A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine

Erfan Fekri,Hooman Latifi,Abdolkarim Zobeidinezhad,Meisam Amani

doi:10.3390/rs13204169

Abstract

Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.

Highlights

For the non-wetland vegetation class, good performance was observed across the study area, especially for the vegetation located in the northwestern part of the study area
10-m spatial resolution using a combination of the Sentinel-1 and Sentinel-2 datasets and the Random Forest (RF) classifier within the Google Earth Engine (GEE) cloud computing platform
We proposed a novel automatic training sample migration method as a potential solution when a shortage of training samples existed for wetland monitoring applications

Summary

Introduction

Wetlands are valuable ecosystems and offer important services to both nature and human beings They play a key role in water purification, carbon cycles, flood and storm control, soil erosion control, and providing wildlife habitats [1,2,3,4,5]. Despite these benefits, a significant portion of wetlands worldwide has been degraded over the past century as a result of climate change and anthropogenic activities such as urbanization and agricultural expansion [6,7]. The dramatically increasing rate of global wetland loss necessitates an effective approach for mapping and monitoring these unique ecosystems [7]

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