Integrating volunteered smartphone data with multispectral remote sensing to estimate forest fuels

Abstract

Volunteered data sources are readily available due to advances in electronic communications technology. For example, smartphones provide tools to collect ground-based observations over broad areas from a diverse set of data collectors, including people with, and without, extensive training. In this study, volunteers used a smartphone application to collect ground-based observations. Forest structural components were then estimated over a broader area using high spatial resolution RapidEye remote sensing imagery (5 spectral bands 440–850 nm, 5 m spatial resolution) and a digital elevation model following a three nearest neighbor approach (K-NN). Participants with professional forestry experience on average chose high-priority fuel load locations near buildings, while nonprofessional participants chose a broader range of conditions over a larger extent. When used together, the professional and nonprofessional observations provided a more complete assessment of forest conditions. A generalized framework is presented that utilizes K-NN imputation tools for estimating the distribution of forest fuels using remote sensing and topography variables, ensuring spatial representation, checking attribute accuracy, and evaluating predictor variables. Frameworks to integrate volunteered data from smartphone platforms with remote sensing may contribute toward more complete Earth observation for Digital Earth.