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Remote Sensing
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Application of Remote Sensing in Forest Ecosystem Functioning and Services
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Application of Remote Sensing in Forest Ecosystem Functioning and Services

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2715

Special Issue Editors

Dr. Jian Yang

E-Mail Website
Guest Editor
Department of Forestry and Natural Resources, University of Kentucky, 730 Rose Street, Lexington, KY 40546, USA
Interests: forest landscape ecology; disturbance ecology; ecosystem modeling; land use and land cover change; ecosystem services; remote sensing and GIS; spatial statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Fang

E-Mail Website
Guest Editor
Environment Research Institute, Shandong University, Qingdao 266237, China
Interests: forest remote sensng; climate change; wildland fire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are the ecosystem upon which life unfolds, and many of its ecological processes operate to provide a wealth of services essential for both humanity and the environment. These services encompass provisioning services (timber and non-timber products), regulating services (e.g., carbon sequestration, water flow regulation, soil erosion, and flood mitigation), supporting services (such as biodiversity and pollination), and cultural services (e.g., recreation, tourism, and spiritual enrichment). Understanding forest ecosystem functioning, including carbon, water, and nutrient cycling, and the cascading effects on the myriad services they provide, is paramount for effective conservation and restoration efforts.

Remote sensing has emerged as a powerful tool for studying forest ecosystems and the services they offer. The ever-growing volume of “big data” collected from satellites, airplanes, and drones presents a remarkable opportunity to quantify forest structure, functions, services, and their dynamic responses to natural and human-induced disturbances at various scales, from individual forest stands to landscapes, regions, and the entire globe.

This Special Issue (SI) seeks original research that utilizes any combination of satellite, airplane, or drone data to investigate forest ecosystem functions and services. We welcome submissions on a broad range of topics to advance our understanding of these critical forest ecosystem functions and services. Topics of interest include, but are not limited to, the following:

  • Spatial patterns and temporal trends in forest carbon stock and sequestration;
  • Influences of forest 3D structure on habitat quality and biodiversity;
  • Forest cover change and its ecological consequences;
  • Identifying invasive species and its impacts on forest health;
  • Quantifying energy, water, and carbon cycling in forests with remote sensing;
  • Use of “big data” methods to evaluate forest ecosystem services and driving factors.

Dr. Jian Yang
Dr. Lei Fang
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

  • forest monitoring
  • forest ecosystem cycling
  • forest disturbances
  • forest structure and functions
  • forest ecosystem services

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

Published Papers (4 papers)

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Research

21 pages, 18239 KiB  
Article
Township-Level Ecological Management for Enhanced Ecosystem Services in the Qinling Mountains
by Yan Zhao, Yiping Chen, Wenqi Wu, Hanwen Tian and Huiwen Zhang
Remote Sens. 2025, 17(2), 272; https://doi.org/10.3390/rs17020272 - 14 Jan 2025
Viewed by 408
Abstract
The Qinling Mountains, known for high forest cover and multiple ecosystem services (ES), present significant potential for advancing ecological management (EM) paradigms. However, existing studies on matching long-term ES sequences with governance units remain limited. By quantifying the assemblage and clustering patterns of [...] Read more.
The Qinling Mountains, known for high forest cover and multiple ecosystem services (ES), present significant potential for advancing ecological management (EM) paradigms. However, existing studies on matching long-term ES sequences with governance units remain limited. By quantifying the assemblage and clustering patterns of ecosystems in the Qinling Mountains over forty years, this study was innovative in analyzing changes in long-term ecosystem interactions and the impact of spatialization drivers, enhancing the significance of administrative-scale adaptations for sustained conservation and EM strategies. The results showed an increasing trend in the multiple ES Landscape Index (MESLI) since 2000. Spatialized trend analysis showed that the MESLI increased by 58.8% in the east. Moreover, the potential of ES bundles (ESB) in matching ecological management scales was demonstrated. Three ESBs were identified at different administrative levels and townships were more responsive to ESB changes. The composite ESB 1 dominated at 50.2%, and ESB 2 exhibited an upward trend. Additionally, spatial analysis of long-term drivers revealed the underlying causes of local ES degradation. Climate change had region-wide impacts, while natural and anthropogenic factors contributed to localized degradation. These findings emphasize the critical role of spatiotemporal analysis in shaping township-level EM strategies in multi-ES regions, providing feasible guidance for accurately enhancing localized management. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Forest Ecosystem Functioning and Services)
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20 pages, 11851 KiB  
Article
Mapping Windthrow Severity as Change in Canopy Cover in a Temperate Eucalypt Forest
by Nina Hinko-Najera, Paul D. Bentley, Samuel Hislop, Alison C. Bennett, Jamie E. Burton, Thomas A. Fairman, Sacha Jellinek, Julio C. Najera Umana and Lauren T. Bennett
Remote Sens. 2024, 16(24), 4710; https://doi.org/10.3390/rs16244710 - 17 Dec 2024
Viewed by 661
Abstract
Storm events are significant disturbance agents that can cause considerable forest damage through windthrow. Assessment and mapping of the extent and severity of windthrow is critical to provide reliable information to forest managers to prioritize post-storm hazard reduction (including public safety and fire [...] Read more.
Storm events are significant disturbance agents that can cause considerable forest damage through windthrow. Assessment and mapping of the extent and severity of windthrow is critical to provide reliable information to forest managers to prioritize post-storm hazard reduction (including public safety and fire risk) and to guide restoration activities. Detailed on-ground assessments after windthrow are often impossible due to lack of access and safety concerns. In 2021, severe windstorms caused unprecedented and extensive windthrow in a temperate eucalypt forest in south-eastern Australia. The purpose of this study is to quantify the severity and extent of the damaged forest area as the change in percentage canopy cover using remotely sensed data. We assessed percentage canopy cover from high-resolution aerial images of 455 randomly selected plots in disturbed and undisturbed areas to train a model and machine learning framework to predict landscape scale canopy cover from Sentinel-2 images. A random forest model using all single bands and percentiles best predicted the canopy cover (R2 = 0.69). Sentinel-2 images were then used to predict canopy cover pre- and post-windthrow to assess and map the severity of windthrow as the change in percentage canopy cover. Of the total 63,471 ha of forest area assessed, 63% (39,987 ha) was impacted by windthrow, with 46% at low severity (<30% canopy cover loss), 11% at moderate (30–50% canopy cover loss) and 6% at high severity (>50% canopy cover loss). Our study provides the first quantitative mapping of windthrow severity mapping for a temperate eucalypt forest in Australia that demonstrates an effective remote assessment methodology and provides critical information to support post-windthrow management decisions. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Forest Ecosystem Functioning and Services)
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21 pages, 5316 KiB  
Article
A Weakly Supervised Multimodal Deep Learning Approach for Large-Scale Tree Classification: A Case Study in Cyprus
by Arslan Amin, Andreas Kamilaris and Savvas Karatsiolis
Remote Sens. 2024, 16(23), 4611; https://doi.org/10.3390/rs16234611 - 9 Dec 2024
Cited by 1 | Viewed by 838
Abstract
Forest ecosystems play an essential role in ecological balance, supporting biodiversity and climate change mitigation. These ecosystems are crucial not only for ecological stability but also for the local economy. Performing a tree census at a country scale via traditional methods is resource-demanding, [...] Read more.
Forest ecosystems play an essential role in ecological balance, supporting biodiversity and climate change mitigation. These ecosystems are crucial not only for ecological stability but also for the local economy. Performing a tree census at a country scale via traditional methods is resource-demanding, error-prone, and requires significant effort by a large number of experts. While emerging technologies such as satellite imagery and AI provide the means for achieving promising results in this task with less effort, considerable effort is still required by experts to annotate hundreds or thousands of images. This study introduces a novel methodology for a tree census classification system which leverages historical and partially labeled data, employs probabilistic data imputation and a weakly supervised training technique, and thus achieves state-of-the-art precision in classifying the dominant tree species of Cyprus. A crucial component of our methodology is a ResNet50 model which takes as input high spatial resolution satellite imagery in the visible band and near-infrared band, as well as topographical features. By applying a multimodal training approach, a classification accuracy of 90% among nine targeted tree species is achieved. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Forest Ecosystem Functioning and Services)
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16 pages, 5417 KiB  
Article
Comparative Analysis of Two Methods for Valuing Local Cooling Effect of Forests in Inner Mongolia Plateau
by Wenjing Bo, Yi Xiao, Jiazhe Sun, Yun Cao and Le Chen
Remote Sens. 2024, 16(23), 4424; https://doi.org/10.3390/rs16234424 - 26 Nov 2024
Viewed by 398
Abstract
Studies have extensively examined the cooling effects of forests. Various methods exist for evaluating climate regulation at regional and global levels. Local-scale cooling effects and their valuing methods, however, remain poorly understood. In this study, the temperature difference and energy balance methods were [...] Read more.
Studies have extensively examined the cooling effects of forests. Various methods exist for evaluating climate regulation at regional and global levels. Local-scale cooling effects and their valuing methods, however, remain poorly understood. In this study, the temperature difference and energy balance methods were compared to assess the value of cooling services of three forest types at a local scale. Using the window searching strategy, land surface temperature and sensible heat flux differences between forest and open land were compared. The average cooling temperature of broad-leaved forests was found to be 0.229 °C, significantly higher than that of coniferous forests, at 0.205 °C, while mixed coniferous–broad-leaved forests were not significantly different to the other two types. The average sensible heat flux differences in broad-leaved, coniferous, and coniferous–broad-leaved forests were found to be 0.23, 0.079, and 0.11 MJ/m2/day, respectively. According to the correlation analysis, the sensible heat flux was significantly correlated with the cooling degree (R = 0.33, p = 0.05), suggesting consistency between the two approaches. However, the total cooling value calculated with the energy balance method was CNY 0.51 billion, significantly higher than the temperature difference method at CNY 0.11 billion. The main reason for the differences between the two approaches is the uncertainty in cooling volume and cooling time for the temperature difference method and energy balance method, respectively. The impact of vegetation on the microclimate depends on the vegetation type, topography, local climate, and other factors. It is also important to note that cooling services are not required at all times of the day, and energy differences can hardly be calculated based on the hour. However, surface radiation and evapotranspiration generally occur during the daytime, which is also when the surface temperature is high. Therefore, there is a certain coincidence with the time when cooling is needed. The energy balance method presented herein provides a novel alternative approach to assessing the cooling services of local-scale forests, offering advantages over the commonly used temperature difference approach, which is associated with large uncertainty. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Forest Ecosystem Functioning and Services)
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