Climate Change Data Portal
| DOI | 10.3832/ifor4183016 |
| Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco | |
| 发表日期 | 2024 |
| ISSN | 1971-7458 |
| 起始页码 | 17 |
| 卷号 | 17 |
| 英文摘要 | Forest resource conservation necessitates a deeper understanding of forest ecosystem processes and how future management decisions and climate change may affect these processes. Argania spinosa (L.) Skeels is one of the most popular species in Morocco. Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offers a powerful resource for mapping, assessing, and monitoring the forest tree species at high spatio-temporal resolution. Multi -spectral Sentinel-2 and Synthetic Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) over the Argan forest in Essaouira province, Morocco, were subjected to pixel -based machine learning classification and analysis. We investigated the influence of different SAR data parameters and DEM layers on the performance of machine learning algorithms. In addition, we evaluated the synergistic effects of integrating remote sensing data, including optical, SAR, and DEM data, for identifying argan trees in the Smimou area. We collected data from Sentinel -2, Sentinel-1, SRTM DEM, and ground truth sources to achieve our goal. Testing different SAR parameters and integrating DEM layers of different resolutions with other remote sensing data showed that the Lee Sigma filter with a size of 11x11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algorithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the combined use of two Sentinel constellation products improved map accuracy and quality. For the best scenario (VV+NDVI), the OA was 88.32% (K = 0.85), while for scenarios NDVI+DEM and VH+NDVI+DEM, the OAs were 93.25% (K = 0.91) and 93.01% (K = 0.91), respectively. Integrating a DEM layer with SAR and optical data has significantly improved the accuracy in the classification of vegetation types, especially in our study area which is characterized by high environmental heterogeneity. |
| 英文关键词 | Argan Forest; Sentinel-2; GLCM Texture; SAR Parameters; DEM; Satellite Image Classification |
| 语种 | 英语 |
| WOS研究方向 | Forestry |
| WOS类目 | Forestry |
| WOS记录号 | WOS:001219215500001 |
| 来源期刊 | IFOREST-BIOGEOSCIENCES AND FORESTRY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/303895 |
| 作者单位 | Cadi Ayyad University of Marrakech |
| 推荐引用方式 GB/T 7714 | . Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco[J],2024,17. |
| APA | (2024).Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco.IFOREST-BIOGEOSCIENCES AND FORESTRY,17. |
| MLA | "Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco".IFOREST-BIOGEOSCIENCES AND FORESTRY 17(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
