Citations (23)
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A. Pascual, J. Guerra-Hernández, D. N. Cosenza & V. Sandoval-Altelarrea. (2021) Using enhanced data co-registration to update Spanish National Forest Inventories (NFI) and to reduce training data under LiDAR-assisted inference. International Journal of Remote Sensing 42:1, pages 126-147.
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Amir Safari & Hormoz Sohrabi. (2020) Integration of synthetic aperture radar and multispectral data for aboveground biomass retrieval in Zagros oak forests, Iran: an attempt on Sentinel imagery. International Journal of Remote Sensing 41:20, pages 8069-8095.
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Bryce Frank, Francisco Mauro, Hailemariam Temesgen & Kevin R. Ford. (2019) Analysis of Classification Methods for Identifying Stands for Commercial Thinning Using LiDAR. Canadian Journal of Remote Sensing 45:5, pages 673-690.
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Articles from other publishers (20)
Alberto López-Amoedo, Marcos Rivas Silvosa, Manuel Beiro Lago, Henrique Lorenzo, Carolina Acuña-Alonso & Xana Álvarez. (2023) Weight estimation models for commercial Pinus radiata wood in small felling stands based on UAV-LiDAR data. Trees, Forests and People, pages 100436.
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Lin Chen, Chunying Ren, Bai Zhang, Zongming Wang, Weidong Man & Mingyue Liu. (2023) Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery. Remote Sensing 15:10, pages 2625.
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Angel Adhikari, Cristian R. Montes & Alicia Peduzzi. (2023) A Comparison of Modeling Methods for Predicting Forest Attributes Using Lidar Metrics. Remote Sensing 15:5, pages 1284.
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Jacob L. Strunk & Robert J. McGaughey. (2023) Stand validation of lidar forest inventory modeling for a managed southern pine forest. Canadian Journal of Forest Research 53:2, pages 71-89.
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Xiandie Jiang, Dengqiu Li, Guiying Li & Dengsheng Lu. (2023) Eucalyptus carbon stock estimation in subtropical regions with the modeling strategy of sample plots – airborne LiDAR – Landsat time series data. Forest Ecosystems 10, pages 100149.
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Zhiyong Qi, Shiming Li, Yong Pang, Guang Zheng, Dan Kong & 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, pages 100135.
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Diogo N. CosenzaPetteri PackalenMatti MaltamoPetri VarviaJanne RätyPaula SoaresMargarida ToméJacob L. StrunkLauri Korhonen. (2022) Effects of numbers of observations and predictors for various model types on the performance of forest inventory with airborne laser scanning. Canadian Journal of Forest Research 52:3, pages 385-395.
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Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Hassan Camil David, Milto Miltiadou & Eija Honkavaara. (2021) Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest. Remote Sensing 13:13, pages 2444.
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Dimitrios Panagiotidis & Azadeh Abdollahnejad. (2021) Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry. Forests 12:6, pages 717.
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Alexandra Stefanidou, Ioannis Z. Gitas, Lauri Korhonen, Nikos Georgopoulos & Dimitris Stavrakoudis. (2020) Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest. Remote Sensing 12:20, pages 3333.
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Vanessa Sousa da Silva, Carlos Alberto Silva, Midhun Mohan, Adrián Cardil, Franciel Eduardo Rex, Gabrielle Hambrecht Loureiro, Danilo Roberti Alves de Almeida, Eben North Broadbent, Eric Bastos Gorgens, Ana Paula Dalla Corte, Emanuel Araújo Silva, Rubén Valbuena & Carine Klauberg. (2020) Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data. Remote Sensing 12:9, pages 1438.
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Adrián Pascual, Juan Guerra-Hernández, Diogo N. Cosenza & Vicente Sandoval. (2020) The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning. Remote Sensing 12:3, pages 413.
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Pascual. (2019) Using Tree Detection Based on Airborne Laser Scanning to Improve Forest Inventory Considering Edge Effects and the Co-Registration Factor. Remote Sensing 11:22, pages 2675.
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Francisco MauroBryce FrankVicente J. MonleonHailemariam TemesgenKevin R. Ford. (2019) Prediction of diameter distributions and tree-lists in southwestern Oregon using LiDAR and stand-level auxiliary information. Canadian Journal of Forest Research 49:7, pages 775-787.
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Trung H Nguyen, Simon Jones, Mariela Soto-Berelov, Andrew Haywood & Samuel Hislop. (2019) Estimate Forest Biomass Dynamics Using Multi-Temporal Lidar And Single-Date Inventory Data. Estimate Forest Biomass Dynamics Using Multi-Temporal Lidar And Single-Date Inventory Data.
Lin Chen, Yeqiao Wang, Chunying Ren, Bai Zhang & Zongming Wang. (2019) Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. Remote Sensing 11:4, pages 414.
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Kun Liu, Xin Shen, Lin Cao, Guibin Wang & Fuliang Cao. (2018) Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations. ISPRS Journal of Photogrammetry and Remote Sensing 146, pages 465-482.
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Lin Chen, Chunying Ren, Bai Zhang, Zongming Wang & Yanbiao Xi. (2018) Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery. Forests 9:10, pages 582.
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Francisco Mauro, Vicente J. Monleon, Hailemariam Temesgen & Kevin R. Ford. (2017) Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information. PLOS ONE 12:12, pages e0189401.
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Jean-Pierre Renaud, Cédric Vega, Sylvie Durrieu, Jonathan Lisein, Steen Magnussen, Philippe Lejeune & Mériem Fournier. (2017) Stand-level wind damage can be assessed using diachronic photogrammetric canopy height models. Annals of Forest Science 74:4.
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