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Forests
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Advancing Forest Management: Remote Sensing for Early Detection and Warning...
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Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats

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: 30 September 2025 | Viewed by 3585

Special Issue Editors

Dr. Haidi Abdullah

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation–ITC, University of Twente, Hallenweg 8, 7522 NH Enschede, The Netherlands
Interests: earth and environmental sciences including environmental DNA (eDNA); vegetation monitoring; vegetation conditions; monitoring vegetation biophysical and biochemical properties using remote sensing; hyperspectral, multispectral, and thermal remote sensing; geostatistics; image analysis; classification
Dr. Elnaz Neinavaz

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation–ITC, University of Twente, Hallenweg 8, 7522 NH Enschede, The Netherlands
Interests: biodiversity and environmental sciences; monitoring vegetation biophysical and biochemical properties using remote sensing data such as hyperspectral, multispectral and thermal infrared remote sensing obtained from different platforms (e.g., UAV, airborne, spaceborne and spectroscopy); land cover classification
Dr. Margarita Huesca

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation–ITC, University of Twente, Hallenweg 8, 7522 NH Enschede, The Netherlands
Interests: forestry; biodiversity; ecosystem dynamics; environmental disturbances such as forest fires and droughts; vegetation monitoring; functional traits; time series analysis; change detection; spaciotemporal modeling; remote sensing; multispectral; hyperspectral; LiDAR and UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the realm of forest management, the early detection of threats and stressors such as insect infestations, fires, and environmental stressors is paramount. The leveraging of remote-sensing data offers a powerful toolset for timely detection and proactive intervention. This Special Issue aims to explore innovative methodologies that integrate remote-sensing technologies into environmental DNA analysis for early detection and warning in forest ecosystems.

We are thrilled to announce a Special Issue of our journal dedicated to "Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats". This Special Issue aims to showcase cutting-edge research that explores the innovative applications of remote sensing in forest studies. We invite researchers from diverse disciplines, including remote-sensing scientists, ecologists, foresters, and environmental modelers, to contribute original, high-quality manuscripts to this endeavor.

Dr. Haidi Abdullah
Dr. Elnaz Neinavaz
Dr. Margarita Huesca
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. 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

  • early detection and warning of forest insect infestations, fires, and other environmental stressors
  • integrating remote-sensing data into environmental DNA for the early detection of forest stress
  • utilizing image spectroscopy and hyperspectral and other remote-sensing data for early detection and warning by identifying spectral signatures and temperature anomalies indicative of forest disturbances
  • time series analysis of remote-sensing data to monitor temporal changes and predict future threats
  • integration of novel platforms and sensors, such as unmanned aerial vehicles (UAVs), for high-resolution forest surveys
  • development of predictive models for forecasting and mitigating forest disturbances based on historical data and environmental parameters

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Published Papers (3 papers)

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Research

16 pages, 4586 KiB  
Article
Real-Time Detection of Smoke and Fire in the Wild Using Unmanned Aerial Vehicle Remote Sensing Imagery
by Xijian Fan, Fan Lei and Kun Yang
Forests 2025, 16(2), 201; https://doi.org/10.3390/f16020201 - 22 Jan 2025
Viewed by 923
Abstract
Detecting wildfires and smoke is essential for safeguarding forest ecosystems and offers critical information for the early evaluation and prevention of such incidents. The advancement of unmanned aerial vehicle (UAV) remote sensing has further enhanced the detection of wildfires and smoke, which enables [...] Read more.
Detecting wildfires and smoke is essential for safeguarding forest ecosystems and offers critical information for the early evaluation and prevention of such incidents. The advancement of unmanned aerial vehicle (UAV) remote sensing has further enhanced the detection of wildfires and smoke, which enables rapid and accurate identification. This paper presents an integrated one-stage object detection framework designed for the simultaneous identification of wildfires and smoke in UAV imagery. By leveraging mixed data augmentation techniques, the framework enriches the dataset with small targets to enhance its detection performance for small wildfires and smoke targets. A novel backbone enhancement strategy, integrating region convolution and feature refinement modules, is developed to facilitate the ability to localize smoke features with high transparency within complex backgrounds. By integrating the shape aware loss function, the proposed framework enables the effective capture of irregularly shaped smoke and fire targets with complex edges, facilitating the accurate identification and localization of wildfires and smoke. Experiments conducted on a UAV remote sensing dataset demonstrate that the proposed framework achieves a promising detection performance in terms of both accuracy and speed. The proposed framework attains a mean Average Precision (mAP) of 79.28%, an F1 score of 76.14%, and a processing speed of 8.98 frames per second (FPS). These results reflect increases of 4.27%, 1.96%, and 0.16 FPS compared to the YOLOv10 model. Ablation studies further validate that the incorporation of mixed data augmentation, feature refinement models, and shape aware loss results in substantial improvements over the YOLOv10 model. The findings highlight the framework’s capability to rapidly and effectively identify wildfires and smoke using UAV imagery, thereby providing a valuable foundation for proactive forest fire prevention measures. Full article
(This article belongs to the Special Issue Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats)
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24 pages, 11680 KiB  
Article
Assessment and Optimization of Forest Aboveground Biomass in Liaoning Province
by Jiapeng Huang and Xinyue Cao
Forests 2024, 15(12), 2095; https://doi.org/10.3390/f15122095 - 26 Nov 2024
Viewed by 974
Abstract
Forests are the largest terrestrial carbon reservoirs and the most cost-effective carbon sinks. Accurate estimation of forest aboveground biomass (AGB) can significantly reduce uncertainty in carbon stock assessments. However, due to the limitations of timely and reliable forestry surveys, as well as high-resolution [...] Read more.
Forests are the largest terrestrial carbon reservoirs and the most cost-effective carbon sinks. Accurate estimation of forest aboveground biomass (AGB) can significantly reduce uncertainty in carbon stock assessments. However, due to the limitations of timely and reliable forestry surveys, as well as high-resolution remote sensing data, mapping high-resolution and spatially continuous forest AGB remains challenging. The Global Ecosystem Dynamics Investigation (GEDI) is a remote sensing mission led by NASA, aimed at obtaining global forest three-dimensional structural information through LiDAR data, and has become an important tool for estimating forest structural parameters at regional scales. In 2019, the GEDI L4A product was introduced to improve AGB estimation accuracy. Currently, forest AGB maps in China have not been consistently evaluated, and research on biomass at the provincial level is still limited. Moreover, scaling GEDI’s footprint-based data to regional-scale gridded data remains a pressing issue. In this study, to verify the accuracy of GEDI L4A data and the reliability of the filtering parameters, the filtered GEDI L4A data were extracted and validated against airborne data, resulting in a Pearson correlation coefficient (ρ) of 0.69 (p < 0.001, statistically significant). This confirms the reliability of both the GEDI L4A data and the proposed filtering parameters. Taking Liaoning Province as an example, this study evaluated three forest AGB maps (Yang’s, Su’s, and Zhang’s maps), which were obtained as nationwide AGB product maps, using GEDI L4A data. The comparison with Su’s map yields the highest ρ value of 0.61. To enhance comparison accuracy, Kriging spatial interpolation was applied to the extracted GEDI footprint data, yielding continuous data. This ρ value increased to 0.75 when compared with Su’s map, with significant increases also observed against Yang’s and Zhang’s maps. The study further proposes a method to subtract the extracted GEDI data from the AGB values of the three maps, followed by Kriging interpolation, resulting in ρ values of 0.70, 0.80, and 0.69 for comparisons with Yang’s, Su’s, and Zhang’s maps, respectively. Additionally, comparisons with field measurements from the Mudanjiang Ecological Research Station yielded ρ values of 0.66, 0.65, and 0.50, indicating substantial improvements over direct comparisons. All the ρ values were statistically significant (p < 0.001). This study also conducted comparisons across different cities and forest cover types. The results indicate that cities in eastern Liaoning Province, such as Dalian and Anshan, which have larger forest cover areas, produced better results. Among the different forest types, evergreen needle-leaved forests and deciduous needle-leaved forests yielded better results. Full article
(This article belongs to the Special Issue Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats)
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22 pages, 11157 KiB  
Article
Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China
by Zhu He, Zhihui Lin, Qianle Xu, Shanshan Ding, Xiaochun Bao, Xuefei Li, Xisheng Hu and Jian Li
Forests 2024, 15(9), 1490; https://doi.org/10.3390/f15091490 - 25 Aug 2024
Viewed by 1096
Abstract
Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the [...] Read more.
Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the road, SSP3: regional rivalry, SSP4: inequality, and SSP5: fossil-fueled development, which were constructed by integrated assessment and climate models. We modeled them with the patch-generating land use simulation (PLUS) and constructed a multi-dimensional landscape connectivity index (MLCI) employing forest landscape connectivity (FLC) indices to assess forest development in Fujian Province, Southeast China. The MLCI visualized by radar charts was based on five metrics, including forest patch size (class area (CA), number (patch density (PD), isolation (landscape division index (DIVISION), aggregation (mean nearest-neighbor index (ENN_MN), and connectance index, (CONNECT). The results indicate that FC will remain above 61.4% until 2030, with growth observed in SSP1 and SSP4. Particularly, FC in SSP4 substantially increased, converted from cropland (1140.809 km2) and grassland (645.741 km2). SSP4 has the largest MLCI values and demonstrates significant enhancements in forest landscape integrity, with CA, ENN_MN and CONNECT increasing greatly. Our study offers valuable approaches to and insights into forest protection and restoration. Full article
(This article belongs to the Special Issue Advancing Forest Management: Remote Sensing for Early Detection and Warning of Environmental Threats)
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