Status: this preprint was under review for the journal GI but the revision was not accepted.
Semi-automated roadside image data collection
Neal Pilger,Aaron Berg,and Pamela Joosse
Abstract. This article describes the development of a mobile roadside survey procedure for obtaining corroboration data for the remote sensing of agricultural land use practices. The key objective was to produce a dataset of geo-referenced roadside digital images that can be used to compare to in-field photos for measuring agricultural land use and land cover associated with crop residue and cover cropping in the non-growing season. It was concluded that a very high level of correspondence (> 90 % level of agreement) could be attained using a mobile survey vehicle, as presented in this research, to detailed in-field ground verification data. Classification correspondence was carried out against 114 field sites with a level of agreement at 93 %. The few discrepancies were in the differentiation of residue levels between 30–60 % and > 60 %, both of which may be considered as achieving conservation practice standards. The mobile roadside image capture has advantages of relatively low cost and insensitivity to cloudy days, which often limits optical remote sensing acquisitions during the study period of interest. We anticipate that this approach can be used to reduce associated field costs for ground surveys, while expanding coverage areas and may be of interest to industry, academic and government organizations for more routine surveys of agricultural soil cover during periods of seasonal cloud cover.
How to cite. Pilger, N., Berg, A., and Joosse, P.: Semi-automated roadside image data collection, Geosci. Instrum. Method. Data Syst. Discuss. [preprint], https://doi.org/10.5194/gi-2019-20, 2019.
Received: 06 Jul 2019 – Discussion started: 12 Aug 2019
This article describes the development of a mobile roadside survey procedure for obtaining corroboration data for the remote sensing of agricultural land use practices over county level areas where atmospheric conditions are unfavourable for satellite remote sensing, while improving on financial, temporal, and safety costs for in-field verification data acquisition.
This article describes the development of a mobile roadside survey procedure for obtaining...