Towards an open and synergistic framework for mapping global land cover.

Jiyao Zhao,Peng Gong,Huabing Huang,Han Liu,Jie Wang,Le Yu

doi:10.7717/peerj.11877

Abstract

Global land-cover datasets are key sources of information for understanding the complex inter-actions between human activities and global change. They are also among the most critical variables for climate change studies. Over time, the spatial resolution of land cover maps has increased from the kilometer scale to 10-m scale. Single-type historical land cover datasets, including for forests, water, and impervious surfaces, have also been developed in recent years. In this study, we present an open and synergy framework to produce a global land cover dataset that combines supervised land cover classification and aggregation of existing multiple thematic land cover maps with the Google Earth Engine (GEE) cloud computing platform. On the basis of this method of classification and mosaicking, we derived a global land cover dataset for 6 years over a time span of 25 years. The overall accuracies of the six maps were around 75% and the accuracy for change area detection was over 70%. Our product also showed good similarity with the FAO and existing land cover maps.

Highlights

Global land cover datasets provide key information for understanding the complex interactions between human activities and global change (Running, 2008)
Multiple datasets were used in this study, including the FROM-GLC global land cover map in 2017, which was the most up to date and accurate land cover map among the three FROM-GLC maps in 2010, 2015 and 2017
The land cover mapping used the full archives of five different satellite missions providing daily observations of the Earth, including National Oceanic and Atmospheric Administration (NOAA)-Advanced Very High-Resolution Radiometer (AVHRR) high-resolution picture transmission (HRPT), Systeme Probatoire d’Observation de la Terre (SPOT) Vegetation, ENVIronmental SATellite (ENVISAT)-Medium Resolution Imaging Spectrometer (MERIS) full spatial resolution (FR) and reduced spatial resolution (RR), ENVISAT-Advanced Synthetic Aperture Radar (ASAR), and PRoject for On-Board Autonomy (PROBA)-Vegetation for the most recent years

Summary

Introduction

Global land cover datasets provide key information for understanding the complex interactions between human activities and global change (Running, 2008). They are some of the most critical variables for studies of climate change (Bounoua et al, 2002; Hibbard et al, 2010), habitat and biodiversity (Buchanan et al, 2009; Hall et al, 2011), carbon cycling (De Moraes et al, 1998; DeFries et al, 1999; DeFries et al, 1995; Poulter et al, 2011), and public health (Liang et al, 2010; Xu et al, 2004). Better frequent land cover observations are desirable for understanding global environmental change (Liu et al, 2021)

Methods

Results

Discussion

Conclusion