Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Enping Yan"'
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1449 (2024)
High-resolution land cover mapping is crucial in various disciplines but is often hindered by the lack of accurately matched labels. Our study introduces an innovative deep learning methodology for effective land cover mapping, independent of matched
Externí odkaz:
https://doaj.org/article/cd7f235275a8461492ae7fec4d7afbe9
Publikováno v:
Remote Sensing, Vol 15, Iss 24, p 5721 (2023)
Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Tradit
Externí odkaz:
https://doaj.org/article/c56962e86edf47ba842f05566c7df443
Publikováno v:
Remote Sensing, Vol 15, Iss 24, p 5785 (2023)
Change detection is a crucial task in remote sensing that finds broad application in land resource planning, forest resource monitoring, natural disaster monitoring, and evaluation. In this paper, we propose a change detection model for cross-domain
Externí odkaz:
https://doaj.org/article/5671e867579a42b3a2bb08b4c10839db
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5218 (2023)
Camellia oleifera is a vital economic crop of southern China. Accurate mapping and monitoring of Camellia oleifera plantations are essential for promoting sustainable operations within the Camellia oleifera industry. However, traditional remote sensi
Externí odkaz:
https://doaj.org/article/a2f14e53a1e94647bdf633d73e2005c4
Autor:
Hui Zhou, Jiaxing Zhu, Jiaxiang Li, Yongfu Xu, Qian Li, Enping Yan, Shaohua Zhao, Yujiu Xiong, Dengkui Mo
Publikováno v:
Remote Sensing in Ecology and Conservation, Vol 7, Iss 4, Pp 638-648 (2021)
Abstract Cliff ecosystems are considered the ‘Last Unknown’ because cliffs may host ancient and unique species that are located in extremely hostile environments and are difficult to reach. However, comprehensive and systematic information and da
Externí odkaz:
https://doaj.org/article/d9fe36c850b34fc4825c136158dd8313
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepne
Externí odkaz:
https://doaj.org/article/33e5f7050ebf44ada95a357d4048a63f
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
As one of the four most important woody oil-tree in the world, Camellia oleifera has significant economic value. Rapid and accurate acquisition of C. oleifera tree-crown information is essential for enhancing the effectiveness of C. oleifera tree man
Externí odkaz:
https://doaj.org/article/64d33d08cec646aab427f6e84aa81e1a
Publikováno v:
Remote Sensing, Vol 15, Iss 3, p 628 (2023)
Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic
Externí odkaz:
https://doaj.org/article/bdc5fd3b3cf04451987c7514285f50ee
Publikováno v:
Remote Sensing, Vol 14, Iss 19, p 5046 (2022)
The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to
Externí odkaz:
https://doaj.org/article/6425a895f06649c28f3bb793ee619825
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a0973a619a3ce90a8f98819c06f858f
https://doi.org/10.1109/lgrs.2022.3188636
https://doi.org/10.1109/lgrs.2022.3188636