Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality

Abstract

AbstractRemote sensing offers an increasingly wide array of imagery with a broad variety of spectral and spatial resolution, but there are relatively few comparisons of how different sources of data impact the accuracy, cost, and utility of analyses. We evaluated the impact of satellite image spatial resolution (1 m from Digital Globe; 30 m from Landsat) on land use classification via ArcGIS Feature Analyst, and on total suspended solids (TSS) load estimates from the Soil and Water Assessment Tool (SWAT) for the Camboriú watershed in Southeastern Brazil. We independently calibrated SWAT models, using both land use map resolutions and short‐term daily streamflow (discharge) and TSS load data from local gauge stations. We then compared the predicted TSS loads with monitoring data outside the model training period. We also estimated the cost difference for land use classification and SWAT model construction and calibration at these two resolutions. Finally, we assessed the value of information (VOI) of the higher‐resolution imagery in estimating the cost‐effectiveness of watershed conservation in reducing TSS at the municipal water supply intake. Land use classification accuracy was 82.3% for 1 m data and 75.1% for 30 m data. We found that models using 1 m data better predicted both annual and peak TSS loads in the full study area, though the 30 m model did better in a sub‐watershed. However, the 1 m data incurred considerably higher costs relative to the 30 m data ($7000 for imagery, plus additional analyst time). Importantly, the choice of spatial resolution affected the estimated return on investment (ROI) in watershed conservation for the municipal water company that finances much of this conservation, although it is unlikely that this would have affected the company's decision to invest in the program. We conclude by identifying key criteria to assist in choosing an appropriate spatial resolution for different contexts.