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
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Published:2024-03-30
Issue:7
Volume:16
Page:1227
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Lodato Francesco12ORCID, Pennazza Giorgio1ORCID, Santonico Marco1ORCID, Vollero Luca3ORCID, Grasso Simone1ORCID, Pollino Maurizio4ORCID
Affiliation:
1. Research Unit of Electronics for Sensor Systems, Faculty of Science and Technology for Sustainable Development and One Health, University Campus Bio-Medico of Rome, 00028 Rome, Italy 2. Department of Biology, University of Naples Federico II, 80138 Napoli, Italy 3. Research Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, 00028 Rome, Italy 4. ENEA—Italian National Agency for New Technologies, Energy and Sustainable Economic Development—Analysis and Protection of Critical Infrastructures Laboratory, Casaccia Research Centre, 00123 Rome, Italy
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 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.
Reference95 articles.
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1. Object-Based Detection of Hazelnut Orchards Using Very High Resolution Aerial Photographs;2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics);2024-07-15
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