Deep learning Using Physically-Informed Input Data for Wetland Identification. (April 2020)

• Record Type: Journal Article
• Authors: O'Neil, Gina L.; Goodall, Jonathan L.; Behl, Madhur; Saby, Linnea
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104665
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Automated and accurate wetland identification algorithms are increasingly important for wetland conservation and environmental planning. Deep learning for wetland identification is an emerging field that shows promise for advancing these efforts. Deep learning is unique to traditional machine learning techniques for its ability to consider the spatial context of object characteristics within a landscape scene. However, applying deep learning typically requires very large datasets for training the algorithms, which limits their application for many environmental applications including wetland identification. Using four study sites across Virginia with field delineated wetlands, we provide insight into the potential for deep learning for wetland detection from limited, but typical, wetland delineation training data. Our proposed workflow performs a wetland semantic segmentation using DeepNets, a deep learning architecture for remote sensing data, and an input dataset consisting of high-resolution topographic indices and the Normalized Difference Vegetation Index. Results show that models trained and evaluated for a single site were able to achieve high accuracy (up to 91% recall and 56% precision) and similar accuracy can be obtained for models trained across multiple sites (up to 91% recall and 57% precision). Through this analysis we found that, across all sites, input data configurations taking advantage of hydrologic properties derived from elevation data consistently outperformed models using the elevation data directly, showing the benefit of physically-informed inputs in deep learning training for wetland identification. By refining the wetland identification workflow presented in this paper and collecting additional training data across landscapes, there is potential for deep learning algorithms to support a range wetland conservation efforts. Highlights: A deep learning workflow is described, able to segment wetlands from geospatial data. Wetlands are segmented at a high spatial resolution for environmental planning. The workflow resulted in accurate wetland predictions (91% recall and 57% precision). Training using physically-informed indicators outperformed using elevation directly.