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IJGI
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The Use of Geo-Spatial Tools in Forestry
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The Use of Geo-Spatial Tools in Forestry

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 22625

Special Issue Editors

Prof. Dr. Palaiologos Palaiologou

E-Mail Website
Guest Editor
Department of Forestry and Natural Environment Management, Agricultural University of Athens, 36100 Karpenisi, Greece
Interests: wildfires; fuel management; forest management; geographic information systems; risk assessment; forest entomology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kostas Kalabokidis

E-Mail Website
Co-Guest Editor
Department of Geography, University of the Aegean, Mytilene, 81100 Lesvos Island,, Greece
Interests: wildfire ecology and management; natural disasters; hazards management; disturbance ecology; forest ecosystem management; human–environment relationships; spatial analysis; geographic information systems

Special Issue Information

During the last two decades of the 20th century, forestry evolved in a way that made it inseparable from the use of geo-spatial tools. The emergence of geographic information systems and remote sensing catalyzed this shift toward a more computerized and spatially- and model-dependent forest science. Major national and international programs, such as LANDFIRE (USA) and Copernicus (EU), offer a wealth of forestry-related spatial data produced by applying advanced geo-informatics methods. These freely available datasets are being used in a wide range of forest and land monitoring-related applications. The combination of forest biometrics with remote sensing and other spatial data with various geo-spatial methods and approaches promises to provide land managers, foresters, and governance organizations with the necessary information that will enable them to better manage the environmental challenges of the next decade. This Special Issue “The Use of Geo-spatial Tools in Forestry” aims to cover recent developments in forestry science from the use of geo-spatial tools and approaches. Submitted papers should clearly show novel contributions and innovative applications of how forestry and geo-spatial tools can support any of the following forestry-related topics (but are not limited to these):

-Wildfire risk estimation and behavior modelling;

-Forest growth and biomass estimation under different scenarios;

-Climate change impacts;

-Carbon pools;

-Water quality;

-Food security;

-Timber economics;

-Habitat protection and conservation;

-Infrastructure;

-Forest expansion and deforestation;

-Land-use changes;

-Forest management;

-Silviculture;

-Infestations.

Dr. Palaiologos Palaiologou
Prof. Dr. Kostas Kalabokidis
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. ISPRS International Journal of Geo-Information 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 1700 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 management
  • forest biometrics
  • remote sensing
  • geographic information systems
  • wildfires
  • forest economics
  • climate change
  • deforestation

Published Papers (7 papers)

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Research

28 pages, 21804 KiB  
Article
Modelling Fire Behavior to Assess Community Exposure in Europe: Combining Open Data and Geospatial Analysis
by Palaiologos Palaiologou, Kostas Kalabokidis, Michelle A. Day, Alan A. Ager, Spyros Galatsidas and Lampros Papalampros
ISPRS Int. J. Geo-Inf. 2022, 11(3), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11030198 - 15 Mar 2022
Cited by 6 | Viewed by 3288
Abstract
Predicting where the next large-scale wildfire event will occur can help fire management agencies better prepare for taking preventive actions and improving suppression efficiency. Wildfire simulations can be useful in estimating the spread and behavior of potential future fires by several available algorithms. [...] Read more.
Predicting where the next large-scale wildfire event will occur can help fire management agencies better prepare for taking preventive actions and improving suppression efficiency. Wildfire simulations can be useful in estimating the spread and behavior of potential future fires by several available algorithms. The uncertainty of ignition location and weather data influencing fire propagation requires a stochastic approach integrated with fire simulations. In addition, scarcity of required spatial data in different fire-prone European regions limits the creation of fire simulation outputs. In this study we provide a framework for processing and creating spatial layers and descriptive data from open-access international and national databases for use in Monte Carlo fire simulations with the Minimum Travel Time fire spread algorithm, targeted to assess cross-boundary wildfire propagation and community exposure for a large-scale case study area (Macedonia, Greece). We simulated over 300,000 fires, each independently modelled with constant weather conditions from a randomly chosen simulation scenario derived from historical weather data. Simulations generated fire perimeters and raster estimates of annual burn probability and conditional flame length. Results were used to estimate community exposure by intersecting simulated fire perimeters with community polygons. We found potential ignitions can grow large enough to reach communities across 27% of the study area and identified the top-50 most exposed communities and the sources of their exposure. The proposed framework can guide efforts in European regions to prioritize fuel management activities in order to reduce wildfire risk. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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19 pages, 2970 KiB  
Article
Earth Observation Systems and Pasture Modeling: A Bibliometric Trend Analysis
by Lwandile Nduku, Ahmed Mukalazi Kalumba, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, George Johannes Chirima, Gbenga Abayomi Afuye and Emmanuel Tolulope Busayo
ISPRS Int. J. Geo-Inf. 2021, 10(11), 793; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10110793 - 20 Nov 2021
Cited by 3 | Viewed by 2459
Abstract
An Earth observation system (EOS) is essential in monitoring and improving our understanding of how natural and managed agricultural landscapes change over time or respond to climate change and overgrazing. Such changes can be quantified using a pasture model (PM), a critical tool [...] Read more.
An Earth observation system (EOS) is essential in monitoring and improving our understanding of how natural and managed agricultural landscapes change over time or respond to climate change and overgrazing. Such changes can be quantified using a pasture model (PM), a critical tool for monitoring changes in pastures driven by the growing population demands and climate change-related challenges and thus ensuring a sustainable food production system. This study used the bibliometric method to assess global scientific research trends in EOS and PM studies from 1979 to 2019. This study analyzed 399 published articles from the Scopus indexed database with the search term “Earth observation systems OR pasture model”. The annual growth rate of 19.76% suggests that the global research on EOS and PM has increased over time during the survey period. The average growth per article is n = 74, average total citations (ATC) = 2949 in the USA, is n = 37, ATC = 488, in China and is n = 22, ATC = 544 in Italy). These results show that the field of the study was inconsistent in terms of ATC per article during the study period. Furthermore, these results show three countries (USA, China, and Italy) ranked as the most productive countries by article publications and the Netherlands had the highest average total citations. This may suggest that these countries have strengthened research development on EOS and PM studies. However, developing counties such as Mexico, Thailand, Sri Lanka, and other African countries had a lower number of publications during the study period. Moreover, the results showed that Earth observation is fundamental in understanding PM dynamics to design targeted interventions and ensure food security. In general, the paper highlights various advances in EOS and PM studies and suggests the direction of future studies. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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16 pages, 4348 KiB  
Article
Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform
by Sifiso Xulu, Nkanyiso Mbatha and Kabir Peerbhay
ISPRS Int. J. Geo-Inf. 2021, 10(8), 511; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080511 - 28 Jul 2021
Cited by 20 | Viewed by 4940
Abstract
Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires [...] Read more.
Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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16 pages, 3036 KiB  
Article
Tree Height Growth Modelling Using LiDAR-Derived Topography Information
by Milan Kobal and David Hladnik
ISPRS Int. J. Geo-Inf. 2021, 10(6), 419; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060419 - 19 Jun 2021
Cited by 4 | Viewed by 2363
Abstract
The concepts of ecotopes and forest sites are used to describe the correlative complexes defined by landform, vegetation structure, forest stand characteristics and the relationship between soil and physiography. Physically heterogeneous landscapes such as karst, which is characterized by abundant sinkholes and outcrops, [...] Read more.
The concepts of ecotopes and forest sites are used to describe the correlative complexes defined by landform, vegetation structure, forest stand characteristics and the relationship between soil and physiography. Physically heterogeneous landscapes such as karst, which is characterized by abundant sinkholes and outcrops, exhibit diverse microtopography. Understanding the variation in the growth of trees in a heterogeneous topography is important for sustainable forest management. An R script for detailed stem analysis was used to reconstruct the height growth histories of individual trees (steam analysis). The results of this study reveal that the topographic factors influencing the height growth of silver fir trees can be detected within forest stands. Using topography modelling, we classified silver fir trees into groups with significant differences in height growth. This study provides a sound basis for the comparison of forest site differences and may be useful in the calibration of models for various tree species. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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13 pages, 3772 KiB  
Article
Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods
by Ping Yan, Kairong Lin, Yiren Wang, Xinjun Tu, Chunmei Bai and Luobin Yan
ISPRS Int. J. Geo-Inf. 2021, 10(6), 366; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060366 - 29 May 2021
Cited by 7 | Viewed by 2392
Abstract
Understanding the spatial variability of soil organic matter (SOM) is crucial for implementing precise land degradation control and fertilization to improve crop productivity. Studying spatial variability provides a scientific basis for precision fertilization and land degradation control. In this study, geostatistics and classical [...] Read more.
Understanding the spatial variability of soil organic matter (SOM) is crucial for implementing precise land degradation control and fertilization to improve crop productivity. Studying spatial variability provides a scientific basis for precision fertilization and land degradation control. In this study, geostatistics and classical statistical methods were used to analyze the spatial variability of SOM and its influencing factors under various degrees of land degradation in the red bed area of southern China. The results demonstrate a declining trend for SOM content with increasing land degradation. The SOM content differs profoundly under different land degradation degrees. The coefficient of variation ranges from 13.61% for extreme land degradation to 8.98% for mild land degradation, 7.96% for moderate land degradation, and 5.64% for severe land degradation. A significant positive correlation is displayed between the altitude and the SOM (p < 0.01) under mild and moderate land degradation conditions. Bulk density and pH value have a significant negative correlation with SOM (p < 0.01). It can be observed that terrain factors, as well as physical and chemical soil parameters, have a great influence on SOM. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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19 pages, 2244 KiB  
Article
Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India
by Praveen Kumar, Akhouri P. Krishna, Thorkild M. Rasmussen and Mahendra K. Pal
ISPRS Int. J. Geo-Inf. 2021, 10(1), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010029 - 13 Jan 2021
Cited by 3 | Viewed by 2899
Abstract
Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a [...] Read more.
Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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20 pages, 3960 KiB  
Article
Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover
by Mihnea Cățeanu and Arcadie Ciubotaru
ISPRS Int. J. Geo-Inf. 2020, 9(4), 224; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040224 - 07 Apr 2020
Cited by 22 | Viewed by 2743
Abstract
A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by [...] Read more.
A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs. Full article
(This article belongs to the Special Issue The Use of Geo-Spatial Tools in Forestry)
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