Evaluating the reliability of bi-temporal canopy height model generated from airborne laser scanning for monitoring forest growth in boreal forest region

References

• Adnan, Syed, Matti Maltamo, Lauri Mehtätalo, Rhei N.L. Ammaturo, Petteri Packalen, and Rubén Valbuena. 2021. “Determining Maximum Entropy in 3D Remote Sensing Height Distributions and Using it to Improve Aboveground Biomass Modelling via Stratification.” Remote Sensing of Environment 260 (July): 112464. https://doi.org/10.1016/j.rse.2021.112464.
   Google Scholar
• Asokan, Anju, and J. Anitha. 2019. “Change Detection Techniques for Remote Sensing Applications: A Survey.” Earth Science Informatics 12 (2): 143–160. https://doi.org/10.1007/s12145-019-00380-5.
   Web of Science ®Google Scholar
• Beese, Lucy, Michele Dalponte, Gregory P. Asner, David A. Coomes, and Tommaso Jucker. 2022. “Using Repeat Airborne LiDAR to Map the Growth of Individual Oil Palms in Malaysian Borneo During the 2015–16 El Niño.” International Journal of Applied Earth Observation and Geoinformation 115 (December): 103117. https://doi.org/10.1016/j.jag.2022.103117.
   Google Scholar
• Blaszczak-Bak, Wioleta, Joanna Janicka, and Anna Sobieraj-Zlobinska. 2016. “Applying Ransac Algorithm for Fitting Scanning Strips from Airborne Laser Scanning.” Civil and Environmental Engineering Reports 23 (4): 29–42. https://doi.org/10.1515/ceer-2016-0048.
   Google Scholar
• Burkhart, Harold E., and Margarida Tomé. 2012. Modeling Forest Trees and Stands. Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-90-481-3170-9
   Google Scholar
• Cao, Lin, Nicholas C. Coops, John L. Innes, Stephen R.J. Sheppard, Liyong Fu, Honghua Ruan, and Guanghui She. 2016. “Estimation of Forest Biomass Dynamics in Subtropical Forests Using Multi-Temporal Airborne LiDAR Data.” Remote Sensing of Environment 178 (June): 158–171. https://doi.org/10.1016/j.rse.2016.03.012.
   Google Scholar
• Cao, Lin, Nicholas C. Coops, Yuan Sun, Honghua Ruan, Guibin Wang, Jinsong Dai, and Guanghui She. 2019. “Estimating Canopy Structure and Biomass in Bamboo Forests Using Airborne LiDAR Data.” ISPRS Journal of Photogrammetry and Remote Sensing 148 (February): 114–129. https://doi.org/10.1016/j.isprsjprs.2018.12.006.
   Google Scholar
• Carrilho, A. C., M. Galo, and R. C. Santos. 2018. “Statistical Outlier Detection Method for Airborne Lidar Data.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII–1 (September): 87–92. https://doi.org/10.5194/isprs-archives-XLII-1-87-2018.
   Google Scholar
• Cheng, Kai, Yanjun Su, Hongcan Guan, Shengli Tao, Yu Ren, Tianyu Hu, Keping Ma, Yanhong Tang, and Qinghua Guo. 2023. “Mapping China’s Planted Forests Using High Resolution Imagery and Massive Amounts of Crowdsourced Samples.” ISPRS Journal of Photogrammetry and Remote Sensing 196: 356–371. https://doi.org/10.1016/j.isprsjprs.2023.01.005.
   Web of Science ®Google Scholar
• Coops, Nicholas C. 2015. “Characterizing Forest Growth and Productivity Using Remotely Sensed Data.” Current Forestry Reports 1 (3): 195–205. https://doi.org/10.1007/s40725-015-0020-x.
   Web of Science ®Google Scholar
• Dalponte, Michele, and David A. Coomes. 2016. “Tree-Centric Mapping of Forest Carbon Density from Airborne Laser Scanning and Hyperspectral Data.” Methods in Ecology and Evolution 7 (10): 1236–1245. https://doi.org/10.1111/2041-210X.12575.
   PubMed Web of Science ®Google Scholar
• Dalponte, Michele, Tommaso Jucker, Sicong Liu, Lorenzo Frizzera, and Damiano Gianelle. 2019. “Characterizing Forest Carbon Dynamics Using Multi-Temporal Lidar Data.” Remote Sensing of Environment 224: 412–420. https://doi.org/10.1016/j.rse.2019.02.018.
   Web of Science ®Google Scholar
• Dong, Pinliang, and Qi Chen. 2017. LiDAR Remote Sensing and Applications. Boca Raton: CRC Press. https://doi.org/10.4324/9781351233354
   Google Scholar
• Ene, Liviu T., Terje Gobakken, Hans-Erik Andersen, Erik Næsset, Bruce D. Cook, Douglas C. Morton, Chad Babcock, and Ross Nelson. 2018. “Large-Area Hybrid Estimation of Aboveground Biomass in Interior Alaska Using Airborne Laser Scanning Data.” Remote Sensing of Environment 204 (January): 741–755. https://doi.org/10.1016/j.rse.2017.09.027.
   Google Scholar
• Fradette, Marie-Soleil, Antoine Leboeuf, Martin Riopel, and Jean Bégin. 2019. “Method to Reduce the Bias on Digital Terrain Model and Canopy Height Model from LiDAR Data.” Remote Sensing 11 (7): 863. https://doi.org/10.3390/rs11070863.
   Web of Science ®Google Scholar
• Gao, Ge, Jianbo Qi, Simei Lin, Ronghai Hu, and Huaguo Huang. 2023. “Estimating Plant Area Density of Individual Trees from Discrete Airborne Laser Scanning Data Using Intensity Information and Path Length Distribution.” International Journal of Applied Earth Observation and Geoinformation 118 (April): 103281. https://doi.org/10.1016/j.jag.2023.103281.
   Google Scholar
• Gao, Weifeng, Yunlong Yao, Hong Liang, Liquan Song, Houcai Sheng, Tijiu Cai, and Dawen Gao. 2019. “Emissions of Nitrous Oxide from Continuous Permafrost Region in the Daxing’an Mountains, Northeast China.” Atmospheric Environment 198: 34–45. https://doi.org/10.1016/j.atmosenv.2018.10.045.
   Web of Science ®Google Scholar
• Guerra-Hernández, Juan, Adrian Pascual, Frederico Tupinambá-Simões, Sergio Godinho, Brigite Botequim, Alfonso Jurado-Varela, and Vicente Sandoval-Altelarrea. 2024. “Using Bi-Temporal ALS and NFI-Based Time-Series Data to Account for Large-Scale Aboveground Carbon Dynamics: The Showcase of Mediterranean Forests.” European Journal of Remote Sensing 57 (1): 2315413. https://doi.org/10.1080/22797254.2024.2315413.
   Web of Science ®Google Scholar
• Guo, Qinghua, Yanjun Su, and Tianyu Hu. 2023. LiDAR Principles, Processing and Applications in Forest Ecology. 1st ed. Amsterdam, Netherlands: Academic Press.
   Google Scholar
• Guo, Futao, Zhangwen Su, Guangyu Wang, Long Sun, Mulualem Tigabu, Xiajie Yang, and Haiqing Hu. 2017. “Understanding Fire Drivers and Relative Impacts in Different Chinese Forest Ecosystems.” Science of The Total Environment 605-606: 411–425. https://doi.org/10.1016/j.scitotenv.2017.06.219.
   PubMed Web of Science ®Google Scholar
• Hawryło, Paweł, Jarosław Socha, Piotr Wężyk, Wojciech Ochał, Wojciech Krawczyk, Jakub Miszczyszyn, and Luiza Tymińska-Czabańska. 2024. “How to Adequately Determine the Top Height of Forest Stands Based on Airborne Laser Scanning Point Clouds?” Forest Ecology and Management 551 (January): 121528. https://doi.org/10.1016/j.foreco.2023.121528.
   Google Scholar
• He, Shan, Huaiyong Shao, Wei Xian, Shuhui Zhang, Jialong Zhong, and Jiaguo Qi. 2021. “Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images.” Remote Sensing 13 (19): 3956. https://doi.org/10.3390/rs13193956.
   Web of Science ®Google Scholar
• Hu, Ronghai, Guangjian Yan, Francoise Nerry, Yunshu Liu, Yumeng Jiang, Shuren Wang, Yiming Chen, Xihan Mu, Wuming Zhang, and Donghui Xie. 2018. “Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index.” IEEE Transactions on Geoscience and Remote Sensing 56 (6): 3196–3209. https://doi.org/10.1109/TGRS.2018.2794504.
   Web of Science ®Google Scholar
• Huang, HuaGuo, and Jun Lian. 2015. “A 3D Approach to Reconstruct Continuous Optical Images Using Lidar and MODIS.” Forest Ecosystems 2 (1): 20. https://doi.org/10.1186/s40663-015-0044-5.
   Google Scholar
• Jean-Romain, Roussel, Auty David, Coops Nicholas, Tompalski Piotr, Goodbody R. H. Tristan, Meador Andrew Sanchez, Bourdon Jean-Francois, de Boissieu Florian, and Achim Alexis. 2020. “lidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data.” Remote Sensing of Environment 251: 112061. https://doi.org/10.1016/j.rse.2020.112061.
   Web of Science ®Google Scholar
• Jia, Bingrui, Hongru Sun, Herman Henry Shugart, Zhenzhu Xu, Peng Zhang, and Guangsheng Zhou. 2021. “Growth Variations of Dahurian Larch Plantations Across Northeast China: Understanding the Effects of Temperature and Precipitation.” Journal of Environmental Management 292 (August): 112739. https://doi.org/10.1016/j.jenvman.2021.112739.
   PubMedGoogle Scholar
• Kershaw Jr, John A, Mark J. Ducey, Thomas W. Beers, and Bertram Husch. 2016. Forest Mensuration. 5th ed. Chichester ; Hoboken, NJ: Wiley-Blackwell.
   Google Scholar
• Khosravipour, Anahita, Andrew K. Skidmore, Martin Isenburg, Tiejun Wang, and Yousif A. Hussin. 2014. “Generating Pit-Free Canopy Height Models from Airborne Lidar.” Photogrammetric Engineering & Remote Sensing 80 (9): 863–872. https://doi.org/10.14358/PERS.80.9.863.
   Web of Science ®Google Scholar
• Lang, Nico, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner. 2023. “A High-Resolution Canopy Height Model of the Earth.” Nature Ecology & Evolution 7 (11): 1778–1789. https://doi.org/10.1038/s41559-023-02206-6.
   PubMed Web of Science ®Google Scholar
• Lefsky, Michael A., Warren B. Cohen, Geoffrey G. Parker, and David J. Harding. 2002. “Lidar Remote Sensing for Ecosystem Studies.” Bioscience 52 (1): 19. https://doi.org/10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2.
   Web of Science ®Google Scholar
• Li, Chenyun, Zhexiu Yu, Shaojie Wang, Fayun Wu, Kunjian Wen, Jianbo Qi, and Huaguo Huang. 2022. “Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China.” Forests 13 (7): 1142. https://doi.org/10.3390/f13071142.
   Web of Science ®Google Scholar
• Liu, Qianwei, Weifeng Ma, Jianpeng Zhang, Yicheng Liu, Dongfan Xu, and Jinliang Wang. 2021. “Point-Cloud Segmentation of Individual Trees in Complex Natural Forest Scenes Based on a Trunk-Growth Method.” Journal of Forestry Research 32 (6): 2403–2414. https://doi.org/10.1007/s11676-021-01303-1.
   Web of Science ®Google Scholar
• Liu, Jing, Andrew K. Skidmore, Simon Jones, Tiejun Wang, Marco Heurich, Xi Zhu, and Yifang Shi. 2018. “Large Off-Nadir Scan Angle of Airborne LiDAR Can Severely Affect the Estimates of Forest Structure Metrics.” ISPRS Journal of Photogrammetry and Remote Sensing 136 (February): 13–25. https://doi.org/10.1016/j.isprsjprs.2017.12.004.
   Google Scholar
• Liu, Yang, Ralph Trancoso, Qin Ma, Philippe Ciais, Lidiane P. Gouvêa, Chaofang Yue, Jorge Assis, and Juan A. Blanco. 2023. “Carbon Density in Boreal Forests Responds Non-Linearly to Temperature: An Example from the Greater Khingan Mountains, Northeast China.” Agricultural and Forest Meteorology 338 (July): 109519. https://doi.org/10.1016/j.agrformet.2023.109519.
   Google Scholar
• Mao, Chunyan, Lubei Yi, Wenqiang Xu, Li Dai, Anming Bao, Zhengyu Wang, and Xueting Zheng. 2022. “Study on Biomass Models of Artificial Young Forest in the Northwestern Alpine Region of China.” Forests 13 (11): 1828. https://doi.org/10.3390/f13111828.
   Web of Science ®Google Scholar
• Matti, Maltamo, Næsset Erik, and Vauhkonen Jari, Eds. 2014. Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies. Vol. 27. Managing Forest Ecosystems. Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-017-8663-8
   Google Scholar
• Michałowska, Maja, and Jacek Rapiński. 2021. “A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers.” Remote Sensing 13 (3): 353. https://doi.org/10.3390/rs13030353.
   Web of Science ®Google Scholar
• Ncutirakiza, Ndamiyehe, Sylvie Gourlet-Fleury Jean-Baptiste, Philippe Lejeune, Xavier Bry, and Catherine Trottier. 2024. “Using High-Resolution Images to Analyze the Importance of Crown Size and Competition for the Growth of Tropical Trees.” Forest Ecology and Management 552 (January): 121553. https://doi.org/10.1016/j.foreco.2023.121553.
   Google Scholar
• Ochal, W., J. Socha, and M. Pierzchalski. 2017. “The Effect of the Calculation Method, Plot Size, and Stand Density on the Accuracy of Top Height Estimation in Norway Spruce Stands.” iForest - Biogeosciences and Forestry 10 (2): 498–505. https://doi.org/10.3832/ifor2108-010.
   Google Scholar
• Økseter, Roar, Ole Martin Bollandsås, Terje Gobakken, and Erik Næsset. 2015. “Modeling and Predicting Aboveground Biomass Change in Young Forest Using Multi-Temporal Airborne Laser Scanner Data.” Scandinavian Journal of Forest Research 30 (5): 458–469. https://doi.org/10.1080/02827581.2015.1024733.
   Web of Science ®Google Scholar
• Pang, Yong, Weiwei Wang, Liming Du, Zhongjun Zhang, Xiaojun Liang, Yongning Li, and Zuyuan Wang. 2021. “Nyström-Based Spectral Clustering Using Airborne LiDAR Point Cloud Data for Individual Tree Segmentation.” International Journal of Digital Earth 14 (10): 1452–1476. https://doi.org/10.1080/17538947.2021.1943018.
   Web of Science ®Google Scholar
• Persson, Henrik Jan. 2022. “Quantify and Account for Field Reference Errors in Forest Remote Sensing Studies.” Remote Sensing of Environment 283 (December): 113302. https://doi.org/10.1016/j.rse.2022.113302.
   Google Scholar
• Popescu, Sorin C., and Randolph H. Wynne. 2004. “Seeing the Trees in the Forest.” Photogrammetric Engineering & Remote Sensing 70 (5): 589–604. https://doi.org/10.14358/PERS.70.5.589.
   Web of Science ®Google Scholar
• Qi, Zhiyong, Shiming Li, Yong Pang, Guang Zheng, Dan Kong, and Zengyuan Li. 2023. “Assessing Spatiotemporal Variations of Forest Carbon Density Using Bi-Temporal Discrete Aerial Laser Scanning Data in Chinese Boreal Forests.” Forest Ecosystems 10: 100135. https://doi.org/10.1016/j.fecs.2023.100135.
   Web of Science ®Google Scholar
• Qi, Guang, Qingli Wang, Xinchuang Wang, Dapao Yu, Li Zhou, Wangming Zhou, Shunlei Peng, and Limin Dai. 2013. “Soil Organic Carbon Storage in Different Aged Larix Gmelinii Plantations in Great Xing’ an Mountains of Northeast China.” The Journal of Applied Ecology 24 (1): 10–16.
   Google Scholar
• Qiu, Zixuan, Zhongke Feng, Yanni Song, Menglu Li, and Panpan Zhang. 2020. “Carbon Sequestration Potential of Forest Vegetation in China from 2003 to 2050: Predicting Forest Vegetation Growth Based on Climate and the Environment.” Journal of Cleaner Production 252 (April): 119715. https://doi.org/10.1016/j.jclepro.2019.119715.
   Google Scholar
• Qu, Yonghua, Shaker Ahmed, Silva Carlos, Klauberg Carine, and Pinagé Ekena. 2018. “Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia.” Remote Sensing 10 (6): 970. https://doi.org/10.3390/rs10060970.
   Web of Science ®Google Scholar
• Riofrio, Jose, Joanne C. White, Piotr Tompalski, Nicholas C. Coops, and Michael A. Wulder. 2022. “Harmonizing Multi-Temporal Airborne Laser Scanning Point Clouds to Derive Periodic Annual Height Increments in Temperate Mixedwood Forests.” Canadian Journal of Forest Research 52 (10): 1334–1352. https://doi.org/10.1139/cjfr-2022-0055.
   Google Scholar
• Sharma, Mahadev, Ralph L. Amateis, and Harold E. Burkhart. 2002. “Top Height Definition and Its Effect on Site Index Determination in Thinned and Unthinned Loblolly Pine Plantations.” Forest Ecology and Management 168 (1-3): 163–175. https://doi.org/10.1016/S0378-1127(01)00737-X.
   Web of Science ®Google Scholar
• Shi, Fang, Mingxing Liu, Jie Qiu, Yali Zhang, Huiyi Su, Xupeng Mao, Xin Li, et al. 2022. “Assessing Land Cover and Ecological Quality Changes in the Forest-Steppe Ecotone of the Greater Khingan Mountains, Northeast China, from Landsat and MODIS Observations from 2000 to 2018.” Remote Sensing 14 (3): 725. https://doi.org/10.3390/rs14030725.
   Web of Science ®Google Scholar
• Socha, Jarosław, Paweł Hawryło, Krzysztof Stereńczak, Stanisław Miścicki, Luiza Tymińska-Czabańska, Wojciech Młocek, and Piotr Gruba. 2020. “Assessing the Sensitivity of Site Index Models Developed Using Bi-Temporal Airborne Laser Scanning Data to Different Top Height Estimates and Grid Cell Sizes.” International Journal of Applied Earth Observation and Geoinformation 91 (September): 102129. https://doi.org/10.1016/j.jag.2020.102129.
   Google Scholar
• Socha, Jarosław, Marcin Pierzchalski, Radomir Bałazy, and Mariusz Ciesielski. 2017. “Modelling Top Height Growth and Site Index Using Repeated Laser Scanning Data.” Forest Ecology and Management 406 (December): 307–317. https://doi.org/10.1016/j.foreco.2017.09.039.
   Google Scholar
• Sun, Hong, and Xin Tian. 2019. “Analysis of Dynamic Changes of Fractional Vegetation Coverage and Influencing Factors of the Genhe in the Great Khingan of Inner Mongolia.” Remote Sensing Technology and Application 33 (6): 1159–1169. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1159.
   Google Scholar
• Tian, Xin, Zengyuan Li, Erxue Chen, Qinhuo Liu, Guangjian Yan, Jindi Wang, Zheng Niu, et al. 2015. “The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE).” PLoS One 10 (9): e0137545. https://doi.org/10.1371/journal.pone.0137545.
   PubMed Web of Science ®Google Scholar
• Tolan, Jamie, Hung-I Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, et al. 2024. “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 (January): 113888. https://doi.org/10.1016/j.rse.2023.113888.
   Google Scholar
• White Joanne, C., Coops Nicholas, Wulder A Michael, Vastaranta Mikko, Hilker Thomas, and Tompalski Piotr. 2016. “Remote Sensing Technologies for Enhancing Forest Inventories: A Review.” Canadian Journal of Remote Sensing 42 (5): 619–641. https://doi.org/10.1080/07038992.2016.1207484.
   Web of Science ®Google Scholar
• Wijedasa, Lahiru S., Sean Sloan, Dimitrios G. Michelakis, and Gopalasamy R. Clements. 2012. “Overcoming Limitations with Landsat Imagery for Mapping of Peat Swamp Forests in Sundaland.” Remote Sensing 4 (9): 2595–2618. https://doi.org/10.3390/rs4092595.
   Web of Science ®Google Scholar
• You, Haotian, Yanqiu Xing, Tao Peng, and Jianhua Ding. 2018. “Research on the Effect of Side-Overlap Between Airborne LiDAR Adjacent Swaths on the Coniferous Forest Structural Parameters Estimation.” SCIENTIA SILVAE SINICAE 54 (6): 109–118. https://doi.org/10.11707/j.1001-7488.20180613.
   Google Scholar
• Yu, Xiaowei, Juha Hyyppä, Markus Holopainen, and Mikko Vastaranta. 2010. “Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes.” Remote Sensing 2 (6): 1481–1495. https://doi.org/10.3390/rs2061481.
   Web of Science ®Google Scholar
• Yu, Xiaowei, Juha Hyyppä, Harri Kaartinen, and Matti Maltamo. 2004. “Automatic Detection of Harvested Trees and Determination of Forest Growth Using Airborne Laser Scanning.” Remote Sensing of Environment 90 (4): 451–462. https://doi.org/10.1016/j.rse.2004.02.001.
   Web of Science ®Google Scholar
• Yu, Xiaowei, Juha Hyyppä, Antero Kukko, Matti Maltamo, and Harri Kaartinen. 2006. “Change Detection Techniques for Canopy Height Growth Measurements Using Airborne Laser Scanner Data.” Photogrammetric Engineering & Remote Sensing 72 (12): 1339–1348. https://doi.org/10.14358/PERS.72.12.1339.
   Web of Science ®Google Scholar
• Yu, Haoyang, and Zhongjun Zhang. 2021. “The Performance of Relative Height Metrics for Estimation of Forest Above-Ground Biomass Using L- and X-Bands TomoSAR Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 1857–1871. https://doi.org/10.1109/JSTARS.2021.3051081.
   Web of Science ®Google Scholar
• Yuan, Shusheng, Tongtong Tang, Minchao Wang, Hao Chen, Aihua Zhang, and Jinghua Yu. 2018. “Regional Scale Determinants of Nutrient Content of Soil in a Cold-Temperate Forest.” Forests 9 (4): 177. https://doi.org/10.3390/f9040177.
   Web of Science ®Google Scholar
• Zhang, Qiyue, Saeid Homayouni, Pengwu Zhao, and Mei Zhou. 2023. “Burned Vegetation Recovery Trajectory and Its Driving Factors Using Satellite Remote-Sensing Datasets in the Great Xing’an Forest Region of Inner Mongolia.” International Journal of Wildland Fire 32 (2): 244–261. https://doi.org/10.1071/WF21167.
   Web of Science ®Google Scholar
• Zhang, Yuzhen, and Shunlin Liang. 2014. “Changes in Forest Biomass and Linkage to Climate and Forest Disturbances Over Northeastern China.” Global Change Biology 20 (8): 2596–2606. https://doi.org/10.1111/gcb.12588.
   PubMed Web of Science ®Google Scholar
• Zhang, Yupan, Yuichi Onda, Hiroaki Kato, Bin Feng, and Takashi Gomi. 2022. “Understory Biomass Measurement in a Dense Plantation Forest Based on Drone-SfM Data by a Manual Low-Flying Drone Under the Canopy.” Journal of Environmental Management 312 (June): 114862. https://doi.org/10.1016/j.jenvman.2022.114862.
   PubMedGoogle Scholar
• Zhang, Wuming, Jianbo Qi, Peng Wan, Hongtao Wang, Donghui Xie, Xiaoyan Wang, and Guangjian Yan. 2016. “An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation.” Remote Sensing 8 (6): 501. https://doi.org/10.3390/rs8060501.
   Web of Science ®Google Scholar
• Zhang, Yongjun, Xiaodong Xiong, Maoteng Zheng, and Xu Huang. 2015. “LiDAR Strip Adjustment Using Multifeatures Matched With Aerial Images.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 976–987. https://doi.org/10.1109/TGRS.2014.2331234.
   Web of Science ®Google Scholar
• Zhao, Kaiguang, Juan C. Suarez, Mariano Garcia, Tongxi Hu, Cheng Wang, and Alexis Londo. 2018. “Utility of Multitemporal Lidar for Forest and Carbon Monitoring: Tree Growth, Biomass Dynamics, and Carbon Flux.” Remote Sensing of Environment 204 (January): 883–897. https://doi.org/10.1016/j.rse.2017.09.007.
   Google Scholar
• Zhou, Hang, Junxiong Zhang, Luzhen Ge, Xiaowei Yu, Yonglin Wang, and Chunlong Zhang. 2021. “Research on Volume Prediction of Single Tree Canopy Based on Three-Dimensional (3D) LiDAR and Clustering Segmentation.” International Journal of Remote Sensing 42 (2): 738–755. https://doi.org/10.1080/01431161.2020.1811917.
   Web of Science ®Google Scholar