Shelterbelt Agroforestry Systems Inventory and Removal Analyzed by Object-based Classification of Satellite Data in Saskatchewan, Canada

Abstract

Shelterbelt agroforestry systems inventory is challenging given their narrow linear feature and extensive distribution. The objective of this study was to evaluate the capability of Sentinel-2A Multispectral Instrument (MSI) and Sentinel-1B Synthetic Aperture Radar (SAR) imagery in (i) delineating shelterbelt tree rows on managed agricultural land in Saskatchewan, Canada, and (ii) detecting shelterbelt removal during the period 2008–2016. Contrast split segmentation for the normalized difference vegetation index and Gaussian filter (line filter) data sets from SAR were used to delineate feature borders. Several feature variables from the spectral bands of MSI were used as inputs for an object-based classification using the random forest classifier. A resulting land cover map, including the linear features of existing shelterbelts, was created with an overall accuracy of 80% and kappa value of 0.69. Shelterbelt change detection analysis using the land cover map (2016) and a legacy shelterbelt inventory map (2008) showed that 354 km of shelterbelts were removed within the study area (1,400 km2), accounting for 29.8% of the total shelterbelt length present in 2008. Our results demonstrated that the combination of Sentinel SAR and MSI imagery can provide sufficient information for mapping future shelterbelt planting, as well as allow the detection of shelterbelt removal.