{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:08:50Z","timestamp":1768820930264,"version":"3.49.0"},"reference-count":95,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T00:00:00Z","timestamp":1673136000000},"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>Plant diversity measurement and monitoring are required for reversing biodiversity loss and ensuring sustainable management. Traditional methods have been using in situ measurements to build multivariate models connecting environmental factors to species diversity. Developments in remotely sensed datasets, processing techniques, and machine learning models provide new opportunities for assessing relevant environmental parameters and estimating species diversity. In this study, geodiversity variables containing the topographic and soil variables and multi-seasonal remote-sensing-based features were used to estimate plant diversity in a rangeland from southwest Iran. Shannon\u2019s and Simpson\u2019s indices, species richness, and vegetation cover were used to measure plant diversity and attributes in 96 plots. A random forest model was implemented to predict and map diversity indices, richness, and vegetation cover using 32 remotely sensed and 21 geodiversity variables. Additionally, the linear regression and Spearman\u2019s correlation coefficient were used to assess the relationship between the spectral diversity, expressed as the coefficient of variation in vegetation indices, and species diversity metrics. The results indicated that the synergistic use of geodiversity and multi-seasonal remotely sensed features provide the highest accuracy for Shannon, Simpson, species richness, and vegetation cover indices (R2 up to 0.57), as compared to a single model for each date (February, April, and July). Furthermore, the strongest relationship between species diversity and the coefficient of variation in vegetation indices was based on the remotely-sensed data of April. The approach of multi-model evaluations using the full geodiversity and remotely sensed variables could be a useful method for biodiversity monitoring.<\/jats:p>","DOI":"10.3390\/rs15020387","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T04:47:08Z","timestamp":1673239628000},"page":"387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Prediction of Plant Diversity Using Multi-Seasonal Remotely Sensed and Geodiversity Data in a Mountainous Area"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6417-9781","authenticated-orcid":false,"given":"Soroor","family":"Rahmanian","sequence":"first","affiliation":[{"name":"Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, 500123 Brasov, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1861-4732","authenticated-orcid":false,"given":"Vahid","family":"Nasiri","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Transilvania University of Brasov, 900152 Brasov, Romania"}]},{"given":"Atiyeh","family":"Amindin","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71348-14336, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0381-2787","authenticated-orcid":false,"given":"Sahar","family":"Karami","sequence":"additional","affiliation":[{"name":"Quantitative Plant Ecology and Biodiversity Research Lab, Department of Biology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad 91779-48974, Iran"}]},{"given":"Sedigheh","family":"Maleki","sequence":"additional","affiliation":[{"name":"Department of Plant Production, Faculty of Agriculture, University of Torbat Heydarieh, Torbat Heydarieh 95161-68595, Iran"},{"name":"Saffron Institute, University of Torbat Heydarieh, Torbat Heydarieh 95161-68595, Iran"}]},{"given":"Soheila","family":"Pouyan","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71348-14336, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4571-7235","authenticated-orcid":false,"given":"Stelian Alexandru","family":"Borz","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, 500123 Brasov, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1038\/s41559-018-0667-3","article-title":"Towards global data products of Essential Biodiversity Variables on species traits","volume":"2","author":"Kissling","year":"2018","journal-title":"Nat. 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