Application of Active and Passive Remote Sensors in the Forest Inventory

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 (20 December 2024) | Viewed by 1982

Special Issue Editors


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

E-Mail Website
Guest Editor
Faculty of Forestry and Environmental Sciences, Juarez University of the State of Durango, Durango 34120, Mexico
Interests: forestry and environmental sciences; analysis of information on forest growth; LiDAR; biomass; forest fires; remote sensing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Forest Sciences, Universidad Juárez del Estado de Durango, Rio Papaloapan y bulevar Durango s/n, col. Valle del Sur, Durango 34120, Mexico
Interests: tree growth modeling; forest monitoring; climate change; ecoinformatics; forest biodiversity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application and analysis of the information derived from active and passive remote sensors in forest inventories allow for the development of new methods of estimation of forest parameters at a lower cost and in less time, in addition to obtaining data from inaccessible areas and covering large areas of land. This Special Issue aims to compile research papers with an overview of the application of active and passive remote sensors with different spatial and spectral resolution capacities as an innovative tool in forest inventory, as well as to provide selected contributions on methodologies, analysis techniques and applications of active and passive remote sensing used in forest inventory. Potential topics include, but are not limited to, the following: ground and airborne LiDAR technology applications; use and applications of satellite images in the forest inventory; use of unmanned aerial vehicles in the forest inventory; evaluation of the error in the estimation of forest attributes; and deep learning algorithms for the estimation of forest parameters with remote sensors.

Prof. Dr. Pablito Marcelo López-Serrano
Prof. Dr. Daniel J. Vega-Nieva
Prof. Dr. José Javier Corral-Rivas
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
  • diameter at breast height estimation: forest height
  • remote sensing
  • satellite images
  • LiDAR
  • unmanned aerial vehicles
  • deep learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 14491 KiB  
Article
Influence of Main Flight Parameters on the Performance of Stand-Level Growing Stock Volume Inventories Using Budget Unmanned Aerial Vehicles
by Marek Lisańczuk, Grzegorz Krok, Krzysztof Mitelsztedt and Justyna Bohonos
Forests 2024, 15(8), 1462; https://doi.org/10.3390/f15081462 - 20 Aug 2024
Viewed by 1082
Abstract
Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes [...] Read more.
Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes these solutions interesting tools for supporting various forest management needs. However, any practical application requires a priori empirical validation and optimization steps, especially if it is to be used under different forest conditions. This study investigates the influence of the main flight parameters, i.e., ground sampling distance and photo overlap, on the performance of individual tree detection (ITD) stand-level forest inventories, based on photogrammetric data obtained from budget unmanned aerial systems. The investigated sites represented the most common forest conditions in the Polish lowlands. The results showed no direct influence of the investigated factors on growing stock volume predictions within the analyzed range, i.e., overlap from 80 × 80 to 90 × 90% and GSD from 2 to 6 cm. However, we found that the tree detection ratio had an influence on estimation errors, which ranged from 0.6 to 15.3%. The estimates were generally coherent across repeated flights and were not susceptible to the weather conditions encountered. The study demonstrates the suitability of the ITD method for small-area forest inventories using photogrammetric UAV data, as well as its potential optimization for larger-scale surveys. Full article
Show Figures

Figure 1

Back to TopTop