Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye
Remote sensing technologies have been extensively used in forest management in predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 satellite images to estimate stand volume, basal area, number of trees, mean diameter, and top height. 180 temporary sample plots were...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2022.2158238 |
| _version_ | 1827814594178973696 |
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| author | Sinan Bulut Alkan Günlü Günay Çakır |
| author_facet | Sinan Bulut Alkan Günlü Günay Çakır |
| author_sort | Sinan Bulut |
| collection | DOAJ |
| description | Remote sensing technologies have been extensively used in forest management in predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 satellite images to estimate stand volume, basal area, number of trees, mean diameter, and top height. 180 temporary sample plots were taken in pure Crimean pine stands with varied structure. Reflectance, vegetation indices, and eight texture values were generated from Landsat 8 and Sentinel-2 satellite images. The stand parameters were modelled with the remotely sensed data using multiple linear regression, support vector machine, and deep learning techniques. The results showed that the support vector machine technique provided the highest level of model performance with 45° orientation for number of trees (R2 = 0.98, RMSE%=5.97) and 90° orientation for basal area (R2=0.91, RMSE%=15.22). The results indicated that the texture values presented better results than the reflectance and the vegetation indices in estimating the stand parameters. |
| first_indexed | 2024-03-11T23:47:18Z |
| format | Article |
| id | doaj.art-ae0123d77ba4467f8b420450397ff2ef |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:47:18Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-ae0123d77ba4467f8b420450397ff2ef2023-09-19T09:13:17ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2022.21582382158238Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in TürkiyeSinan Bulut0Alkan Günlü1Günay Çakır2Department of Forest Management and Planning, Faculty of Forestry, Çankırı Karatekin UniversityDepartment of Forest Management and Planning, Faculty of Forestry, Çankırı Karatekin UniversityDepartment of Forestry, Gümüşhane University, Gümüşhane Vocational SchoolRemote sensing technologies have been extensively used in forest management in predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 satellite images to estimate stand volume, basal area, number of trees, mean diameter, and top height. 180 temporary sample plots were taken in pure Crimean pine stands with varied structure. Reflectance, vegetation indices, and eight texture values were generated from Landsat 8 and Sentinel-2 satellite images. The stand parameters were modelled with the remotely sensed data using multiple linear regression, support vector machine, and deep learning techniques. The results showed that the support vector machine technique provided the highest level of model performance with 45° orientation for number of trees (R2 = 0.98, RMSE%=5.97) and 90° orientation for basal area (R2=0.91, RMSE%=15.22). The results indicated that the texture values presented better results than the reflectance and the vegetation indices in estimating the stand parameters.http://dx.doi.org/10.1080/10106049.2022.2158238stand parametersremote sensing datamultiple linear regressionsupport vector machinedeep learning |
| spellingShingle | Sinan Bulut Alkan Günlü Günay Çakır Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye Geocarto International stand parameters remote sensing data multiple linear regression support vector machine deep learning |
| title | Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye |
| title_full | Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye |
| title_fullStr | Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye |
| title_full_unstemmed | Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye |
| title_short | Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye |
| title_sort | modelling some stand parameters using landsat 8 oli and sentinel 2 satellite images by machine learning techniques a case study in turkiye |
| topic | stand parameters remote sensing data multiple linear regression support vector machine deep learning |
| url | http://dx.doi.org/10.1080/10106049.2022.2158238 |
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