Remote Sensing in Mountainous Vegetation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4917
Special Issue Editors
Interests: vegetation indices; topographic correction; mountain vegetation monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: precision agriculture; pest management; airborne; image processing; multispectral, hyperspectral and thermal imaging systems; unmanned aircraft systems; electronic and spectral sensors
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing images processing in mountainous areas; spatiotemporal fusion methods for mountain remote sensing images
Special Issues, Collections and Topics in MDPI journals
Interests: microwave remote sensing; glacier mapping; monitoring of environmental changes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Mountainous vegetation, e.g., forest, shrub, grass and moss, plays an important role in achieving regional biodiversity conservation, ecological service, carbon neutrality, and eco-society sustainable development, from tropical to frigid zones. It is significant to use multisource remote sensing data in mountainous vegetation species detection, community classification, change monitoring, and biophysical parameter retrieval, such as fractional vegetation coverage (FVC), leaf area index (LAI), canopy height and biomass, etc. However, mountainous vegetation in remote sensing images suffers from many influences, e.g., topographic effect, mixed species, anthropogenic activities, and the temporal–spatial–spectral feature of images. Hence, innovative and advanced techniques are encouraged to eliminate these disturbances to improve the accuracy of retrieved vegetation information in mountains from local to global scales, e.g., topographic corrections, vegetation indices, machine learning, deep learning, and so on.
This proposed Special Issue of Remote Sensing addresses research on “Remote Sensing in Mountainous Vegetation” using diversified approaches and multisource images. We welcome original research articles and reviews which provide the community with the most recent advancements, including but not limited to innovative topographic correction approaches, vegetation indices, machine learning and deep learning algorithms, and their applications in mountainous vegetation species detection, community classification, change monitoring, and biophysical parameter retrieval. Original research or review articles on one or more of the following topics are welcome:
(1) New topographic correction approaches for removal topographic effects, e.g., the cast shadow and the self-shadow in rugged terrain;
(2) Applicability of integrated or mixed techniques (e.g., topographic corrections, vegetation indices, machine learning and deep learning algorithms, and regression models) in specified targets (e.g., forest, shrub, grass and\or moss in mountains);
(3) Integration and assimilation of multisource images (e.g., optical, LiDAR, SAR from satellite and UAS platforms) and other data (e.g., situ survey, statistics) for mountainous vegetation study;
(4) Case study of applications in a range of scenarios (e.g., regional mountain, mountainous protected areas).
We look forward to receiving your contributions.
Dr. Hong Jiang
Dr. Chenghai Yang
Dr. Jinhu Bian
Dr. Zhaohui Chi
Guest Editors
Manuscript Submission Information
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Keywords
- topographic correction
- vegetation indices
- mountainous vegetation
- FVC\LAI\biomass
- detection\monitoring\classification
- machine learning\deep learning
- satellite\UAS images
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