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Forests
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Application of Remote Sensing and Geographic Information Systems for Natural...
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Application of Remote Sensing and Geographic Information Systems for Natural Resource Management of Forest Ecosystems

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: closed (30 April 2025) | Viewed by 5210

Special Issue Editors

Dr. Pablito M. López-Serrano

E-Mail Website
Guest Editor
Institute of Forestry and Wood Industry, Juarez University of the State of Durango, Durango 34239, Mexico
Interests: geomatics applied to forest and environmental resources; management of geoinformatics (GIS); passive and active remote sensors; forest management; forestry; multivariate analysis and machine learning with spectral information
Special Issues, Collections and Topics in MDPI journals
Dr. Marín Pompa-García

E-Mail Website
Guest Editor
Dendroecology Lab, Forestry Sciences Faculty, Juarez University of the State of Durango, Durango, Mexico
Interests: forest ecology; dendroecology; climate change; spatial analysis; remote sensing; UAV technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing and Geographic Information Systems (GISs) are essential technologies and tools for the effective management of natural resources in forest ecosystems. Their integration allows for the analysis of not only information obtained remotely, but also information collected directly, whether meteorological, soil, or field measurements of forest attributes. Technological advancement and the development of new tools for forest management have enabled better decision making in modern silviculture. Information derived from unmanned aerial vehicles equipped with sensors and cameras that can capture data through high-resolution images using Light Detection and Ranging (LiDAR) technology is integrated into GIS through novel machine learning algorithms for more accurate and up-to-date analysis.

This Special Issue of Forests focuses on the integration of remote sensing with GIS applied to forest management and monitoring. Research articles can focus on any aspect in which GIS is applied to assess forest ecosystems using satellite imagery, drone imagery, LiDAR technology, and the analysis of direct measurement data such as meteorological and historical records. We also welcome studies in which remote sensing and GIS support research in forest ecology and the development of data-driven management strategies for ecological modeling and the performance of studies on forest ecosystem dynamics.

Dr. Pablito M. López-Serrano
Dr. Marín Pompa-García
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

  • remote sensing
  • geographic information systems (GISs)
  • machine learning
  • forest management
  • geoprocessing and spatial analysis
  • unmanned aerial vehicles

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Published Papers (5 papers)

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Research

19 pages, 5177 KiB  
Article
Comparison of LiDAR Operation Methods for Forest Inventory in Korean Pine Forests
by Lan Thi Ngoc Tran, Myeongjun Kim, Hongseok Bang, Byung Bae Park and Sung-Min Choi
Forests 2025, 16(4), 643; https://doi.org/10.3390/f16040643 - 7 Apr 2025
Viewed by 339
Abstract
Precise forest inventory is the key to sustainable forest management. LiDAR technology is widely applied to tree attribute extraction. Therefore, this study compared DBH and tree height derived from Handheld Mobile Laser Scanning (HMLS), Airborne Laser Scanning (ALS), and Integrated ALS and HMLS [...] Read more.
Precise forest inventory is the key to sustainable forest management. LiDAR technology is widely applied to tree attribute extraction. Therefore, this study compared DBH and tree height derived from Handheld Mobile Laser Scanning (HMLS), Airborne Laser Scanning (ALS), and Integrated ALS and HMLS and determined the applicability of integrating HMLS and ALS scanning methods to estimate individual tree attributes such as diameter at breast height (DBH) and tree height in pine forests of South Korea. There were strong correlations for DBH at the individual tree level (r > 0.95; p < 0.001). HMLS and Integrated ALS-HMLS achieved high accuracy for DBH estimations, showing Root Mean Squared Error (RMSE) of 1.46 cm (rRMSE 3.7%) and 1.38 cm (rRMSE 3.5%), respectively. In contrast, tree height obtained from HMLS was lower than expected, showing an RMSE of 2.85 m (12.74%) along with a bias of −2.34 m. ALS data enhanced the precision of tree height estimations, achieving a RMSE of 1.81 m and a bias of −1.24 m. However, integrating ALS and HMLS data resulted in the most precise tree height estimations resulted in a reduced RMSE to 1.43 m and biases to −0.3 m. Integrated ALS and HMLS and its advantages are a beneficial solution for accurate forest inventory, which in turn supports forest management and planning. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Geographic Information Systems for Natural Resource Management of Forest Ecosystems)
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32 pages, 31711 KiB  
Article
Optimization and Construction of Forestland Ecological Security Pattern: A Case Study of the Huai River Source–Dabie Mountains in China
by Xiaofang Wang, Shilin Xu, Xin Huang, Chaochen Yang and Yongsheng Li
Forests 2025, 16(3), 426; https://doi.org/10.3390/f16030426 - 26 Feb 2025
Viewed by 379
Abstract
In this research, we chose six indicators—soil conservation, water conservation, carbon sequestration, windbreak and sand fixation, biodiversity conservation, and forest recreation—to compute the forestland ecosystem service index for forestland within the study region, utilizing time series data. The outcomes reveal that the aggregate [...] Read more.
In this research, we chose six indicators—soil conservation, water conservation, carbon sequestration, windbreak and sand fixation, biodiversity conservation, and forest recreation—to compute the forestland ecosystem service index for forestland within the study region, utilizing time series data. The outcomes reveal that the aggregate index of forestland ecosystem services exhibits a spatial distribution characterized by higher values in the northeastern part and lower values in the southwestern part, with an upward trend over time. Among these functions, windbreak and sand fixation, water conservation, carbon sequestration, and forest recreation all maintained relatively high growth rates. We selected 10 factors that are closely related to the natural environment and human activities and employed spatial principal component analysis to develop a comprehensive resistance surface. Based on the assessment results of forestland ecosystem functions, in conjunction with morphological spatial pattern analysis (MSPA) as well as landscape connectivity analysis, we optimized the method for identifying ecological source sites and extracted 38 ecological source sites. Subsequently, leveraging circuit theory, we extracted 91 ecological corridors and pinpointed 25 ecological nodes, ultimately constructing a forestland ecosystem security pattern (ESP) in the study area and proposing restoration strategies. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Geographic Information Systems for Natural Resource Management of Forest Ecosystems)
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19 pages, 22182 KiB  
Article
Modeling Spongy Moth Forest Mortality in Rhode Island Temperate Deciduous Forest
by Liubov Dumarevskaya and Jason R. Parent
Forests 2025, 16(1), 93; https://doi.org/10.3390/f16010093 - 8 Jan 2025
Cited by 1 | Viewed by 681
Abstract
Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015–2017 Spongy moth outbreak in [...] Read more.
Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015–2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Geographic Information Systems for Natural Resource Management of Forest Ecosystems)
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27 pages, 3310 KiB  
Article
Evaluation of Correction Algorithms for Sentinel-2 Images Implemented in Google Earth Engine for Use in Land Cover Classification in Northern Spain
by Iyán Teijido-Murias, Marcos Barrio-Anta and Carlos A. López-Sánchez
Forests 2024, 15(12), 2192; https://doi.org/10.3390/f15122192 - 12 Dec 2024
Cited by 3 | Viewed by 1874
Abstract
This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern [...] Read more.
This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern Spain and made use of the Spanish National Forest Inventory plots and other systematically located plots to cover non-forest classes. A total of 2991 photo-interpreted ground plots and 15 Sentinel-2 images, acquired in summer at a spatial resolution of 10–20 m per pixel, were used for this purpose. The overall goal was to determine the optimal level of image correction in GEE for subsequent use in time series analysis of images for accurate forest cover classification. Particular attention was given to the classification of cover by the major commercial forest species: Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, and Pinus radiata. The Second Simulation of the Satellite Signal in the Solar Spectrum (Py6S) algorithm, used for atmospheric correction, provided the best compromise between execution time and image size, in comparison with other algorithms such as Sentinel-2 Level 2A Processor (Sen2Cor) and Sensor Invariant Atmospheric Correction (SIAC). To correct the topographic effect, we tested the modified Sun-canopy-sensor topographic correction (SCS + C) algorithm with digital elevation models (DEMs) of three different spatial resolutions (90, 30, and 10 m per pixel). The combination of Py6S, the SCS + C algorithm and the high-spatial resolution DEM (10 m per pixel) yielded the greatest precision, which demonstrated the need to match the pixel size of the image and the spatial resolution of the DEM used for topographic correction. We used the Ross-Thick/Li-Sparse-Reciprocal BRDF to correct the variation in reflectivity captured by the sensor. The BRDF corrections did not significantly improve the accuracy of the land cover classification with the Sentinel-2 images acquired in summer; however, we retained this correction for subsequent time series analysis of the images, as we expected it to be of much greater importance in images with larger solar incidence angles. Our final proposed dataset, with image correction for atmospheric (Py6S), topographic (SCS + C), and BRDF (Ross-Thick/Li-Sparse-Reciprocal BRDF) effects and a DEM of spatial resolution 10 m per pixel, yielded better goodness-of-fit statistics than other datasets available in the GEE catalogue. The Sentinel-2 images currently available in GEE are therefore not the most accurate for constructing land cover classification maps in areas with complex orography, such as northern Spain. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Geographic Information Systems for Natural Resource Management of Forest Ecosystems)
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34 pages, 11382 KiB  
Article
Evaluation of Two-Dimensional DBH Estimation Algorithms Using TLS
by Jorge Luis Compeán-Aguirre, Pablito Marcelo López-Serrano, José Luis Silván-Cárdenas, Ciro Andrés Martínez-García-Moreno, Daniel José Vega-Nieva, José Javier Corral-Rivas and Marín Pompa-García
Forests 2024, 15(11), 1964; https://doi.org/10.3390/f15111964 - 7 Nov 2024
Viewed by 944
Abstract
Terrestrial laser scanning (TLS) has become a vital tool in forestry for accurately measuring tree parameters, such as diameter at breast height (DBH). However, its application in Mexican forests remains underexplored. This study evaluates the performance of five two-dimensional DBH estimation algorithms (Nelder–Mead, [...] Read more.
Terrestrial laser scanning (TLS) has become a vital tool in forestry for accurately measuring tree parameters, such as diameter at breast height (DBH). However, its application in Mexican forests remains underexplored. This study evaluates the performance of five two-dimensional DBH estimation algorithms (Nelder–Mead, least squares, Hough transform, RANSAC, and convex hull) within a temperate Mexican forest and explores their broader applicability across diverse ecosystems, using published point cloud data from various scanning devices. Results indicate that algorithm accuracy is influenced by local factors like point cloud density, occlusion, vegetation, and tree structure. In the Mexican study area, the Nelder–Mead algorithm achieved the highest accuracy (R² = 0.98, RMSE = 1.59 cm, MAPE = 6.12%), closely followed by least squares (R² = 0.98, RMSE = 1.67 cm, MAPE = 6.42%), with different outcomes in other sites. These findings advance DBH estimation methods by highlighting the importance of tailored algorithm selection and environmental considerations, thereby contributing to more accurate and efficient forest management across various landscapes. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Geographic Information Systems for Natural Resource Management of Forest Ecosystems)
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