Primitives as building blocks for constructing land cover maps

Abstract Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using primitive map layers. Primitive map layers are a suite of biophysical and end member maps, with land cover primitives representing the raw information needed to make decisions in a dichotomous key for land cover classification. We present best practices to create and assemble primitives from optical satellite using computing technologies, decision tree logic and Monte Carlo simulations to integrate their uncertainties. The concept is presented in the context of a regional land cover map based on a shared regional typology with 18 land cover classes agreed on by stakeholders from Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam. We created annual map and uncertainty layers for the period 2000–2017. We found an overall accuracy of 94% when taking uncertainties into account. RLCMS produces consistent time series products using free long term historical Landsat and MODIS data. The customizable architecture can include a variety of sensors and machine learning algorithms to create primitives and the best suited smoothing can be applied on a primitive level. The system is transferable to all regions around the globe because of its use of publicly available global data (Landsat and MODIS) and easily adaptable architecture that allows for the incorporation of a customizable assembly logic to map different land cover typologies based on the user's landscape monitoring objectives

[1]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[2]  P. Eilers.  A perfect smoother. , 2003, Analytical chemistry.

[3]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[4]  Ian W. Housman,et al.  Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States , 2019, Remote Sensing of Environment.

[5]  Hankui K. Zhang,et al.  Examination of Sentinel-2A Multi-spectral Instrument (MSI) Reflectance Anisotropy and the Suitability of a General Method to Normalize MSI Reflectance to Nadir BRDF Adjusted Reflectance , 2017 .

[6]  David Saah,et al.  A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data , 2017, Remote. Sens..

[7]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[8]  David Saah,et al.  Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification , 2019, Remote. Sens..

[9]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[10]  Scott J. Goetz,et al.  Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data , 2016 .

[11]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[12]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

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

[14]  Aris Perperoglou,et al.  Two dimensional smoothing via an optimised Whittaker smoother , 2017 .

[15]  Michael A. Wulder,et al.  Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas , 2002 .

[16]  A. Gillespie,et al.  Topographic Normalization of Landsat TM Images of Forest Based on Subpixel Sun–Canopy–Sensor Geometry , 1998 .

[17]  Alan H. Strahler,et al.  Validation of the global land cover 2000 map , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[19]  David J. Ganz,et al.  Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing , 2018, Remote. Sens..

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

[21]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Ian W. Housman,et al.  An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States , 2018, Remote. Sens..

[23]  J. Scepan,et al.  Thematic validation of high-resolution Global Land-Cover Data sets , 1999 .

[24]  Feng Gao,et al.  Landsat Ecosystem Disturbance Adaptive Processing System LEDAPS algorithm description , 2013 .

[25]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[26]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[27]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[28]  Zhe Zhu,et al.  Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative , 2016 .

[29]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

[30]  Xuezhi Feng,et al.  Accuracy assessment of seven global land cover datasets over China , 2017 .

[31]  Steven W. Running,et al.  Ecosystem Disturbance, Carbon, and Climate , 2008, Science.

[32]  Damien Sulla-Menashe,et al.  Enhancing MODIS land cover product with a spatial–temporal modeling algorithm , 2014 .

[33]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[34]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[35]  Hideki Kanamaru,et al.  Projected Impact of Climate Change on Hydrological Regimes in the Philippines , 2016, PloS one.

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

[37]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[38]  Anthony G. Vorster,et al.  A survival guide to Landsat preprocessing. , 2017, Ecology.

[39]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[40]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[41]  Peng Gong,et al.  An assessment of MODIS Collection 5 global land cover product for biological conservation studies , 2010, 2010 18th International Conference on Geoinformatics.

[42]  R. Shumway,et al.  Time Series Regression and Exploratory Data Analysis , 2011 .

[43]  V. Salomonson,et al.  Estimating fractional snow cover from MODIS using the normalized difference snow index , 2004 .

[44]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[45]  Matthew Patterson,et al.  Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities , 2019, Front. Environ. Sci..

[46]  Zhuosen Wang,et al.  Angular Effects and Correction for Medium Resolution Sensors to Support Crop Monitoring , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  Edmund Taylor Whittaker.  On a New Method of Graduation , 1922, Proceedings of the Edinburgh Mathematical Society.

[48]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[49]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[50]  Christiane Schmullius,et al.  Land-Cover Observations as Part of a Global Earth Observation System of Systems (GEOSS): Progress, Activities, and Prospects , 2008, IEEE Systems Journal.

[51]  D. Roberts,et al.  Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon , 2004 .

[52]  T. Lin,et al.  The Lambertian assumption and Landsat data. , 1980 .

[53]  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 .

[54]  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 .

[55]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[56]  F. Javier García-Haro,et al.  A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion , 2015 .

[57]  J. Michaelsen,et al.  The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes , 2015, Scientific Data.

[58]  Lee C. Wensel,et al.  Notes and Observations: Aspect Transformation in Site Productivity Research , 1966 .

[59]  Zhenlong Li,et al.  Big Data and cloud computing: innovation opportunities and challenges , 2017, Int. J. Digit. Earth.

[60]  Kenneth B. Pierce,et al.  Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches , 2010 .

[61]  A. D. Gregorio,et al.  Land Cover Classification System (LCCS): Classification Concepts and User Manual , 2000 .

[62]  Christopher E. Holden,et al.  An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations , 2016 .

[63]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[64]  Steffen Fritz,et al.  A land‐cover map for South and Southeast Asia derived from SPOT‐VEGETATION data , 2007 .

[65]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[66]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[67]  Martin Herold,et al.  A joint initiative for harmonization and validation of land cover datasets , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[68]  Guang Yang,et al.  Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions , 2016, ISPRS Int. J. Geo Inf..

[69]  N. Pettorelli,et al.  Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions , 2016 .

[70]  J. Dymond,et al.  Correcting satellite imagery for the variance of reflectance and illumination with topography , 2003 .

[71]  Joanne C. White,et al.  Land cover 2.0 , 2018 .

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

[73]  David Riaño,et al.  Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003) , 2003, IEEE Trans. Geosci. Remote. Sens..

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

[75]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[76]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[77]  David J. Ganz,et al.  On Spatially Distributed Hydrological Ecosystem Services: Bridging the Quantitative Information Gap using Remote Sensing and Hydrological Models , 2017 .

[78]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[79]  A. Belward,et al.  The IGBP-DIS global 1km land cover data set, DISCover: First results , 1997 .

[80]  Hanqiu Xu,et al.  A new index for delineating built‐up land features in satellite imagery , 2008 .

[81]  Clement Atzberger,et al.  A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America , 2011, Int. J. Digit. Earth.

[82]  Raghavan Srinivasan,et al.  Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid Hydrologic Modeling and Basin Planning , 2018, Remote. Sens..

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

[84]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .

[85]  D. Roy,et al.  Continental-scale Validation of MODIS-based and LEDAPS Landsat ETM+ Atmospheric Correction Methods , 2012 .

[86]  Neil Flood,et al.  Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median) , 2013, Remote. Sens..

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

[88]  Huiping Liu,et al.  Accuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison , 2004 .

[89]  Stefano Ricci,et al.  Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation , 2016, Remote. Sens..

[90]  P. Eilers,et al.  Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements , 2011 .

[91]  P. Teillet,et al.  On the Slope-Aspect Correction of Multispectral Scanner Data , 1982 .

[92]  Leo Stroosnijder,et al.  Lumped surface and sub‐surface runoff for erosion modeling within a small hilly watershed in northern Vietnam , 2013 .

[93]  Don Faber-Langendoen,et al.  Contours of the Revised U.S. National Vegetation Classification Standard , 2009 .

[94]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[95]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

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

[97]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[98]  C. Justice,et al.  Application of digital terrain data to quantify and reduce the topographic effect on Landsat data , 1981 .

[99]  Giovanna Trianni,et al.  Urban Mapping in Landsat Images Based on Normalized Difference Spectral Vector , 2014, IEEE Geoscience and Remote Sensing Letters.

[100]  J. Colby,et al.  Topographic Normalization in Rugged Terrain , 1991 .

[101]  Clayton C. Kingdon,et al.  Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976-2006 Landsat time series , 2009 .

[102]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[103]  Chaowei Phil Yang,et al.  Redefining the possibility of digital Earth and geosciences with spatial cloud computing , 2013, Int. J. Digit. Earth.

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

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

[106]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[107]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[108]  S. Fritz,et al.  Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa , 2010 .

[109]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

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

[111]  David Saah,et al.  Collect Earth: An online tool for systematic reference data collection in land cover and use applications , 2019, Environ. Model. Softw..

[112]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[113]  Stephen V. Stehman,et al.  Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles , 1998 .

[114]  Austin Troy,et al.  An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale , 2018, Remote. Sens..

[115]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[116]  Peter H. Verburg,et al.  Mapping ecosystem services: The supply and demand of flood regulation services in Europe , 2014 .