A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing

ABSTRACT 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.

[1]  Geoffrey J. Hay,et al.  How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains , 2012 .

[2]  D. Vitt,et al.  Canadian wetlands: Environmental gradients and classification , 1995, Vegetatio.

[3]  C. Hopkinson,et al.  A Physically Based Terrain Morphology and Vegetation Structural Classification for Wetlands of the Boreal Plains, Alberta, Canada , 2016 .

[4]  Floyd M. Henderson,et al.  Radar detection of wetland ecosystems: a review , 2008 .

[5]  C. Rubec,et al.  The Canadian Wetland Classification System , 2016 .

[6]  Timothy G. Whiteside,et al.  Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery , 2015, Remote. Sens..

[7]  Debbie Whitall,et al.  WETLANDS , 1995, Restoration & Management Notes.

[8]  Brian Brisco,et al.  Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada , 2017 .

[9]  Weimin Huang,et al.  A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Shao Yun,et al.  Compact polarimetry assessment for rice and wetland mapping , 2013 .

[11]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[12]  Onisimo Mutanga,et al.  High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Jennifer N. Hird,et al.  Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping , 2017, Remote. Sens..

[14]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[15]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Mohamed Shehata,et al.  A Multiple Classifier System to improve mapping complex land covers: a case study of wetland classification using SAR data in Newfoundland, Canada , 2018 .

[17]  Stacy L. Ozesmi,et al.  Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.

[18]  Weimin Huang,et al.  Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data , 2017 .

[19]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[20]  Mohammed Dabboor,et al.  Change Detection with Compact Polarimetric SAR for Monitoring Wetlands , 2015 .

[21]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[22]  B. Brisco,et al.  Spectral analysis of wetlands using multi-source optical satellite imagery , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[23]  Brian Brisco,et al.  Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration , 2017 .

[24]  Richard A. Fournier,et al.  An object-based method to map wetland using RADARSAT-1 and Landsat ETM images: test case on two sites in Quebec, Canada , 2007 .

[25]  Mohammed Dabboor,et al.  A Collection of SAR Methodologies for Monitoring Wetlands , 2015, Remote. Sens..

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  F. Achard,et al.  Object‐oriented and textural image classification of the Siberia GBFM radar mosaic combined with MERIS imagery for continental scale land cover mapping , 2007 .

[28]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[29]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

[30]  Brian Brisco,et al.  Remote sensing for wetland classification: a comprehensive review , 2018 .

[31]  D. Lobell,et al.  A scalable satellite-based crop yield mapper , 2015 .

[32]  Nicholas C. Coops,et al.  A National Assessment of Wetland Status and Trends for Canada's Forested Ecosystems Using 33 Years of Earth Observation Satellite Data , 2018, Remote. Sens..

[33]  Manabu Watanabe,et al.  Comparative Assessment of Supervised Classifiers for Land Use–Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Onisimo Mutanga,et al.  Google Earth Engine Applications Since Inception: Usage, Trends, and Potential , 2018, Remote. Sens..

[35]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[36]  Yi Peng,et al.  Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data , 2014 .

[37]  Charles R. Lane,et al.  Mapping Isolated Wetlands in a Karst Landscape: GIS and Remote Sensing Methods , 2009 .

[38]  Bin Li,et al.  Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery , 2015, Remote. Sens..

[39]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[40]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[41]  Laura L. Bourgeau-Chavez,et al.  Improving Wetland Characterization with Multi-Sensor, Multi-Temporal SAR and Optical/Infrared Data Fusion , 2009 .

[42]  Alexei Novikov,et al.  Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..

[43]  Weimin Huang,et al.  Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results , 2019, Remote. Sens..

[44]  D. Mcalpine,et al.  Assessment of species diversity in the Atlantic Maritime Ecozone , 2012 .