Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.

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

[2]  Zhiqiang Yang,et al.  Continuous monitoring of land disturbance based on Landsat time series , 2020, Remote Sensing of Environment.

[3]  Junichi Imanishi,et al.  Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data , 2011, Landscape and Ecological Engineering.

[4]  Anthony J. T. Lee,et al.  Mining frequent trajectory patterns in spatial-temporal databases , 2009, Inf. Sci..

[5]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[6]  GamanyaRuvimbo,et al.  Object-oriented change detection for the city of Harare, Zimbabwe , 2009 .

[7]  Ke Wang,et al.  NDVI-Based Lacunarity Texture for Improving Identification of Torreya Using Object-Oriented Method , 2011 .

[8]  P. Vitousek.  Beyond Global Warming: Ecology and Global Change , 1994 .

[9]  Peijun Du,et al.  A review of supervised object-based land-cover image classification , 2017 .

[10]  Brian Johnson,et al.  Unsupervised image segmentation evaluation and refinement using a multi-scale approach , 2011 .

[11]  Xudong Guan,et al.  Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance , 2016, Remote. Sens..

[12]  Lidia Vlassova,et al.  Effects of post-fire wood management strategies on vegetation recovery and land surface temperature (LST) estimated from Landsat images , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Patrick Bogaert,et al.  Forest change detection by statistical object-based method , 2006 .

[14]  Russell G. Congalton.  Considerations And Techniques For Assessing The Accuracy Of Remotely Sensed Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[15]  K. Moffett,et al.  Remote Sens , 2015 .

[16]  Qingming Zhan,et al.  Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network , 2016 .

[17]  A-Xing Zhu,et al.  Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions , 2019, Remote. Sens..

[18]  Francisca López-Granados,et al.  Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery , 2009 .

[19]  M. Mhawej,et al.  Global trends analysis of the main vegetation types throughout the past four decades , 2018, Applied Geography.

[20]  Joanne C. White,et al.  An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites , 2015 .

[21]  Albert Rango,et al.  Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[22]  M. Hereher,et al.  Surface area change detection of the Burullus Lagoon, North of the Nile Delta, Egypt, using water indices: A remote sensing approach , 2013 .

[23]  Patrick Bogaert,et al.  An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution , 2008 .

[24]  Nicolas Baghdadi,et al.  A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images , 2018, Sensors.

[25]  K. Seto,et al.  Quantifying Spatiotemporal Patterns of Urban Land-use Change in Four Cities of China with Time Series Landscape Metrics , 2005, Landscape Ecology.

[26]  Yen-Liang Chen,et al.  Discovering multi-label temporal patterns in sequence databases , 2011, Inf. Sci..

[27]  Deren Li,et al.  Study on geographic ontology based on object-oriented remote sensing analysis , 2008, International Conference on Earth Observation for Global Changes.

[28]  Clemens Eisank,et al.  Automated object-based classification of topography from SRTM data , 2012, Geomorphology.

[29]  Lei Ma,et al.  Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery , 2015 .

[30]  David Morin,et al.  An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series , 2015, Remote. Sens..

[31]  M. Buchroithner,et al.  Quantitative assessment of 2014–2015 land-cover changes in Azerbaijan using object-based classification of LANDSAT-8 timeseries , 2016, Modeling Earth Systems and Environment.

[32]  G. Foody.  Assessing the accuracy of land cover change with imperfect ground reference data , 2010 .

[33]  Eamonn Keogh.  Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[34]  Lucy Marshall,et al.  Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest , 2013 .

[35]  Xiang Li,et al.  Detecting spatio-temporal and typological changes in land use from Landsat image time series , 2017 .

[36]  Lifeng Luo,et al.  Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data , 2018 .

[37]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[38]  Hermann Ney,et al.  Dynamic programming search for continuous speech recognition , 1999, IEEE Signal Process. Mag..

[39]  Stuart A. Roberts,et al.  Object-based approach to integrate remotely sensed data within a GIS context for land use changes detection at urban-rural fringe areas , 1997, Remote Sensing.

[40]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[41]  S. Franklin,et al.  Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico , 2015 .

[42]  Yang Liu,et al.  The relationship between threshold-based and inflexion-based approaches for extraction of land surface phenology , 2017 .

[43]  C. Munyati,et al.  Landsat TM image segmentation for delineating geological zone correlated vegetation stratification in the Kruger National Park, South Africa , 2013 .

[44]  Olufemi A. Omitaomu,et al.  Weighted dynamic time warping for time series classification , 2011, Pattern Recognit..

[45]  Gilberto Câmara,et al.  A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Philippe De Maeyer,et al.  Object-oriented change detection for the city of Harare, Zimbabwe , 2009, Expert Syst. Appl..

[47]  Manchun Li,et al.  Generation of Pixel-Level SAR Image Time Series Using a Locally Adaptive Matching Technique , 2014 .

[48]  Nicolas Passat,et al.  Spatio-temporal reasoning for the classification of satellite image time series , 2012, Pattern Recognit. Lett..

[49]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[50]  Francesca Bovolo,et al.  A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[51]  K. Venkata Reddy,et al.  Air Pollution Modeling from Remotely Sensed Data Using Regression Techniques , 2013, Journal of the Indian Society of Remote Sensing.

[52]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[53]  Lizhe Wang,et al.  A time-series classification approach based on change detection for rapid land cover mapping , 2019 .

[54]  Yumin Tan,et al.  Time series remote sensing based dynamic monitoring of land use and land cover change , 2016, 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA).

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

[56]  Shi Peirong,et al.  Parameters of Multi-Segmentation based on Segmentation Evaluation Function , 2018 .

[57]  T. Warner,et al.  Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects , 2011 .

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

[59]  Ying Wang,et al.  Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation , 2017, Remote. Sens..

[60]  V. Radeloff,et al.  Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series , 2018, Remote Sensing of Environment.

[61]  Thomas Blaschke,et al.  New contextual approaches using image segmentation for objectbased classification , 2004 .

[62]  P. Siqueira,et al.  Use of time-series L-band UAVSAR data for the classification of agricultural fields in the San Joaquin Valley , 2017 .

[63]  Hassiba Nemmour,et al.  Multiple support vector machines for land cover change detection: An application for mapping urban extensions , 2006 .

[64]  François Petitjean,et al.  Satellite Image Time Series Analysis Under Time Warping , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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