Abstract
The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.
Highlights
Land-cover mapping and monitoring is one of the main applications of Earth observation satellite data [1], and is essential for the development and management of natural resource [2], modelling of environmental variables [3] and understanding of habitat distribution [4]
The National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) dataset [9] was incorporated into the training pool of the 2011 National Land Cover Database (NLCD) product [10]; the global Web-enabled Landsat Data (GWELD) were classified automatically using the training data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover product [11]; and The European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product, combined with the MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance (NBAR) product (MCD43A4), was used to extract the spatial–temporal spectral signature of each land-cover type in order to classify Landsat Operational Land Imager (OLI) imagery [12]
The intention of this study was to develop a method for automatic land-cover mapping at a scale of 30 m based on the Google Earth Engine cloud-based platform
Summary
Land-cover mapping and monitoring is one of the main applications of Earth observation satellite data [1], and is essential for the development and management of natural resource [2], modelling of environmental variables [3] and understanding of habitat distribution [4]. The National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) dataset [9] was incorporated into the training pool of the 2011 National Land Cover Database (NLCD) product [10]; the global Web-enabled Landsat Data (GWELD) were classified automatically using the training data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover product [11]; and The European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product, combined with the MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance (NBAR) product (MCD43A4), was used to extract the spatial–temporal spectral signature of each land-cover type in order to classify Landsat Operational Land Imager (OLI) imagery [12] These studies have demonstrated that the use of existing land-cover maps as a source of training data can automatically generate accurate Landsat-based 30 m land-cover maps at the national and continental scale
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