{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T20:58:33Z","timestamp":1771016313619,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"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>In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.<\/jats:p>","DOI":"10.3390\/rs11232800","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T03:55:51Z","timestamp":1574826951000},"page":"2800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9212-9615","authenticated-orcid":false,"given":"Alon","family":"Dadon","sequence":"first","affiliation":[{"name":"The Remote Sensing Laboratory, Department of Geography and Human Environment, The Porter School of the Environment and Earth Sciences, Tel-Aviv University, Tel Aviv 699780, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moshe","family":"Mandelmilch","sequence":"additional","affiliation":[{"name":"The Remote Sensing Laboratory, Department of Geography and Human Environment, The Porter School of the Environment and Earth Sciences, Tel-Aviv University, Tel Aviv 699780, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eyal","family":"Ben-Dor","sequence":"additional","affiliation":[{"name":"The Remote Sensing Laboratory, Department of Geography and Human Environment, The Porter School of the Environment and Earth Sciences, Tel-Aviv University, Tel Aviv 699780, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-7468","authenticated-orcid":false,"given":"Efrat","family":"Sheffer","sequence":"additional","affiliation":[{"name":"The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Faculty of Agriculture, The Hebrew University of Jerusalem, Rehovot 7610001, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2014.11.011","article-title":"Quantifying forest canopy traits: Imaging spectroscopy versus field survey","volume":"158","author":"Asner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1641\/0006-3568(2004)054[0523:UISTSE]2.0.CO;2","article-title":"Using Imaging Spectroscopy to Study Ecosystem Processes and Properties","volume":"54","author":"Ustin","year":"2004","journal-title":"BioScience"},{"key":"ref_3","first-page":"1","article-title":"Forest fire risk zone mapping from satellite imagery and GIS","volume":"4","author":"Jaiswal","year":"2002","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1007\/s10021-007-9041-z","article-title":"Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests","volume":"10","author":"Carlson","year":"2007","journal-title":"Ecosystems"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Francis, E.J., and Asner, G.P. (2019). High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests. Remote Sens., 11.","DOI":"10.3390\/rs11030351"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Peng, Y., Fan, M., Bai, L., Sang, W., Feng, J., Zhao, Z., and Tao, Z. (2019). Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands. Remote Sens., 11.","DOI":"10.3390\/rs11050588"},{"key":"ref_8","first-page":"326","article-title":"Mapping rock forming minerals at Boundary Canyon, Death Valey National Park, California, using aerial SEBASS thermal infrared hyperspectral image data","volume":"64","author":"Aslett","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1177\/0003702817739013","article-title":"Exploring the impact of different input data types on soil variable estimation using the ICRAF-ISRIC global soil spectral database","volume":"72","author":"Aitkenhead","year":"2018","journal-title":"Appl. Spectrosc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/22797254.2017.1274571","article-title":"Retrieval of leaf fuel moisture contents from hyperspectral indices developed from dehydration experiments","volume":"50","author":"Cao","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Carmon, N., and Ben-Dor, E. (2018). Mapping Asphaltic Roads\u2019 Skid Resistance Using Imaging Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030430"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/LGRS.2016.2539301","article-title":"Rapid Assessment of Dynamic Friction Coefficient of Asphalt Pavement Using Reflectance Spectroscopy","volume":"13","author":"Carmon","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/10643389.2018.1447717","article-title":"Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives","volume":"48","author":"Gholizadeh","year":"2018","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.oregeorev.2017.11.023","article-title":"Identification of new base metal mineralization in Kumaon Himalaya, India, using hyperspectral remote sensing and hydrothermal alteration","volume":"92","author":"Govil","year":"2018","journal-title":"Ore Geol. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecocom.2013.06.003","article-title":"Review of optical-based remote sensing for plant trait mapping","volume":"15","author":"Clevers","year":"2013","journal-title":"Ecol. Complex."},{"key":"ref_16","first-page":"1","article-title":"Advances in remote sensing of vegetation function and traits","volume":"43","author":"Houborg","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","first-page":"55","article-title":"Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 \u03bcm","volume":"43","author":"Kokaly","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kopa\u010dkov\u00e1, V., Ben-Dor, E., Carmon, N., and Notesco, G. (2017). Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine. Remote Sens., 9.","DOI":"10.3390\/rs9020134"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1080\/01431161.2016.1148291","article-title":"Normalizing reflectance from different spectrometers and protocols with an internal soil standard","volume":"37","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0034-4257(01)00346-7","article-title":"Multidate adaptive unmixing and its application to analysis of ecosystem transitions along a climatic gradient","volume":"82","author":"Shoshany","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.catena.2006.10.007","article-title":"Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a Mediterranean landscape, Mt. Carmel Israel","volume":"71","author":"Wittenberg","year":"2007","journal-title":"Catena"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"115","article-title":"Principal component analysis for hyperspectral image classification","volume":"62","author":"Rodarmel","year":"2002","journal-title":"Surv. Land Inf. Sci."},{"key":"ref_26","unstructured":"Jensen, J.R. (2000). Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall."},{"key":"ref_27","unstructured":"Jensen, J.R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s12517-017-3090-1","article-title":"Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA","volume":"10","author":"Liu","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/01431160500444772","article-title":"Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field-based results","volume":"28","author":"Wynne","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.3390\/rs70202046","article-title":"Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery","volume":"7","author":"Burai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1080\/10106049.2014.883439","article-title":"Mediterranean forest species mapping using classification of Hyperion imagery","volume":"30","author":"Galidaki","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-Based Edge-Preserving Features for Hyperspectral Image Classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s12524-018-0803-1","article-title":"Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis","volume":"46","author":"Kavzoglu","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.rse.2004.03.006","article-title":"Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping","volume":"91","author":"Pu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s10661-007-9843-7","article-title":"Invasive species change detection using artificial neural networks and CASI hyperspectral imagery","volume":"140","author":"Pu","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"895","DOI":"10.13031\/2013.16087","article-title":"Hyperspectral image data mining for band selection in agricultural applications","volume":"47","author":"Bajwa","year":"2004","journal-title":"Trans. ASAE"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2016.2636877","article-title":"Hyperspectral image classification with rotation random forest via KPCA","volume":"10","author":"Xia","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.infrared.2018.06.026","article-title":"Comparison assessment of low rank sparse-PCA based-clustering\/classification for automatic mineral identification in long wave infrared hyperspectral imagery","volume":"93","author":"Yousefi","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e00542","DOI":"10.1016\/j.heliyon.2018.e00542","article-title":"Landsat-8 data for chromite prospecting in the Logar Massif, Afghanistan","volume":"4","author":"Abdelaziz","year":"2018","journal-title":"Heliyon"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.landusepol.2018.02.019","article-title":"Land use\/cover change in Ghana\u2019s oil city: Assessing the impact of neoliberal economic policies and implications for sustainable development goal number one\u2014A remote sensing and GIS approach","volume":"73","author":"Acheampong","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s40965-017-0029-0","article-title":"Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data","volume":"2","author":"Alexandris","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.geoderma.2017.01.018","article-title":"Homogenisation of a soil properties map by principal component analysis to define index agricultural insurance policies","volume":"311","author":"Arias","year":"2018","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bell\u00f3n, B., B\u00e9gu\u00e9, A., Seen, D.L., De Almeida, C.A., and Sim\u00f5es, M. (2017). A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9060600"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e02155","DOI":"10.1002\/ecs2.2155","article-title":"Springs as hydrologic refugia in a changing climate? A remote-sensing approach","volume":"9","author":"Cartwright","year":"2018","journal-title":"Ecosphere"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sassa, K., Guzzetti, F., Yamagishi, H., Arbanas, Z., Casagli, N., McSaveney, M., and Dang, K. (2018). TXT-tool 2.039-3.1: Satellite remote sensing techniques for landslides detection and mapping. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools, Springer.","DOI":"10.1007\/978-3-319-57774-6"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s11069-016-2663-8","article-title":"Joint use of remote sensing data and volunteered geographic information for exposure estimation: Evidence from Valpara\u00edso, Chile","volume":"86","author":"Riedlinger","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, J., Luo, C., Huang, H., Zhao, H., and Wang, S. (2017). Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network. Remote Sens., 9.","DOI":"10.3390\/rs9030225"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2018). Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation, CRC Press.","DOI":"10.1201\/9781315164151"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.3390\/s90604869","article-title":"Applications of Remote Sensing to Alien Invasive Plant Studies","volume":"9","author":"Huang","year":"2009","journal-title":"Sensors"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1890\/1051-0761(2000)010[0689:BICEGC]2.0.CO;2","article-title":"Biotic invasions: Causes, epidemiology, global consequences, and control","volume":"10","author":"Mack","year":"2000","journal-title":"Ecol. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2016). Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Boisvenue, C., and White, J.C. (2019). Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science. Remote Sens., 11.","DOI":"10.3390\/rs11040463"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1111\/nph.15810","article-title":"Improving plant allometry by fusing forest models and remote sensing","volume":"223","author":"Fischer","year":"2019","journal-title":"New Phytol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.scienta.2012.02.031","article-title":"Non-destructive prediction of sweetness of intact mango using near infrared spectroscopy","volume":"138","author":"Jha","year":"2012","journal-title":"Sci. Hortic."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Moreno, A., Neumann, M., Mohebalian, P.M., Thurnher, C., and Hasenauer, H. (2019). The Continental Impact of European Forest Conservation Policy and Management on Productivity Stability. Remote Sens., 11.","DOI":"10.3390\/rs11010087"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.tree.2018.12.012","article-title":"Advances in Microclimate Ecology Arising from Remote Sensing","volume":"34","author":"Zellweger","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_58","unstructured":"Blondel, J., and Aronson, J. (1999). Biology and Wildlife of the Mediterranean Region, Oxford University Press."},{"key":"ref_59","unstructured":"Kruger, F.J., Mitchell, D.T., and Jarvis, J.U.M. (2012). Mediterranean-Type Ecosystems: The Role of Nutrients, Springer Science Business Media."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Miller, C.J. (2002). Performance Assessment of ACORN Atmospheric Correction Algorithm. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, International Society for Optics and Photonics.","DOI":"10.1117\/12.478777"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6196","DOI":"10.3390\/rs70506196","article-title":"Supervised Vicarious Calibration (SVC) of Multi-Source Hyperspectral Remote-Sensing Data","volume":"7","author":"Brook","year":"2015","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/01969727308546046","article-title":"A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters","volume":"3","author":"Dunn","year":"1973","journal-title":"J. Cybern."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1080\/01431160903475241","article-title":"Coastal wetland vegetation classification with a Landsat Thematic Mapper image","volume":"32","author":"Zhang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_65","first-page":"69","article-title":"A Quantitative Method to Test for Consistency and Correctness in Photointerpretation","volume":"49","author":"Congalton","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_66","first-page":"421","article-title":"Correct formulation of the Kappa coefficient of agreement","volume":"53","author":"Hudson","year":"1987","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis.","DOI":"10.1201\/9781420048568"},{"key":"ref_69","unstructured":"Jensen, J.R., McMaster, R.B., and Rizos, C. (2001). Manual of Geospatial Science and Technology, Informa UK Limited."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"53507","DOI":"10.1117\/1.3553234","article-title":"Examination of spaceborne imaging spectroscopy data utility for stratigraphic and lithologic mapping","volume":"5","author":"Dadon","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press.","DOI":"10.1201\/b19322"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1002\/ecy.1734","article-title":"Liana effects on biomass dynamics strengthen during secondary forest succession","volume":"98","author":"Lai","year":"2017","journal-title":"Ecology"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0169-5347(02)02491-6","article-title":"The ecology of lianas and their role in forests","volume":"17","author":"Schnitzer","year":"2002","journal-title":"Trends Ecol. Evol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1111\/1365-2745.12815","article-title":"Tree species vary widely in their tolerance for liana infestation: A case study of differential host response to generalist parasites","volume":"106","author":"Visser","year":"2017","journal-title":"J. Ecol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1111\/1365-2745.12635","article-title":"Lianas and soil nutrients predict fine-scale distribution of above-ground biomass in a tropical moist forest","volume":"104","author":"Ledo","year":"2016","journal-title":"J. Ecol."},{"key":"ref_76","unstructured":"Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2004). Remote Sensing and Image Interpretation, Wiley. [5th ed.]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2800\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:49Z","timestamp":1760189869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,27]]},"references-count":76,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232800"],"URL":"https:\/\/doi.org\/10.3390\/rs11232800","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,27]]}}}