{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:25:34Z","timestamp":1760149534121,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Council for Scientific and Industrial Research (CSIR)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral sensors capture and compute spectral reflectance of objects over many wavelength bands, resulting in a high-dimensional space with enough information to differentiate between spectrally similar objects. Due to the curse of dimensionality, high spectral dimensionality can also be difficult to handle and analyse, demanding complex processing and the use of advanced analytical techniques. Moreover, when hyperspectral measurements are taken at different temporal frequencies, separation is likely to improve; however, additional complexities in modelling time variability concurrently with this high spectral dimensionality may be created. As a result, the applicability of ensemble-based techniques suitable for high-dimensional data is examined in this research, together with the statistical evaluation of time-induced variability, since spectral measurements of tree species were taken at different time periods. Classification errors for the stochastic gradient boosting (SGB) and random forest (RF) methods ranged between 5.6% and 13.5%, respectively. Differences in classification accuracy or errors were also accounted for in the assessment of the models, with up to 46% of variation in classification error due to the effect of time in the RF model, indicating that measurement time is important in improving discrimination between tree species. This is because optical leaf characteristics can vary during the course of the year due to seasonal effects, health status, or the developmental stage of a tree. Different spectral properties (assumed from relevant wavelength bands) were found to be key factors impacting the models\u2019 discrimination performance at various measurement times.<\/jats:p>","DOI":"10.3390\/rs15174117","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T09:10:56Z","timestamp":1692695456000},"page":"4117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distinguishing Tree Species from In Situ Hyperspectral and Temporal Measurements through Ensemble Statistical Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8853-3121","authenticated-orcid":false,"given":"Nontembeko","family":"Dudeni-Tlhone","sequence":"first","affiliation":[{"name":"Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa"},{"name":"Discipline of Geography, University of KwaZulu-Natal, P Bag X01, Scottsville, Pietermaritzburg 3209, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-8111","authenticated-orcid":false,"given":"Onisimo","family":"Mutanga","sequence":"additional","affiliation":[{"name":"Discipline of Geography, University of KwaZulu-Natal, P Bag X01, Scottsville, Pietermaritzburg 3209, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4870-988X","authenticated-orcid":false,"given":"Pravesh","family":"Debba","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa"},{"name":"Department of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg 2000, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-5375","authenticated-orcid":false,"given":"Moses Azong","family":"Cho","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa"},{"name":"Department of Plant and Soil Science, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria 0002, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"294540","DOI":"10.1100\/2012\/294540","article-title":"Temporal aspects of surface water quality variation using robust statistical tools","volume":"2012","author":"Mustapha","year":"2012","journal-title":"Sci. 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