Abstract
Accurate information on change types is widely used in areas such as territorial spatial planning and dynamic monitoring of land cover, etc. Change type information is usually obtained using the post-classification comparison method. However, such methods are susceptible to the accumulation of errors due to the classification results, which in turn affects the accuracy of change type information identification. In response to the above problems, a change type determination method based on knowledge of spectral changes in land cover types (KSC-LCT) is proposed. Firstly, the change vector analysis method is used to obtain the change magnitude map, and the threshold value is calculated using the OTSU method to obtain the change regions. Secondly, some samples of land cover types are selected to calculate the mean value of each land cover type, and the spectral knowledge information of different land cover type changes is carved from the perspectives of spectral mean change, spectral angle change and thematic index change. Finally, based on the classification data of the previous moment and spectral change knowledge information, knowledge matching is carried out with the spectral change information of remote sensing images in the changed areas, and the minimum value of the matching result is selected as the judgment criterion to obtain the final change type of land cover. The experimental results show that the KSC-LCT method can effectively improve the recognition accuracy of land cover change type information and further reduce the phenomenon of over-detection in the change areas.
Similar content being viewed by others
Data Availability
Data are available on the United States Geological Survey website (https://earthexplorer.usgs.gov/).
References
Alam SMR, Hossain MS (2020) A rule-based classification method for Mapping Saltmarsh Land-Cover in South-Eastern Bangladesh from Landsat-8 OLI. Can J Remote Sens 0:1–25. https://doi.org/10.1080/07038992.2020.1789852
Amani M, Ghorbanian A, Ahmadi SA et al (2020) IEEE J Sel Top Appl Earth Obs Remote Sens 13:5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review
Asokan A, Anitha J (2019) Change detection techniques for remote sensing applications: a survey. Earth Sci Informatics 12:143–160. https://doi.org/10.1007/s12145-019-00380-5
Carvalho Júnior OA, Guimarães RF, Gillespie AR et al (2011) A new approach to change vector analysis using distance and similarity measures. Remote Sens 3:2473–2493. https://doi.org/10.3390/rs3112473
Chen J, Chen X, Cui X, Chen J (2011) Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geosci Remote Sens Lett 8:317–321. https://doi.org/10.1109/LGRS.2010.2068537
Chen J, Lu M, Chen X et al (2013) A spectral gradient difference based approach for land cover change detection. ISPRS J Photogramm Remote Sens 85:1–12. https://doi.org/10.1016/j.isprsjprs.2013.07.009
Du P, Wang X, Chen D et al (2020) An improved change detection approach using tri-temporal logic-verified change vector analysis. ISPRS J Photogramm Remote Sens 161:278–293. https://doi.org/10.1016/j.isprsjprs.2020.01.026
Fan J, Lin K, Han M (2019) A Novel Joint Change Detection Approach based on weight-clustering sparse autoencoders. IEEE J Sel Top Appl Earth Obs Remote Sens 12:685–699. https://doi.org/10.1109/JSTARS.2019.2892951
Fang B, Chen G, Ouyang G et al (2022) Content-invariant dual learning for change detection in Remote sensing images. IEEE Trans Geosci Remote Sens 60:1–17. https://doi.org/10.1109/TGRS.2021.3064501
Gelabert PJ, Rodrigues M, de la Riva J et al (2021) LandTrendr smoothed spectral profiles enhance woody encroachment monitoring. Remote Sens Environ 262. https://doi.org/10.1016/j.rse.2021.112521
Gordon SI (1980) Utilizing LANDSAT imagery to Monitor Land. Use change : a Case Study in Ohio. Remote Sens Environ 9:189–196
Grabska E, Frantz D, Ostapowicz K (2020) Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the polish Carpathians. Remote Sens Environ 251:112103. https://doi.org/10.1016/j.rse.2020.112103
Han X, Chen X, Feng L (2015) Four decades of winter wetland changes in Poyang Lake based on landsat observations between 1973 and 2013. Remote Sens Environ 156:426–437. https://doi.org/10.1016/j.rse.2014.10.003
Han M, Zhang C, Zhou Y (2018) Object-wise joint-classification change detection for remote sensing images based on entropy query-by fuzzy ARTMAP. GIScience Remote Sens 55:265–284. https://doi.org/10.1080/15481603.2018.1430100
Himalaya K, Solanki A, Gupta V, Joshi M (2022) Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Application of machine learning algorithms in landslide. Geocarto Int 0:1–26. https://doi.org/10.1080/10106049.2022.2120546
Huang J, Liu Y, Wang M et al (2019) Change detection of high spatial resolution images based on region-line primitive association analysis and evidence fusion. Remote Sens 11:1–23. https://doi.org/10.3390/rs11212484
Jin S, Liu Y, Fagherazzi S et al (2021) River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method. Remote Sens Environ 255. https://doi.org/10.1016/j.rse.2021.112297
Johnson RD, Kasischke ES (1998) Change vector analysis: a technique for the multispectral monitoring of land cover and condition. Int J Remote Sens 19:411–426. https://doi.org/10.1080/014311698216062
Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90. https://doi.org/10.1016/j.compag.2018.02.016
Liu C, Zhang Q, Luo H et al (2019) An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense landsat time series stacks. Remote Sens Environ 229:114–132. https://doi.org/10.1016/j.rse.2019.04.025
Lv ZY, Liu TF, Zhang P et al (2019) Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images. IEEE Trans Geosci Remote Sens 57:9554–9574. https://doi.org/10.1109/TGRS.2019.2927659
Madasa A, Orimoloye IR, Ololade OO (2021) Application of geospatial indices for mapping land cover/use change detection in a mining area. J Afr Earth Sci 175:104108. https://doi.org/10.1016/j.jafrearsci.2021.104108
Massetti A, Gil A (2020) Mapping and assessing land cover/land use and aboveground carbon stocks rapid changes in small oceanic islands’ terrestrial ecosystems: a case study of Madeira Island, Portugal (2009–2011). Remote Sens Environ 239:111625. https://doi.org/10.1016/j.rse.2019.111625
Meng X, Gao X, Li S, Lei J (2020) Spatial and temporal characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015. Remote Sens 12:603. https://doi.org/10.3390/rs12040603
Mirasi A, Mahmoudi A, Navid H et al (2021) Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data. Geocarto Int 36:1309–1324. https://doi.org/10.1080/10106049.2019.1641561
Ni H, Gong P, Li X (2021) Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks. Remote Sens 13:2438–2455. https://doi.org/10.3390/rs13132438
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. https://doi.org/10.1109/tsmc.1979.4310076
Özelkan E (2020) Water body detection analysis using NDWI indices derived from landsat-8 OLI. Pol J Environ Stud 29:1759–1769. https://doi.org/10.15244/pjoes/110447
Roy DP, Huang H, Houborg R, Martins VS (2021) A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery. Remote Sens Environ 264:112586. https://doi.org/10.1016/j.rse.2021.112586
Saha S, Bovolo F, Bruzzone L (2019) Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Trans Geosci Remote Sens 57:3677–3693. https://doi.org/10.1109/TGRS.2018.2886643
Tu Y, Lang W, Yu L et al (2020) Improved mapping results of 10 m Resolution Land Cover classification in Guangdong, China using Multisource Remote Sensing Data with Google Earth Engine. IEEE J Sel Top Appl Earth Obs Remote Sens 13:5384–5397. https://doi.org/10.1109/JSTARS.2020.3022210
Walshe D, McInerney D, De Kerchove R et al (2020) Detecting nutrient deficiency in spruce forests using multispectral satellite imagery. Int J Appl Earth Obs Geoinf 86:101975. https://doi.org/10.1016/j.jag.2019.101975
Wan L, Xiang Y, You H (2019) A Post-Classification Comparison Method for SAR and Optical images change detection. IEEE Geosci Remote Sens Lett 16:1026–1030. https://doi.org/10.1109/LGRS.2019.2892432
Wang Y, Li Z, Zeng C et al (2020) An Urban Water extraction Method Combining Deep Learning and Google Earth Engine. IEEE J Sel Top Appl Earth Obs Remote Sens 13:768–781. https://doi.org/10.1109/JSTARS.2020.2971783
Wittke S, Yu X, Karjalainen M et al (2019) Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest. Int J Appl Earth Obs Geoinf 76:167–178. https://doi.org/10.1016/j.jag.2018.11.009
Wu C, Du B, Cui X, Zhang L (2017) A post-classification change detection method based on iterative slow feature analysis and bayesian soft fusion. Remote Sens Environ 199:241–255. https://doi.org/10.1016/j.rse.2017.07.009
Xing H, Zhu L, Chen B et al (2021a) A Novel Change Detection Method using remotely sensed Image Time Series Value and shape based Dynamic Time Warping. Geocarto Int 0:1–16. https://doi.org/10.1080/10106049.2021.2022013
Xing H, Zhu L, Feng Y et al (2021b) An adaptive change threshold selection method based on land cover posterior probability and spatial Neighborhood Information. IEEE J Sel Top Appl Earth Obs Remote Sens 14:11608–11621. https://doi.org/10.1109/JSTARS.2021.3124491
Xing H, Zhu L, Hou D, Zhang T (2021c) Integrating change magnitude maps of spectrally enhanced multi-features for land cover change detection. Int J Remote Sens 42:4284–4308. https://doi.org/10.1080/01431161.2021.1892860
Xing H, Zhu L, Niu J et al (2021d) A land cover change detection method combing spectral values and class probabilities. IEEE Access 9:83727–83739. https://doi.org/10.1109/access.2021.3087206
Xing H, Zhu L, Chen B et al (2022) A comparative study of threshold selection methods for change detection from very high-resolution remote sensing images. Earth Sci Informatics. https://doi.org/10.1007/s12145-021-00734-y
Xu L, Jing W, Song H, Chen G (2019) High-resolution remote sensing image change detection combined with pixel-level and object-level. IEEE Access 7:78909–78918. https://doi.org/10.1109/ACCESS.2019.2922839
Xue D, Lei T, Jia X et al (2021) Unsupervised change detection using Multiscale and Multiresolution Gaussian-Mixture-Model guided by saliency enhancement. IEEE J Sel Top Appl Earth Obs Remote Sens 14:1796–1809. https://doi.org/10.1109/JSTARS.2020.3046838
Yan L, Xia W, Zhao Z, Wang Y (2018) A novel approach to unsupervised change detection based on hybrid spectral difference. Remote Sens 10:841–862. https://doi.org/10.3390/rs10060841
Zakeri F, Huang B, Saradjian MR (2019) Fusion of change vector analysis in posterior probability space and postclassification comparison for change detection from multispectral remote sensing data. Remote Sens 11:1–14. https://doi.org/10.3390/rs11131511
Zakeri F, Saradjian MR (2020) Change detection in multispectral images based on fusion of change vector analysis in posterior probability space and posterior probability space angle mapper. Geocarto Int 0:1–15. https://doi.org/10.1080/10106049.2020.1768595
Zheng Z, Wan Y, Zhang Y et al (2021) CLNet: cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS J Photogramm Remote Sens 175:247–267. https://doi.org/10.1016/j.isprsjprs.2021.03.005
Zhu Z (2017) Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS J Photogramm Remote Sens 130:370–384. https://doi.org/10.1016/j.isprsjprs.2017.06.013
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.
Funding
This work was supported by the key program of National Natural Science Foundation of China [grant number 41930650] and the general program of National Natural Science Foundation of China [grant number 42271435].
Author information
Authors and Affiliations
Contributions
All authors were involved in the conception and design of the study. Material preparation, data collection, analysis and methodology were carried out by Linye Zhu, Huaqiao Xing, Longfei Zhao, Hui Qu, Wenbin Sun. The first draft of the manuscript was written by Linye Zhu, Huaqiao Xing and Wenbin Sun, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Communicated by H. Babaie
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhu, L., Xing, H., Zhao, L. et al. A change type determination method based on knowledge of spectral changes in land cover types. Earth Sci Inform 16, 1265–1279 (2023). https://doi.org/10.1007/s12145-023-00968-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-00968-y