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Forests
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Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote...
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Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote Sensing

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2534

Special Issue Editor

Dr. Uroš Durlević

E-Mail Website
Guest Editor
Geographical Institute "Jovan Cvijić", Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia
Interests: natural hazards; environmental modeling; geographic information systems; forest management; multi-sensor data fusion

Special Issue Information

Dear Colleagues,

Global population growth and intensified climate extremes have further exacerbated biodiversity and forest loss, two significant environmental challenges in the 21st century. Geospatial tools such as geographic information systems (GISs) and remote sensing (RS) are vital for analyzing vegetation dynamics and environmental monitoring. This Special Issue aims to compile cutting-edge research that utilizes GISs and RS technologies to monitor forest ecosystems, assess environmental risks, and support sustainable forest management. A comparative analysis of satellite imagery (Landsat, MODIS, VIIRS, and Sentinel) enables the long-term and large-scale tracking of vegetation. Vegetation indices (NDVI, EVI, SAVI, LAI, and GCI) allow precise vegetation health monitoring.

The integration of GISs and RS with methods such as machine learning, numerical modeling, and multi-criteria analysis enables the identification of processes such as deforestation, desertification, and land use change, as well as zoning areas at risk of wildfires. The timely identification of negative environmental changes will facilitate the application of adequate nature protection measures (recultivation, revitalization, and afforestation) to implement sustainable development and management strategies for forest ecosystems.

This Special Issue welcomes contributions including, but not limited to, the following topics:

  1. Forest monitoring and management;
  2. Wildfire risk assessment;
  3. Vegetation indices;
  4. Multi-sensor satellite data fusion;
  5. Monitoring changes in forest ecosystems;
  6. GISs and remote sensing applications;
  7. Artificial intelligence (AI);
  8. Numerical modeling;
  9. Fuzzy logic-based multi-criteria decision making;
  10. Forest protection measures.

Dr. Uroš Durlević
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Forests is an international peer-reviewed open access monthly 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 2600 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.

Publisher’s Notice

At the request of Dr. Gordana Kaplan, a member of the original Guest Editor team for the Special Issue “Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote Sensing”, she will no longer be involved in the editorial handling of the Special Issue as of 24 February 2026. This change has been agreed upon by the remaining Guest Editor and the Editorial Office, and this Special Issue website has been updated accordingly. The Special Issue will continue to be handled by the remaining Guest Editor in accordance with MDPI’s Special Issue and editorial policies.

Keywords

  • forest vegetation dynamics
  • remote sensing (RS)
  • geographic information systems (GIS)
  • wildfire risk assessment
  • land use
  • forest protection and restoration
  • vegetation indices
  • machine learning
  • multi-criteria decision making
  • numerical simulations

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

13 pages, 5353 KB  
Article
Abiotic Factors Exert a Predominant Influence on the Annual Aboveground Biomass Dynamics of Chinese Abies Mill. Forests Relative to Biotic Factors
by Zichun Gao, Huayong Zhang and Yanan Wei
Forests 2026, 17(4), 466; https://doi.org/10.3390/f17040466 - 10 Apr 2026
Viewed by 320
Abstract
The mean annual change in aboveground biomass (ΔAGB) is a pivotal indicator for assessing forest carbon cycle dynamics. This study analyzed 791 independent Abies Mill. forest patches across China to elucidate their driving mechanisms by integrating abiotic, anthropogenic, and biotic factors. We employed [...] Read more.
The mean annual change in aboveground biomass (ΔAGB) is a pivotal indicator for assessing forest carbon cycle dynamics. This study analyzed 791 independent Abies Mill. forest patches across China to elucidate their driving mechanisms by integrating abiotic, anthropogenic, and biotic factors. We employed a spatially explicit framework, including spatial error regression and structural equation modeling (SEM), to account for significant spatial autocorrelation (Moran’s I = 0.375, p < 0.001). Our results show that abiotic factors predominantly dictate ΔAGB, with soil fertility (pH and Total Nitrogen), elevation (DEM), and soil physical properties (Coarse Fragments and Thickness) explaining the majority of deterministic variance. This relatively low explanatory variance (marginal R2 = 0.09) likely reflects the high environmental stochasticity inherent in alpine ecosystems. Specifically, soil fertility exerted the strongest positive influence (Std. Estimate = 0.33), while elevation and soil physical constraints were the primary limiting factors. Biotic factors (Stand Age, Height, and Tree Cover) played a subordinate role, contributing only a marginal 2% gain in explained variance (increasing marginal R2 from 0.07 to 0.09). Path analysis revealed an “environmental filtering” hierarchy where abiotic factors shape stand structure, which in turn has limited impact on growth dynamics. These findings underscore that carbon management in alpine forests should prioritize habitat quality conservation over simple biotic structural manipulation. Full article
(This article belongs to the Special Issue Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote Sensing)
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17 pages, 7554 KB  
Article
The Impact of Inundation Frequency on the Distribution of Floodplain Vegetation in the Jingjiang Section of the Yangtze River
by Jiefeng Kou, Xiaolong Huang, Jingjing Lin, Haihua Zhuo, Zheng Zhou and Chao Yang
Forests 2026, 17(1), 133; https://doi.org/10.3390/f17010133 - 19 Jan 2026
Viewed by 423
Abstract
Floodplain vegetation is an essential part of riverine wetland ecosystems. Hydrological fluctuations significantly influence its survival and distribution. This study examines the floodplain vegetation of the Jingjiang section of the Yangtze River. This study uses annual mean NDVI data over six time periods [...] Read more.
Floodplain vegetation is an essential part of riverine wetland ecosystems. Hydrological fluctuations significantly influence its survival and distribution. This study examines the floodplain vegetation of the Jingjiang section of the Yangtze River. This study uses annual mean NDVI data over six time periods from 2000 to 2023 to represent the changes in floodplain vegetation. The driving factors include inundation frequency, annual mean temperature, annual mean precipitation, elevation, and slope gradient. To analyze the data, this study employs multiple analytical methods, including threshold segmentation, pixel-by-pixel linear regression (using the least squares method), Geodetector, and Pearson’s correlation analysis. This study clarifies the spatiotemporal evolution of the NDVI and the distribution of vegetation in these floodplain. It also quantitatively assesses the influence of multiple drivers and reveals the areas and extent of vegetation distribution affected by different inundation frequencies. The findings indicate: (1) Over six time periods from 2000 to 2023, NDVI values and the area covered by vegetation in the Jingjiang section of the Yangtze River floodplain exhibited fluctuating growth trends. The area covered by vegetation increased by 66.94 km2 in 2023 compared with that in 2000. (2) NDVI values were influenced by multiple interacting drivers, with inundation frequency being the dominant factor affecting vegetation change in the Jingjiang section (q-value: 0.79–0.86), followed by slope (q-value: 0.46–0.56). Interactions between different drivers amplify their impact on the annual average NDVI value. (3) Areas with inundation frequencies of 20%–40% exhibit positive spatial correlation with NDVI values. The maximum area of positive correlation is 112.51 km2, which is predominantly distributed across the central and marginal bars of the Jingjiang section. Within this range, inundation frequency has the strongest positive effect on vegetation growth. Full article
(This article belongs to the Special Issue Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote Sensing)
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21 pages, 87393 KB  
Article
Divergent Responses of Leaf Area Index to Abiotic Drivers Across Abies Forest Types in China
by Zichun Gao, Huayong Zhang, Xi Luo, Yiwen Zhang and Yunxiang Han
Forests 2026, 17(1), 103; https://doi.org/10.3390/f17010103 - 12 Jan 2026
Viewed by 339
Abstract
The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain [...] Read more.
The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain underexplored. This study employed Random Forest (RF) and Structural Equation Modeling (SEM) to disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on LAI across 17 distinct Abies forest types. The results revealed that temperature was the dominant positive driver for the overall Abies forests (Total effect = 2.197), whereas Elevation (DEM) exerted the strongest negative regulation (Total effect = −0.335). However, driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii (Viguié & Gaussen) W.C.Cheng & L.K.Fu forest (Type 55), while DEM determined LAI in Abies fargesii Franch. forest (Type 49). Notably, we found that HFP could exert positive effects on LAI in specific communities (e.g., Abies densa Griff. forest, Type 58), likely due to understory compensation under moderate disturbance. These findings highlight the necessity of type-specific management strategies and provide a theoretical basis for predicting alpine forest dynamics under changing environments. Full article
(This article belongs to the Special Issue Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote Sensing)
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19 pages, 6483 KB  
Article
Mapping Forest Climate-Sensitivity Belts in a Mountainous Region of Namyangju, South Korea, Using Satellite-Derived Thermal and Vegetation Phenological Variability
by Joon Kim, Whijin Kim, Woo-Kyun Lee and Moonil Kim
Forests 2026, 17(1), 14; https://doi.org/10.3390/f17010014 - 22 Dec 2025
Viewed by 843
Abstract
Mountain forests play a key role in buffering local climate, yet their climate sensitivity is seldom mapped in a way that is directly usable for spatial planning. This study investigates how phenological thermal and vegetation variability are organized within the forested landscape of [...] Read more.
Mountain forests play a key role in buffering local climate, yet their climate sensitivity is seldom mapped in a way that is directly usable for spatial planning. This study investigates how phenological thermal and vegetation variability are organized within the forested landscape of Namyangju, a mountainous region in central Korea, and derives spatial indicators of forest climate sensitivity. Using monthly, cloud-screened Landsat-8/9 land surface temperature (LST) and normalized difference vegetation index (NDVI) images over a recent multi-year period, we calculated phenological coefficients of variation for 34,123 forest grid cells and applied local clustering analysis to identify belts of high and low variability. Forest areas where LST and NDVI variability simultaneously occupied the upper tail of their distributions (top 5%/10%/20%) were interpreted as climate-sensitivity hotspots, whereas co-located coldspots were treated as microclimatic refugia. Across the mountainous terrain, sensitivity hotspots formed continuous belts along high-elevation ridges and steep, dissected slopes, while coldspots were concentrated in sheltered valley floors. Notably, the most sensitive belts were dominated by high-elevation conifer stands, despite the limited seasonal fluctuation typically expected in evergreen canopies. This pattern suggests that elevation strongly amplifies the coupling between thermal responsiveness and vegetation health, whereas valley-bottom forests act as stabilizers that maintain comparatively constant microclimatic and phenological conditions. We refer to these patterns as “forest climate-sensitivity belts,” which translate satellite observations into spatially explicit information on where climate-buffering functions are most vulnerable or resilient. Incorporating climate-sensitivity belts into forest plans and adaptation strategies can guide elevation-aware species selection in new afforestation, targeted restoration and fuel-load management in upland sensitivity zones, and the protection of valley refugia that support biodiversity, thermal buffering, and hydrological regulation. Because the framework relies on standard satellite products and transparent calculations, it can be updated as new imagery becomes available and transferred to other seasonal, mountainous regions, providing a practical basis for climate-resilient forest planning. Full article
(This article belongs to the Special Issue Forest Vegetation Dynamics and Environmental Monitoring Using GIS and Remote Sensing)
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