Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

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.

[1]  Liangyun Liu,et al.  Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China , 2014, Remote. Sens..

[2]  Patrick Hostert,et al.  A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[4]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[5]  C. Woodcock,et al.  Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data , 2012 .

[6]  David P. Roy,et al.  Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project , 2011 .

[7]  F. Gao,et al.  Improved forest change detection with terrain illumination corrected Landsat images , 2013 .

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

[9]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[10]  Hugo Carrão,et al.  Contribution of multispectral and multitemporal information from MODIS images to land cover classification , 2008 .

[11]  Stefan Dech,et al.  A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data , 2017 .

[12]  Hankui K. Zhang,et al.  Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification , 2017 .

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[15]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[16]  Suming Jin,et al.  Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information , 2015 .

[17]  Chengquan Huang,et al.  Quality assessment of Landsat surface reflectance products using MODIS data , 2012, Comput. Geosci..

[18]  W. Cohen,et al.  Using Landsat-derived disturbance history (1972-2010) to predict current forest structure , 2012 .

[19]  Paul M. Mather,et al.  MULTITEMPORAL CLASSIFICATION OF AGRICULTURAL CROPS USING THE SPECTRAL-TEMPORAL RESPONSE SURFACE , 2002 .

[20]  S. P. Abercrombie,et al.  Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product , 2019, Remote Sensing of Environment.

[21]  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 .

[22]  Xiangming Xiao,et al.  Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data , 2003 .

[23]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[24]  Robert E. Wolfe,et al.  An illumination correction algorithm on Landsat-TM data , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[25]  Siamak Khorram,et al.  Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach , 2006 .

[26]  Darrel L. Williams,et al.  Landsat-7 Long-Term Acquisition Plan: Development and Validation , 2006 .

[27]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[28]  D. Lobell,et al.  Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .

[29]  Darrel L. Williams,et al.  Landsat sensor performance: history and current status , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[30]  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 .

[31]  Brian W. Barrett,et al.  Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[32]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[33]  Forrest R. Stevens,et al.  Multitemporal settlement and population mapping from Landsat using Google Earth Engine , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Paul E. Gessler,et al.  Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments , 2008 .

[35]  P. Beckschäfer.  Obtaining rubber plantation age information from very dense Landsat TM & ETM + time series data and pixel-based image compositing , 2017 .

[36]  D. Roy,et al.  A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin , 2008 .

[37]  M. Claverie,et al.  Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products , 2015 .

[38]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[39]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[40]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[41]  M. Hansen,et al.  Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive , 2015 .

[42]  Joanne C. White,et al.  Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics , 2015 .

[43]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[44]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[45]  Hankui K. Zhang,et al.  A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance , 2016 .

[46]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[47]  J. Cihlar.  Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[48]  J. L. Barker,et al.  Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets , 2001 .

[49]  M. Herold,et al.  Evolving standards in land cover characterization , 2006 .

[50]  Robert C. Balling,et al.  Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover , 2018 .

[51]  Yuan Gao,et al.  Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach , 2019, Remote. Sens..

[52]  R. Mueller,et al.  The 2009 Cropland Data Layer. , 2010 .

[53]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.