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

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.

Publisher

MDPI AG

Reference95 articles.

1. FAO (2023). FAOSTAT Food and Agriculture Data, FAO.

2. Allegrini, A., Salvaneschi, P., Schirone, B., Cianfaglione, K., and Di Michele, A. (2022). Multipurpose plant species and circular economy: Corylus avellana L. as a study case. Front. Biosci.-Landmark, 27.

3. Franco, S., Pancino, B., and Cristofori, V. (2012, January 19–22). Hazelnut production and local development in Italy. Proceedings of the VIII International Congress on Hazelnut, Temuco City, Chile.

4. Istat (2023, June 06). Coltivazioni Superfici e Produzione, Available online: http://dati.istat.it/Index.aspx?QueryId=37850.

5. Economic performance and risk of farming systems specialized in perennial crops: An analysis of Italian hazelnut production;Zinnanti;Agric. Syst.,2019

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3