{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:15:00Z","timestamp":1773098100365,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T00:00:00Z","timestamp":1673654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Esp\u00edrito Santo Research and Innovation Support Foundation (FAPES)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The definition of strategies for forest restoration projects depends on information of the successional stage of the area to be restored. Usually, classification of the successional stage is carried out in the field using forest inventory campaigns. However, these campaigns are costly, time-consuming, and limited in terms of spatial coverage. Currently, forest inventories are being improved using 3D data obtained from remote sensing. The objective of this work was to estimate several parameters of interest for the classification of the successional stages of secondary vegetation areas using 3D digital aerial photogrammetry (DAP) data obtained from unmanned aerial vehicles (UAVs). A cost analysis was also carried out considering the costs of equipment and data collection, processing, and analysis. The study was carried out in southeastern Brazil in areas covered by secondary Atlantic Forest. Regression models were fit to estimate total height (h), diameter at breast height (dbh), and basal area (ba) of trees in 40 field inventory plots (0.09 ha each). The models were fit using traditional metrics based on heights derived from DAP and a portable laser scanner (PLS). The prediction models based on DAP data yielded a performance similar to models fit with LiDAR, with values of R\u00b2 ranging from 88.3% to 94.0% and RMSE between 11.1% and 28.5%. Successional stage maps produced by DAP were compatible with the successional classes estimated in the 40 field plots. The results show that UAV photogrammetry metrics can be used to estimate h, dbh, and ba of secondary vegetation with an accuracy similar to that obtained from LiDAR. In addition to presenting the lowest cost, the estimates derived from DAP allowed for the classification of successional stages in the analyzed secondary forest areas.<\/jats:p>","DOI":"10.3390\/rs15020509","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:31:32Z","timestamp":1673843492000},"page":"509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mapping of the Successional Stage of a Secondary Forest Using Point Clouds Derived from UAV Photogrammetry"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4631-2603","authenticated-orcid":false,"given":"Ricardo Pinheiro","family":"Cabral","sequence":"first","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"given":"Gilson Fernandes","family":"da Silva","sequence":"additional","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"given":"Andr\u00e9 Quint\u00e3o","family":"de Almeida","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Sergipe, S\u00e3o Crist\u00f3v\u00e3o 49100-000, SE, Brazil"}]},{"given":"Santiago","family":"Bonilla-Bedoya","sequence":"additional","affiliation":[{"name":"Research Center for Territory and Sustainable Habitat, Universidad Tecnol\u00f3gica Indoam\u00e9rica, Quito 170103, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2217-7846","authenticated-orcid":false,"given":"Henrique Machado","family":"Dias","sequence":"additional","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3307-8579","authenticated-orcid":false,"given":"Adriano Ribeiro","family":"De Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3750-0813","authenticated-orcid":false,"given":"N\u00edvea Maria Mafra","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"given":"Carem Cristina Araujo","family":"Valente","sequence":"additional","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8051-9387","authenticated-orcid":false,"given":"Klisman","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Forestry and Wood Science, Federal University of Esp\u00edrito Santo, Jer\u00f4nimo Monteiro 29550-000, ES, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6925-3012","authenticated-orcid":false,"given":"F\u00e1bio Guimar\u00e3es","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Canopy Remote Sensing Solutions, Florian\u00f3polis 88032, SC, Brazil"}]},{"given":"Tathiane Santi","family":"Sarcinelli","sequence":"additional","affiliation":[{"name":"Aracruz Unit, Department of Forest Environment, Suzano S.A., Aracruz 29197-900, ES, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,14]]},"reference":[{"key":"ref_1","unstructured":"ONU (2022, August 04). Relat\u00f3rio Anual Das Na\u00e7\u00f5es Unidas No Brasil 2021\u2013Portal ODS. Available online: https:\/\/portalods.com.br\/publicacoes\/relatorio-anual-das-nacoes-unidas-no-brasil-2021\/."},{"key":"ref_2","unstructured":"Mma, M.d.M.A. (2016). ENREDD+ National REDD+ Strategy, Ministry of the Environment."},{"key":"ref_3","first-page":"3","article-title":"Degrada\u00e7\u00e3o Ambiental e Teoria Econ\u00f4mica","volume":"12","author":"Andrade","year":"2011","journal-title":"Rev. Econ. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1590\/S0103-20032008000200002","article-title":"A Intensidade Da Explora\u00e7\u00e3o Agropecu\u00e1ria Como Indicador Da Degrada\u00e7\u00e3o Ambiental Na Regi\u00e3o Dos Cerrados, Brasil","volume":"46","author":"Cunha","year":"2008","journal-title":"Rev. De Econ. E Sociol. Rural."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Rojas, M., Pereira, P., Brevik, E.C., Cerd\u00e0, A., and Jord\u00e1n, A. (2017). Soil Mapping and Processes Models for Sustainable Land Management Applied to Modern Challenges. Soil Mapping and Process Modeling for Sustainable Land Use Management, Elsevier.","DOI":"10.1016\/B978-0-12-805200-6.00006-2"},{"key":"ref_6","first-page":"1","article-title":"Climate Change Adaptation and Its Impact on Household Farm Income and Revenue Risk Exposure","volume":"10","author":"Dhakal","year":"2022","journal-title":"Resour. Environ. Sustain."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2015.02.012","article-title":"Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series","volume":"161","author":"DeVries","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"148952","DOI":"10.1016\/j.scitotenv.2021.148952","article-title":"Assessing Reforestation Failure at the Project Scale: The Margin for Technical Improvement under Harsh Conditions. A Case Study in a Mediterranean Dryland","volume":"796","author":"Campo","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_9","unstructured":"Pellico Netto, S., and Brena, D.A. (1997). Invent\u00e1rio Florestal, UFPR. [1st ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5558\/tfc2017-012","article-title":"Unmanned Aerial Systems for Precision Forest Inventory Purposes: A Review and Case Study","volume":"93","author":"Goodbody","year":"2017","journal-title":"For. Chron."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tompalski, P., Coops, N.C., White, J.C., and Wulder, M.A. (2016). Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching. Forests, 7.","DOI":"10.3390\/f7110255"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1590\/1809-43921982121091","article-title":"Tamanho de Parcela Amostral Para Invent\u00e1rios Florestais","volume":"12","author":"Higuchi","year":"1982","journal-title":"Acta Amaz."},{"key":"ref_13","unstructured":"Nogueira, M.M., Lentini, M.W., Pires, I.P., Bittencourt, P.G., and Zweede, J.C. (2010). Procedimentos Simplificados em Seguran\u00e7a e Sa\u00fade do Trabalho no Manejo Florestal Manual T\u00e9cnico, Instituto Floresta Tropical-Funda\u00e7\u00e3o Floresta Tropical. [1st ed.]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Morin, D., Planells, M., Baghdadi, N., Bouvet, A., Fayad, I., le Toan, T., Mermoz, S., and Villard, L. (2022). Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process. Remote Sens., 14.","DOI":"10.3390\/rs14092079"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14214\/sf.9923","article-title":"Value of Airborne Laser Scanning and Digital Aerial Photogrammetry Data in Forest Decision Making","volume":"52","author":"Kangas","year":"2018","journal-title":"Silva Fennica"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1093\/forestry\/75.3.305","article-title":"Approaches to quantifying forest structures","volume":"75","author":"Pommerening","year":"2002","journal-title":"Forestry"},{"key":"ref_18","first-page":"1","article-title":"Est\u00e1gio sucessional de uma floresta estacional semidecidual secund\u00e1ria com distintos hist\u00f3ricos de uso do solo no sul do Esp\u00edrito Santo","volume":"70","author":"Abreu","year":"2019","journal-title":"Rodrigu\u00e9sia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s10980-007-9119-1","article-title":"Observing succession on aspen-dominated landscapes using a remote sensing-ecosystem approach","volume":"22","author":"Bergen","year":"2007","journal-title":"Landsc. Ecol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111194","DOI":"10.1016\/j.rse.2019.05.013","article-title":"Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry","volume":"232","author":"Bispo","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8300","DOI":"10.3390\/rs70708300","article-title":"Mapping secondary forest succession on abandoned agricultural land with LiDAR point clouds and terrestrial photography","volume":"7","author":"Kolecka","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/35002501","article-title":"Biodiversity hotspots for conservation priorities","volume":"403","author":"Myers","year":"2000","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1016\/j.biocon.2009.02.021","article-title":"BrazilianAtlantic forest: How much is left and how is the remaining forest distributed? Implications for conservation","volume":"142","author":"Ribeiro","year":"2009","journal-title":"Biol. Conserv."},{"key":"ref_24","unstructured":"INCAPER (2020). Programa de Assist\u00eancia T\u00e9cnica e Extens\u00e3o Rural, Proater 2020\u20132023, INCAPER."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s Climate Classification Map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_26","unstructured":"IBGE (2022, August 06). BDIA\u2013Banco de Dados de Informa\u00e7\u00f5es Ambientais, Available online: https:\/\/bdiaweb.ibge.gov.br\/#\/consulta\/pedologia."},{"key":"ref_27","unstructured":"Brasil (2022, August 05). Resolu\u00e7\u00e3o Conama 29, de 7 de dezembro de 1994 Conselho Nacional de Meio Ambiente, Available online: http:\/\/conama.mma.gov.br."},{"key":"ref_28","unstructured":"(2022, October 27). Suunto PM-5\/360 PC Clinometer\u2013Inclination Tool for Professionals. Available online: https:\/\/www.suunto.com\/Products\/Compasses\/Suunto-PM-5\/Suunto-PM-5360-PC\/."},{"key":"ref_29","unstructured":"Soares, C.P.B., Paula Neto, F., and Souza, A.L. (2011). Dendrometria e Invent\u00e1rio Florestal, Editora UFV."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.3390\/rs71013895","article-title":"Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure","volume":"7","author":"Dandois","year":"2015","journal-title":"Remote Sens."},{"key":"ref_31","unstructured":"Brasil (2023, January 04). ICA 100-40: Aeronaves n\u00e3o Tripuladas e o Acesso A\u00e9reo Brasileiro. Available online: https:\/\/publicacoes.decea.mil.br\/publicacao\/ica-100-40."},{"key":"ref_32","first-page":"43","article-title":"Dos levantamento com ve\u00edculo a\u00e9reo n\u00e3o tripulado para gera\u00e7\u00e3o demodelo digital do terreno em bacia experimental com vegeta\u00e7\u00e3o florestal esparsa","volume":"39","author":"Hung","year":"2017","journal-title":"RA\u2019E GA\u2013O Espac. Geogr. Em Anal."},{"key":"ref_33","unstructured":"(2022, August 07). Western Digital Corporation Cart\u00e3o MicroSDXCTM SanDisk Extreme\u00ae PRO UHS-I, Melhor Cart\u00e3o Micro SD|Western Digital. Available online: https:\/\/www.westerndigital.com\/pt-br\/products\/memory-cards\/sandisk-extreme-pro-uhs-i-microsd#SDSQXCD-128G-GN6MA."},{"key":"ref_34","unstructured":"(2022, August 07). Agisoft Agisoft Metashape: Agisoft Metashape. Available online: https:\/\/www.agisoft.com\/."},{"key":"ref_35","unstructured":"(2022, August 07). GEOSLAM ZEB Horizon: The Ultimate Mobile Mapping Solution. Available online: https:\/\/geoslam.com\/solutions\/zeb-horizon\/."},{"key":"ref_36","unstructured":"(2022, August 07). GEOSLAM GeoSLAM Hub: Transform 3D Data into Actionable Information. Available online: https:\/\/geoslam.com\/hub\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","article-title":"LidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"(2022, August 07). R Core Team R: The R Project for Statistical Computing. Available online: https:\/\/www.r-project.org\/."},{"key":"ref_39","unstructured":"McGaughey, R. (2022). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization 2022. V3.42, USDA Forest Service."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Almeida, A., Gon\u00e7alves, F., Silva, G., Souza, R., Treuhaft, R., Santos, W., Loureiro, D., and Fernandes, M. (2020). Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds. Remote Sens., 12.","DOI":"10.3390\/rs12213560"},{"key":"ref_41","unstructured":"Lumley, T. (2022, February 04). Package \u201cLeaps\u201d: Regression Subset Selection. Available online: https:\/\/cran.r-project.org\/web\/packages\/leaps\/leaps.pdf."},{"key":"ref_42","unstructured":"SEEA, AEFES, and CREA-ES (2012). Tabela de Servi\u00e7os e Honor\u00e1rios Profissionais No Campo Da Engenharia Agron\u00f4mica Para o Estado Do Esp\u00edrito Santo, SEEA."},{"key":"ref_43","first-page":"396","article-title":"A Comparison between LiDAR and Photogrammetry Digital Terrain Models in a Forest Area on Tenerife Island","volume":"39","author":"Gil","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.biocon.2015.03.031","article-title":"Using Lightweight Unmanned Aerial Vehicles to Monitor Tropical Forest Recovery","volume":"186","author":"Zahawi","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1080\/01431161.2016.1264028","article-title":"Determining Tree Height and Crown Diameter from High-Resolution UAV Imagery","volume":"38","author":"Panagiotidis","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mlambo, R., Woodhouse, I., Gerard, F., and Anderson, K. (2017). Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries. Forests, 8.","DOI":"10.3390\/f8030068"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Guerra-Hern\u00e1ndez, J., Gonz\u00e1lez-Ferreiro, E., Monle\u00f3n, V., Faias, S., Tom\u00e9, M., and D\u00edaz-Varela, R. (2017). Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus Pinea Stands. Forests, 8.","DOI":"10.3390\/f8080300"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kachamba, D., \u00d8rka, H., Gobakken, T., Eid, T., and Mwase, W. (2016). Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens., 8.","DOI":"10.3390\/rs8110968"},{"key":"ref_49","first-page":"143","article-title":"Allometric Estimation of the Biomass of Musa spp. in Homegardens of Tabasco, Mexico","volume":"22","year":"2019","journal-title":"Trop. Subtrop. Agroecosyst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2017.01.016","article-title":"Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity across Spatial Scales in a Pine Barrens Ecosystem","volume":"191","author":"Meng","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ganz, S., K\u00e4ber, Y., and Adler, P. (2019). Measuring Tree Height with Remote Sensing\u2014A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements. Forests, 10.","DOI":"10.3390\/f10080694"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2013.04.005","article-title":"High Spatial Resolution Three-Dimensional Mapping of Vegetation Spectral Dynamics Using Computer Vision","volume":"136","author":"Dandois","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Schneider, P.R., and Schneider, P.S.P. (2008). Introdu\u00e7\u00e3o Ao Manejo Florestal, FACOS-UFSM. [2nd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111434","DOI":"10.1016\/j.rse.2019.111434","article-title":"Quantifying the Contribution of Spectral Metrics Derived from Digital Aerial Photogrammetry to Area-Based Models of Forest Inventory Attributes","volume":"234","author":"Tompalski","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gyawali, A., Aalto, M., Peuhkurinen, J., Villikka, M., and Ranta, T. (2022). Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests. Sustainability, 14.","DOI":"10.3390\/su14073720"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s40725-019-00087-2","article-title":"Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions","volume":"5","author":"Goodbody","year":"2019","journal-title":"Current Forestry Reports."},{"key":"ref_57","first-page":"102658","article-title":"Integrating Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry to Estimate Individual Tree Attributes in Managed Coniferous Forests in Japan","volume":"106","author":"Shimizu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Moe, K.T., Owari, T., Furuya, N., Hiroshima, T., and Morimoto, J. (2020). Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests. Remote Sens., 12.","DOI":"10.3390\/rs12172865"},{"key":"ref_59","first-page":"11","article-title":"Image Matching as a Data Source for Forest Inventory\u2013Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction Algorithms in a Typical Managed Boreal Forest Environment","volume":"60","author":"Kukkonen","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","first-page":"231","article-title":"A Comparison of Area-Based Forest Attributes Derived from Airborne Laser Scanner, Small-Format and Medium-Format Digital Aerial Photography","volume":"76","author":"Iqbal","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ullah, S., Dees, M., Datta, P., Adler, P., Schardt, M., and Koch, B. (2019). Potential of Modern Photogrammetry Versus Airborne Laser Scanning for Estimating Forest Variables in a Mountain Environment. Remote Sens., 11.","DOI":"10.3390\/rs11060661"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/02827581.2014.961954","article-title":"Comparing Biophysical Forest Characteristics Estimated from Photogrammetric Matching of Aerial Images and Airborne Laser Scanning Data","volume":"30","author":"Gobakken","year":"2015","journal-title":"Scand. J. For. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111747","DOI":"10.1016\/j.rse.2020.111747","article-title":"The Application of Unmanned Aerial Vehicles (UAVs) to Estimate above-Ground Biomass of Mangrove Ecosystems","volume":"242","author":"Navarro","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_64","unstructured":"Pereira, I.S. (2018). Desempenho de Dispositivos Eletr\u00f4nicos para An\u00e1lise Estrutural da floresta de terra firme na Amaz\u00f4nia Central, Instituto nacional de pesquisas da Amaz\u00f4nia\u2013INPA."},{"key":"ref_65","unstructured":"Berbert, M.L.D.G. (2016). Potencial do LiDAR Terrestre Como Ferramenta para o Manejo de Florestas Naturais, UFRRJ."},{"key":"ref_66","unstructured":"Ara\u00fajo, J.P.d.C., Niemann, R.S., Dourado, F., Fernandes, M.C., and Fernandes, N.F. (2022). Revis\u00f5es de Literatura de Geomorfologia Brasileira, UnB."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Pires, P.F. (2020). Geoci\u00eancias, Sociedade e Sustentabilidade, Conhecimento Livre. [1st ed.].","DOI":"10.37423\/2020.a3"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone Remote Sensing for Forestry Research and Practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. For. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/509\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:21Z","timestamp":1760119581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/509"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,14]]},"references-count":68,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020509"],"URL":"https:\/\/doi.org\/10.3390\/rs15020509","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,14]]}}}