Estimation Stand Volume, Basal Area and Quadratic Mean Diameter Using Landsat 8 OLI and Sentinel-2 Satellite Image With Different Machine Learning Techniques

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dc.authoridAksoy, Hasan/0000-0003-1980-3834
dc.contributor.authorAksoy, Hasan
dc.date.accessioned2025-03-23T19:31:44Z
dc.date.available2025-03-23T19:31:44Z
dc.date.issued2024
dc.departmentSinop Üniversitesi
dc.description.abstractThe data required for sustainable forest planning is provided by traditional forest inventories, which are labor, time, and cost-intensive. Providing this data quickly, reliably, and accurately is crucial for planners and researchers. The objective of this study was to predict stand basal area (BA), stand volume (V), and quadratic mean diameter (dq) by leveraging vegetation indices (VIs) and reflectance (R) derived from Landsat 8 OLI and Sentinel 2 satellite images, along with topographic (T) data obtained from ALOS-PALSAR satellite imagery. Forest inventory data for a total of 250 sample plots were used for modeling in the study. Stand parameters were estimated using support vector machines (SVM), multiple linear regression (MLR), decision tree (DT), and random forest (RF) algorithms. In modeling V, BA, and dq, both individual and combinations of R, VIs, and T values obtained from satellite imagery were used as independent variables. Using the generated datasets, each of the stand parameters was modeled separately with MLR, SVM, RF, and DT algorithms, and the success of the models was compared to determine the modeling technique and dataset with the highest success for the relevant parameter. The results showed that for each stand parameter, the highest model success was achieved in the combined dataset, which was created by combining all datasets. However, in terms of modeling techniques, the highest success for each stand parameter was achieved with different modeling techniques. The highest success for V is obtained in the model using the SVM method (R-2 = 0.78; RMSE = 0.28 m(3)/ha), the RF method yielded the highest model performance for BA (R-2 = 0.70; RMSE = 2.53 m(2)/ha), and finally, the highest success for dq was obtained in the DT method (R-2 = 0.74; RMSE = 0.02 cm). In general, the datasets obtained from Sentinel 2 images showed higher model success than the datasets obtained from Landsat 8 OLI images.
dc.identifier.doi10.1111/tgis.13265
dc.identifier.endpage2704
dc.identifier.issn1361-1682
dc.identifier.issn1467-9671
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85206696945
dc.identifier.scopusqualityQ2
dc.identifier.startpage2687
dc.identifier.urihttps://doi.org/10.1111/tgis.13265
dc.identifier.urihttps://hdl.handle.net/11486/5351
dc.identifier.volume28
dc.identifier.wosWOS:001335896900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAksoy, Hasan
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofTransactions in Gis
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectmachine learning
dc.subjectremote sensing
dc.subjectstand metrics
dc.subjectterrain topography
dc.titleEstimation Stand Volume, Basal Area and Quadratic Mean Diameter Using Landsat 8 OLI and Sentinel-2 Satellite Image With Different Machine Learning Techniques
dc.typeArticle

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