An integrative modeling approach to mapping wetlands and riparian areas in a heterogeneous Rocky Mountain watershed. Issue 2 (18th September 2017) - Chignell, Stephen M.; Luizza, Matthew W.; Skach, Sky; Young, Nicholas E.; Evangelista, Paul H.
Abstract: Accurate maps of wetlands and riparian areas are critical for targeting conservation and monitoring efforts. However, detailed inventories in mountain regions are largely non‐existent, as conventional mapping approaches are hindered by high costs, remoteness, and landscape variability. Contemporary modeling techniques can circumvent many of these issues, but are often difficult to interpret and tend to rely on specialized datasets that prevent their wider application. In this study, we used machine learning, Landsat 8 imagery and geomorphometric indices to map the distribution of wetlands and riparian areas in the Cache la Poudre River watershed, Colorado, USA. We used a presence‐background approach to develop and compare predictions from three popular algorithms: boosted regression trees, MaxEnt and random forests. In addition, we developed the models within three elevation‐based life zones to account for altitudinal changes in ecohydrology and land use. Our results showed strong predictive performance, with top‐performing models achieving area under the curve values as high as 0.98 and correctly classifying up to 95% of test data. Model performance varied by elevation zone, and no algorithm consistently outperformed the others. The boosted regression trees approach was uniquely able to differentiate wetlands from irrigated agriculture and residential areas in lower elevations. Multi‐seasonal greenness and wetness indices were highly influential predictors in all models, underscoring the importance of capturing local phenological characteristics and hydrological regimes. Dissection and roughness terrain metrics were key predictors for identifying valley bottom meadows and emergent wetlands in high‐elevation forests. We demonstrate how integrating ecological interpretation into the modeling workflow can inform conventional accuracy statistics and help bridge field‐based and remote sensing perspectives. We also show how continuous model outputs can facilitate this process by depicting nuances of the wetland‐upland continuum. Our approach requires only public data that are widely available, and can be easily adapted to other heterogeneous mountain settings. Abstract : We used machine learning, Landsat 8 imagery and geomorphometric indices to map the distribution of wetlands and riparian areas in a highly variable Rocky Mountain watershed. We used a presence‐background approach to develop and compare predictions from three popular algorithms: boosted regression trees, MaxEnt and random forests. Results were highly accurate, with top‐performing models achieving area under the curve values as high as 0.98 and correctly classifying up to 95% of test data. Multi‐seasonal greenness and wetness indices were highly influential predictors in all models. Dissection and roughness terrain metrics were key predictors for identifying valley‐bottom meadows and emergent wetlands in high‐elevation forests. We demonstrate how integrating ecological interpretation into the modeling workflow can inform conventional accuracy statistics and help bridge field‐based and remote sensing perspectives. Our approach requires only public data that are widely available, and can be easily adapted to other heterogeneous mountain settings.