Wide-Scale Identification of Small Woody Features of Landscape from Remote Sensing

Alessio Patriarca, Eros Caputi, Lorenzo Gatti, Ernesto Marcheggiani, Fabio Recanatesi, Carlo Maria Rossi and Maria Nicolina Ripa ()
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Alessio Patriarca: Geospatial Analysis and-Remote Sensing Laboratory, Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Eros Caputi: Geospatial Analysis and-Remote Sensing Laboratory, Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Lorenzo Gatti: Geospatial Analysis and-Remote Sensing Laboratory, Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Ernesto Marcheggiani: Department of Agricultural, Food and Environmental Sciences, Marche Polytechnic University, 60131 Ancona, Italy
Fabio Recanatesi: Geospatial Analysis and-Remote Sensing Laboratory, Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Carlo Maria Rossi: Geospatial Analysis and-Remote Sensing Laboratory, Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, 01100 Viterbo, Italy
Maria Nicolina Ripa: Geospatial Analysis and-Remote Sensing Laboratory, Department of Agricultural and Forestry Sciences (DAFNE), University of Tuscia, 01100 Viterbo, Italy

Land, 2024, vol. 13, issue 8, 1-20

Abstract: Small landscape features (i.e., trees outside forest, small woody features) and linear vegetation such as hedgerows, riparian vegetation, and green lanes are vital ecological structures in agroecosystems, enhancing the biodiversity, landscape diversity, and protecting water bodies. Therefore, their monitoring is fundamental to assessing a specific territory’s arrangement and verifying the effectiveness of strategies and financial measures activated at the local or European scale. The size of these elements and territorial distribution make their identification extremely complex without specific survey campaigns; in particular, remote monitoring requires data of considerable resolution and, therefore, is very costly. This paper proposes a methodology to map these features using a combination of open-source or low-cost high-resolution orthophotos (RGB), which are typically available to local administrators and are object-oriented classification methods. Additionally, multispectral satellite images from the Sentinel-2 platform were utilized to further characterize the identified elements. The produced map, compared with the other existing layers, provided better results than other maps at the European scale. Therefore, the developed method is highly effective for the remote and wide-scale assessment of SWFs, making it a crucial tool for defining and monitoring development policies in rural environments.

Keywords: hedgerows; SWF mapping; remote sensing; RGB classification; natural elements of landscape (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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