Estimation of Forest Canopy Closure Based on Spaceborne LiDAR ICESat–2/ATLAS Data

Wei Zhiyue; Li Hao; Shu Qingtai; Xi Lei; Song Hanyue; Qiu Shuang; Yang Zezhi

doi:10.11929/j.swfu.202211067

Journal of Southwest Forestry University > 2024 > 44(2): 127-134. > DOI: 10.11929/j.swfu.202211067

Wei Zhiyue, Li Hao, Shu Qingtai, Xi Lei, Song Hanyue, Qiu Shuang, Yang Zezhi. Estimation of Forest Canopy Closure Based on Spaceborne LiDAR ICESat–2/ATLAS Data[J]. Journal of Southwest Forestry University, 2024, 44(2): 127-134. DOI: 10.11929/j.swfu.202211067

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Estimation of Forest Canopy Closure Based on Spaceborne LiDAR ICESat–2/ATLAS Data

1.
College of Forestry, Southwest Forestry University, Kunming Yunnan 650233, China
2.
United Front Work Department of the Party Committee, Southwest Forestry University, Kunming Yunnan 650233, China

More Information

Received Date: November 21, 2022
Revised Date: February 15, 2023
Accepted Date: April 22, 2023
Available Online: April 25, 2023
Published Date: March 19, 2024

Graphical Abstract

Abstract

Abstract

Taking Shangri-La City, Yunnan Province as the research area, based on ICESat‒2 / ATLAS data, the remote sensing forest canopy density estimation models were established by random forest regression, gradient boosting tree regression and nearest neighbor regression, respectively. The optimal model was selected to invert the forest canopy closure within the study area spots. The results showed that random forest modeling was the best method to estimate forest canopy closure, the coefficient of determination(R²) was 0.9446, mean square error (RMSE) was 0.0560 and the prediction accuracy(P) was 90.60%. The predicted values of canopy closure corresponding to 74873 effective forest spots in Shangri-La City were obtained, and the spatial distribution map of canopy closure of all forest spots in the city was obtained by combining the spot center coordinates. The results can provide a reference for remote sensing estimation of forest canopy closure at low-high altitude areas.
- LiDAR,
- ICESat–2,
- canopy closure,
- random forest

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References (38)

References

孙忠秋, 高金萍, 吴发云, 等. 基于机载激光雷达点云和随机森林算法的森林蓄积量估测 [J]. 林业科学, 2021, 57(8): 68−81.

胡振华, 王丽媛, 岳彩荣, 等. 基于Hyperion数据的香格里拉森林郁闭度遥感估测研究 [J]. 西南林业大学学报(自然科学), 2017, 37(3): 159−164.

杨妍婷. 基于高分遥感数据的森林郁闭度估测方法研究 [J]. 智能计算机与应用, 2019, 9(2): 67−72.

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.

张王菲, 陈尔学, 李增元, 等. 干涉、极化干涉SAR技术森林高度估测算法研究进展 [J]. 遥感技术与应用, 2017, 32(6): 983−997.

黄佳鹏. 基于ICESat‒2/ATLAS光子计数LiDAR数据反演森林冠层高度研究[D]. 哈尔滨: 东北林业大学, 2021.

朱笑笑. 基于ICESat‒2和GEDI数据的中国30米分辨率森林高度反演研究[D]. 北京: 中国科学院大学, 2021.

李彬彬, 谢欢, 童小华, 等. 基于随机森林的ICESat‒2卫星数据地表覆盖分类 [J]. 红外与激光工程, 2020, 49(11): 115−121.

Pang S Y, Li G Y, Jiang X D, et al. Retrieval of forest canopy height in a mountainous region with ICESat‒2 ATLAS [J]. Forest Ecosystems, 2022, 9: 100046. DOI: 10.1016/j.fecs.2022.100046

Lin X J, Xu M, Cao C X, et al. Estimates of forest canopy height using a combination of ICESat-2/ATLAS data and stereo-photogrammetry [J]. Remote Sensing, 2020, 12(21): 3649. DOI: 10.3390/rs12213649

Neuenschwander A, Magruder L. The potential impact of vertical sampling uncertainty on ICESat-2/ATLAS terrain and canopy height retrievals for multiple ecosystems [J]. Remote Sensing, 2016, 8(12): 1039. DOI: 10.3390/rs8121039

孙映通, 夏礼庆, 林琳, 等. 香格里拉亚高山森林带退化群落土壤种子库特征与土壤理化性质分析 [J]. 云南大学学报(自然科学版), 2020, 42(5): 1014−1026.

谢福明, 舒清态, 字李, 等. 基于k-NN非参数模型的高山松生物量遥感估测研究 [J]. 江西农业大学学报, 2018, 40(4): 743−750.

廖易, 张加龙, 鲍瑞, 等. 基于Landsat的高山松地上生物量动态变化估测模型构建 [J]. 西南林业大学学报(自然科学), 2023, 43(1): 117−125.

Gwenzi D, Lefsky M A, Suchdeo V P, et al. Prospects of the ICESat‒2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 118: 68−82. DOI: 10.1016/j.isprsjprs.2016.04.009

席磊, 舒清态, 孙杨, 等. 基于ICESat2的西南山地森林LAI遥感估测模型优化 [J]. 自然资源遥感, 2023, 35(3): 160−169.

蒋维成. 基于主成分回归及遥感的贵阳市林分郁闭度估测 [J]. 林业调查规划, 2020, 45(3): 6−9, 23.

蔺昊宇, 巫兆聪, 孙小虎. 利用ICESat‒2 ATLAS&JL‒1反演输电线走廊附近森林冠层高度 [J]. 应用科学学报, 2022, 40(5): 809−819.

Zhang L J, Zeng Y J, Zhuang R D, et al. In situ observation-constrained global surface soil moisture using random forest model [J]. Remote Sensing, 2021, 13(23): 4893. DOI: 10.3390/rs13234893

Nie S, Wang C, Xi X H, et al. Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data [J]. Optics Express, 2018, 26(10): 520−540. DOI: 10.1364/OE.26.00A520

Zhang J S, Kerekes J. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data [J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 726−730. DOI: 10.1109/LGRS.2014.2360367

Zhu X X, Nie S, Wang C, et al. A ground elevation and vegetation height retrieval algorithm using micro-pulse photon-counting lidar data [J]. Remote Sensing, 2018, 10(12): 1962. DOI: 10.3390/rs10121962

Nie S, Wang C, Dong P, et al. A revised progressive TIN densification for filtering airborne LiDAR data[J]. Measurement, 2017, 104: 70−77.

Zhang J, Tian J Y, Li X J, et al. Leaf area index retrieval with ICESat‒2 photon counting LiDAR [J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 103: 102488. DOI: 10.1016/j.jag.2021.102488

孙珊珊. 森林郁闭度遥感估测方法研究[D]. 北京: 中国林业科学研究院, 2019.

刘银萍, 马少辉. 基于多模型融合的车辆通过时间预测 [J]. 计算机与现代化, 2019(2): 66−71, 81.

陆驰. 基于Landsat的香格里拉市高山松地上生物量及其动态变化建模研究[D]. 昆明: 西南林业大学, 2017.

张加龙, 胥辉, 陆驰. 应用Landsat8 OLI和GBRT对高山松地上生物量的估测 [J]. 东北林业大学学报, 2018, 46(8): 25−30. DOI: 10.3969/j.issn.1000-5382.2018.08.005

谢福明, 字李, 舒清态. 基于优化k-NN模型的高山松地上生物量遥感估测 [J]. 浙江农林大学学报, 2019, 36(3): 515−523. DOI: 10.11833/j.issn.2095-0756.2019.03.012

朱妍. 基于Landsat 8 OLI和ALOS-2 PALSAR-2数据的北京市森林生物量估测研究[D]. 北京: 北京林业大学, 2020.

何丹丹. 基于大间隔最近邻分类的水稻叶绿素含量估测研究[D]. 哈尔滨: 东北农业大学, 2012.

李盈昌. 森林地上生物量的遥感估测模型优化及时空分析方法[D]. 南京: 南京林业大学, 2021.

Zhu X X, Wang C, Nie S, et al. Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: a case study in Virginia and North Carolina, USA [J]. Ecological Indicators, 2020, 114: 106287. DOI: 10.1016/j.ecolind.2020.106287

Fabian P, Gaël V, Alexandre G, et al. Scikit-learn: Machine Learning in Python. [J]. Journal of Machine Learning Research, 2011, 12.

Narine L L, Popescu S C, Malambo L. Synergy of ICESat‒2 and landsat for mapping forest aboveground biomass with deep learning [J]. Remote Sensing, 2019, 11(12): 1503. DOI: 10.3390/rs11121503

Li W, Niu Z, Shang R, et al. High-resolution mapping of forest canopy height using machine learning by coupling ICESat‒2 LiDAR with Sentinel-1, Sentinel‒2 and Landsat-8 data [J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 92: 102163. DOI: 10.1016/j.jag.2020.102163

Shu Q T, Xi L, Wang K R, et al. Optimization of samples for remote sensing estimation of forest aboveground biomass at the regional scale [J]. Remote Sensing, 2022, 14(17): 4187. DOI: 10.3390/rs14174187

宋涵玥, 舒清态, 席磊, 等. 基于星载ICESat‒2/ATLAS数据的森林地上生物量估测 [J]. 农业工程学报, 2022, 38(10): 191−199. DOI: 10.11975/j.issn.1002-6819.2022.10.023

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