{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:59:40Z","timestamp":1774641580066,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,18]],"date-time":"2016-08-18T00:00:00Z","timestamp":1471478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economy and Competitiveness","award":["CGL2013-48202-C2"],"award-info":[{"award-number":["CGL2013-48202-C2"]}]},{"DOI":"10.13039\/501100004895","name":"European Social Fund","doi-asserted-by":"publisher","award":["JAE-Doc Program"],"award-info":[{"award-number":["JAE-Doc Program"]}],"id":[{"id":"10.13039\/501100004895","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish National Research Council (CSIC)","award":["JAE-Doc Program"],"award-info":[{"award-number":["JAE-Doc Program"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk areas. This research aims to assess the viability of combining Geographic Object-Based Image Analysis (GEOBIA) and the fusion of a WorldView-2 (WV2) image and low density Light Detection and Ranging (LiDAR) data in order to produce fuel type maps within an area of complex orography and vegetation distribution located in the island of Tenerife (Spain). Independent GEOBIAs were applied to four datasets to create four fuel type maps according to the Prometheus classification. The following fusion methods were compared: Image Stack (IS), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), as well as the WV2 image alone. Accuracy assessment of the maps was conducted by comparison against the fuel types assessed in the field. Besides global agreement, disagreement measures due to allocation and quantity were estimated, both globally and by fuel type. This made it possible to better understand the nature of disagreements linked to each map. The global agreement of the obtained maps varied from 76.23% to 85.43%. Maps obtained through data fusion reached a significantly higher global agreement than the map derived from the WV2 image alone. By integrating LiDAR information with the GEOBIAs, global agreement improvements by over 10% were attained in all cases. No significant differences in global agreement were found among the three classifications performed on WV2 and LiDAR fusion data (IS, PCA, MNF). These study\u2019s findings show the validity of the combined use of GEOBIA, high-spatial resolution multispectral data and low density LiDAR data in order to generate fuel type maps in the Canary Islands.<\/jats:p>","DOI":"10.3390\/rs8080669","type":"journal-article","created":{"date-parts":[[2016,8,18]],"date-time":"2016-08-18T09:58:54Z","timestamp":1471514334000},"page":"669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2204-2936","authenticated-orcid":false,"given":"Alfonso","family":"Alonso-Benito","sequence":"first","affiliation":[{"name":"Earth and Atmospheric Observation Group (GOTA), Departamento de F\u00edsica, Universidad de La Laguna (ULL), 38200 La Laguna, Spain"}]},{"given":"Lara","family":"Arroyo","sequence":"additional","affiliation":[{"name":"Institute of Economy, Geography y Demography (IEGD), Spanish Council for Scientific Research (CSIC), Calle Albasanz 26-28, 28037 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6853-4442","authenticated-orcid":false,"given":"Manuel","family":"Arbelo","sequence":"additional","affiliation":[{"name":"Earth and Atmospheric Observation Group (GOTA), Departamento de F\u00edsica, Universidad de La Laguna (ULL), 38200 La Laguna, Spain"}]},{"given":"Pedro","family":"Hern\u00e1ndez-Leal","sequence":"additional","affiliation":[{"name":"Earth and Atmospheric Observation Group (GOTA), Departamento de F\u00edsica, Universidad de La Laguna (ULL), 38200 La Laguna, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bond, W.J., and van Wilgen, B.W. (1996). Surviving Fires-Vegetative and Reproductive Responses, Springer Science & Business Media.","DOI":"10.1007\/978-94-009-1499-5_3"},{"key":"ref_2","unstructured":"Pyne, S.J. (2001). Year of the Fires: The Story of the Great Fires of 1910, Mountain Press Publishing Company."},{"key":"ref_3","unstructured":"Moore, P. (2005, January 16). Fire management: Imbalanced and misunderstood?. Proceedings of the Forests, Wood and Livelihoods: Finding a Future for All Conference, Canberra, Australia."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ehlers, E., and Krafft, T. (2006). Earth System Science in the Anthropocene: Emerging Issues and Problems, Springer.","DOI":"10.1007\/b137853"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/0034-4257(89)90023-0","article-title":"Application of remote sensing and geographic systems to forest fire hazard mapping","volume":"29","author":"Chuvieco","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chuvieco, E. (2009). Earth Observation of Wildland Fires in Mediterranean Ecosystems, Springer.","DOI":"10.1007\/978-3-642-01754-4"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1071\/WF9980159","article-title":"Fuel model and fire potential from satellite and surface observations","volume":"8","author":"Burgan","year":"1998","journal-title":"Int. J. Wildland Fire"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1071\/WF01033","article-title":"Fire modeling and information system technology","volume":"10","author":"Andrews","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_9","unstructured":"Merrill, D.F., and Alexander, M.E. (1987). Glossary of Forest Fire Management Terms."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1071\/WF01028","article-title":"Mapping wildland fuel for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling","volume":"10","author":"Keane","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1071\/WF01036","article-title":"Characterizing fuels in the 21st century","volume":"10","author":"Sandberg","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_12","unstructured":"Ottmar, R.D., and Alvarado, E. (2004). Linking Vegetation Patterns to Potential Smoke Production and Fire Hazard, General Technical Report."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Perera, A.H., Drew, C.A., and Johnson, C.J. (2012). Expert Knowledge and Its Application in Landscape Ecology, Springer.","DOI":"10.1007\/978-1-4614-1034-8"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.foreco.2008.06.048","article-title":"Fire models and methods to map fuel types: The role of remote sensing","volume":"256","author":"Arroyo","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1139\/x02-052","article-title":"Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems","volume":"32","author":"Chuvieco","year":"2002","journal-title":"Can. J. For. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rollins, M.G., and Frame, C.K. (2006). The LANDFIRE Prototype Project: Nationally Consistent and Locally Relevant Geospatial Data for Wildland Fire Management.","DOI":"10.2737\/RMRS-GTR-175"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.foreco.2005.06.013","article-title":"Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling","volume":"217","author":"Falkowski","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_18","first-page":"225","article-title":"Remotely sensed characterization of forest fuel types by using satellite ASTER data","volume":"9","author":"Lasaponara","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1071\/WF11068","article-title":"Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data","volume":"22","author":"Arroyo","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bajocco, S., Dragoz, E., Gitas, I., Smiraglia, D., Salvati, L., and Ricotta, C. (2015). Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0119811"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.3390\/rs6021684","article-title":"A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape","volume":"6","author":"Mallinis","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3556","DOI":"10.1016\/j.foreco.2008.01.077","article-title":"Reliable, accurate and timely forest mapping for wildfire management using ASTER and Hyperion satellite imagery","volume":"255","author":"Keramitsoglou","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2006.02.025","article-title":"Assessing spatial patterns of forest fuel using AVIRIS data","volume":"102","author":"Jia","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1080\/01431160500227631","article-title":"Multiscale Fuel Type Mapping in Fragmented Ecosystems: Preliminary Results from Hyperspectral MIVIS and Multispectral Landsat TM data","volume":"27","author":"Lasaponara","year":"2006","journal-title":"Int. J. Rem. Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Arroyo, L.A., Healey, S.P., Cohen, W.B., Cocero, D., and Manzanera, J.A. (2006). Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region. J. Geophys. Res., 111.","DOI":"10.1029\/2005JG000120"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gitas, I.Z., Mitri, G.H., Kazakis, G., Ghosn, D., and Xanthopoulos, G. (2006). Fuel type mapping in Annapolis, Crete by employing QuickBird imagery and object-based classification. For. Ecol. Manag., 234.","DOI":"10.1016\/j.foreco.2006.08.255"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.isprsjprs.2006.05.002","article-title":"Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment","volume":"60","author":"Hollaus","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/36.921414","article-title":"A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners","volume":"39","author":"Kelle","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.rse.2007.10.009","article-title":"Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data","volume":"112","author":"Hudak","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"923","DOI":"10.14358\/PERS.72.8.923","article-title":"Isolating individual trees in a savanna woodland using small footprint LiDAR data","volume":"72","author":"Chen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"950","DOI":"10.3390\/rs4040950","article-title":"An international comparison of individual tree detection and extraction using airborne laser scanning","volume":"4","author":"Kaartinen","year":"2012","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7892","DOI":"10.3390\/rs70607892","article-title":"Invidual tree segmentation from LiDAR point clouds for urban forest inventory","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1016\/j.rse.2008.03.004","article-title":"Estimation of above-and below-ground biomass across regions of the boreal forest zone using airborne laser","volume":"112","author":"Naesset","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0034-4257(03)00098-1","article-title":"Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behaviour modeling","volume":"86","author":"Meier","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating forest canopy fuel parameter using LiDAR data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.rse.2007.06.011","article-title":"A voxel-based LiDAR method for estimating crown base height for deciduous and pine trees","volume":"112","author":"Popescu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.rse.2009.11.002","article-title":"Fusion of LiDAR and Imagery for Estimating Forest Canopy Fuels","volume":"114","author":"Erdody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"251","DOI":"10.14358\/PERS.77.3.251","article-title":"Dynamic range-based intensity normalization for airborne, discrete return LiDAR data for forest canopies","volume":"77","author":"Gatziolis","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","unstructured":"Rogers, R.H., and Wood, L. (1990). The History and Status of Merging Multiple Sensor Data: An Overview, Technical Report for 1990 ACSMASPRS Annual Conference."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.foreco.2009.11.018","article-title":"Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas","volume":"259","author":"Arroyo","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of hyperspectral and LiDAR remote sensing data for classification of complex forest areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6897","DOI":"10.3390\/rs6086897","article-title":"Decision fusion based on hyperspectral and multispectral satellite imagery for accurate forest species mapping","volume":"6","author":"Stavrakoudis","year":"2014","journal-title":"Remote Sens."},{"key":"ref_45","first-page":"262","article-title":"Comparison of vector stacking, multi-SVMs fuzzy output, and multi-SVMs voting methods for multiscale VHR urban mapping","volume":"7","author":"Huang","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3354","DOI":"10.1080\/01431161.2011.591444","article-title":"A multilevel decision fusion approach for urban mapping using very-high-resolution multi\/hyper-spectral imagery","volume":"33","author":"Huang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/01431160903439882","article-title":"Information fusion of aerial images and LiDAR data in urban areas: Vector stacking, re-classification, and post-processing approaches","volume":"32","author":"Huang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","first-page":"2971","article-title":"Fusion of hyper-spectral and LiDAR data using morphological profiles","volume":"8","author":"Pedergnana","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1016\/j.rse.2010.08.027","article-title":"Mapping biomass and stress in the Sierra Nevada using LiDAR and hyperspectral data fusion","volume":"115","author":"Swatantran","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/j.rse.2011.01.017","article-title":"Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules","volume":"115","author":"Chuvieco","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.rse.2007.05.005","article-title":"Mapping surface fuel models using LiDAR and multispectral data fusion for fire behavior","volume":"112","author":"Mutlu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.14358\/PERS.79.1.37","article-title":"Predicting surface fuel models and fuel metrics using LiDAR and CIR imagery in a dense, mountainous forest","volume":"79","author":"Jakubowski","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/JSTARS.2009.2012475","article-title":"An examination of the effects of spatial resolution and image analysis technique on indirect fuel mapping","volume":"1","author":"Tanase","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","first-page":"167","article-title":"Bioclimatology and climatophilous vegetation of Tenerife (Canary Islands)","volume":"43","author":"Acebes","year":"2006","journal-title":"Ann. Bot. Fennici."},{"key":"ref_56","first-page":"396","article-title":"A comparison between LiDAR and photogrammetrty digital terrain models in a forest area on Tenerife Island","volume":"39","author":"Isenburg","year":"2013","journal-title":"Can. J. For. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1080\/01431160903380565","article-title":"LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques","volume":"31","author":"Gaulton","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","unstructured":"Rapidlasso GmbH. Available online: http:\/\/rapidlasso.com\/."},{"key":"ref_59","unstructured":"Updike, T., and Comp, C. (2010). Radiometric Use of WorldView-2 Imagery, Digital Globe Inc.. Technical Note 2010."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Matthew, M.W., Adler-Golden, S.M., Berk, A., Richtsmeier, S.C., Levine, R.Y., Bernstein, L.S., Acharya, P.K., Anderson, G.P., Felde, G.W., and Hoke, M.P. (2000, January 24). Status of Atmospheric Correction Using a MODTRAN4-based Algorithm. Proceedings of the SPIE 2000, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, Orlando, FL, USA.","DOI":"10.1117\/12.410341"},{"key":"ref_61","unstructured":"Jensen, J.R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall. [3rd ed.]."},{"key":"ref_62","unstructured":"Smith, L.I. A Tutorial on Principal Components Analysis. Available online: http:\/\/goo.gl\/fqufn."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A Transform for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal","volume":"26","author":"Green","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Angewandte Geographische Informationsverarbeitung, Wichmann-Verlag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"7543","DOI":"10.1080\/2150704X.2014.969814","article-title":"Quantity, Exchange, and Shift Components of Difference in a Square Contingency Table","volume":"35","author":"Pontius","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5959","DOI":"10.1080\/01431161.2015.1110265","article-title":"Relative quantity and allocation disagreement measures for category-level accuracy assessment","volume":"36","author":"Warrens","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5273","DOI":"10.1080\/01431160903130937","article-title":"Sample size determination for image classification accuracy assessment and comparison","volume":"30","author":"Foody","year":"2009","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/8\/669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:28:43Z","timestamp":1760210923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/8\/669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,18]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2016,8]]}},"alternative-id":["rs8080669"],"URL":"https:\/\/doi.org\/10.3390\/rs8080669","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8,18]]}}}