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Istituto di biologia agro-ambientale e forestale
Soppresso il 19/09/2018

Torna all'elenco Contributi in atti di convegno anno 2018

Contributo in atti di convegno

Tipo: Poster

Titolo: REMOTE SENSING AND GIS METHODS TO DETECT TREE HEDGEROWS IN AGROFORESTRY LANDSCAPES

Anno di pubblicazione: 2018

Formato: Cartaceo

Autori: Chiocchini F., Ciolfi M., Sarti M., Lauteri M., Cherubini M., Leonardi L., Paris P.

Affiliazioni autori: Istituto di Biologia Agroambientale e Forestale, Consiglio Nazionale delle Ricerche, Porano (TR)

Autori CNR:

  • MARCELLO CHERUBINI
  • FRANCESCA CHIOCCHINI
  • MARCO CIOLFI
  • MARCO LAUTERI
  • LUCA LEONARDI
  • PIERLUIGI PARIS
  • MAURIZIO SARTI

Lingua: inglese

Abstract: Agroforestry denotes land use systems in which trees grow in combination with agricultural crops and/or livestock. The woody component usually consists of scattered or linear trees (planted or naturally growing), that can be located either inside the field or along the field boundaries, as tree hedgerows. This land use approach is aimed at the optimization of both ecological interactions and economical revenue. Agroforestry is increasingly perceived as providing ecosystem services, environmental benefits, and economic commodities as part of a multifunctional working landscape. These services and benefits occur over a range of spatial and temporal scales: from the farm/local scale, through the landscape/regional scale up to the global scale. The use of Remote Sensing and GIS spatial analysis is of the utmost importance for detecting landscape patterns, understanding the interactions between biological and physical components, and for assessing, mapping and quantifying the socio-economic values of the agroforestry systems services. Agroforestry systems have traditionally been used in different places of Europe employing several types of practices at different levels of intensity. However, a decline of this land use system occurred all through the 20th century due to agricultural intensification and mechanization. To slow down the decrease of these good practices, the European Common Agricultural Policy is currently supporting establishment and preservation of agroforestry systems, because of their higher ecological and socio-economic value. Most of the Italian territory is naturally suited for agroforestry systems due to its environmental setting, geomorphological and climatic conditions, as well as for the historical and cultural land management practices. Although that, few information is available concerning the current extent of agroforestry systems because tree detection in large agricultural areas is time consuming. Additionally, the magnitude of ecosystems services of agroforestry systems strongly depends on tree number and dimension. Thus, detecting these tree parameters by remote sensing is of dramatic importance. This study is focused on an agroforestry landscape located in the Umbria region (central Italy), where we investigated a farm managing over 600 ha of arable land and woods. The main land uses include herbaceous crops, tree hedgerows, shelterbelts and forest belts. In these systems, trees grow only at the edges of fields, within hedgerows, or on scarps and drainage ditches between fields; the trees provide established positive effects on soil erosion, wind shielding and ecological enrichment as well as an aesthetic enhancement of the landscape. We combined different methodologies comprising Remote Sensing, photo interpretation, GIS analysis and field survey to detect the spatial distribution of the land cover/use of the study area and to reveal the spatial interactions between the crop and tree components of the system. In particular, we used the hemispherical canopy ground photography technique to assess the shading effect of trees on crops. The aims of this study were: i) to map and estimate the extant of Tree Hedge Rows (THR) in the study area; ii) to quantify the influence of THRs on the yield of crops at the plot scale. Basing on the land use classification, performed by photo interpretation (Data source: AGEA 2011), we identified two experimental sites (ES) to study the continuous and discontinuous THRs along the margins of the cultivated fields. Each site contains a plot of annual crops and THRs along at least one of the borders consisting of oaks, mainly Quercus pubescens and Quercus cerris. Through the aerial photos (2011) and Google Earth images (2017), we identified two test areas (TA) of 100 ha (1km x 1km squares) each one containing one of the two ES. We tested a procedure for the GIS inventory of THR over the two TAs, consisting in: 1) survey of THRs with GPS device in the ES, for the proper georeferencing and actual measurement of the tree linear systems, measurement of height (H), diameter at breast height (DBH) for each tree of the THRs and of the distance between adjacent individuals; 2) recognition of THRs by photo interpretation of aerial and satellite images; 3) comparison of field measurements against estimates by photo interpretation for the error evaluation; 4) estimation of the incidence of THRs per hectare of cultivated area over the two TAs and over the whole farmland. The recognition of THRs was based on photo interpretation of high-resolution multispectral Sentinel2 (HRS2) images. In particular, evaluating the NDVI (Normalized Difference Vegetation Index, NDVI = (NIR-VIS) / (NIR + VIS)), we could easily discriminate between areas with dense vegetation coverage (0.6 Lingua abstract: inglese

Stato della pubblicazione: Preprint

Parole chiave:

  • Tree Hedgerows
  • agroforestry
  • NDVI
  • sentinel2

Congresso nome: AIT2018: THE IX CONFERENCE OF THE ITALIAN SOCIETY OF REMOTE SENSING

Congresso luogo: Firenze

Congresso data: 04-06/07/2018

Congresso rilevanza: Nazionale

Congresso relazione: Contributo

Strutture CNR:

 
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