Remote Sensing in Mountainous Vegetation

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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• topographic correction
• vegetation indices
• mountainous vegetation
• FVC\LAI\biomass
• detection\monitoring\classification
• machine learning\deep learning
• satellite\UAS images