{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:49:43Z","timestamp":1760240983511,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,11,3]],"date-time":"2019-11-03T00:00:00Z","timestamp":1572739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way.<\/jats:p>","DOI":"10.3390\/rs11212579","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T04:13:08Z","timestamp":1572840788000},"page":"2579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7791-0991","authenticated-orcid":false,"given":"Fernando","family":"Carvajal-Ram\u00edrez","sequence":"first","affiliation":[{"name":"Department of Engineering, Mediterranean Research Center of Economics and Sustainable Development (CIMEDES), University of Almer\u00eda (Agrifood Campus of International Excellence, ceiA3), La Ca\u00f1ada de San Urbano, s\/n. 04120 Almer\u00eda, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5178-8158","authenticated-orcid":false,"given":"Jo\u00e3o Manuel Pereira Ramalho","family":"Serrano","sequence":"additional","affiliation":[{"name":"Rural Engineering Department, University of \u00c9vora, Instituto de Ci\u00eancias Agr\u00e1rias e Ambientais Mediterr\u00e2nicas (ICAAM), Apartado 94, 7002-554 \u00c9vora, Portugal"}]},{"given":"Francisco","family":"Ag\u00fcera-Vega","sequence":"additional","affiliation":[{"name":"Department of Engineering, Mediterranean Research Center of Economics and Sustainable Development (CIMEDES), University of Almer\u00eda (Agrifood Campus of International Excellence, ceiA3), La Ca\u00f1ada de San Urbano, s\/n. 04120 Almer\u00eda, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9556-7998","authenticated-orcid":false,"given":"Patricio","family":"Mart\u00ednez-Carricondo","sequence":"additional","affiliation":[{"name":"Department of Engineering, Mediterranean Research Center of Economics and Sustainable Development (CIMEDES), University of Almer\u00eda (Agrifood Campus of International Excellence, ceiA3), La Ca\u00f1ada de San Urbano, s\/n. 04120 Almer\u00eda, Spain"},{"name":"Peripheral Service of Research and Development based on drones, University of Almeria, La Ca\u00f1ada de San Urbano, s\/n. 04120 Almer\u00eda, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.accre.2014.11.002","article-title":"A Study of the Validation of Atmospheric CO2 from Satellite Hyper Spectral Remote Sensing","volume":"5","author":"Miao","year":"2014","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4155\/cmt.11.18","article-title":"Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change","volume":"2","author":"Goetz","year":"2011","journal-title":"Carbon Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2012.10.017","article-title":"A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing","volume":"128","author":"Zolkos","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"99","article-title":"Fitomasa a\u00e9rea en los ecosistemas de matorral en el monte Can Vilallonga (T.M. DE Cass\u00e0 de la Selva-Girona)","volume":"18","author":"Navarro","year":"2004","journal-title":"Ecolog\u00eda"},{"key":"ref_5","first-page":"197","article-title":"Estimation of above-ground biomass in shrubland ecosystems of southern Spain","volume":"15","author":"Cerrillo","year":"2006","journal-title":"Investig. Agrar. Sist. y Recur. For."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Alonso-Benito, A., Arroyo, L.A., Arbelo, M., and Hern\u00e1ndez-Leal, P. (2016). Fusion of WorldView-2 and LiDAR data to map fuel types in the Canary Islands. Remote Sens., 8.","DOI":"10.3390\/rs8080669"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3970","DOI":"10.3390\/s8063970","article-title":"Spatio-temporal analysis of forest fire risk and danger using Landsat imagery","volume":"8","author":"Bilgili","year":"2008","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11258-005-3448-4","article-title":"Fire risk and vegetation structural dynamics in Mediterranean shrubland","volume":"187","author":"Baeza","year":"2006","journal-title":"Plant Ecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1017\/S0376892900014132","article-title":"Ecosystems of the World II: Mediterranean-type Shrublands, Edited by F. Di Castri, D.W. Goodall & R.L. Specht. Elsevier Scientific Publishing Company, Amsterdam\u2013Oxford\u2013New York: xii + 643 pp., with numerous text-figures and tables, 27 \u00d7 20 \u00d7 38 cm, clothbound, US $136.50, Dfl. 280, 1981","volume":"11","author":"Wells","year":"1984","journal-title":"Environ. Conserv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Le, A.V., Paull, D.J., and Griffin, A.L. (2018). Exploring the inclusion of small regenerating trees to improve above-ground forest biomass estimation using geospatial data. Remote Sens., 10.","DOI":"10.3390\/rs10091446"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Jaramillo, V., Fries, A., Zeilinger, J., Homeier, J., Paladines-Benitez, J., and Bendix, J. (2018). Estimation of above ground biomass in a tropical mountain forest in southern Ecuador using airborne LiDAR data. Remote Sens., 10.","DOI":"10.3390\/rs10050660"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Knapp, N., Huth, A., Kugler, F., Papathanassiou, K., Condit, R., Hubbell, S.P., and Fischer, R. (2018). Model-assisted estimation of tropical forest biomass change: A comparison of approaches. Remote Sens., 10.","DOI":"10.3390\/rs10050731"},{"key":"ref_14","first-page":"87","article-title":"Descripci\u00f3n Y Evaluaci\u00f3n De La Fitomasa Presente En \u00c1reas No Cultivadas De La Comarca De Monegros (Arag\u00f3n)","volume":"25","author":"Enguita","year":"1995","journal-title":"Pastos"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1016\/j.rse.2010.08.029","article-title":"Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors","volume":"115","author":"Clark","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1051\/forest:19930106","article-title":"Shrub biomass, bulk volume and structure in the French Mediterranean region","volume":"50","author":"Armand","year":"1993","journal-title":"Ann. Des. Sci. For."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1111\/j.1654-109X.2012.01214.x","article-title":"Do goats preserve the forest? Evaluating the effects of grazing goats on combustible Mediterranean scrub","volume":"16","year":"2013","journal-title":"Appl. Veg. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.3390\/rs6032134","article-title":"Artificial neural network modeling of high arctic phytomass using synthetic aperture radar and multispectral data","volume":"6","author":"Collingwood","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"540","DOI":"10.3390\/rs6010540","article-title":"Modeling fire danger in Galicia and asturias (Spain) from MODIS images","volume":"6","author":"Bisquert","year":"2013","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cartus, O., Santoro, M., Wegm\u00fcller, U., and Rommen, B. (2019). Benchmarking the Retrieval of Biomass in Boreal Forests Using P-Band SAR Backscatter with Multi-Temporal C- and L-Band Observations. Remote Sens., 11.","DOI":"10.3390\/rs11141695"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, X., Ziniti, B., Torbick, N., and Ducey, M.J. (2018). Assessment of forest above ground biomass estimation using multi-temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10091424"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Doughty, C.L., and Cavanaugh, K.C. (2019). Mapping coastal wetland biomass from high resolution unmanned aerial vehicle (UAV) imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050540"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, H., Sun, Y., Chang, L., Qin, Y., Chen, J., Qin, Y., Du, J., Yi, S., and Wang, Y. (2018). Estimation of grassland canopy height and aboveground biomass at the quadrat scale using unmanned aerial vehicle. Remote Sens., 10.","DOI":"10.3390\/rs10060851"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2013). Remote Sensing Digital Image Analysis, Springer. [5th ed.].","DOI":"10.1007\/978-3-642-30062-2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Guisuraga, J.M., Sanz-Ablanedo, E., Su\u00e1rez-Seoane, S., and Calvo, L. (2018). Using unmanned aerial vehicles in postfire vegetation survey campaigns through large and heterogeneous areas: Opportunities and challenges. Sensors, 18.","DOI":"10.3390\/s18020586"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Carvajal-Ram\u00edrez, F., da Silva, J.R.M., Ag\u00fcera-Vega, F., Mart\u00ednez-Carricondo, P., Serrano, J., and Moral, F.J. (2019). Evaluation of fire severity indices based on pre- and post-fire multispectral imagery sensed from UAV. Remote Sens., 11.","DOI":"10.3390\/rs11090993"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dash, J.P., Pearse, G.D., and Watt, M.S. (2018). UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sens., 10.","DOI":"10.3390\/rs10081216"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","article-title":"Accurate, Dense, and Robust Multiview Stereopsis","volume":"32","author":"Furukawa","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","first-page":"1","article-title":"Accuracy of Digital Surface Models and Orthophotos Derived from Unmanned Aerial Vehicle Photogrammetry","volume":"143","year":"2016","journal-title":"J. Surv. Eng."},{"key":"ref_32","first-page":"1","article-title":"Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points","volume":"72","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","first-page":"127","article-title":"Reconstruction of extreme topography from UAV structure from motion photogrammetry","volume":"121","year":"2018","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5194\/isprs-annals-IV-1-W1-3-2017","article-title":"Classification of Aerial Photogrammetric 3D Point Clouds","volume":"4","author":"Becker","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_36","unstructured":"(2019, March 13). Junta de Andaluc\u00eda Interreg-Sudoe OPEN2PRESERVE. Available online: https:\/\/open2preserve.eu\/estudi\/experiencia-piloto-en-andalucia\/."},{"key":"ref_37","unstructured":"(2019, March 13). Gobierno de Espa\u00f1a La Red Natura 2000 en Espa\u00f1a. Available online: https:\/\/www.miteco.gob.es\/es\/biodiversidad\/temas\/espacios-protegidos\/red-natura-2000\/rn_espana.aspx."},{"key":"ref_38","unstructured":"(2019, March 13). Parrot Drones SAS Parrot Sequoia. Available online: https:\/\/www.parrot.com\/soluciones-business\/profesional\/parrot-sequoia#parrot-sequoia-."},{"key":"ref_39","unstructured":"(2019, March 13). SPH Engineering SIA UgCS Mission Planning Sofware for UAV Professionals. Available online: https:\/\/www.ugcs.com\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"805","DOI":"10.14358\/PERS.71.7.805","article-title":"Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy","volume":"71","author":"Aguilar","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Glocker, M., Landau, H., Leandro, R., and Nitschke, M. (2012, January 5\u20137). Global precise multi-GNSS positioning with trimble centerpoint RTX. Proceedings of the 2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands.","DOI":"10.1109\/NAVITEC.2012.6423060"},{"key":"ref_42","unstructured":"(2019, March 13). Pix4D SA Pix4D Make Better Decisions with Accurate 3D Maps and Models. Available online: https:\/\/www.pix4d.com\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4025","DOI":"10.1080\/01431161.2013.772313","article-title":"Using AHS hyper-spectral images to study forest vegetation recovery after a fire","volume":"34","author":"Huesca","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"71","DOI":"10.2307\/4003531","article-title":"Dry-Weight-Rank Method Assessment in Heterogenous Communities","volume":"54","author":"Dowhower","year":"2007","journal-title":"J. Range Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"438","DOI":"10.2111\/04-166R2.1","article-title":"Comparison of comparative yield and stubble height for estimating herbage standing crop in annual rangelands","volume":"59","author":"George","year":"2006","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_46","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_47","unstructured":"Kazhdan, M., Bolitho, M., and Hoppe, H. (2006, January 26\u201328). Poisson Surface Reconstruction. Proceedings of the Eurographics Symposium on Geometry Processing, Cagliari, Italy."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.2307\/2577276","article-title":"A Measure of Association for Nonparametric Statistics","volume":"57","author":"Acock","year":"1979","journal-title":"Soc. Forces"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2579\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:31:35Z","timestamp":1760189495000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,3]]},"references-count":48,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11212579"],"URL":"https:\/\/doi.org\/10.3390\/rs11212579","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,11,3]]}}}