Agroforestry systems are widespread in many countries, supporting the coexistence of tree, crop and livestock components, offering a wide range of economic, social and environmental benefits, over a range of spatial and temporal scales. The landscape level mapping of agroforestry systems has become a fundamental approach in agroforestry, which has recently developed as an autonomous science aiming to increase productivity and profitability for the farmers, whilst ensuring the land use sustainability. One of the major geospatial issues in agroforestry is detecting, mapping and estimating the forest component of the systems: scattered trees or linear forest formations located either inside the field or along the field boundaries, also known as Trees Outside Forest (TOF). Data on TOF are scarce at regional and national levels and up to now, there are no guidelines for TOF inventory in agroforestry systems. The integrated use of GIS, Remote Sensing and field survey is particularly suited for assessing, mapping and quantifying the intrinsic spatial heterogeneity of such complex ecological systems. Traditional tree-based agriculture systems involving different multipurpose trees such as chestnuts (Castanea spp.), oaks (Quercus spp.), and olive (Olea europaea), are common in Italy and other Mediterranean countries. In this study we combined GIS, Remote Sensing and field survey to detect, map and estimate the vegetation coverage of TOF in a traditional silvoarable system, located in Umbria region (central Italy), where oaks tree hedgerows (THRs) coexist with herbaceous crops. High-resolution multispectral Sentinel-2 satellite images were processed in order to detect the vegetation cover of TOF (such as scattered trees, small woods, tree hedgerows) and several optical indices based on different bands combination were calculated. In particular, Standard-, Green-, Blue- and Pan-Normalized Difference Vegetation Indices, resulting by the combination of different visible bands vs near infrared band, and the Negative Luminance index were considered. A ground truth survey of TOF was based on GPS measurements of the vegetated areas and visual photointerpretation of aerial imagery and google earth images. TOF objects identified according to vegetation indices classification were compared with ground truth survey. The statistical comparison of the TOF ground truth vs the TOF vegetation coverage derived by the Sentinel-2 classification shows that the automated classification of TOFs is within the reach of the current satellites sensors resolution. The Negative Luminance index shows the best scores for the TOF identification in this agroforestry system. Further developments require the application of this classification technique to different agroforestry landscapes.
Geospatial techniques for mapping TOF in Italian traditional Agroforestry systems
Chiocchini Francesca;Sarti Maurizio;Ciolfi Marco;Lauteri Marco;Paris Pierluigi
2019
Abstract
Agroforestry systems are widespread in many countries, supporting the coexistence of tree, crop and livestock components, offering a wide range of economic, social and environmental benefits, over a range of spatial and temporal scales. The landscape level mapping of agroforestry systems has become a fundamental approach in agroforestry, which has recently developed as an autonomous science aiming to increase productivity and profitability for the farmers, whilst ensuring the land use sustainability. One of the major geospatial issues in agroforestry is detecting, mapping and estimating the forest component of the systems: scattered trees or linear forest formations located either inside the field or along the field boundaries, also known as Trees Outside Forest (TOF). Data on TOF are scarce at regional and national levels and up to now, there are no guidelines for TOF inventory in agroforestry systems. The integrated use of GIS, Remote Sensing and field survey is particularly suited for assessing, mapping and quantifying the intrinsic spatial heterogeneity of such complex ecological systems. Traditional tree-based agriculture systems involving different multipurpose trees such as chestnuts (Castanea spp.), oaks (Quercus spp.), and olive (Olea europaea), are common in Italy and other Mediterranean countries. In this study we combined GIS, Remote Sensing and field survey to detect, map and estimate the vegetation coverage of TOF in a traditional silvoarable system, located in Umbria region (central Italy), where oaks tree hedgerows (THRs) coexist with herbaceous crops. High-resolution multispectral Sentinel-2 satellite images were processed in order to detect the vegetation cover of TOF (such as scattered trees, small woods, tree hedgerows) and several optical indices based on different bands combination were calculated. In particular, Standard-, Green-, Blue- and Pan-Normalized Difference Vegetation Indices, resulting by the combination of different visible bands vs near infrared band, and the Negative Luminance index were considered. A ground truth survey of TOF was based on GPS measurements of the vegetated areas and visual photointerpretation of aerial imagery and google earth images. TOF objects identified according to vegetation indices classification were compared with ground truth survey. The statistical comparison of the TOF ground truth vs the TOF vegetation coverage derived by the Sentinel-2 classification shows that the automated classification of TOFs is within the reach of the current satellites sensors resolution. The Negative Luminance index shows the best scores for the TOF identification in this agroforestry system. Further developments require the application of this classification technique to different agroforestry landscapes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
