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Big Data and Remote Sensing for Smart Forestry

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1466

Special Issue Editors


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Guest Editor
Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, 01100 Viterbo, IT, Italy
Interests: smart forestry; digital forest resources monitoring; landscape ecology; remote sensing; spatial analysis; geoprocessing techniques; ecological indicators
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Agriculture Department, Mediterranea University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: remote sensing; change detection; spatial analysis; multispectral data analysis; LiDAR data analysis; UAV data analysis; GIS
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
National Research Council of Italy—Institute for BioEconomy (CNR-IBE), Via Giovanni Caproni 8, 50145 Firenze, Italy
Interests: remote sensing; multispectral data analysis; hyperspectral data analysis; SAR data analysis; machine learning; image classification; geoprocessing; forest fire/post-fire analysis

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Guest Editor
Dipartimento di Scienze Veterinarie, Università degli Studi di Messina, Viale G. Palatucci s.n., 98168 Messina, Italy
Interests: land cover and land use change dynamics; satellite and UAV remote sensing; landscape analysis and interpretation; remote sensing of vegetation; geographic object-based image analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forestry, as a cornerstone of environmental sustainability, stands at the intersection of technological innovation and ecological preservation. In response to the pressing challenges of climate change and resource management, this Special Issue, “Big Data and Remote Sensing for Smart Forestry”, investigates the transformative role of data-driven solutions and remote sensing technologies in the realm of forestry. The aim of this Special Issue is to explore several pivotal themes:

Precision Forestry: cutting-edge technologies, such as unpiloted aerial vehicles (UAVs), mounting multispectral, thermal, or LiDAR sensors, as well as satellite imagery, enable precise large-scale measurement and monitoring of forests. These tools optimize dendrometric measurement campaigns and aid in assessing forest health, detecting pests, and predicting fire risks and effects with real-time data.

Ecosystem Modelling: advanced data analytics and remote sensing data inform complex ecosystem models. These models predict forest dynamics, carbon sequestration, and species distribution, guiding conservation and restoration efforts.

Forest Biodiversity: remote sensing and big data analytics support biodiversity monitoring, habitat assessment, and protection of endangered species, promoting sustainable forest ecosystems.

Sustainable Forest Management: balancing conservation and economic interests, the integration of big data and remote sensing optimizes timber harvesting, minimizes environmental impact, and ensures long-term forest sustainability.

Climate Change Mitigation: forests play a critical role in mitigating climate change by sequestering carbon. This Special Issue explores how these technologies quantify carbon stocks, track deforestation and afforestation, and support international climate agreements.

Papers will serve as a comprehensive resource for researchers, practitioners, and policymakers seeking to harness the potential of big data and remote sensing in advancing smart forestry practices. Contributions based on multidisciplinary approaches, resulting from collaboration between researchers and practitioners, and highlighting the effects of technological innovations on smart forestry are also welcome. Our collective aim is to promote sustainable forestry, safeguard forest ecosystems, and ensure a sustainable future for generations to come.

Dr. Francesco Solano
Dr. Salvatore Praticò
Dr. Giandomenico De Luca
Prof. Dr. Giuseppe Modica
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • forestry technology
  • remote sensing data
  • big data analytics
  • sustainable forest management
  • ecosystem modeling
  • climate change mitigation
  • time-series monitoring of forest ecosystems
  • multiscale and multi temporal analysis of forest landscapes

Published Papers (1 paper)

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Research

27 pages, 28795 KiB  
Article
In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy
by Francesco Lodato, Giorgio Pennazza, Marco Santonico, Luca Vollero, Simone Grasso and Maurizio Pollino
Remote Sens. 2024, 16(7), 1227; https://doi.org/10.3390/rs16071227 - 30 Mar 2024
Viewed by 1035
Abstract
The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into [...] Read more.
The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system. Full article
(This article belongs to the Special Issue Big Data and Remote Sensing for Smart Forestry)
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