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

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3984

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Special Issue Editors

Prof. Dr. Xiuchun Yang

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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
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Dr. Francois Girard

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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
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Dr. Cong (Vega) Xu

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Guest Editor

New Zealand School of Forestry, University of Canterbury, Christchurch 8140, New Zealand
Interests: forestry; GIS; remote sensing; LiDAR; UAV
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Prof. Dr. Yungang Cao

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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
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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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Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring in Remote Sensing (16 articles)

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Research

19 pages, 7516 KB

Open AccessArticle

ForSOC-UA: A Novel Framework for Forest Soil Organic Carbon Estimation and Uncertainty Assessment with Multi-Source Data and Spatial Modeling

by Qingbin Wei, Miao Li, Zhen Zhen, Shuying Zang, Hongwei Ni, Xingfeng Dong and Ye Ma

Remote Sens. 2026, 18(8), 1106; https://doi.org/10.3390/rs18081106 - 8 Apr 2026

Viewed by 469

Abstract

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition))

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28 pages, 5013 KB

Open AccessArticle

Forest Transition Under Climate Pressure: Land Use Land Cover Change in the Greater Shawnee National Forest

by Saroj Thapa, David J. Gibson and Ruopu Li

Remote Sens. 2026, 18(7), 1079; https://doi.org/10.3390/rs18071079 - 3 Apr 2026

Viewed by 633

Abstract

The Land Use and Land Cover (LULC) of many regional landscapes are changing due to natural effects and anthropogenic activities, impacting biodiversity and ecosystem services. LULC dynamics reflect the altered flow of energy, water, and greenhouse gases, influencing the pillars of sustainability: society, environment, and economy. Thus, assessing LULC changes is vital for understanding the relationship between nature and society. This study used multi-temporal remotely sensed imagery to examine LULC change between 1990 and 2019 in the context of Forest Transition Theory (FTT) across the Greater Shawnee National Forest (GSNF) area of southern Illinois, USA, using a random forest algorithm, and projecting change to 2050 with a Land Change Model integrated with IPCC temperature and precipitation scenarios. From 1990 to 2019, LULC analysis showed increases in deciduous forest (1.35%), mixed forest (26.40%), agriculture (2.15%), and built-up areas (6.70%), while hay/grass/pasture declined (16.0%). LULC change intensity was highest from 1990 to 2001 (2.35% annually), slowing to 0.23% (2001–2010) and 0.18% (2010–2019). The overall accuracy (OA) of LULC classification ranged from 0.9 to 0.95 at a 95% confidence interval (CI). Projections to 2050 showed consistent increases in built-up areas (17.12–42.61%), water (28.75–39.70%), and hay/grass/pasture (6.23–38.38%), while overall forest cover declined in all scenarios. Deciduous forests decreased by 3.11–19.87% and were replaced by mixed forests in some scenarios (12.45–23.63%), while evergreen forests showed mixed responses, ranging from a decline of up to 17.13% to an increase of 2.90%. The OA of projected LULC ranged from 0.71 to 0.83 (95% CI) across SSP-RCP-based temperature and precipitation scenarios. The results showed that the GSNF broadly follows the FTT framework: forest recovery since 2001 coincided with rural depopulation, slow agricultural expansion, and rising incomes. However, climate change is expected to disrupt this recovery, pushing transitions toward mixed and evergreen forests. Findings demonstrate the importance of integrating remote sensing-based LULC with socio-economic trends and climate adaptation strategies to sustain forests and ecosystem services under future environmental pressures. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition))

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22 pages, 1911 KB

Open AccessArticle

A Two-Step Framework for Mapping, Classification, and Area Estimation of Stand- and Non-Stand-Replacing Forest Disturbances

by Isabel Aulló-Maestro, Saverio Francini, Gherardo Chirici, Cristina Gómez, Icíar Alberdi, Isabel Cañellas, Francesco Parisi and Fernando Montes

Remote Sens. 2026, 18(7), 1038; https://doi.org/10.3390/rs18071038 - 30 Mar 2026

Viewed by 730

Abstract

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition))

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21 pages, 7848 KB

Open AccessArticle

Multidimensional Validation of FVC Products over Qinghai–Tibetan Plateau Alpine Grasslands: Integrating Spatial Representativeness Metrics with Machine Learning Optimization

by Junji Li, Jianjun Chen, Xue Cheng, Jiayuan Yin, Qingmin Cheng, Haotian You, Xiaowen Han and Xinhong Li

Remote Sens. 2026, 18(2), 228; https://doi.org/10.3390/rs18020228 - 10 Jan 2026

Viewed by 531

Abstract

Fractional Vegetation Cover (FVC) dynamics on the Qinghai–Tibetan Plateau (QTP) are critical indicators for assessing ecosystem condition. However, uncertainties persist in the accuracy of existing FVC products over the QTP due to retrieval differences, scale effects, and limited validation data. This study utilized the Google Earth Engine platform to integrate unmanned aerial vehicle (UAV) observations, Sentinel-2, MODIS, climate, and topography datasets, and proposed a comprehensive framework incorporating dual-index screening, machine learning optimization, and multidimensional validation to systematically assess the accuracy of GEOV3, GLASS, and MuSyQ FVC products in the alpine grasslands. The dual-index screening reduced validation uncertainty by improving the spatial representativeness of ground data. To build a high-precision evaluation dataset with limited inter-class coverage, recursive feature elimination and grid search were applied to optimize five ML models, and CatBoost achieved the superior performance (R² = 0.880, RMSE = 0.122), followed by XGBoost, GBM, LightGBM, and RF models. Four validation scenarios were implemented, including direct validation using 250 m UAV plot FVC and multi-scale validation using a 10 m FVC reference aggregated to product grids. Results show that GEOV3 (R² = 0.909–0.925, RMSE = 0.082–0.103) outperformed GLASS (R² = 0.742–0.771, RMSE = 0.138–0.175) and MuSyQ (R² = 0.739–0.746, RMSE = 0.138–0.181), both of which exhibited systematic underestimation. This framework significantly enhances FVC product validation reliability, providing a robust solution for remote sensing product validation in alpine grassland ecosystems. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition))

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35 pages, 18467 KB

Open AccessArticle

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

Viewed by 1056

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 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

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring (Second Edition))

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