Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada

Abstract

Wetlands in the Prairie Pothole Region (PPR) of Canada and the United States represent a unique mapping challenge. They are dynamic both seasonally and year-to-year, are very small, and frequently altered by human activity. Many efforts have been made to estimate the loss of these important habitats but a high-quality inventory of pothole wetlands is needed for data-driven conservation and management of these resources. Typical landcover classifications using one or two image dates from optical or Synthetic Aperture Radar (SAR) Earth Observation (EO) systems often produce reasonable wetland inventories for less dynamic, forested landscapes, but will miss many of the temporary and seasonal wetlands in the PPR. Past studies have attempted to capture PPR wetland dynamics by using dense image stacks of optical or SAR data. We build upon previous work, using 2017–2020 Sentinel-2 imagery processed through the Google Earth Engine (GEE) cloud computing platform to capture seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada. Using 36 different image dates, wetland flood frequency (hydroperiod) was calculated by classifying water/flooding in each image date. This product along with the Global Ecosystem Dynamics Investigation (GEDI) Canopy Height Model (CHM) was then used to generate a seven-class wetland inventory with wetlands classified as areas with seasonal but not permanent water/flooding. Overall accuracies of the resulting inventory were between 95% and 96% based on comparisons with local photo-interpreted inventories at the Canadian Wetland Classification System class level, while wetlands themselves were classified with approximately 70% accuracy. The high overall accuracy is due, in part, to a dominance of uplands in the PPR. This relatively simple method of classifying water through time generates reliable wetland maps but is only applicable to ecosystems with open/non-complex wetland types and may be highly sensitive to the timing of cloud-free optical imagery that captures peak wetland flooding (usually post snow melt). Based on this work, we suggest that expensive field or photo-interpretation training data may not be needed to map wetlands in the PPR as self-labeling of flooded and non-flooded areas in a few Sentinel-2 images is sufficient to classify water through time. Our approach demonstrates a framework for the operational mapping of small, dynamic PPR wetlands that relies on open-access EO data and does not require costly, independent training data. It is an important step towards the effective conservation and management of PPR wetlands, providing an efficient method for baseline and ongoing mapping in these dynamic environments.

Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1&2 Data Applied to Small Seasonal Ponds in Semiarid Environments

“…Similar outcomes have also been evidenced for seasonal waters (Bangira et al, 2019; Huang et al, 2018). For small water bodies, recent studies have reported high accuracy in surface water occurrence and extent for sizes ranging from 0.5 ha (Wang et al, 2022) up to 5 ha (DeLancey et al, 2022; Peña-Luque et al, 2021). To date, there is a scarcity of research specifically addressing very small water bodies (<0.5 ha), and the few existing studies focusing on this range of sizes (0-0.5 ha) have consistently demonstrated suboptimal performance, especially when employing publicly available EOS data (Cordeiro et al, 2021).…”

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

“…In summary, the table reveals predominant transitions into wetland from barren and vegetated surfaces, highlighting wetland expansion mainly occurring in formerly non-vegetated bare lands. It also quantifies the minor exchanges between wetlands and built-up impervious surfaces in the landscape surrounding Khinjhir Lake [75,76].…”

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

“…Additionally, Fekri et al [39] developed a RF algorithm and an automatic change detection method in GEE to map and identify changes in the International Shadegan Wetland (ISW) areas in southwestern Iran from 2018 to 2021 using a combination of Sentinel-1 and Sentinel-2 data. Finally, DeLancey et al [40] used Sentinel-2 imagery from 2017 to 2020 in GEE to monitor the seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada.…”