A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

作者：Masoud Mahdianpari, Hamid Jafarzadeh, Jean Granger, Fariba Mohammadimanesh, Brian Brisco, Bahram Salehi, Saeid Homayouni, Qihao Weng · 发表于：GIScience & Remote Sensing · 年份：2020 · DOI：10.1080/15481603.2020.1846948 · 被引用次数：164 · 研究领域：Land Use and Ecosystem Services、Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications

Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of climate change. The availability of cloud-based geospatial platforms has allowed for the production of wetland maps at scales previously unfeasible due to technical limitations, yet the assessment of changes to wetlands at the level of the wetland class (bog, fen, swamp, and marsh) has yet to be implemented across Canada. Class-level change information is important when considering changes and impacts to wetland functions and services. To demonstrate this possibility, this study assessed 30 years of change to wetlands across the province of Newfoundland using Landsat imagery, spectral indices, and Random Forest classification within the Google Earth Engine (GEE) cloud-computing platform. Overall accuracies were high, ranging from 84.37% to 88.96%. In a comparison of different classifiers, Random Forest produced the highest over accuracy results and allowed for the estimation of variable importance, when compared Classification and Regression Tree (CART) and Minimum Distance (MD). The most important variables include the thermal infrared band (TIR), elevation, the difference vegetation index (DVI), the shortwave infrared bands (SWIR),...