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Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition)

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 278

Special Issue Editors


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Guest Editor
School of Grassland Science, Beijing Forestry University, Beijing 100083, China
Interests: remote sensing monitoring grassland vegetation structure and function changes; monitoring grassland resources quality; assessment of grassland ecosystem degradation and health
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geography, Université de Montréal, Montreal, QC, Canada
Interests: plant ecology; forest biogeography; geographic information systems and their applications; modelling and statistics; dendro-ecology and dendro-climatology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor

E-Mail Website
Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: remote sensing of ecosystem and environment; spatial-temporal-spectral information fusion; deep learning for remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests and grasslands are two of our planet's most vital ecosystems, providing a multitude of critical ecosystem services that underpin environmental health and human well-being. These services include erosion control, climate regulation, nutrient cycling, raw material provision, forage production, habitat for diverse species, and recreational opportunities. Under the combined effects of natural factors and human disturbances, forest and grassland ecosystems are constantly evolving. With advancements in remote sensing and GIS technology, the efficiency, level, and scientific decision-making processes of forest and grassland ecosystem monitoring have been significantly enhanced. Effectively monitoring and understanding these ecosystems is essential for informed decision making and conservation efforts. This Special Issue focuses on the “Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring.” It aims to explore the latest advancements in these technologies and their applications in managing and preserving these invaluable ecosystems.

Our goal is to collect state-of-the-art research that showcases the innovative use of remote sensing and GIS for monitoring forest and grassland ecosystems. We welcome contributions that investigate various aspects, from monitoring changes in forest and grassland vegetation structures and functions, assessing land cover changes, tracking biodiversity, and quantifying carbon sequestration, to monitoring wildfire events and improving the sustainability of forest and grassland management practices.

We invite researchers, scientists, and professionals to submit original research papers and review articles that explore the integration of remote sensing and GIS technologies in the monitoring and management of forest and grassland ecosystems. Topics of interest include, but are not limited to, the following:

  • Advanced remote sensing techniques: The use of cutting-edge remote sensing technologies, such as hyperspectral, LiDAR, and synthetic aperture radar (SAR), for precise ecosystem monitoring.
  • Vegetation dynamic monitoring: Monitoring dynamic changes in forest and grassland ecosystem structure and function.
  • Biodiversity assessment: The application of remote sensing and GIS in biodiversity assessment, habitat modelling, and conservation efforts.
  • Land cover and land use change: Investigations into land cover and land use changes in forest and grassland ecosystems and their environmental consequences.
  • Carbon sequestration: Studies on carbon sequestration estimation and its relation to climate change mitigation in these ecosystems.
  • Ecosystem degradation/health and resilience: Papers focusing on assessing ecosystem degradation, health and resilience using remote sensing indicators, as well as their driving mechanisms.
  • Wildfire and disturbance monitoring: Research on monitoring wildfires, disturbances, and post-fire recovery in these ecosystems

Prof. Dr. Xiuchun Yang
Dr. Francois Girard
Dr. Cong (Vega) Xu
Prof. Dr. Yungang Cao
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 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. 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

  • remote sensing
  • geographic information systems (GISs)
  • forest ecosystem
  • grassland ecosystem
  • key ecosystem parameters
  • vegetation change
  • biodiversity assessment
  • land cover change
  • carbon sequestration
  • ecosystem degradation
  • ecosystem resilience
  • wildfire monitoring
  • environmental conservation

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Published Papers (1 paper)

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Research

35 pages, 18467 KB  
Article
Monitoring Rubber Plantation Distribution and Biomass with Sentinel-2 Using Deep Learning and Machine Learning Algorithm (2019–2024)
by Yingtan Chen, Jialong Duanmu, Zhongke Feng, Jun Qian, Zhikuan Liu, Huiqing Pei, Pietro Grimaldi and Zixuan Qiu
Remote Sens. 2025, 17(24), 4042; https://doi.org/10.3390/rs17244042 - 16 Dec 2025
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Abstract
The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used [...] Read more.
The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R2 = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations. Full article
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