{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:42:23Z","timestamp":1770748943987,"version":"3.50.0"},"reference-count":62,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T00:00:00Z","timestamp":1678492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of C\u00f3rdoba"},{"name":"Center for Applied Research in Agroforestry Development (IDAF)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne laser scanning (ALS) technology is fully implemented in forest resource assessment processes, providing highly accurate and spatially continuous results throughout the area of interest, thus reducing inventory costs when compared with traditional sampling inventories. Several approaches have been employed to estimate forest parameters using ALS data, such as the Area-Based Approach (ABA) and Individual Tree Crown (ITC). These two methodologies use different information processing and field data collection approaches; thus, it is important to have a selection criterion for the method to be used based on the expected results and admissible errors. The objective of this study was to compare the prediction errors of forest inventory attributes in the functioning of ABA and ITC approaches. A plantation of 500 ha of Pinus radiata (400\u2013600 trees ha\u22121) in Chile was selected; a forest inventory was conducted using the ABA and ITC methods and the accuracy of both methods was analyzed. The ITC models performed better than the ABA models at low tree densities for all forest inventory attributes (15% MAPE in tree density\u2014N\u2014and 11% in volume\u2014V). There was no significant difference in precision regarding the volume and basal area (G) estimations at medium densities, although ITC obtained better results for density and dominant height (Ho). At high densities, ABA performed better for all the attributes except for height (6.5% MAPE in N, 8.7% in G, and 8.9% in V). Our results showed that the precision of forest inventories based on ALS data can be adjusted depending on tree density to optimize the selected approach (ABA and ITC), thus reducing the inventory costs. Hence, field efforts can be greatly decreased while achieving better prediction accuracies.<\/jats:p>","DOI":"10.3390\/rs15061544","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1544","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Comparison of Errors Produced by ABA and ITC Methods for the Estimation of Forest Inventory Attributes at Stand and Tree Level in Pinus radiata Plantations in Chile"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6075-5945","authenticated-orcid":false,"given":"Miguel \u00c1ngel","family":"Lara-G\u00f3mez","sequence":"first","affiliation":[{"name":"IDAF-Center for Applied Research in Agroforestry Development, Rabanales 21 Science & Technology Park, 14014 C\u00f3rdoba, Spain"},{"name":"Mediterranean Forest Global Change Observatory, Digitalization and Development in Forestry Ecosystems Laboratory, DigiFoR+-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV km. 396, 14071 C\u00f3rdoba, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3470-8640","authenticated-orcid":false,"given":"Rafael M.","family":"Navarro-Cerrillo","sequence":"additional","affiliation":[{"name":"Dendrochronology and Climate Change Laboratory, DendrodatLab-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV km. 396, 14071 C\u00f3rdoba, Spain"}]},{"given":"Inmaculada","family":"Clavero Rumbao","sequence":"additional","affiliation":[{"name":"IDAF-Center for Applied Research in Agroforestry Development, Rabanales 21 Science & Technology Park, 14014 C\u00f3rdoba, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8786-0211","authenticated-orcid":false,"given":"Guillermo","family":"Palacios-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Mediterranean Forest Global Change Observatory, Digitalization and Development in Forestry Ecosystems Laboratory, DigiFoR+-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV km. 396, 14071 C\u00f3rdoba, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1139\/cjfr-2018-0196","article-title":"Applications of the United States Forest Inventory and Analysis dataset: A review and future directions","volume":"48","author":"Tinkham","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/02827581.2017.1416666","article-title":"Remote sensing and forest inventories in Nordic countries\u2014Roadmap for the future","volume":"33","author":"Kangas","year":"2017","journal-title":"Scand. J. For. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Latifi, H., and Heurich, M. (2019). Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects. Remote Sens., 11.","DOI":"10.3390\/rs11111260"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1139\/cjfr-2020-0322","article-title":"From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data\u2014A brief history of management inventories in the Nordic countries","volume":"51","author":"Maltamo","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_5","first-page":"167","article-title":"Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest","volume":"76","author":"Wittke","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Luther, J.E., Fournier, R.A., van Lier, O.R., and Bujold, M. (2019). Extending ALS-Based Mapping of Forest Attributes with Medium Resolution Satellite and Environmental Data. Remote Sens., 11.","DOI":"10.3390\/rs11091092"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.3390\/f6114059","article-title":"Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA","volume":"6","author":"Rahlf","year":"2015","journal-title":"Forests"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2012.02.001","article-title":"Lidar Sampling for Large-Area Forest Characterization: A Review","volume":"121","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Frank, B., Mauro, F., and Temesgen, H. (2020). Model-Based Estimation of Forest Inventory Attributes Using Lidar: A Comparison of the Area-Based and Semi-Individual Tree Crown Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12162525"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4163","DOI":"10.3390\/rs5094163","article-title":"Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches","volume":"5","author":"Jakubowski","year":"2013","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.3390\/rs2061481","article-title":"Comparison of area-based and individual tree-based methods for pre-dicting plot-level forest attributes","volume":"2","author":"Yu","year":"2010","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1080\/01431160701736471","article-title":"Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images","volume":"29","author":"Holmgren","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9206","DOI":"10.1080\/01431161.2018.1508916","article-title":"Development of a robust canopy height model derived from ALS point clouds for predicting individual crown attributes at the species level","volume":"39","author":"Erfanifard","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.rse.2011.10.009","article-title":"Airborne scanning LiDAR in a double sampling forest carbon inventory","volume":"117","author":"Stephens","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s13595-014-0389-x","article-title":"Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources","volume":"72","author":"Bergseng","year":"2015","journal-title":"Ann. For. Sci."},{"key":"ref_18","first-page":"27","article-title":"Comparative testing of single-tree detection algorithms under different types of forest","volume":"85","author":"Vauhkonen","year":"2012","journal-title":"For. Int. J. For. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1139\/X08-037","article-title":"Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs","volume":"38","author":"Maltamo","year":"2008","journal-title":"Can. J. For. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1139\/X10-223","article-title":"Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands","volume":"41","author":"Peuhkurinen","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_21","unstructured":"Vastaranta, M., Holopainen, M., Haapanen, R., Yu, X., Melkas, T., Hyypp\u00e4, J., and Hyypp\u00e4, H. (2009, January 1\u20132). Comparison between an area based and individual tree detection method for low-pulse density ALS-based forest inventory. Proceedings of the Laser Scanning 2009, Paris, France. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives)."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.14358\/PERS.72.12.1369","article-title":"Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest","volume":"72","author":"Solberg","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saukkola, A., Melkas, T., Riekki, K., Sirparanta, S., Peuhkurinen, J., Holopainen, M., and Vastaranta, M. (2019). Predicting forest inventory attributes using airborne laser scanning, aerial imagery, and harvester data. Remote Sens., 11.","DOI":"10.3390\/rs11070797"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1139\/cjfr-2014-0276","article-title":"Modelling and localizing a stem taper function for Pinus radiata in Spain","volume":"45","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_25","unstructured":"McGaughey, R.J. (2007). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3390\/f5050936","article-title":"Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates","volume":"5","author":"Ruiz","year":"2014","journal-title":"Forests"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0924-2716(98)00009-4","article-title":"Determination of terrain models in wooded areas with airborne laser scanner data","volume":"53","author":"Kraus","year":"1998","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.geoderma.2005.06.004","article-title":"Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM","volume":"132","author":"Anderson","year":"2006","journal-title":"Geoderma"},{"key":"ref_29","unstructured":"Isenburg, M. (2017). LAStools, Rapidlasso GmbH."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"863","DOI":"10.14358\/PERS.80.9.863","article-title":"Generating Pit-free Canopy Height Models from Airborne Lidar","volume":"80","author":"Khosravipour","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.14358\/PERS.78.10.1079","article-title":"Mapping Individual Tree Species in an Urban Forest Using Airborne Lidar Data and Hyperspectral Imagery","volume":"78","author":"Zhang","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","unstructured":"R Core Team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Quinn, G.P., and Keough, M.J. (2002). Experimental Design and Data Analysis for Biologists, Cambridge University Press.","DOI":"10.1017\/CBO9780511806384"},{"key":"ref_34","unstructured":"Mead, D.J. (2013). Sustainable Management of Pinus radiata Plantations, FAO. FAO Forestry Paper No. 170."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1139\/cjfr-2020-0326","article-title":"Evolution, history, and use of stem taper equations: A review of their development, application, and implementation","volume":"51","author":"McTague","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5892","DOI":"10.1080\/01431161.2013.798053","article-title":"Development of a national model of Pinus radiata stand volume from lidar metrics for New Zealand","volume":"34","author":"Watt","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2011.10.006","article-title":"Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data","volume":"7","author":"Vastaranta","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","unstructured":"Koch, B., Kattenborn, T., Straub, C., and Vauhkonen, J. (2014). Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Springer."},{"key":"ref_39","first-page":"162","article-title":"Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest","volume":"42","author":"Latifi","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1080\/01431161.2016.1214302","article-title":"How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review","volume":"37","author":"Yin","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1016\/j.rse.2021.112307","article-title":"Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach","volume":"256","author":"Yun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"White, J.C., Tompalski, P., Vastaranta, M.A., Wulder, M.A., Saarinen, N.P., Stepper, C., and Coops, N.C. (2017). A Model Development and Application Guide for Generating an Enhanced Forest Inventory Using Airborne Laser Scanning Data and an Area-Based Approach, Natural Resources Canada. Canadian Wood Fibre Centre Information Report, No. FI-X-018."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1139\/cjfr-2021-0217","article-title":"Evaluation of UAS LiDAR data for tree segmentation and diameter estimation in boreal forests using trunk- and crown-based methods","volume":"52","author":"Kukkonen","year":"2022","journal-title":"Can. J. For. Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Novo-Fern\u00e1ndez, A., Barrio-Anta, M., Recondo, C., C\u00e1mara-Obreg\u00f3n, A., and L\u00f3pez-S\u00e1nchez, C.A. (2019). Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain. Remote Sens., 11.","DOI":"10.3390\/rs11141693"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kandare, K., Dalponte, M., \u00d8rka, H.O., Frizzera, L., and N\u00e6sset, E. (2017). Prediction of Species-Specific Volume Using Different In-ventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data. Remote Sens., 9.","DOI":"10.3390\/rs9050400"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gonzalez-Ferreiro, E., Arellano-P\u00e9rez, S., Castedo-Dorado, F., Hevia, A., Vega, J.A., Vega-Nieva, D.J., \u00c1lvarez-Gonz\u00e1lez, J.G., and Ruiz-Gonz\u00e1lez, A.D. (2017). Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176114"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2014.10.004","article-title":"Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data","volume":"156","author":"Bouvier","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1093\/sjaf\/28.4.205","article-title":"High- Versus Low-Density LiDAR in a Double-Sample Forest Inventory","volume":"28","author":"Parker","year":"2004","journal-title":"South. J. Appl. For."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.rse.2019.01.022","article-title":"Resolution dependence in an area-based approach to forest inventory with airborne laser scanning","volume":"224","author":"Packalen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"382","DOI":"10.5589\/m13-046","article-title":"Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update","volume":"39","author":"Vastaranta","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Barnes, C., Balzter, H., Barrett, K., Eddy, J., Milner, S., and Su\u00e1rez, J.C. (2017). Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands. Remote Sens., 9.","DOI":"10.3390\/rs9030231"},{"key":"ref_52","first-page":"102327","article-title":"Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories","volume":"100","author":"Kukkonen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ecolind.2017.10.066","article-title":"Predicting stem diameters and aboveground biomass of individual trees using remote sensing data","volume":"85","author":"Dalponte","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rse.2017.11.010","article-title":"Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR","volume":"205","author":"Goldbergs","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ma, Z., Pang, Y., Wang, D., Liang, X., Chen, B., Lu, H., Weinacker, H., and Koch, B. (2020). Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sens., 12.","DOI":"10.3390\/rs12071078"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Leite, R.V., Amaral, C.H.d., Pires, R.d.P., Silva, C.A., Soares, C.P.B., Macedo, R.P., Silva, A.A.L.d., Broadbent, E.N., Mohan, M., and Leite, H.G. (2020). Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and In-dividual Tree-Based Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12091513"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1080\/00049158.2016.1153770","article-title":"Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation","volume":"79","author":"Kathuria","year":"2016","journal-title":"Aust. For."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1093\/forestry\/cpab007","article-title":"Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches","volume":"94","author":"Kotivuori","year":"2021","journal-title":"Forestry"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1139\/cjfr-2013-0147","article-title":"Characterizing forest structural types and shelter-wood dynamics from Lorenz-based indicators predicted by airborne laser scanning","volume":"43","author":"Valbuena","year":"2013","journal-title":"Can. J. For. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Peuhkurinen, J., Tokola, T., Plevak, K., Sirparanta, S., Kedrov, A., and Pyankov, S. (2018). Predicting Tree Diameter Distributions from Airborne Laser Scanning, SPOT 5 Satellite, and Field Sample Data in the Perm Region, Russia. Forests, 9.","DOI":"10.3390\/f9100639"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"127195","DOI":"10.1016\/j.ufug.2021.127195","article-title":"Improving methods to calculate the loss of ecosystem services provided by urban trees using LiDAR and aerial orthophotos","volume":"63","author":"Matczak","year":"2021","journal-title":"Urban For. Urban Green."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cabrera-Ariza, A.M., Lara-G\u00f3mez, M.A., Santelices-Moya, R.E., Mero\u00f1o de Larriva, J.-E., and Mesas-Carrascosa, F.-J. (2022). Individu-alization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices. Sensors, 22.","DOI":"10.3390\/s22041331"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1544\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:52:57Z","timestamp":1760122377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1544"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,11]]},"references-count":62,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061544"],"URL":"https:\/\/doi.org\/10.3390\/rs15061544","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,11]]}}}