Wetlands are important natural resources due to their numerous ecological services.Consequently,identifying their locations and extents is imperative.The stability,repeatability,cost-effectiveness,multi-scale coverage...Wetlands are important natural resources due to their numerous ecological services.Consequently,identifying their locations and extents is imperative.The stability,repeatability,cost-effectiveness,multi-scale coverage,and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications,such as provincial wetland mapping.To do so,it is required to(1)process and classify big geo data(i.e.a large amount of satellite datasets)in a time-and computationally-efficient approach and(2)collect a large amount of field samples.In this study,Google Earth Engine(GEE)and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba,Quebec,and Newfoundland and Labrador(NL).Additionally,using GEE,a generalized supervised classification method is proposed to produce a regional wetland map from a large area(e.g.,a province)when lacking field samples.In fact,using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics,the wetland maps were generated for the other two provinces.The overall classification accuracies for Manitoba,Quebec,and NL were 84%,78%,and 82%,respectively,indicating the high potential of the proposed method for aiding provincial wetland inventory systems.展开更多
基金supported by the Canada Centre for Mapping and Earth Observation of Natural Resources Canada(NRCan).
文摘Wetlands are important natural resources due to their numerous ecological services.Consequently,identifying their locations and extents is imperative.The stability,repeatability,cost-effectiveness,multi-scale coverage,and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications,such as provincial wetland mapping.To do so,it is required to(1)process and classify big geo data(i.e.a large amount of satellite datasets)in a time-and computationally-efficient approach and(2)collect a large amount of field samples.In this study,Google Earth Engine(GEE)and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba,Quebec,and Newfoundland and Labrador(NL).Additionally,using GEE,a generalized supervised classification method is proposed to produce a regional wetland map from a large area(e.g.,a province)when lacking field samples.In fact,using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics,the wetland maps were generated for the other two provinces.The overall classification accuracies for Manitoba,Quebec,and NL were 84%,78%,and 82%,respectively,indicating the high potential of the proposed method for aiding provincial wetland inventory systems.
