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

Abstract

Remote 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 Cambori u 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 subwatershed. 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.

High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

“…Here, we compare classification with Landsat-8 (LS8) and with high-resolution data (WV) and demonstrate why, as described in [49], higher-resolution data is preferable to lower-resolution data when a landscape has small features and/or fine-scale variation in LULC, when a large portion of LULC change patches are smaller than the pixel size at lower-resolutions, and when high accuracy is necessary to inform decisions.…”

New and Enhanced Tools for Civil Military Operations (NET-CMO)

“…As discussed in the introduction, resampling to a smaller pixel size introduces redundancy into the data, which can increase spatial autocorrelation. Spatial autocorrelation violates the assumption of independent observations (Dormann 2007;Legendre 1993;Kühn and Dormann 2012) and redundancy can slow down processing times and take up unnecessary storage (Fisher et al 2017).…”

“…If redundant pixels are overly present, minor background pixels could become overly represented and skew geospatial models (Rodriguez-Carrion et al 2014;Costanza and Maxwell 1994). Thus, it is preferable to size pixels at the point where only relevant information is preserved (Fisher et al 2017).…”

Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

“…High-resolution satellite imagery is preferable for classification of small sites with small and heterogeneous field sizes and small features present like in the current study. 54 Features with sizes smaller than the low Landsat resolution pixels were not distinguishable.…”