算法的适用性分析[J]. 激光技术, 2018, 42(3): 410−416. doi: 10.7510/jgjs.issn.1001-3806.2018.03.023 Zhang C S, Liu Z J, Yang S W, et al. Applicability analysis of cloth simulation filtering algorithm based on LiDAR data[J]. Laser Technology, 2018, 42(3): 410−416. doi: 10.7510/jgjs.issn.1001-3806.2018.03.023
[22]	国家测绘地理信息局. 机载激光雷达数据获取技术规范: CH/T8024−2011[S]. 北京: 国家测绘地理信息局, 2011: 1−17. National Administration of Surveying, Specifications for data acquisition of airbore LIDAR : CH/T 8024−2011 [S]. Beijing: China Standard Press, 2011: 1−17.
[23]	陈祖刚, 巴图娜存, 徐芝英, 等. 基于数码相机的草地植被盖度测量方法对比研究[J]. 草业学报, 2014, 23(6): 20−27. doi: 10.11686/cyxb20140603 Chen Z G, Batunacun, Xu Z Y, et al. Measuring grassland vegetation cover using digital camera images[J]. Journal of Pratacultural Science, 2014, 23(6): 20−27. doi: 10.11686/cyxb20140603
[24]	Korhonen L, Korpela I, Heiskanen J, et al. Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index[J]. Remote Sensing of Environment, 2011, 115(4): 1065−1080. doi: 10.1016/j.rse.2010.12.011
[25]	Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research-Atmospheres, 2001, 106(D7): 7183−7192. doi: 10.1029/2000JD900719
[26]	谢栋博, 雷雅凯, 张宇超, 等. 基于机载LiDAR数据的崇礼冬奥核心区树冠覆盖率估算[J]. 林业科学, 2022, 58(10): 24−34. Xie D B, Lei Y K, Zhang Y C, et al. Estimation of canopy cover in the core area of Winter Olympic Games based on airborne LiDAR data[J]. Scientia Silvae Sinicae, 2022, 58(10): 24−34.
[27]	Zhao K, Popescu S, Meng X, et al. Characterizing forest canopy structure with lidar composite metrics and machine learning[J]. Remote Sensing of Environment, 2011, 115(8): 1978−1996. doi: 10.1016/j.rse.2011.04.001
[28]	Hopkinson C. The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution[J]. Canadian Journal of Remote Sensing, 2007, 33(4): 312−324. doi: 10.5589/m07-029
[29]	Liu Q W, Fu L Y, Wang G, et al. Improving estimation of forest canopy cover by introducing loss ratio of laser pulses using airborne LiDAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 567−585. doi: 10.1109/TGRS.2019.2938017
[30]	Jakubowski M K, Guo Q, Kelly M. Tradeoffs between lidar pulse density and forest measurement accuracy[J]. Remote Sensing of Environment, 2013, 130: 245−253. doi: 10.1016/j.rse.2012.11.024
[31]	Vauhkonen J, Tokola T, Maltamo M, et al. Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data[J]. Canadian Journal of Remote Sensing, 2008, 34(Suppl. 2): S441−S459. doi: 10.5589/m08-052
[32]	刘鹤, 顾玲嘉, 任瑞治. 基于无人机遥感技术的森林参数获取研究进展[J]. 遥感技术与应用, 2021, 36(3): 489−501. Liu H, Gu L J, Ren R Z. Research progress of forest parameter acquisition based on UAV remote sensing technology[J]. Remote Sensing Technology and Application, 2021, 36(3): 489−501.
[33]	秦海明, 王成, 习晓环, 等. 机载激光雷达测深技术与应用研究进展 [J]. 遥感技术与应用, 2016, 31(04): 617−624. Qin H M, Wang C, Xi X H, et al. Development of airborne laser bathymetric technology and applications [J]. Remote Sensing Technology and Application, 2016, 31(4): 617−624.
[34]	张瑞英, 庞勇, 李增元, 等. 结合机载LiDAR和LANDSAT ETM+ 数据的温带森林郁闭度估测 [J]. 植物生态学报, 2016, 40(2): 102−115. Zhang R Y, Pang Y, Li Z Y, et al. Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM + data [J]. Chinese Journal of Plant Ecology, 2016, 40(2): 102−115.
[35]	Hershey J L, McDill M E, Miller D A, et al. A voxel-based individual tree stem detection method using airborne LiDAR in mature northeastern US forests[J]. Remote Sensing, 2022, 14(3): 806. doi: 10.3390/rs14030806

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Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data