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Forests
Special Issues
Remote Sensing-Driven Insights into Forest Growth and Sustainable Resource Management
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Remote Sensing-Driven Insights into Forest Growth and Sustainable Resource Management

A Special Issue of Forests (ISSN 1999-4907) belonging to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1511

Editors

Dr. Chunyan Wu

E-Mail Website
Guest Editor
State Key Laboratory of Efficient Production of Forest Resources, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
Interests: machine learning; climate change; physiological ecology; forest monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marta Szostak

E-Mail Website
Guest Editor
Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Krakow, Poland
Interests: land use; land cover; forest succession; remote sensing data; point cloud

Special Issue Information

Dear Colleagues,

Forests play a crucial role in maintaining global ecological balance, biodiversity, and carbon sequestration. However, under the context of climate change and intense human activities, sustainable forest resource management faces unprecedented challenges. Traditional forest monitoring methods are often time-consuming and labor-intensive. In recent years, the rapid advancement of remote sensing technologies (such as LiDAR, multispectral/hyperspectral imaging, and radar) from various platforms (satellite, UAV, and terrestrial) has provided unprecedented opportunities for monitoring forest ecosystems with high precision and spatiotemporal resolution.

This Special Issue, "Remote Sensing-Driven Insights into Forest Growth and Sustainable Resource Management," aims to gather the latest research applying advanced remote sensing techniques to forest sciences. We welcome original research articles and reviews that explore innovative methodologies for estimating forest parameters, monitoring forest health and growth dynamics, and supporting sustainable management practices.

Potential topics include, but are not limited to:

  • Forest biomass and carbon stock estimation;
  • Monitoring of forest health, disturbances, and resilience;
  • UAV and LiDAR applications in forest inventory;
  • Integration of multi-source remote sensing data for silviculture;
  • Remote sensing-informed sustainable forest management and policy-making.

Dr. Chunyan Wu
Prof. Dr. Marta Szostak
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-anonymized 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.

Keywords

  • remote sensing
  • forest growth
  • sustainable forest management
  • LiDAR
  • UAV
  • forest biomass
  • ecosystem monitoring
  • forest health

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

Published Papers (2 papers)

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Research

20 pages, 16044 KB  
Article
Hyperspectral Estimation of Chlorophyll Density in Populus pruinosa Incorporating Leaf Water Content
by Bingling Zhang, Jiaqiang Wang, Huixia Li and Chongfa Cai
Forests 2026, 17(6), 692; https://doi.org/10.3390/f17060692 - 11 Jun 2026
Viewed by 359
Abstract
Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water content (LWC) were measured for Populus pruinosa in the [...] Read more.
Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water content (LWC) were measured for Populus pruinosa in the Tarim River headwater region and Awati County, Xinjiang, from July to October 2023. The aim was to estimate CHD using hyperspectral data combined with machine learning and to evaluate the effect of LWC on model accuracy. Raw spectra were preprocessed using Savitzky–Golay (SG) smoothing and continuous wavelet transform (CWT). A two-step feature selection strategy comprising Random Frog and iterative retaining informative variables (IRIV) was applied to extract characteristic bands. Three machine learning models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—were developed for CHD estimation with and without LWC as an additional input. Incorporating LWC consistently improved the predictive performance of all models. Without LWC, the RF model achieved the best accuracy (training R2 = 0.842, test R2 = 0.830), whereas after LWC integration, XGBoost reached the optimal performance (training R2 = 0.871, test R2 = 0.865). SHAP analysis identified the 687 nm wavelength and its interaction with LWC as the most important predictors. These results indicate that combining spectral information with LWC effectively improves the accuracy and stability of CHD estimation for Populus pruinosa, providing a reliable non-destructive approach for assessing forest ecosystem physiological status—a key contribution to the sustainable management of arid riparian forests. Full article
(This article belongs to the Special Issue Remote Sensing-Driven Insights into Forest Growth and Sustainable Resource Management)
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26 pages, 82459 KB  
Article
Forest Fire Risk Early Warning Based on Dynamic Fuel Moisture Content
by Yuanzong Li, Cui Zhou, Junxiang Zhang, Wenjun Wang, Zhenyu Chen and Yongfeng Luo
Forests 2026, 17(5), 532; https://doi.org/10.3390/f17050532 - 28 Apr 2026
Viewed by 742
Abstract
Accurate prediction of forest fires is crucial for enhancing regional fire prevention and control. Existing models frequently rely on static factors such as weather and terrain, while insufficiently taking into account the Fuel Moisture Content (FMC), a critical internal factor that directly determines [...] Read more.
Accurate prediction of forest fires is crucial for enhancing regional fire prevention and control. Existing models frequently rely on static factors such as weather and terrain, while insufficiently taking into account the Fuel Moisture Content (FMC), a critical internal factor that directly determines fire behavior. Instead, proxies like the Normalized Difference Vegetation Index (NDVI) are commonly employed, which weakens the physical foundation of predictions. This study assesses the marginal contribution of integrating dynamic FMC into fire prediction models. Concentrating on California, we developed a random-forest-based model that incorporates high-resolution FMC products retrieved by our team, along with meteorological, topographic, vegetation, and anthropogenic data. Through comparative experiments and SHapley Additive exPlanations (SHAP) analysis, we evaluated model improvements and the contribution mechanisms of key drivers. The results indicated that: (1) Incorporating FMC significantly enhanced model performance, with precision and specificity increasing by 3.93% and 3.60%, respectively, and the Area Under the Curve (AUC) showing improvements, suggesting heightened sensitivity in detecting actual fire occurrences. (2) SHAP analysis disclosed nonlinear effects and threshold dynamics: temperature was the dominant positive driver (the fire risk soared above 20 °C); FMC demonstrated a negative correlation with fire risk, with 100% serving as a potential threshold; elevation presented an inverted U-shaped pattern (the peak risk occurred at 1000–1500 m); and population density exhibited a shifting influence from positive to negative. (3) The monthly risk maps for California in 2023 captured the seasonal progression of fire risk and spatial patterns consistent with historical fire points. The fire risk map for 9 September 2020 also demonstrated consistency with the spatial distribution of the actual fire points on that day. This study validates that the integration of dynamic FMC strengthens the mechanistic foundation and early-warning capacity of fire prediction models, providing scientific backing for targeted fire management. Full article
(This article belongs to the Special Issue Remote Sensing-Driven Insights into Forest Growth and Sustainable Resource Management)
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