Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

Abstract

Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.

Keywords

• grassland
• remote sensing
• machine learning
• mixed grassland ecoregion
• big data