Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

References

• Ahlqvist, O., et al., 2005. Not just objects: reconstructing objects. In: P. Fisher and D. Unwin, eds. Re-presenting GIS. London: John Wiley & Sons, 17–25.
   Google Scholar
• Ahmed, N.K., et al., 2022. Role-based graph embeddings. IEEE Transactions on Knowledge and Data Engineering, 34 (5), 2401–2415.
   Google Scholar
• Aldwaik, S.Z. and Pontius Jr, R.G., 2012. Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition. Landscape and Urban Planning, 106 (1), 103–114.
   Web of Science ®Google Scholar
• Arévalo, P., et al., 2020. A suite of tools for continuous land change monitoring in google earth engine. Frontiers in Climate, 2, 576740.
   Google Scholar
• Auber, D., et al., 2003. Multiscale visualization of small world networks. In: IEEE symposium on information visualization 2003 (IEEE Cat. No. 03TH8714), 19–21 October 2003, Seattle, WA, USA.Los Alamitos, CA: IEEE Computer Society, 75–81.
   Google Scholar
• Bacciu, D., et al., 2020. A gentle introduction to deep learning for graphs. Neural Networks: The Official Journal of the International Neural Network Society, 129, 203–221.
   PubMed Web of Science ®Google Scholar
• Bender, E.A. and Williamson, S.G., 2010. Lists, decisions and graphs. University of California, San Diego: S. Gill Williamson.
   Google Scholar
• Bettinger, P., et al., 2005. A hierarchical spatial framework for forest landscape planning. Ecological Modelling, 182 (1), 25–48.
   Web of Science ®Google Scholar
• Biswas, S.R. and Wagner, H.H., 2012. Landscape contrast: a solution to hidden assumptions in the metacommunity concept? Landscape Ecology, 27 (5), 621–631.
   Web of Science ®Google Scholar
• Blaschke, T., et al., 2014. Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191.
   PubMed Web of Science ®Google Scholar
• Cao, S., et al., 2017. A win-win strategy for ecological restoration and biodiversity conservation in Southern China. Environmental Research Letters, 12 (4), 044004.
   Web of Science ®Google Scholar
• Censi, A.M., et al., 2021. Attentive spatial temporal graph CNN for land cover mapping from multi temporal remote sensing data. IEEE Access., 9, 23070–23082.
   Web of Science ®Google Scholar
• Chan, J., Bailey, J., and Leckie, C., 2008. Discovering correlated spatio-temporal changes in evolving graphs. Knowledge and Information Systems, 16 (1), 53–96.
   Web of Science ®Google Scholar
• Chaudhuri, U., et al., 2021. Attention-driven graph convolution network for remote sensing image retrieval. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
   Web of Science ®Google Scholar
• Chen, C., et al., 2019a. China and India lead in greening of the world through land-use management. Nature Sustainability, 2 (2), 122–129.
   PubMed Web of Science ®Google Scholar
• Chen, Y., et al., 2019b. A survey on visualization approaches for exploring association relationships in graph data. Journal of Visualization, 22 (3), 625–639.
   Web of Science ®Google Scholar
• Cheung, A., 2015. Spatial and temporal topological analysis of landscape structure using graph theory. Auckland, New Zealand: ResearchSpace@ Auckland.
   Google Scholar
• Cheung, A.K.L., O’sullivan, D., and Brierley, G., 2015. Graph-assisted landscape monitoring. International Journal of Geographical Information Science, 29 (4), 580–605.
   Web of Science ®Google Scholar
• Dale, M. and Fortin, M.-J., 2010. From graphs to spatial graphs. Annual Review of Ecology, Evolution, and Systematics, 41 (1), 21–38.
   Google Scholar
• Dara, A., et al., 2018. Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series. Remote Sensing of Environment, 213, 49–60.
   Web of Science ®Google Scholar
• De Cola, L., 2010. A network representation of raster land-cover patches. Photogrammetric Engineering & Remote Sensing, 76 (1), 61–72.
   Web of Science ®Google Scholar
• Del Mondo, G., et al., 2013. Modeling consistency of spatio-temporal graphs. Data & Knowledge Engineering, 84, 59–80.
   Web of Science ®Google Scholar
• Donnat, C., et al., 2018. Learning structural node embeddings via diffusion wavelets. In: Y. Guo and F. Farooq, eds. Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 19–23 August 2018, London, UK. New York: Association for Computing Machinery, 1320–1329.
   Google Scholar
• Fall, A., et al., 2007. Spatial graphs: principles and applications for habitat connectivity. Ecosystems, 10 (3), 448–461.
   Web of Science ®Google Scholar
• Fang, X., et al., 2018. Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: a case study in Quebec, Canada. Remote Sensing of Environment, 206, 391–402.
   Web of Science ®Google Scholar
• Ferrari, J.R., Lookingbill, T.R., and Neel, M.C., 2007. Two measures of landscape-graph connectivity: assessment across gradients in area and configuration. Landscape Ecology, 22 (9), 1315–1323.
   Web of Science ®Google Scholar
• Ferrarini, A. and Tomaselli, M., 2010. A new approach to the analysis of adjacencies: potentials for landscape insights. Ecological Modelling, 221 (16), 1889–1896.
   Web of Science ®Google Scholar
• Foltête, J.-C. and Vuidel, G., 2017. Using landscape graphs to delineate ecologically functional areas. Landscape Ecology, 32 (2), 249–263.
   Web of Science ®Google Scholar
• Friedl, M. and Sulla-Menashe, D., 2015. MCD12Q1 MODIS/Terra + aqua land cover type yearly L3 global 500m SIN grid V006 [data set. ]. NASA EOSDIS Land Processes DAAC, 10, 200.
   Google Scholar
• Friedl, M.A., et al., 2010. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114 (1), 168–182.
   Web of Science ®Google Scholar
• Friedl, M.A., et al., 2018. Towards a moderate spatial resolution data record of 21st century global land cover, land use, and land cover change. AGU Fall Meeting Abstracts, 2018, B22A-01.
   Google Scholar
• Galpern, P., Manseau, M., and Fall, A., 2011. Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biological Conservation, 144 (1), 44–55.
   Web of Science ®Google Scholar
• Gao, Y., et al., 2011. A degradation threshold for irreversible loss of soil productivity: a long‐term case study in China. Journal of Applied Ecology, 48 (5), 1145–1154.
   Web of Science ®Google Scholar
• Gong, P., et al., 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510.
   Web of Science ®Google Scholar
• Goodwin, N.R., et al., 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 112 (9), 3680–3689.
   Web of Science ®Google Scholar
• Gorelick, N., et al., 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
   Web of Science ®Google Scholar
• Gross, J.L., Yellen, J., and Anderson, M., 2018. Graph theory and its applications. New York: Chapman and Hall/CRC.
   Google Scholar
• Guttler, F., et al., 2017. A graph-based approach to detect spatiotemporal dynamics in satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 92–107.
   Web of Science ®Google Scholar
• Güttler, F., et al., 2014. Exploring high repetitivity remote sensing time series for mapping and monitoring natural habitats—a new approach combining OBIA and k-partite graphs. In: 2014 IEEE geoscience and remote sensing symposium, IGARSS 2014, 13–18 July 2014, Quebec City, QC, Canada. IEEE, 3930–3933.
   Google Scholar
• Hartigan, J.A. and Wong, M.A., 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28 (1), 100–108.
   Google Scholar
• Hay, G.J. and Castilla, G., 2008. Geographic Object-Based Image Analysis (GEOBIA): a new name for a new discipline. In: T. Blaschke, S. Lang, and G. Hay, eds. Object-based image analysis. Heidelberg, Berlin: Springer, 75–89.
   Google Scholar
• He, Y., Lee, E., and Warner, T.A., 2017. A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sensing of Environment, 199, 201–217.
   Web of Science ®Google Scholar
• He, Z., et al., 2020. Mining spatiotemporal association patterns from complex geographic phenomena. International Journal of Geographical Information Science, 34 (6), 1162–1187.
   Web of Science ®Google Scholar
• Héas, P. and Datcu, M., 2005. Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning. IEEE Transactions on Geoscience and Remote Sensing, 43 (7), 1635–1647.
   Web of Science ®Google Scholar
• Heinz, F. and Güting, R.H., 2016. Robust high-quality interpolation of regions to moving regions. GeoInformatica, 20 (3), 385–413.
   Web of Science ®Google Scholar
• Henderson, K., et al., 2011. It’s who you know: graph mining using recursive structural features. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, 21–24 August 2011, San Diego, California, USA. New York: Association for Computing Machinery, 663–671.
   Google Scholar
• Hermosilla, T., et al., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment, 170, 121–132.
   Web of Science ®Google Scholar
• Hersperger, A.M., 2006. Spatial adjacencies and interactions: neighborhood mosaics for landscape ecological planning. Landscape and Urban Planning, 77 (3), 227–239.
   Web of Science ®Google Scholar
• Hossain, M.D. and Chen, D., 2019. Segmentation for Object-Based Image Analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 115–134.
   Web of Science ®Google Scholar
• Hostert, P., et al., 2015. Time series analyses in a new era of optical satellite data. In: C. Kuenzer, S. Dech, and W. Wagner, eds. Remote sensing time series. Switzerland: Springer, 25–41.
   Google Scholar
• Hou, W. and Walz, U., 2016. An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones. Nature Conservation, 14, 25–39.
   Google Scholar
• Huang, J., et al., 2012. Use of intensity analysis to link patterns with processes of land change from 1986 to 2007 in a coastal watershed of southeast China. Applied Geography, 34, 371–384.
   Web of Science ®Google Scholar
• Hussain, M., et al., 2013. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106.
   Web of Science ®Google Scholar
• Kennedy, R.E., Yang, Z., and Cohen, W.B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sensing of Environment, 114 (12), 2897–2910.
   Web of Science ®Google Scholar
• Khiali, L., et al., 2019. Detection of spatio-temporal evolutions on multi-annual satellite image time series: a clustering based approach. International Journal of Applied Earth Observation and Geoinformation, 74, 103–119.
   Web of Science ®Google Scholar
• Khiali, L., Ienco, D., and Teisseire, M., 2018. Object-oriented satellite image time series analysis using a graph-based representation. Ecological Informatics, 43, 52–64.
   Web of Science ®Google Scholar
• Li, S. and Yang, B., 2015. Introducing a new method for assessing spatially explicit processes of landscape fragmentation. Ecological Indicators, 56, 116–124.
   Web of Science ®Google Scholar
• Liu, J., et al., 2019. A process-oriented spatiotemporal clustering method for complex trajectories of dynamic geographic phenomena. IEEE Access., 7, 155951–155964.
   Web of Science ®Google Scholar
• Lorrain, F. and White, H.C., 1971. Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology, 1 (1), 49–80.
   Google Scholar
• Loveland, T.R. and Belward, A., 1997. The IGBP-DIS global 1km land cover data set, DISCover: first results. International Journal of Remote Sensing, 18 (15), 3289–3295.
   Web of Science ®Google Scholar
• Macqueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: L.M. Le Cam and J. Neyman, eds. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 21 June–18 July, 1965 and 27 December 1965–7 January 1966, Statistical Laboratory of the University of California, Berkeley. Univ of California Press, 281–297.
   Google Scholar
• Massey, D., 1999. Space‐time, ‘science’ and the relationship between physical geography and human geography. Transactions of the Institute of British Geographers, 24 (3), 261–276.
   Web of Science ®Google Scholar
• Matasci, G., et al., 2018. Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216, 697–714.
   Web of Science ®Google Scholar
• Mcgarigal, K., 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station.
   Google Scholar
• Mcgee, F., et al., 2019. The state of the art in multilayer network visualization. Computer Graphics Forum, 38,125–149.
   Google Scholar
• Mckenney, M. and Webb, J., 2010. Extracting moving regions from spatial data. In: A.E. Abbadi, D. Agrawal, M. Mokbel, and P. Zhang, eds. Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, 2–5 November 2010, San Jose, California. New York: Association for Computing Machinery, 438–441.
   Google Scholar
• Mckennney, M. and Frye, R., 2015. Generating moving regions from snapshots of complex regions. ACM Transactions on Spatial Algorithms and Systems, 1 (1), 1–30.
   Google Scholar
• Mertes, C.M., et al., 2015. Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment, 158, 331–347.
   Web of Science ®Google Scholar
• Oberoi, K.S. and Del Mondo, G., 2021. Graph-based pattern detection in spatio-temporal phenomena. In: 16th spatial analysis and geomatics conference (SAGEO 2021), May 2021, La Rochelle, (virtuel), France. hal-03232523.
   Google Scholar
• OriginPro, 2022. Northampton, MA: OriginLab Corporation.
   Google Scholar
• Pedregosa, F., et al., 2011. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Pekel, J.-F., et al., 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540 (7633), 418–422.
   PubMed Web of Science ®Google Scholar
• Pinson, L., Del Mondo, G., and Tranouez, P., 2019. Representation of interdependencies between urban networks by a multi-layer graph. In: S. Timpf, C. Schlieder, M. Kattenbeck, B. Ludwig, and K. Stewart, eds. 14th International Conference on Spatial Information Theory, COSIT 2019, 9–13 September 2019, Regensburg, Germany. LIPIcs 142, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2019.
   Google Scholar
• Réjichi, S. and Chaabane, F., 2014. SVM spatio-temporal classification of HR satellite image time series using graph based kernel. In: 2014 1st international conference on advanced technologies for signal and image processing (ATSIP), 17–19 March 2014, Sousse, Tunisia. IEEE, 390–395.
   Google Scholar
• Réjichi, S. and Chaabane, F., 2016. Spatio-temporal regions’ similarity framework for VHR satellite image time series analysis. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS), 10–15 July 2016, Beijing, China. IEEE, 2845–2848.
   Google Scholar
• Réjichi, S., Chaabane, F., and Tupin, F., 2015. Expert knowledge-based method for satellite image time series analysis and interpretation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (5), 2138–2150.
   Web of Science ®Google Scholar
• Riitters, K., 2019. Pattern metrics for a transdisciplinary landscape ecology. Landscape Ecology, 34, 2057–2063.
   Google Scholar
• Rossi, R.A. and Ahmed, N.K., 2015. Role discovery in networks. IEEE Transactions on Knowledge and Data Engineering, 27 (4), 1112–1131.
   Web of Science ®Google Scholar
• Rozemberczki, B., Kiss, O., and Sarkar, R., 2020. Karate Club: an API oriented open-source python framework for unsupervised learning on graphs. In: M. d'Aquin, S. Dietze, C. Hauff, E. Curry, and P.C. Mauroux, eds. Proceedings of the 29th ACM international conference on information & knowledge management, 19–23 October 2020, Virtual Event, Ireland. New York: Association for Computing Machinery, 3125–3132.
   Google Scholar
• Shifaw, E., Sha, J., and Li, X., 2020. Detection of spatiotemporal dynamics of land cover and its drivers using remote sensing and landscape metrics (Pingtan Island, China). Environment, Development and Sustainability, 22 (2), 1269–1298.
   Web of Science ®Google Scholar
• Singh, A., 1989. Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10 (6), 989–1003.
   Web of Science ®Google Scholar
• Smiraglia, D., et al., 2007. The use of adjacency analysis for quantifying landscape changes. Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology, 141 (3), 384–389.
   Google Scholar
• Song, X.-P., et al., 2018. Global land change from 1982 to 2016. Nature, 560 (7720), 639–643.
   PubMed Web of Science ®Google Scholar
• Su, K., Wei, D.-Z., and Lin, W.-X., 2020. Evaluation of ecosystem services value and its implications for policy making in China–a case study of Fujian province. Ecological Indicators, 108, 105752.
   Web of Science ®Google Scholar
• Tøssebro, E. and Güting, R.H., 2001. Creating representations for continuously moving regions from observations. In: C.S. Jensen, M. Schneider, B. Seeger, and V.J. Tsotras, eds. International symposium on spatial and temporal databases, SSTD 2001, 12–15 July 2001, Redondo Beach, CA, USA. Heidelberg, Berlin: Springer, 321–344.
   Google Scholar
• Urban, D. and Keitt, T., 2001. Landscape connectivity: a graph‐theoretic perspective. Ecology, 82 (5), 1205–1218.2.0.CO;2]
   Web of Science ®Google Scholar
• Vaglio Laurin, G., et al., 2021. Satellite open data to monitor forest damage caused by extreme climate-induced events: a case study of the Vaia storm in Northern Italy. Forestry: An International Journal of Forest Research, 94 (3), 407–416.
   Google Scholar
• Van der Maaten, L. and Hinton, G., 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, 9 (86), 2579–2605.
   Google Scholar
• Verbesselt, J., et al., 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114 (1), 106–115.
   Web of Science ®Google Scholar
• Wang, X., et al., 2020. Gainers and losers of surface and terrestrial water resources in China during 1989–2016. Nature Communications, 11 (1), 1–12.
   PubMed Web of Science ®Google Scholar
• Woodcock, C.E., et al., 2020. Transitioning from change detection to monitoring with remote sensing: a paradigm shift. Remote Sensing of Environment, 238, 111558.
   Web of Science ®Google Scholar
• Wu, B., et al., 2021. A spatiotemporal structural graph for characterizing land cover changes. International Journal of Geographical Information Science, 35 (2), 397–425.
   Web of Science ®Google Scholar
• Wu, Z., et al., 2021. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32 (1), 4–24.
   PubMed Web of Science ®Google Scholar
• Wulder, M.A., et al., 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147.
   Web of Science ®Google Scholar
• Xu, Y., et al., 2020. Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm. Science China Earth Sciences, 63 (9), 1390–1407.
   Web of Science ®Google Scholar
• Xue, C., Xu, Y., and He, Y., 2021. A global process-oriented sea surface temperature anomaly dataset retrieved from remote sensing products. Big Earth Data, 6 (2), 179–195.
   Google Scholar
• Zhang, Y., et al., 2015. An efficient graph-based method for long-term land-use change statistics. Sustainability, 8 (1), 9.
   Web of Science ®Google Scholar
• Zhang, Z., Cui, P., and Zhu, W., 2020. Deep learning on graphs: a survey. IEEE Transactions on Knowledge and Data Engineering, 34 (1), 249–270.
   Google Scholar
• Zhou, J., et al., 2020. Graph neural networks: a review of methods and applications. AI Open, 1, 57–81.
   Google Scholar
• Zhu, L., et al., 2021. Detection of paddy rice cropping systems in southern China with time series Landsat images and phenology-based algorithms. GIScience & Remote Sensing, 58 (5), 733–755.
   Web of Science ®Google Scholar
• Zhu, Z., 2017. Change detection using Landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384.
   Web of Science ®Google Scholar
• Zhu, Z. and Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.
   Web of Science ®Google Scholar