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Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Natural Hazards and Risk Management".

Deadline for manuscript submissions: closed (24 January 2024) | Viewed by 16756

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Special Issue Editors

Dr. Paul Sestras

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Guest Editor

Department of Land Measurements and Cadastre, Faculty of Civil Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: topography; construction survey; mapping; cadastre; UAV photogrammetry; GIS
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Dr. Ștefan Bilașco

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Cluj-Napoca Subsidiary Geography Section, Romanian Academy, 400015 Cluj-Napoca, Romania
Interests: GIS; risk assessment; soil studies; spatial planning
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Prof. Dr. Mihai Nita

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Department of Forest Engineering, Universitatea Transilvania Brasov, Braşov, Romania
Interests: remote sensing; GIS; forest and water; forest management; machine learning
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Dr. Sanda Roșca

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Guest Editor

1. Faculty of Geography, Babes-Bolyai University, Clinicilor Street 5-7, 400006 Cluj-Napoca, Romania
2. Academy of Romanian Scientists, Ilfov 3, 050044 Bucharest, Romania
Interests: multirisk; soil favourability; GIS; statistical analysis; drones
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Special Issue Information

Dear Colleagues,

In the current context of urban area expansion, population growth, and the increase in industrial and agricultural activity near forest areas, the importance of sustainable forest management (SFM) with hazard mapping and monitoring is imperative. The backbone to many applications of SFM and hazard mapping is the access to accurate and efficiently acquired geospatial data. Because forests are one of the most complex ecological systems on Earth, forest management and inventory was always a challenge to specialists, researchers, and public authorities. In addition to the constant issues related to social, economic, and legal aspects related to forest management, the technical aspects have progressed significantly and modern data acquisition has never been more accessible to the public and research sector. This Special Issue aims to present and promote original scientific contributions in regard to modern and efficient data acquisition in a wide range of interdisciplinary applications related to sustainable forest management and natural hazards within forests. Thus, new tools and best practices, both in terms of data capture and in their processing and modelling, can be implemented in this rapidly evolving field.

Topics of interest include, but are not limited to:

New tools and techniques in tree and forest measurement;
Dendrometry, from the traditional forest inventory to modern solutions and practices;
Land surveying in forests, from the established topographic field instrumentations (total stations, GNSS systems, TLS, etc.) to the new methods based on optical remote sensing (UAV or airborne platforms for LiDAR and digital photogrammetry, InSAR, satellite images, etc.);
Challenges and advances in forest cadastre;
Integrating models, methods, techniques and tools for geospatial applications in forestry;
GIS applications in forest management, policy, and decision-making;
Best practices, guidelines, and planning using acquired or open-sourced geospatial data;
Mapping and monitoring urban forests;
Precision forestry for SFM;
Integration of field data and sensors in decision support systems;
Spatial analysis and the influence of geographical origin on tree characteristics;
Geospatial data for landscape and ecology assessment;
Techniques for vegetation structure modelling and biomass assessment;
Forest dynamics and the environmental/ecological implications;
Forest hazard susceptibility and mapping based on acquired geospatial data;
Assessment of landslides and flash floods in forests based on geomatics tools;
Disaster prevention and risk mitigation of forest hazards based on geostatistics and field data.

Dr. Paul Sestras
Prof. Dr. Ștefan Bilașco
Prof. Dr. Mihai Nita
Dr. Sanda Roșca
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 surveying methods
sustainable forest management
forest cadastre
forest management planning
dendrometry
monitoring and mapping
GIS and remote sensing
data integration
natural hazards
hazard and risk assessment
susceptibility and hazard mapping

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Published Papers (7 papers)

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Research

19 pages, 7593 KiB

Open AccessArticle

Evaluation of Different Geographic Provenances of Silver Fir (Abies alba) as Seed Sources, Based on Seed Traits and Germination

by Irina M. Morar, Catalina Dan, Radu E. Sestras, Roxana L. Stoian-Dod, Alina M. Truta, Adriana F. Sestras and Paul Sestras

Forests 2023, 14(11), 2186; https://doi.org/10.3390/f14112186 - 2 Nov 2023

Viewed by 1519

Abstract

The evaluation of the diversity of silver fir (Abies alba Mill.) populations and the reproductive traits of the trees are of great importance for the conservation of genetic resources and forest management. Therefore, important reproductive characteristics of A. alba from seven Romanian provenances, considered as different geographical populations, were evaluated. Significant differences between the provenances were observed for the female cones, seed morphology, and germination. Due to the relatively low germination of silver fir seeds, germination tests were conducted to identify treatments that can stimulate the germination capacity. Thus, the seed germination capacity was determined using four different stimulation treatments and the data were compared with those of untreated seeds, designed as the control. Considerable differences were recorded not only depending on the seed provenances, but also regarding the treatments applied to stimulate germination (Atonik biostimulator, scarification, acetone, H₂SO₄). The biostimulator seed treatment gave the highest germination percentage, while sulfuric acid caused the lowest germination. The research also revealed that not all the forest seed sources provide high-quality reproductive material. Furthermore, for some of the seed resources, even the germination stimulation treatments did not result in adequate germination. The findings are pertinent and valuable for identifying suitable forest populations as seed sources, as well as for their use in silver fir reforestation programs. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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36 pages, 27116 KiB

Open AccessArticle

Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China

by Weiting Yue, Chao Ren, Yueji Liang, Xiaoqi Lin, Anchao Yin and Jieyu Liang

Forests 2023, 14(8), 1616; https://doi.org/10.3390/f14081616 - 10 Aug 2023

Cited by 2 | Viewed by 1749

Abstract

Wildfire disasters pose a significant threat to the stability and sustainability of ecosystems. The assessment of wildfire risk based on a seasonal dimension has contributed to improving the spatiotemporal targeting of fire prevention efforts. In this study, Nanning, China, was selected as the research area. The wildfire driving factors were chosen from both seasonal and nonseasonal aspects, and the datasets were divided into five periods: all seasons, spring, summer, autumn, and winter. The light gradient boosting machine (LGBM) was employed to construct wildfire danger models for different periods, evaluating the spatial distribution of high-wildfire-danger areas during these periods and the predictive performance differences. The SHapley Additive exPlanations (SHAP) method was utilized to analyze the differential contributions of various factors to wildfire occurrence in different seasons. Subsequently, the remote sensing ecological index (RSEI) was calculated using four indicators, greenness, heat, wetness, and dryness, to assess the ecological vulnerability in different seasons. Finally, by integrating danger and vulnerability information, wildfire risk models were developed to systematically assess the risk of wildfire disasters causing losses to the ecological environment in different seasons. The results indicate that: (1) The evaluation of wildfire danger based on individual seasons effectively compensates for the shortcomings of analyzing danger across all seasons, exhibiting higher predictive performance and richer details. (2) Wildfires in Nanning primarily occur in spring and winter, while the likelihood of wildfires in summer and autumn is relatively lower. In different seasons, NDVI is the most critical factor influencing wildfire occurrence, while slope is the most important nonseasonal factor. The influence of factors varies among different seasons, with seasonal factors having a more significant impact on wildfire danger. (3) The ecological vulnerability in Nanning exhibits significant differences between different seasons. Compared to spring and winter, the ecological environment is more vulnerable to wildfire disasters during summer and autumn. (4) The highest wildfire risk occurs in spring, posing the greatest threat to the ecological environment, while the lowest wildfire risk is observed in winter. Taking into account information on danger and vulnerability in different seasons enables a more comprehensive assessment of the risk differences in wildfire disasters causing ecological losses. The research findings provide a scientific theoretical basis for relevant departments regarding the prevention, control, and management of seasonal wildfires. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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27 pages, 12074 KiB

Open AccessArticle

Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset

by Nazanin Bahadori, Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Khalifa M. Al-Kindi, Tamer Abuhmed, Behrokh Nazeri and Soo-Mi Choi

Forests 2023, 14(7), 1325; https://doi.org/10.3390/f14071325 - 28 Jun 2023

Cited by 6 | Viewed by 3148

Abstract

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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19 pages, 9255 KiB

Open AccessArticle

The Development of a Set of Novel Low Cost and Data Processing-Free Measuring Instruments for Tree Diameter at Breast Height and Tree Position

by Linhao Sun, Zhongke Feng, Yakui Shao, Linxin Wang, Jueying Su, Tiantian Ma, Dangui Lu, Jiayi An, Yongqi Pang, Shahzad Fahad, Wenbiao Wang and Zhichao Wang

Forests 2023, 14(5), 891; https://doi.org/10.3390/f14050891 - 26 Apr 2023

Cited by 3 | Viewed by 1762

Abstract

In current forestry investigation studies, the research hotspots have tended to concentrate on ascertaining the precision of certain tree parameters. This has resulted in an augmented intricacy of the technique in terms of algorithms and observation instruments. The complexity of the technology and the cost of the equipment make it impossible to use for large-scale forest surveys, for example, a national forest inventory (NFI). The aim of our study was to design a new type of low-cost measuring method that could be utilized in a NFI and in developing countries. Meanwhile, the newly designed method was expected to be able to output certain forest measurement factors without necessitating data processing by NFI field investigators. Based on these objectives, we developed a measuring method that included hardware comprised of two tools. The first tool was an electronic measuring tape that contained a microcontroller unit (MCU) and could automatically record and collaborate with other equipment via wireless protocols. The second tool was a tree stem position mapper that utilized our own designed mechanisms. The results showed that the tree DBH measurements exhibited a 0.05 cm (0.20%) bias and a 0.36 cm (1.45%) root mean square error (RMSE), and the biases on the x-axis and the y-axis of the tree position estimations were −15.92–9.92 cm and −25.90–10.88 cm, respectively, accompanied by corresponding RMSEs of 15.27–29.40 cm and 14.49–34.68 cm. Moreover, an efficiency test determined that the average measurement time per tree was 20.34 s, thus, demonstrating a marked improvement in speed by nearly one-fold compared to the conventional method. Meanwhile, this measurement kit costs less than 150 Euros and is economically suitable for large-scale applications. We posit that our method has the potential to serve as a standard tool in a Chinese NFI and in developing countries in the future. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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20 pages, 6900 KiB

Open AccessArticle

Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods

by Slobodan Milanović, Jan Kaczmarowski, Mariusz Ciesielski, Zoran Trailović, Miłosz Mielcarek, Ryszard Szczygieł, Mirosław Kwiatkowski, Radomir Bałazy, Michał Zasada and Sladjan D. Milanović

Forests 2023, 14(1), 46; https://doi.org/10.3390/f14010046 - 26 Dec 2022

Cited by 9 | Viewed by 3046

Abstract

In recent years, forest fires have become an important issue in Central Europe. To model the probability of the occurrence of forest fires in the Lower Silesian Voivodeship of Poland, historical fire data and several types of predictors were collected or generated, including topographic, vegetation, climatic, and anthropogenic features. The main objectives of this study were to determine the importance of the predictors of forest fire occurrence and to map the probability of forest fire occurrence. The H2O driverless artificial intelligence (DAI) cloud platform was used to model forest fire probability. The gradient boosted machine (GBM) and random forest (RF) methods were applied to assess the probability of forest fire occurrence. Evaluation the importance of the variables was performed using the H2O platform permutation method. The most important variables were the presence of coniferous forest and the distance to agricultural land according to the GBM and RF methods, respectively. Model validation was conducted using receiver operating characteristic (ROC) analysis. The areas under the curve (AUCs) of the ROC plots from the GBM and RF models were 83.3% and 81.3%, respectively. Based on the results obtained, the GBM model can be recommended for the mapping of forest fire occurrence in the study area. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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25 pages, 3333 KiB

Open AccessArticle

Assessment of the Annual Erosion Rate along Three Hiking Trails in the Făgăraș Mountains, Romanian Carpathians, Using Dendrogeomorphological Approaches of Exposed Roots

by Mihai Jula and Mircea Voiculescu

Forests 2022, 13(12), 1993; https://doi.org/10.3390/f13121993 - 25 Nov 2022

Cited by 2 | Viewed by 2392

Abstract

Mountain hiking trails are vital components of tourist infrastructure and provide recreational opportunities for a large number of tourists. Exposed roots along the tourist trails in the forested mountains are impacted by tourist trampling and various natural processes, thus becoming even more exposed and eroded. The aim of our study was to estimate the annual erosion rates along three hiking trails in the Făgăraș Mountains using dendrogeomorphological approaches. The three used routes were: Bâlea Hotel—Bâlea Waterfall (BWFHT), Bâlea Hotel—Bâlea Glacial Lake (BLHT), and Bâlea Hotel—Doamnei Glacial Valley (DVHT). The average annual erosion rates in BWFHT, BLHT, and DVHT were 10.6 ± 4.4, 6.8 ± 3.9, and 6.1 ± 3.3 mm·y⁻¹, respectively. Over a 56-year interval (1965–2021), 610 scars were recorded among the annual growth rings of the sampled tree roots; 172, 213, and 225 scars were recorded in BWFHT, BLHT, and DVHT, respectively. Moreover, we identified 1022 rows of traumatic resin ducts (TRDs) associated with scars: 237, 343, and 442 in BWFHT, BLHT, and DVHT, respectively. Additionally, the climate of the Făgăraș Mountains is humid with a multiannual average precipitation of 1366.2 mm; the precipitation in 24 h, between 1979 and 2021 in seven and three cases exceeded 70 mm/24 h and 100 mm/24 h, respectively. Thus, there were synchronous situations of root exposure with 24 h rainfall. However, it is unclear whether precipitation plays a decisive role in root exposure or in triggering erosion processes on tourist trails. We considered that tourist traffic plays a decisive role in root exposure and erosion, however locally and complementarily, 24 h precipitation must also be considered. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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16 pages, 3786 KiB

Open AccessFeature PaperArticle

Measuring Distances and Areas under Forest Canopy Conditions—A Comparison of Handheld Mobile Laser Scanner and Handheld Global Navigation Satellite System

by Petru Tudor Stăncioiu, Ioan Dutcă, Sergiu Constantin Florea and Marius Paraschiv

Forests 2022, 13(11), 1893; https://doi.org/10.3390/f13111893 - 11 Nov 2022

Cited by 3 | Viewed by 1770

Abstract

Measuring distances and areas under forest canopy conditions is often required for a broad range of forest research and management-related activities. While modern technologies, such as handheld mobile laser scanning (MLS), made possible the tridimensional representation of forests with great accuracy, the practical application is still limited by its high costs and challenging data processing. The handheld Global Navigation Satellite System (GNSS) represents the classical alternative, determining the distances and areas based on point coordinates. In this study, we aimed to assess the accuracy of a handheld GNSS, relative to the handheld MLS, in measuring distances and areas under forest canopy conditions. The material consists of 209 ant nests, which were mapped in a mixed-species deciduous forest of North-Eastern Romania. The GNSS- and MLS-based distances among nests were compared using the Bland–Altman plots. The differences in size and shape of the areas described by the nests were analyzed using (i) the shape compactness and (ii) the form factor of the convex polygons. In general, the GNSS-based distances were shorter compared with those based on MLS. However, for most cases, the intervals of agreement between the two instruments were within the limits of GNSS accuracy (i.e., ±10 m). The largest mean differences occurred when nests were in dense canopy conditions and on rugged terrain. The GNSS-based area of the convex polygons was smaller in most cases, but no significant correlation between the size of the area and the size of the relative difference was found. Furthermore, both the shape compactness and the form factor of the polygons were also smaller for the GNSS-based method compared with the MLS-based method, with differences up to 10%. In conclusion, measurements recorded by GNSS were less accurate, and under certain forest conditions (dense canopies, rugged terrain), large systematic errors can occur and therefore limit its use. Full article

(This article belongs to the Special Issue Data Acquisition, Methods and Techniques Applied in Sustainable Forest Management and Hazard Mapping)

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