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Forests
Special Issues
Forest Inventory and Forest Carbon Assessments with Remote Sensing Technologies
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Forest Inventory and Forest Carbon Assessments with Remote Sensing Technologies

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 June 2025 | Viewed by 2865

Special Issue Editors

Dr. Giovanni D'Amico

E-Mail Website
Guest Editor
Department of Agricultural, Food, Environmental and Forestry Sciences and Technologies, University of Florence, 50145 Florence, Italy
Interests: forestry; remote sensing; forest inventory; airborne laser scanning
Special Issues, Collections and Topics in MDPI journals
Dr. Elia Vangi

E-Mail Website
Guest Editor
Forest Modelling Lab., Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Via Madonna Alta 128, 06128 Perugia, Italy
Interests: environment; remote sensing; forest modelling; forest conservation
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Travaglini

E-Mail Website
Guest Editor
Department of Agricultural, Food, Environmental and Forestry Sciences and Technologies, University of Florence, 50145 Florence, Italy
Interests: forest inventory; forest monitoring; forest management

Special Issue Information

Dear Colleagues,

Forests are key resources for preserving life on Earth and, as carbon sinks, they contribute to global carbon neutrality and assist in mitigating climate change effects, simultaneously providing several valuable ecosystem services. However, forests suffer from increased anthropogenic pressure and environmental hazards (e.g., fires, floods, droughts, extreme weather, deforestation, insects, and diseases, among others). Accurately monitoring forest ecosystems is therefore essential to promoting sustainable forest management.

In this context, national forest inventories represent the most comprehensive and accurate surveys for forest monitoring and forest carbon assessment, and remote sensing data collected from different sensor platforms, exploiting machine learning and deep learning techniques along with ground-acquired data, enable analysis of forest ecosystems and forest carbon assessment at different spatial resolutions. The purpose of this Special Issue is to gather research on forest inventorying and forest carbon assessment through the use of remote sensing optical data from multispectral or hyperspectral sensors, along with the structural data that are often provided by radar and LiDAR sensors, and the integration of data from multiple sources.

Dr. Giovanni D'Amico
Dr. Elia Vangi
Dr. Davide Travaglini
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

  • forest inventory
  • forest mapping
  • forest carbon assessments
  • remote sensing
  • biodiversity
  • sustainable forest management
  • machine learning
  • climate change
  • satellite
  • LiDAR

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

Published Papers (3 papers)

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Research

23 pages, 20340 KiB  
Article
Forest Height and Volume Mapping in Northern Spain with Multi-Source Earth Observation Data: Method and Data Comparison
by Iyán Teijido-Murias, Oleg Antropov, Carlos A. López-Sánchez, Marcos Barrio-Anta and Jukka Miettinen
Forests 2025, 16(4), 563; https://doi.org/10.3390/f16040563 - 24 Mar 2025
Viewed by 246
Abstract
Accurate forest monitoring is critical for achieving the objectives of the European Green Deal. While national forest inventories provide consistent information on the state of forests, their temporal frequency is inadequate for monitoring fast-growing species with 15-year rotations when inventories are conducted every [...] Read more.
Accurate forest monitoring is critical for achieving the objectives of the European Green Deal. While national forest inventories provide consistent information on the state of forests, their temporal frequency is inadequate for monitoring fast-growing species with 15-year rotations when inventories are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable the continuous acquisition of land cover data with high temporal frequency (annually or shorter), at a spatial resolution of 10-30 m per pixel. This study focused on northern Spain, a highly productive forest region. This study aimed to improve models for predicting forest variables in forest plantations in northern Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 and TanDEM-X) datasets supported by climatic and terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, and XGBoost. The study findings show an improvement in R2 from 0.24 when only Sentinel-2 data are used with MultiLinear Regression to 0.49 when XGboost is used with multi-source EO data. It can be concluded that the combination of multi-source datasets, regardless of the model used, significantly enhances model performance, with TanDEM-X data standing out for their remarkable ability to provide valuable radar information on forest height and volume, particularly in a complex terrain such as northern Spain. Full article
(This article belongs to the Special Issue Forest Inventory and Forest Carbon Assessments with Remote Sensing Technologies)
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27 pages, 7991 KiB  
Article
Estimation of the Total Carbon Stock of Dudles Forest Based on Satellite Imagery, Airborne Laser Scanning, and Field Surveys
by Kornél Czimber, Botond Szász, Norbert Ács, Dávid Heilig, Gábor Illés, Diána Mészáros, Gábor Veperdi, Bálint Heil and Gábor Kovács
Forests 2025, 16(3), 512; https://doi.org/10.3390/f16030512 - 14 Mar 2025
Cited by 1 | Viewed by 422
Abstract
We present our carbon stock estimation method developed for mixed coniferous and deciduous forests in the Hungarian hilly region, covering diverse site conditions. The method consists of four complex steps, integrating traditional field surveys with modern remote sensing and GIS. The first step [...] Read more.
We present our carbon stock estimation method developed for mixed coniferous and deciduous forests in the Hungarian hilly region, covering diverse site conditions. The method consists of four complex steps, integrating traditional field surveys with modern remote sensing and GIS. The first step involves comprehensive field data collection at systematically distributed sampling points. The second step is tree species mapping based on satellite image time series. The third step uses Airborne Laser Scanning to estimate aboveground biomass and derive the carbon stock of roots. The final step involves evaluating and spatially extending field and laboratory data on litter and humus from sampling points using geostatistical methods, followed by aggregating the results for the forest block and individual forest sub-compartments. New elements were developed and implemented into the complex methodology, such as aboveground biomass calculation with voxel aggregation and underground carbon stock spatial extension with EBK regression prediction. Additionally, we examined how the accuracy of our method, designed for a 200 m sampling grid, decreases as the distance between sampling points increases. Full article
(This article belongs to the Special Issue Forest Inventory and Forest Carbon Assessments with Remote Sensing Technologies)
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19 pages, 7409 KiB  
Article
Satellite Remote Sensing Images of Crown Segmentation and Forest Inventory Based on BlendMask
by Zicheng Ji, Jie Xu, Lingxiao Yan, Jiayi Ma, Baozhe Chen, Yanfeng Zhang, Li Zhang and Pei Wang
Forests 2024, 15(8), 1320; https://doi.org/10.3390/f15081320 - 29 Jul 2024
Cited by 2 | Viewed by 1451
Abstract
This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed [...] Read more.
This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the dataset independently, for model training. The experimental results show that the F1-score of Sophora japonica, Pinus tabulaeformis, and Koelreuteria paniculata reached 87.4%, 85.7%, and 86.3%, respectively. Meanwhile, we tested for the study area with a total area of 146 ha, and 27,403 tree species were identified in nine categories, with a total crown projection area of 318,725 m2. We also fitted a biomass calculation model for oil pine (Pinus tabulaeformis) based on field measurements and assessed 205,199.69 kg of carbon for this species across the study area. Additionally, we compared the model to U-net, and the results showed that BlendMask has strong crown-segmentation capabilities. This study demonstrates that BlendMask can effectively perform crown segmentation and forest inventory in large-scale complex forest areas, showing its great potential for forest resource management. Full article
(This article belongs to the Special Issue Forest Inventory and Forest Carbon Assessments with Remote Sensing Technologies)
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