{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:38:27Z","timestamp":1765233507400,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"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>Forests are some of the major ecosystems that help in mitigating the effects of climate change. Understanding the relation between the surface temperatures of different vegetation and trees and their heights is very crucial in understanding events such as wildfires. In this work, relationships between tree canopy temperature and canopy height with respect to vegetation types were extracted. The southern part of Sardinia Island, which has dense forests and is often affected by wildfires, was selected as the region of interest. PRISMA hyperspectral imagery has been used to map all the available vegetation types in the region of interest using the support vector machine classifier with an accuracy of &gt;80% for all classes. The Global Ecosystem Dynamics Investigation\u2019s (GEDI) L2A Raster Canopy Top Height product provides canopy height measurements in spatially discrete footprints, and to overcome this issue of discontinuous sampling, Random Forest Regression was used on Sentinel-1 SAR data, Sentinel-2 multispectral data, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to estimate the canopy heights of various vegetation classes, with a root mean squared error (RMSE) value of 2.9176 m and a coefficient of determination (R2) value of 0.791. Finally, the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and emissivity product provides ground surface temperature regardless of land use and land cover (LULC) types. LST measurements over tree canopies are considered as the tree canopy temperature. We estimated the relationship between the canopy temperature of five vegetation types (evergreen oak, olive, juniper, silicicole, riparian trees) and the corresponding canopy heights and vegetation types. The resulting scatter plots showed that lower tree canopy temperatures correspond with higher tree canopies with a correlation coefficient in the range of \u22120.4 to \u22120.5 for distinct types of vegetation.<\/jats:p>","DOI":"10.3390\/rs15082080","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T10:29:16Z","timestamp":1681468156000},"page":"2080","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Unveiling Temperature Patterns in Tree Canopies across Diverse Heights and Types"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8581-3374","authenticated-orcid":false,"given":"Riyaaz Uddien","family":"Shaik","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Sapienza University of Rome, 00138 Rome, Italy"}]},{"given":"Sriram Babu","family":"Jallu","sequence":"additional","affiliation":[{"name":"Wageningen University & Research, 6700 HB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8534-2808","authenticated-orcid":false,"given":"Katarina","family":"Doctor","sequence":"additional","affiliation":[{"name":"Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, DC 20375, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34002","DOI":"10.1088\/1748-9326\/ac4657","article-title":"Biophysical feedback of forest canopy height on land surface temperature over contiguous United States","volume":"17","author":"Zhang","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_2","unstructured":"Hulley, G.C., Ghent, D., G\u00f6ttsche, F.M., Guillevic, P.C., Mildrexler, D.J., and Coll, C. (2019). Taking the Temperature of the Earth, Elsevier BV."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/B978-0-12-409548-9.10375-6","article-title":"Land Surface Temperature","volume":"1\u20139","author":"Li","year":"2018","journal-title":"Compr. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1016\/j.agrformet.2009.05.016","article-title":"Advances in thermal infrared remote sensing for land surface modeling","volume":"149","author":"Kustas","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Teufel, B., Sushama, L., Poitras, V., Dukhan, T., B\u00e9lair, S., Miranda-Moreno, L., Sun, L., Sasmito, A., and Bitsuamlak, G. (2021). Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics. Atmosphere, 12.","DOI":"10.3390\/atmos12020243"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100056","DOI":"10.1016\/j.nexus.2022.100056","article-title":"Assessing the effect of COVID-19 lockdown on surface urban heat island for different land use\/cover types using remote sensing","volume":"5","author":"Jallu","year":"2022","journal-title":"Energy Nexus"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2004.01.019","article-title":"Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating","volume":"92","author":"Chuvieco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/01431160701281072","article-title":"Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM\/ETM images","volume":"29","author":"Escuin","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kuenzer, C., and Dech, S. (2013). Thermal Infrared Remote Sensing, Springer.","DOI":"10.1007\/978-94-007-6639-6"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6136","DOI":"10.3390\/rs6076136","article-title":"Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images","volume":"6","author":"Vlassova","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e10668","DOI":"10.1016\/j.heliyon.2022.e10668","article-title":"Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine","volume":"8","author":"Roy","year":"2022","journal-title":"Heliyon"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1002\/ecy.1580","article-title":"Global patterns and determinants of forest canopy height","volume":"97","author":"Tao","year":"2016","journal-title":"Ecology"},{"key":"ref_13","first-page":"217","article-title":"Diverse responses of different structured forest to drought in Southwest China through remotely sensed data","volume":"69","author":"Xu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127807","DOI":"10.1016\/j.ufug.2022.127807","article-title":"Can canopy temperature acquired from an airborne level be a tree health indicator in an urban environment?","volume":"79","author":"Zakrzewska","year":"2023","journal-title":"Urban For. Urban Green."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1127\/0941-2948\/2012\/0363","article-title":"Effects of different vegetation on temperature in an urban building environment. Micro-scale numerical experiments","volume":"21","author":"Gross","year":"2012","journal-title":"Meteorol. Z."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Helletsgruber, C., Gillner, S., Guly\u00e1s, \u00c1., Junker, R.R., Tan\u00e1cs, E., and Hof, A. (2020). Identifying Tree Traits for Cooling Urban Heat Islands\u2014A Cross-City Empirical Analysis. Forests, 11.","DOI":"10.3390\/f11101064"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1007\/s11069-022-05530-5","article-title":"Hotspot and trend analysis of forest fires and its relation to climatic factors in the western Himalayas","volume":"114","author":"Kumar","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1109\/JSTARS.2016.2571838","article-title":"Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends","volume":"9","author":"Chaparro","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1111\/gcb.13275","article-title":"A review of the relationships between drought and forest fire in the United States","volume":"Volume 22","author":"Littell","year":"2016","journal-title":"Global Change Biology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1111\/j.1365-2486.2004.00812.x","article-title":"Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years","volume":"10","author":"Brovkin","year":"2004","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8381","DOI":"10.1175\/JCLI-D-19-0725.1","article-title":"Sensitivities and Responses of Land Surface Temperature to Deforestation-Induced Biophysical Changes in Two Global Earth System Models","volume":"33","author":"Liao","year":"2020","journal-title":"J. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1126\/science.1120529","article-title":"Land Use and Climate Change","volume":"310","author":"Pielke","year":"2005","journal-title":"Science"},{"key":"ref_23","first-page":"1000145","article-title":"Application of Geographic Information System (GIS) in Forest Management","volume":"5","author":"Sh","year":"2015","journal-title":"J. Geogr. Nat. Disasters"},{"key":"ref_24","unstructured":"Venkata, K.M., and Yelisetty, N. (2006, January 27\u201330). Use of Geo-spatial database in Sustainable Forest Management. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Goa, India."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1890\/ES15-00203.1","article-title":"On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene","volume":"6","author":"Allen","year":"2015","journal-title":"Ecosphere"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1038\/s41558-022-01528-w","article-title":"Younger trees in the upper canopy are more sensitive but also more resilient to drought","volume":"12","author":"Au","year":"2022","journal-title":"Nat. Clim. Chang."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1038\/s41467-019-12380-6","article-title":"Tree height explains mortality risk during an intense drought","volume":"10","author":"Stovall","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112477","DOI":"10.1016\/j.rse.2021.112477","article-title":"Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends","volume":"260","author":"Coops","year":"2021","journal-title":"Remote. Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote. Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1080\/17538947.2022.2059114","article-title":"Health assessment of plantations based on LiDAR canopy spatial structure parameters","volume":"15","author":"Meng","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Curkovic, S. (2012). Sustainable Development\u2014Authoritative and Leading Edge Content for Environmental Management, InTech Press.","DOI":"10.5772\/2562"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2374","DOI":"10.1109\/JSTARS.2023.3244866","article-title":"Large Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data","volume":"16","author":"Choi","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/fee.2587","article-title":"A theoretical framework for the ecological role of three-dimensional structural diversity","volume":"21","author":"LaRue","year":"2023","journal-title":"Front. Ecol. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111347","DOI":"10.1016\/j.rse.2019.111347","article-title":"Country-wide high-resolution vegetation height mapping with Sentinel-2","volume":"233","author":"Lang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112844","DOI":"10.1016\/j.rse.2021.112844","article-title":"Neural network guided interpolation for mapping canopy height of China\u2019s forests by integrating GEDI and ICESat-2 data","volume":"269","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1002\/wcc.144","article-title":"Land use\/land cover changes and climate: Modeling analysis and observational evidence","volume":"2","author":"Pielke","year":"2011","journal-title":"Wiley Interdiscip. Rev. Clim. Change"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.agrformet.2019.01.022","article-title":"The biophysical effects of the vegetation restoration program on regional climate metrics in the Loess Plateau, China","volume":"268","author":"Cao","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2018.11.024","article-title":"Clarifying the role of radiative mechanisms in the spatiotemporal changes of land surface temperature across the Horn of Africa","volume":"221","author":"Abera","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1111\/1365-2745.13825","article-title":"Changes in forest structure drive temperature preferences of boreal understorey plant communities","volume":"110","author":"Christiansen","year":"2022","journal-title":"J. Ecol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"687988","DOI":"10.3389\/ffgc.2021.687988","article-title":"A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery","volume":"4","author":"Hasnat","year":"2021","journal-title":"Front. For. Glob. Chang."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"24010","DOI":"10.1088\/1748-9326\/aa9e93","article-title":"Impacts of land cover transitions on surface temperature in China based on satellite observations","volume":"13","author":"Zhang","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liang, S. (2008). Advances in Land Remote Sensing: System, Modeling, Inversion and Application, Springer.","DOI":"10.1007\/978-1-4020-6450-0"},{"key":"ref_44","first-page":"182","article-title":"Transition Modeling of Land-Use Dynamics in the Pipestem Creek, North Dakota, USA","volume":"05","author":"Rozario","year":"2017","journal-title":"J. Geosci. Environ. Prot."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.inffus.2022.08.032","article-title":"Multispectral and hyperspectral image fusion in remote sensing: A survey","volume":"89","author":"Vivone","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"112303","DOI":"10.1016\/j.rse.2021.112303","article-title":"Hyperspectral imagery to monitor crop nutrient status within and across growing seasons","volume":"255","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shaik, R.U., Laneve, G., and Fusilli, L. (2022). An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach. Remote Sens., 14.","DOI":"10.3390\/rs14051264"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shaik, R.U., Periasamy, S., and Zeng, W. (2023). Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications. Remote Sens., 15.","DOI":"10.3390\/rs15051378"},{"key":"ref_49","first-page":"1","article-title":"Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy","volume":"187","author":"Salis","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Santarsiero, V. (2021, January 13\u201316). A Remote Sensing Methodology to Assess the Abandoned Arable Land Using NDVI Index in Basilicata Region. Proceedings of the Computational Science and Its Applications\u2013ICCSA 2021, Cagliari, Italy.","DOI":"10.1007\/978-3-030-86979-3_49"},{"key":"ref_51","unstructured":"Tucci, B., Nol\u00e8, G., Lanorte, A., Santarsiero, V., Cillis, G., Scorza, F., and Murgante, B. (2021). Information for a Better World: Shaping the Global Future, Springer Science and Business Media LLC."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-94-007-7969-3_5","article-title":"CORINE Land Cover and Land Cover Change Products","volume":"Volume 18","author":"Manakos","year":"2014","journal-title":"Land Use and Land Cover Mapping in Europe"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1117\/1.1766301","article-title":"New hyperspectral discrimination measure for spectral characterization","volume":"43","author":"Chang","year":"2004","journal-title":"Opt. Eng."},{"key":"ref_54","first-page":"70","article-title":"A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery","volume":"15","author":"Laliberte","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1080\/10106049.2012.665498","article-title":"Spectral material mapping using hyperspectral imagery: A review of spectral matching and library search methods","volume":"28","author":"Vishnu","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13638-019-1346-z","article-title":"Hyperspectral image classification with SVM and guided filter","volume":"2019","author":"Guo","year":"2019","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_58","first-page":"32","article-title":"Image Classification using Support Vector Machine and Artificial Neural Network","volume":"4","author":"Thai","year":"2012","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral\u2013Spatial Hyperspectral Image Classification With Edge-Preserving Filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1111\/nph.17321","article-title":"Imaging canopy temperature: Shedding (thermal) light on ecosystem processes","volume":"230","author":"Still","year":"2021","journal-title":"New Phytol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.agwat.2019.06.005","article-title":"Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery","volume":"222","author":"Hou","year":"2019","journal-title":"Agric. Water Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2080\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:16:12Z","timestamp":1760123772000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2080"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,14]]},"references-count":61,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082080"],"URL":"https:\/\/doi.org\/10.3390\/rs15082080","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,4,14]]}}}