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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 3285

Special Issue Editors

Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China
Interests: vegetation indices; topographic correction; mountain vegetation monitoring
Special Issues, Collections and Topics in MDPI journals
U.S. Department of Agriculture, 3103 F&B Road, College Station, TX 77845, USA
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
Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: remote sensing images processing in mountainous areas; spatiotemporal fusion methods for mountain remote sensing images
Special Issues, Collections and Topics in MDPI journals
Department of Geography, Texas A&M University, College Station, TX 77843, USA
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Keywords

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

Published Papers (2 papers)

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Research

21 pages, 10352 KiB  
Article
Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
Remote Sens. 2023, 15(8), 1986; https://doi.org/10.3390/rs15081986 - 09 Apr 2023
Cited by 6 | Viewed by 1540
Abstract
In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (NPP) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the [...] Read more.
In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (NPP) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest > meadow steppe > coniferous forest > cropland > shrub > typical steppe > sandy land > alpine steppe > desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (q-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (q-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) > precipitation (0.33) > soil moisture (0.16) > temperature (0.14) > solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. Full article
(This article belongs to the Special Issue Remote Sensing in Mountainous Vegetation)
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25 pages, 15087 KiB  
Article
Quantitative Assessment of the Spatial Scale Effects of the Vegetation Phenology in the Qinling Mountains
Remote Sens. 2022, 14(22), 5749; https://doi.org/10.3390/rs14225749 - 14 Nov 2022
Viewed by 1301
Abstract
Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology [...] Read more.
Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas. Full article
(This article belongs to the Special Issue Remote Sensing in Mountainous Vegetation)
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