{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:01:52Z","timestamp":1791194512251,"version":"4.1.0"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2008,5,6]],"date-time":"2008-05-06T00:00:00Z","timestamp":1210032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation.<\/jats:p>","DOI":"10.3390\/s8053020","type":"journal-article","created":{"date-parts":[[2008,5,26]],"date-time":"2008-05-26T05:25:21Z","timestamp":1211779521000},"page":"3020-3036","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object- Oriented Approach"],"prefix":"10.3390","volume":"8","author":[{"given":"Matthew","family":"Voss","sequence":"first","affiliation":[{"name":"GeoTREE Center, 205 Innovative Teaching & Technology Center, Cedar Falls, Iowa, USA 50614-0406"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramanathan","family":"Sugumaran","sequence":"additional","affiliation":[{"name":"GeoTREE Center, 205 Innovative Teaching & Technology Center, Cedar Falls, Iowa, USA 50614-0406"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2008,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/TGRS.2003.815384","article-title":"The use of high-resolution imagery for identification of urban climax forest species using traditional and rule-based classification approach","volume":"41","author":"Sugumaran","year":"2003","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0378-1127(00)00449-7","article-title":"Species diversity of three major urban forest types in Guangzhou City, China","volume":"146","author":"Jim","year":"2001","journal-title":"Forest Ecology and Management"},{"key":"ref_3","unstructured":"Newton, W. (2007). Tree species identification."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"383","DOI":"10.14358\/PERS.72.4.383","article-title":"Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters","volume":"72","author":"Chubey","year":"2006","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2004.10.011","article-title":"Automated tree recognition in old growth conifer stands with high resolution digital imagery","volume":"94","author":"Leckie","year":"2005","journal-title":"Remote Sensing of Environment"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"508","DOI":"10.2307\/1309693","article-title":"Studying the Earth's Vegetation from Space","volume":"34","author":"Botkin","year":"1984","journal-title":"BioScience"},{"key":"ref_7","unstructured":"Kan, E., and Weber, F. (1978). The Ten-Ecosystem Study- Landsat ADP mapping of forest and rangeland in the United States. International Symposium on Remote Sensing of Environment, 12th, Manila, Philippines, 1809\u20131825."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/0924-2716(96)00003-2","article-title":"Semi-automated procedures for tree species identification in high spatial resolution data from digitized colour infrared-aerial photography","volume":"51","author":"Meyer","year":"1996","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"ref_9","first-page":"717","article-title":"Subpixel classification of Bald Cypress and Tupelo Gum trees in Thematic Mapper imagery","volume":"63","author":"Huguenin","year":"1997","journal-title":"Photogrammetric Engineering & Remote Sensing"},{"key":"ref_10","unstructured":"Lennartz, S.P., and Congalton, R.G. (2004). Classifying and mapping forest cover types using Ikonos imagery in the northeastern United States. Proceedings of the ASPRS 2004 Annual Conference."},{"key":"ref_11","unstructured":"Kosaka, N., Akiyama, T., Tsai, B., and Kojima, T. (2005). Proceedings of the IGARSS 2005."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"135","DOI":"10.14358\/PERS.70.1.135","article-title":"Exploitation of very high resolution satellite data for tree species identification","volume":"70","author":"Carleer","year":"2004","journal-title":"PE&RS, Photogrammetric Engineering & Remote Sensing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/S0034-4257(00)00159-0","article-title":"A Comparison of Multispectral and Multitemporal Information in High Spatial Resolution Imagery for Classification of Individual Tree Species in a Temperate Hardwood Forest","volume":"75","author":"Key","year":"2001","journal-title":"Remote Sensing of Environment"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2003.11.018","article-title":"Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests","volume":"90","author":"Thenkabail","year":"2004","journal-title":"Remote Sensing of Environment"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5637","DOI":"10.1080\/01431160412331291224","article-title":"Using AVIRIS data and multiple-masking techniques to map urban forest tree species","volume":"25","author":"Xiao","year":"2004","journal-title":"International Journal of Remote Sensing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements","volume":"89","author":"Francois","year":"2004","journal-title":"Remote Sensing of Environment"},{"key":"ref_17","unstructured":"Greiwe, A., and Ehlers, M. (2005). Combined analysis of hyperspectral and high resoltuion image data in an object oriented classification approach. Proceedings of 3rd International Symposium on Remote Sensing and Data Fusion over Urban Areas, 13\u201315."},{"key":"ref_18","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 Sensing of Environment"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0031-3203(80)90041-2","article-title":"The development of a spectral-spatial classifier for earth observational data","volume":"12","author":"Landgrebe","year":"1980","journal-title":"Pattern Recognition"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/S0924-2716(02)00161-2","article-title":"Automated analysis of ultra-high resolution remote sensing data for biotype mapping: new possibilities and challenges","volume":"57","author":"Ehlers","year":"2002","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1080\/01431160050021303","article-title":"Using vegetation reflectance variability for species level classification of hyperspectral data","volume":"21","author":"Cochrane","year":"2000","journal-title":"International Journal of Remote Sensing"},{"key":"ref_22","unstructured":"Kristof, D., Csato, E., and Ritter, D. (2002). Ottawa, Canada."},{"key":"ref_23","unstructured":"H\u00e1jek, F. (2005). Object-oriented classification of remote sensing data for the identification of tree species composition. Proceedings of ForestSat 2005 conference."},{"key":"ref_24","unstructured":"Ke, Y., and Quackenbush, L. (2007). Forest species classification and tree crown delineation using Quickbird imagery. Proceedings of the ASPRS 2007 Annual Conference."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iovan, C., Boldo, D., and Cord, M. (2007). Automatic extraction of urban vegetation structures from high resolution imagery and digital elevation models. Proceedings of the Urban Remote Sensing Joint Event 2007.","DOI":"10.1109\/URS.2007.371840"},{"key":"ref_26","unstructured":"van Aardt, J.A.N., and Wynne, R.H. (2004). A multi-resoltuion approach to forest segmentation as a precursor to estimation of volume and biomass. Proceedings: ASPRS Annual Conference, 23\u201328."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/S0034-4257(03)00140-8","article-title":"Identifying species of individual trees using airborne laser scanner","volume":"90","author":"Holmgren","year":"2004","journal-title":"Remote Sensing of Environment"},{"key":"ref_28","unstructured":"Collins, C.A., Parker, R.C., and Evans, D.L. (2004). Using multispectral imagery and multi-return LIDAR to estimate tree stand attributes in a southern bottomland hardwood forest. Proceedings ASPRS 2004 Annual Conference, Denver, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2003.11.003","article-title":"Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sensing of Environment"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/S0034-4257(03)00008-7","article-title":"Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America","volume":"85","author":"Brandtberg","year":"2003","journal-title":"Remote Sensing of Environment"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.rse.2005.04.001","article-title":"Estimating forest growth using canopy metrics derived from airborne laser scanner data","volume":"96","author":"Naesset","year":"2005","journal-title":"Remote Sensing of Environment"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.foreco.2005.03.025","article-title":"Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions","volume":"213","author":"Roberts","year":"2005","journal-title":"Forest Ecology and Management"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.rse.2004.02.008","article-title":"Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape","volume":"91","author":"Clark","year":"2004","journal-title":"Remote Sensing of Environment"},{"key":"ref_34","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":"Photogrammetric Engineering and Remote Sensing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.14358\/PERS.72.12.1389","article-title":"Using tree clusters to derive forest properties from small footprint LiDAR data","volume":"72","author":"Bortolot","year":"2006","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.14358\/PERS.72.12.1379","article-title":"Using laser altimetry-based segmentation to refine automated tree identification in managed forests of the Black Hills, South Dakota","volume":"72","author":"Rowell","year":"2006","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"ref_37","unstructured":"Kato, A., Schreuder, G., Calhoun, D., Schiess, P., and Stuetzle, W. (2007). ASPRS 2007 Annual Conference."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.rse.2005.08.011","article-title":"On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification","volume":"98","author":"McVicar","year":"2005","journal-title":"Remote Sensing of Environment"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2006.03.004","article-title":"Training set size requirements for the classification of a specific class","volume":"104","author":"Foody","year":"2006","journal-title":"Remote Sensing of Environment"},{"key":"ref_40","unstructured":"Jensen, J.R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall PTR Upper Saddle River."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/8\/5\/3020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:20:37Z","timestamp":1760221237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/8\/5\/3020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,5,6]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2008,5]]}},"alternative-id":["s8053020"],"URL":"https:\/\/doi.org\/10.3390\/s8053020","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,5,6]]}}}