Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Abstract Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.

[1]  Laurent Lesnard,et al.  Setting Cost in Optimal Matching to Uncover Contemporaneous Socio-Temporal Patterns , 2010 .

[2]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[3]  M. Perry,et al.  The development of a new set of long‐term climate averages for the UK , 2005 .

[4]  Claudia Kuenzer,et al.  European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products , 2012, Remote. Sens..

[5]  Hongjie Xie,et al.  New methods for studying the spatiotemporal variation of snow cover based on combination products of MODIS Terra and Aqua , 2009 .

[6]  Dennis P. Lettenmaier,et al.  Trends in 20th century drought over the continental United States , 2006 .

[7]  Gilbert Ritschard,et al.  Discrepancy Analysis of State Sequences , 2011 .

[8]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[9]  Gilbert Ritschard,et al.  Analyzing and Visualizing State Sequences in R with TraMineR , 2011 .

[10]  András Bárdossy,et al.  Cloud removal methodology from MODIS snow cover product , 2009 .

[11]  Laura Poggio,et al.  Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates , 2013 .

[12]  T. Hlásny,et al.  Snow disturbances in secondary Norway spruce forests in Central Europe: Regression modeling and its implications for forest management , 2011 .

[13]  A. Rampini,et al.  Author ' s personal copy A regional snowline method for estimating snow cover from MODIS during cloud cover , 2010 .

[14]  S. Kampf,et al.  Spatiotemporal index for analyzing controls on snow climatology: application in the Colorado Front Range , 2013 .

[15]  Hongjie Xie,et al.  Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements , 2008 .

[16]  Hongjie Xie,et al.  Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA , 2010 .

[17]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[18]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[19]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[20]  G. Blöschl,et al.  Distributed Snowmelt Simulations in an Alpine Catchment: 1. Model Evaluation on the Basis of Snow Cover Patterns , 1991 .

[21]  Iain Brown,et al.  Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland , 2012 .

[22]  David H. Douglas,et al.  Detection of Surface-Specific Points by Local Parallel Processing of Discrete Terrain Elevation Data , 1975 .

[23]  Günter Blöschl,et al.  Spatio‐temporal combination of MODIS images – potential for snow cover mapping , 2008 .

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

[25]  Chris Derksen,et al.  Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements , 2011 .

[26]  N. DiGirolamo,et al.  MODIS snow-cover products , 2002 .

[27]  Shihyan Lee,et al.  A review of global satellite-derived snow products , 2012 .

[28]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[29]  Markus Metz,et al.  GRASS GIS: A multi-purpose open source GIS , 2012, Environ. Model. Softw..

[30]  Peter Romanov,et al.  An assessment of the differences between three satellite snow cover mapping techniques , 2002 .

[31]  K. R. Clarke,et al.  Non‐parametric multivariate analyses of changes in community structure , 1993 .

[32]  Mario Chica-Olmo,et al.  Downscaling Cokriging for Super-Resolution Mapping of Continua in Remotely Sensed Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Francesco C. Billari,et al.  The analysis of early life courses: Complex descriptions of the transition to adulthood , 2001 .

[34]  Michael Schmidt,et al.  Geostatistical interpolation of SLC-off Landsat ETM+ images , 2009 .

[35]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[36]  Melinda Mills,et al.  Introducing Survival and Event History Analysis , 2011 .

[37]  A. Stein,et al.  A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA-AVHRR images , 1998 .

[38]  Gilbert Ritschard,et al.  The De-Standardization of the Life Course: Are Men and Women Equal? , 2009 .

[39]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[40]  G. N. Lance,et al.  A general theory of classificatory sorting strategies: II. Clustering systems , 1967, Comput. J..

[41]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[42]  Richard Fernandes,et al.  Validation of VEGETATION, MODIS, and GOES + SSM/I snow‐cover products over Canada based on surface snow depth observations , 2003 .

[43]  J. Hofierka,et al.  The solar radiation model for Open source GIS: implementation and applications , 2002 .

[44]  D. Hall,et al.  Accuracy assessment of the MODIS snow products , 2007 .

[45]  G. Blöschl,et al.  The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models , 2008 .

[46]  D. Lettenmaier,et al.  Trends in 20 th century drought over the continental United States , 2006 .

[47]  Monica Musio,et al.  Modeling Spatiotemporal Forest Health Monitoring Data , 2009 .

[48]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[49]  Jan M. H. Hendrickx,et al.  Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements , 2005 .

[50]  D. Warton,et al.  Distance‐based multivariate analyses confound location and dispersion effects , 2012 .

[51]  Mark A. Templin,et al.  Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region , 2006 .

[52]  Keshav Prasad Paudel,et al.  Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology , 2011 .

[53]  Qiuhong Tang,et al.  Remote sensing: hydrology , 2009 .

[54]  Yves Arnaud,et al.  Snow cover monitoring in the Northern Patagonia Icefield using MODIS satellite images (2000–2006) , 2008 .

[55]  Zuhal Akyürek,et al.  Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey , 2005 .

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

[57]  Aart C. Liefbroer,et al.  De-standardization of Family-Life Trajectories of Young Adults: A Cross-National Comparison Using Sequence Analysis , 2007 .

[58]  Günter Blöschl,et al.  Advances in the use of observed spatial patterns of catchment hydrological response , 2002 .

[59]  A. Abbott,et al.  Sequence Analysis and Optimal Matching Methods in Sociology , 2000 .

[60]  Roger Bivand,et al.  Bindings for the Geospatial Data Abstraction Library , 2015 .

[61]  J. Dozier Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper , 1989 .

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

[63]  Thomas H. Painter,et al.  Assessment of methods for mapping snow cover from MODIS , 2011 .

[64]  J. Seibert,et al.  On the calculation of the topographic wetness index: evaluation of different methods based on field observations , 2005 .

[65]  André G. Journel,et al.  Modelling Uncertainty and Spatial Dependence: Stochastic Imaging , 1996, Int. J. Geogr. Inf. Sci..

[66]  G. Jenkins,et al.  The climate of the United Kingdom and recent trends , 2007 .

[67]  Andrew G. Klein,et al.  Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000–2001 snow year , 2003 .

[68]  Laura Poggio,et al.  Soil available water capacity interpolation and spatial uncertainty modelling at multiple geographical extents , 2010 .