Semi-automated roadside image data collection for characterization of agricultural land management practices


Pilger, N., Berg, A., Joosse, P. (2020). Semi-automated roadside image data collection for characterization of agricultural land management practices, 12(14),

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Scientists are interested in using satellites to measure conditions in agricultural fields because satellites can take measurements over a large area at one time, saving much time and effort compared to ground surveys. Some on-the-ground checking is still required though to test the accuracy and ensure the correct interpretation of the satellite results. This study tested the ability of a vehicle mounted, video camera system to collect soil cover data compared to in-field photo measurements of soil cover. The vehicle was mounted with two video cameras, one pointing towards each side of the road as it was driven. Two routes were driven in spring 2016 in southern Ontario past 114 fields where in-field photos of soil cover were also being collected that same day. Single images of agricultural fields were taken from the video footage and assessed visually to belong to either conventional (0-30% residue cover), conservation (30-60% residue cover), no-till (>60% residue cover) or green cover (green crop growing) classes. The classes determined using the vehicle mounted, video camera system agreed very well (93%) with the classes determined from the in-field photographs. This vehicle mounted, video camera method of collecting conditions in the field may serve as a quick way, up to 500 fields per hour, to collect data to interpret satellite imagery. The method may even replace satellite imagery measurements which can often be hampered by cloud cover particularly during the fall, winter and spring periods of interest in southern Ontario.


© 2020 by the authors.Land cover management practices, including the adoption of cover crops or retaining crop residue during the non-growing season, has important impacts on soil health. To broadly survey these practices, a number of remotely sensed products are available but issues with cloud cover and access to agriculture fields for validation purposes may limit the collection of data over large regions. In this study, we describe the development of a mobile roadside survey procedure for obtaining ground reference 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 in comparison to in-field photos to measure agricultural land use and land cover associated with crop residue and cover cropping in the non-growing season. We found a very high level of correspondence (>90% level of agreement) between the mobile roadside survey to in-field ground verification data. Classification correspondence was carried out with a portion of the county-level census image data against 114 in-field manually categorized sites with a level of agreement of 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 described mobile roadside image capture system 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 that it may be of interest to industry, academic, and government organizations for more routine surveys of agricultural soil cover during periods of seasonal cloud cover.

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