Remote Sensing Technology and Application, Volume. 40, Issue 1, 202(2025)
Estimation of Canopy Height is Conducted by Integrating Multi-source Remote Sensing Data from ICESat-2 and GEDI
[1] ZHU Xiaoxiao. Forest height retrieval of China with a resolution of 30 m using ICESat-2 and GEDI data(2021).
[2] HOU Bo, YANG Yanrong. Estimation of forest canopy height using neural network fitting multi-source remote sensing information. Journal of Forest and Environment, 43, 426-432(2023).
[3] HAN Minghui, XING Yanqiu, LI Guoyuan et al. Comparison of the accuracy of the maximum canopy height and biomass inversion of the data of different GEDI algorithm groups. Journal of Central South University of Forestry & Technology, 42, 72-82(2022).
[4] JUSTICE C, TOWNSHEND J. Special issue on the moderate resolution imaging spectroradiometer (MODIS):A new generation of land surface monitoring. Remote Sensing of Environment, 83, 1-2(2002).
[5] LEFSKY M A, TURNER D P, GUZY M et al. Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity. Remote Sensing of Environment, 95, 549-558(2005).
[6] CUI Shaowei, FAN Wenyi, JIN Sen et al. Extraction of individual tree height using quickbird images based on Tree Shadow. Journal of Northeast Forestry University, 39, 47-50(2011).
[7] ZHANG Wangfei, CHEN Erxue, LI Zengyuan et al. Development of forest height estimation on using InSAR/PolInSAR technology. Remote Sensing Technology and Application, 32, 983-997(2017).
[8] ZHU Junfeng, LIU Qingwang, CUI Ximin et al. Estimation of volume based on fusion point cloud of terrestrial and UAV LiDAR. Remote Sensing Technology and Application, 39, 45-54(2024).
[9] XING Yanqiu, ZHANG Jinxiu, CHEN Shipei et al. Mean canopy height estimation by combing ZY-3 data and airborne LiDAR. Journal of Central South University of Forestry & Technology, 38, 10-16(2018).
[10] LIN X, XU M, CAO C et al. Estimates of forest canopy height using a combination of ICESat-2/ATLAS data and stereo-Photogrammetry. Remote Sensing, 12, 36-49(2020).
[11] GLENN N F, NEUENSCHWANDER A, VIERLING L A et al. Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass. Remote Sensing of Environment, 185(2016).
[12] NEUENSCHWANDER A, GUENTHER E, WHITE J C et al. Validation of ICESat-2 terrain and canopy heights in boreal forests. Remote Sensing of Environment, 251(2020).
[13] MALAMBO L, POPESCU S, LIU M. Landsat-scale regional forest canopy height mapping using ICESat-2 Along-track heights: Case study of Eastern Texas. Remote Sensing, 15, 1-17(2023).
[14] NEUENSCHWANDER A, PITTS K. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment, 221, 247-259(2019).
[15] SILVA C A, DUNCANSON L, HANCOCK S et al. Fusing simulated GEDI,ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 253, 232-247(2021).
[16] TANG H, STOKER J, LUTHCKE S et al. Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI. Remote Sensing of Environment, 291, 113571(2023).
[17] POTAPOV P, LI X Y, HERNANDEZ-SERNA A et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253(2021).
[18] TOLAN J, YANG H, NOSARZEWSKI B et al. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment, 300(2024).
[19] LANG N, KALISCHEK N, ARMSTON J et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268(2022).
[20] CHEN Wenjie, CHEN Yang, XIA Jiangzhou. Review of forest age datasets and their estimation methods. Remote Sensing Technology and Application, 39, 1039-1053(2024).
[21] ZHU X, NIE S, WANG C et al. Consistency analysis of forest height retrievals between GEDI and ICESat-2. Remote Sensing of Environment, 281(2022).
[22] LIU A B, CHENG X, CHEN Z Q. Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals. Remote Sensing of Environment, 264(2021).
[23] SOTHE C, GONSAMO A, LOURENCO R B et al. Spatially continuous mapping of forest canopy height in canada by combining GEDI and ICESat-2 with PALSAR and Sentinel. Remote Sensing, 14, 51-58(2022).
[24] LIU X Q, SU Y J, HU T Y et al. Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data. Remote Sensing of Environment, 269(2022).
[25] ZHANG Zihui, WU Shixin, ZHAO Zifei et al. Estimation of grassland biomass using machine learning methods: A case study of grassland in Qilian Mountains. Acta Ecologica Sinica, 2022, 42(22):8953-8963.张子慧,吴世新,赵子飞,. 生态学报, 42, 8953-63(2022).
[26] JI X, YANG B, WEI Z et al. Benthic habitat sediments mapping in coral reef area using amalgamation of multi-source and multi-modal remote sensing data. Remote Sensing of Environment, 291-304(2024).
[27] SONG L J, SONG C Q, LUO S X et al. Integrating ICESat-2 altimetry and machine learning to estimate the seasonal water level and storage variations of national-scale lakes in China. Remote Sensing of Environment, 294(2023).
[28] PENG Qing. Simulation and prediction of ecological environment in Qilian Mountain National Park under climate change(2022).
[29] SONG Jie, LIU Xuelu. Estimation of forest aboveground carbon density in QilianMountains National Park based on remote sensing. ARID LAND GEOGRAPHY, 44, 1045-1057(2021).
[30] HUANG Zhuo, SUN Jianguo, FENG Chunyue et al. ES change-based ecological restoration zoning for the Hexi region. Remote Sensing for Natural Resoutces, 35, 236-243(2023).
[31] CHEN Siwen. Forest biomass estimation in alpine mountainbased on GEDI:The case of Qilian MountainNational Park(2023).
[32] ZHANG Mengxu, LIU Wei, ZHU MENG et al. Responses of soil properties and vegetation biomass to slope aspect and position in forest-steppezone of the Qilian Mountains. Journal of Glaciology and Geocryology, 43, 233-241(2021).
[33] NEUENSCHWANDER A L, MAGRUDER L A. Canopy and Terrain Height Retrievals with ICESat-2: A First Look. Remote Sensing, 11, 1721-1734(2019).
[34] LIU Meiyan, NIE Sheng, WANG Cheng et al. Study on forest stock volume inversion based on ICESat-2 and Sentinel-2A data. Remote Sensing for Natural Resoutces, 36, 210-216(2024).
[35] HUANG Jiapeng, XIA Tingting, YU Y. Evaluation of underforest terrain performance estimation using GEDI and Tandem-X DEM data in dense forests. Transactions of the Chinese Society for Agricultural Machinery, 54, 279-287(2023).
[36] LIU Lijuan, WANG Cheng, NIE Sheng et al. Aanlysis of the influence of different algorithms of GEDI L2A on the accuracy of ground elevation and forest canopy height. Journal of University of Chinese Academy of Sciences, 39, 502-511(2022).
[37] GHOSH S M, BEHERA M D, KUMAR S et al. Predicting the forest canopy height from LiDAR and multi-sensor data using Machine Learning over India. Remote Sensing, 14, 59-68(2022).
[38] LIU Y, GONG W, XING Y et al. Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B,multispectral Sentinel-2A,and DEM imagery. Isprs Journal of Photogrammetry and Remote Sensing, 151, 277-289(2019).
[39] ZHANG Zhitao, HE Yujie, YING Haoyuan et al. Synergistic estimation of soil salinity based on Sentinel-1/2 improved polarization combination index and texture features. Transactions of the Chinese Society for Agricultural Machinery, 55, 175-185(2024).
[40] MATASCI G, HERMOSILLA T, WULDER M A et al. Large-area mapping of Canadian boreal forest cover,height,biomass and other structural attributes using Landsat composites and Lidar plots. Remote Sensing of Environment, 209, 90-106(2018).
[41] SIMARD M, PINTO N, FISHER J B et al. Mapping forest canopy height globally with spaceborne Lidar. Journal of Geophysical Research-Biogeosciences, 90-116(2011).
[42] LIN Xinyi, WANG Xiaoqing, TANG Zixia et al. Different spatial resolutions based on object-oriented CNN and RF research on agricultural greenhouse extraction from remote sensing images. Remote Sensing Technolo⁃gy and Application, 39, 315-327(2024).
[43] CHE Miao, WANG Hairong, XU Xi et al. PSO-DF: A hyperspectral model for estimating nitrogen content in rice leaves. Remote Sensing Technology and Application, 39, 280-289(2024).
[44] LUO Yichen. Mapping the forest canopy height and aboveground biomass by fusion of ICESat-2 and multi-source remote sensing data: A Case Study in Jiangxi Province,China(2023).
[45] ZHAO Weijun, LIU Xiande, JIN Ming et al. Analysis on community structure of Picea crassifolia Forests in the Qilian Mountains. Arid Zone Research, 29, 615-620(2012).
[46] BIAN Rui, NIAN Yanyun, GOU Xizohua et al. Analysis of forest canopy height based on UAV LiDAR:A case studyof picea crassifolia in the East and Central of the Qilian Mountains. Remote Sensing Technology And Application, 36, 511-520(2021).
[47] WU Jinjin, JIAO Liang, ZHANG Hua et al. Vegetation coverage variation in the Qilian Mountains before and after ecological restoration. Acta Ecologica Sinica, 43, 408-418(2023).
[48] BIE Qiang, ZHAO Chuan, QIANG Wenli et al. Dynamic change of Picea crassifolia in Qilian mountain in recent 40 years. Journal of Arid Land Resources and Environment, 27, 176-180(2013).
[50] LANG N, JETZ W, SCHINDLER K, WEGNER J D. A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7, 1778-1789(2023).
[51] XU Zongxue, WANG Zifeng, ZHANG Shurong. Vegetation quadrat survey data in the middle of Heihe River Basin (2013-2014)[DB/OL].
[52] CHANG Xuexiang. Forest investigation data about Qinghai spruce stand in Pailougou watershed (2011)[DB/OL].
[53] SONG Jinling, FU Zhuo, LI Shihua et al. WATER: Dataset of forest structure parameter measurements for the fixed forest sampling plots in the Dayekou and Pailugou watershed foci experimental areas. National Tibetan Plateau Data Center, 2003-3007(2012).
[54] LING Feilong, LI Zengyuan, CHEN Erxue et al. Leaf area index estimation for Qinghai spruce forest using digital hemispherical photogtaphy. Advances In Earth Science, 24, 803-809(2009).
[55] LIU Qingwang. Study on the Estimation Method of Forest Parameters Using Airboenr LIDAR(2009).
[56] YANG Y. Comparison of Dual-Source Evapotranspiration Models in Estimating Potential Evaporation and Transpiration(2015).
[57] TIAN X, Li ZY, van der Tol C et al. Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data. Remote Sensing of Environment, 115, 2330-2341(2011).
[58] SONG Jinling, FU Zhuo, GUO Xinping et al. WATER: Dataset of spectral reflectance observations of the Picea crassifolia at the super site around the Dayekou Guantan forest station(2008).
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Huajun LIANG, Qiang BIE, Ying SHI, Xinru DENG, Xinzhang LI. Estimation of Canopy Height is Conducted by Integrating Multi-source Remote Sensing Data from ICESat-2 and GEDI[J]. Remote Sensing Technology and Application, 2025, 40(1): 202
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Received: Mar. 26, 2024
Accepted: --
Published Online: May. 22, 2025
The Author Email: Qiang BIE (bieq@lzjtu.edu.cn)