The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine

Abstract Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. Iin order to increase wetland classification accuracy, the main contributions of this new study are adding more training data to the classification process and training Random Forest (RF) models on the Google Earth Engine (GEE) platform within the boundaries of ecozones rather than provinces. A considerable effort has been devoted to data collection, preparation, standardization of datasets for each ecozone. The data cleaning reveals a data gap in several Northern ecozones. Accordingly, high-resolution optical data, from Worldview-2 and Pleiades, were acquired to delineate wetland training data based on visual interpretation in those regions. By using this well-distributed training data, this second generation wetland inventory map represents an improvement of 7% compared to the first generation map. Accuracy varied from 76% to 91% in different ecozones depending on available resources. Furthermore, the results of RF variable importance, which was carried out for each ecozone, demonstrate that and NDVI extracted from Sentinel-1 and Sentinel-2 data, respectively, were the most important features for wetland mapping.

[1]  Masoud MahdianPari,et al.  Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review , 2020, Remote. Sens..

[2]  Saeid Homayouni,et al.  Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America , 2020, Remote. Sens..

[3]  Masoud Mahdianpari,et al.  Google Earth Engine for geo-big data applications: A meta-analysis and systematic review , 2020 .

[4]  Lianpeng Zhang,et al.  Mapping national-scale aquaculture ponds based on the Google Earth Engine in the Chinese coastal zone , 2020 .

[5]  Koreen Millard,et al.  Big geospatial data analysis for Canada’s Air Pollutant Emissions Inventory (APEI): using google earth engine to estimate particulate matter from exposed mine disturbance areas , 2020, GIScience & Remote Sensing.

[6]  Saeid Homayouni,et al.  Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2020, Canadian Journal of Remote Sensing.

[7]  Jahan Kariyeva,et al.  Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada , 2019, Remote. Sens..

[8]  R. Whaley,et al.  Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario , 2019, Remote Sensing of Environment.

[9]  Brian Brisco,et al.  Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification , 2019, Remote. Sens..

[10]  Saeid Homayouni,et al.  The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2018, Remote. Sens..

[11]  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..

[12]  W. A. Kurz,et al.  Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands , 2018, Carbon Balance and Management.

[13]  Brian Brisco,et al.  Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands , 2018 .

[14]  Brian Brisco,et al.  Wetland Water Level Monitoring Using Interferometric Synthetic Aperture Radar (InSAR): A Review , 2018, Canadian Journal of Remote Sensing.

[15]  Yun Zhang,et al.  Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Bahram Salehi,et al.  A New Hierarchical Object-Based Classification Algorithm for Wetland Mapping in Newfoundland, Canada , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[17]  Yun Zhang,et al.  Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery , 2018, Remote. Sens..

[18]  Brigitte Leblon,et al.  Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data , 2018, International Journal of Applied Earth Observation and Geoinformation.

[19]  Joanne C. White,et al.  Land cover 2.0 , 2018 .

[20]  Joanne C. White,et al.  Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series , 2018 .

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

[22]  N. Clinton,et al.  A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform , 2017 .

[23]  M. Mahdianpari,et al.  Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery , 2017 .

[24]  Z. Niu,et al.  Global Wetland Datasets: a Review , 2017, Wetlands.

[25]  Lori White,et al.  Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands , 2017, Remote. Sens..

[26]  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..

[27]  D. Pennock,et al.  Prairie Pothole Wetlands – Suggestions for Practical and Objective Definitions and Terminology , 2016, Wetlands.

[28]  Zhuang Fengqing,et al.  Patients’ Responsibilities in Medical Ethics , 2016 .

[29]  Paul D. Pickell,et al.  Evolution of Canada’s Boreal Forest Spatial Patterns as Seen from Space , 2016, PloS one.

[30]  Chris Davies,et al.  Object-based image analysis of optical and radar variables for wetland evaluation , 2015 .

[31]  Yuhong He,et al.  An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery ☆ , 2015 .

[32]  Koreen Millard,et al.  On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping , 2015, Remote. Sens..

[33]  Peng Liu,et al.  A survey of remote-sensing big data , 2015, Front. Environ. Sci..

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

[35]  Yuhong He,et al.  NDVI variation and its relation to climate in Canadian ecozones , 2012 .

[36]  Josef Aschbacher,et al.  The European Earth monitoring (GMES) programme: Status and perspectives , 2012 .

[37]  Lu,et al.  Wetland Definitions：Creation,Evolution and Application , 2010 .

[38]  Kevin L. Erwin.  Wetlands and global climate change: the role of wetland restoration in a changing world , 2009, Wetlands Ecology and Management.

[39]  Richard A. Fournier,et al.  Towards a strategy to implement the Canadian Wetland Inventory using satellite remote sensing , 2007 .

[40]  Gregory S. Okin,et al.  Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring , 2020 .

[41]  C. Finlayson,et al.  Wetland Classification: Overview , 2018 .