What is the Direction of Land Change? A New Approach to Land-Change Analysis
Abstract
:1. Introduction
2. Methods
2.1. Maximum Cross-Correlation (MCC) Method Overview
2.2. Data, Pre-Processing, Image Classification, and Classification Accuracy Assessment
2.3. Sensitivity Analysis
3. Results and Discussion
3.1. Classification Accuracy Assessment
3.2. Sensitivity to Template Size
3.3. Sensitivity to Search Window Size
3.4. Sensitivity to MCC Threshold
3.5. MCC Land-Change Analysis
3.6. Suggestions for Future Research
- An appropriate change-analysis accuracy-assessment procedure needs to be developed for the MCC algorithm in this context. Accuracy assessment is necessary to validate a change-analysis technique. However, given the specialized nature of the combination of the change-analysis method (involving MCC displacement vectors) and the application domain considered, a graphical correspondence assessment was performed in the present research. Although the results of the present research are promising, a more rigorous assessment procedure needs to be developed. Perhaps one possible means of accuracy assessment would be to measure angles and distances/change magnitudes for a given phenomenon, for example, at time t and the same phenomenon at time t + 1. Such measurements could be performed in situ at different times, or perhaps utilizing higher spatial-resolution multitemporal images. Also note that, as discussed in Foody (2004; 2008) [38,39], comparison of accuracies between different methods can be conducted by comparing Kappa coefficients derived from the confusion matrices or other statistical approaches. However, for the MCC application presented here, MCC provides directional change information for quantitative historical land-cover change analysis, which is unlike information produced by other traditional change-detection methods. Thus, a conventional confusion matrix-based assessment method cannot be used to evaluate the accuracy of the directional information contained in the MCC displacement vectors. Based on the MCC results though, from-to class change information could possibly be derived, at least at the template level. Results from other change-detection methods (e.g., CVA) could be generalized via, for example, resampling from the pixel up to the template level, and based on the majority class of the pixels within a given template area. This could potentially allow comparison of MCC class results with those from other change-detection methods. However, again, MCC output is primarily comprised of directional information (i.e., class-by-class spatial displacements across the landscape) in the form of the MCC displacement vectors, which is not comparable to results from traditional change-detection approaches.
- An improved filtering rule is needed. Besides the MCC threshold, some original MCC applications to ocean currents applied other criteria, e.g., displacement vector length compared with physical movement and velocity of currents in order to filter-out invalid vectors [7]. As noted above, our terrestrial application entailed several invalid vectors that had not been rejected. Thus, a stricter filtering rule based on certain physical/geomorphological/ecological criteria related to the scene is worth investigation in order to eliminate such invalid vectors.
- Comprehensive interpretation of MCC change/displacement vectors would likely be beneficial. Unlike original ocean-current applications, terrestrial MCC applications contain multiple sets of vectors, where a set of displacement vectors corresponds to a given land-cover class. As different land-cover classes interact with each other during the process of land change, in addition to a separate interpretation for each set of MCC land-cover change/displacement vectors for a given land-cover class, a joint interpretation of multiple or all sets of change/displacement vectors associated with the different respective land-cover classes should be considered. Such a combined analysis may provide a more comprehensive synthesis and assessment of land change. Comprehension of such multifaceted data sets (e.g., regarding how MCC change/displacement vectors represent underlying interactions and processes among multiple land-cover classes) based on visualization will be challenging, and quantitative methods for such joint analyses should thus be a focus of further research. For instance, part of such research may involve converting change/displacement vector information for each fuzzy membership layer into from-to change-analysis information.
- Finally, note that there are some classes of landscape change for which MCC may not be ideally suited. If a vegetation assemblage, for example, were to completely disappear/be removed from the study area during the time period between two image-acquisition dates, MCC may not capture such a change. Thus, in some such land-change scenarios, MCC may be used in conjunction with other change-analysis algorithms (i.e., pixel- and/or object-based algorithms), with MCC providing a unique and complementary class of change-analysis information.
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Lunetta, R.S.; Johnson, D.M.; Lyon, J.G.; Crotwell, J. Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sens. Environ. 2004, 89, 444–454. [Google Scholar] [CrossRef]
- Tewkesbury, A.P.; Comber, A.J.; Tate, N.J.; Lamb, A.; Fisher, P.F. A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens. Environ. 2015, 160, 1–14. [Google Scholar] [CrossRef]
- Wei, C.; Blaschke, T.; Kazakopoulos, P.; Taubenböck, H.; Tiede, D. Is spatial resolution critical in urbanization velocity analysis? Investigations in the Pearl River Delta. Remote Sens. 2017, 9, 80. [Google Scholar]
- Kong, F.; Nakagoshi, N. Spatial-temporal gradient analysis of urban green spaces in Jinan, China. Landsc. Urban Plan. 2006, 78, 147–164. [Google Scholar] [CrossRef]
- McGillem, C.D.; Svedlow, M. Image registration error variance as a measure of overlay quality. IEEE Trans. Geosci. Electron. 1976, 14, 44–49. [Google Scholar] [CrossRef]
- Bowen, M.M.; Emery, W.J.; Wilkin, J.L.; Tildesley, P.C.; Barton, I.J.; Knewtson, R. Extracting multiyear surface currents from sequential thermal imagery using the maximum cross-correlation technique. J. Atmos. Ocean. Technol. 2002, 19, 1665–1676. [Google Scholar] [CrossRef]
- Crocker, R.I.; Matthews, D.K.; Emery, W.J.; Baldwin, D.G. Computing coastal ocean surface currents from infrared and ocean color satellite imagery. IEEE Trans. Geosci. Remote Sens. 2007, 45, 435–447. [Google Scholar] [CrossRef]
- Crocker, R.I.; Matthews, D.K.; Emery, W.J.; Qazi, W.A.; Baldwin, D. Near-Realtime U.S. Coastal Ocean Surface Currents Derived From Sequential Thermal Satellite Imagery. Available online: http://ccar.colorado.edu/colors/mcc.html (accessed on 27 September 2016).
- Emery, W.J.; Thomas, A.C.; Collins, M.J.; Crawford, W.R.; Mackas, D.L. An objective method for computing advective surface velocities from sequential infrared satellite images. J. Geophys. Res. 1986, 91, 12865–12878. [Google Scholar] [CrossRef]
- Gao, J.; Lythe, M.B. The maximum cross-correlation approach to detecting translational motions from sequential remote-sensing images. Comput. Geosci. 1996, 22, 525–534. [Google Scholar] [CrossRef]
- Leese, J.A.; Novak, C.S.; Clark, B.B. An automated technique for obtaining cloud motion from geosynchronous satellite data using cross correlation. J. Appl. Meteorol. 1971, 10, 118–132. [Google Scholar] [CrossRef]
- Ninnis, R.M.; Emery, W.J.; Collins, M.J. Automated extraction of pack ice motion from advanced very high resolution radiometer imagery. J. Geophys. Res. 1986, 91, 10725. [Google Scholar] [CrossRef]
- Civco, D.L.; Hurd, J.D.; Wilson, E.H.; Song, M.; Zhang, Z. A comparison of land use and land cover change detection methods. In Proceedings of the ASPRS-ACSM Annual Conference, Washington, DC, USA, 22–26 April 2002. [Google Scholar]
- Kim, J.G. Assessment of recent industrialization in wetlands near Ulsan, Korea. J. Paleolimnol. 2005, 33, 433–444. [Google Scholar] [CrossRef]
- Im, J.; Jensen, J.; Tullis, J. Object-based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens. 2008, 29, 399–423. [Google Scholar] [CrossRef]
- Koeln, G.; Bissonnette, J. Cross-correlation analysis: Mapping landcover change with a historic landcover database and a recent, single-date multispectral image. In Proceedings of the 2000 ASPRS Annual Convention, Washington, DC, USA, 22–26 May 2000. [Google Scholar]
- Hurd, J.D.; Wilson, E.H.; Lammey, S.G.; Civco, D.L. Characterization of forest fragmentation and urban sprawl using time sequential Landsat imagery. In Proceedings of the ASPRS Annual Convention, St. Louis, MO, USA, 23–27 April 2001. [Google Scholar]
- Moughal, T.A.; Yu, F.; Mazher, A.; Liu, S.; Razzaq, A. Enhanced detection of burned area using cross- and autocorrelation. J. Appl. Remote Sens. 2015, 9, 096018. [Google Scholar] [CrossRef]
- Tarantino, C.; Adamo, M.; Lucas, R.; Blonda, P. Detection of changes in semi-natural grasslands by cross correlation analysis with WorldView-2 images and new Landsat 8 data. Remote Sens. Environ. 2016, 175, 65–72. [Google Scholar] [CrossRef] [PubMed]
- Im, J.; Jensen, J.R. A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens. Environ. 2005, 99, 326–340. [Google Scholar] [CrossRef]
- Debella-Gilo, M.; Kääb, A. Locally adaptive template sizes for matching repeat images of Earth surface mass movements. ISPRS J. Photogramm. Remote Sens. 2012, 69, 10–28. [Google Scholar] [CrossRef]
- Perkins, T.; Adler-Golden, S.; Matthew, M.; Berk, A.; Anderson, G.; Gardner, J.; Felde, G. Retrieval of atmospheric properties from hyper- and multi-spectral imagery with the FLAASH atmospheric correction algorithm. Proc. SPIE 2005, 5979. [Google Scholar] [CrossRef]
- Eastman, J.R. IDRISI Taiga Guide to GIS and Image Processing; Clark Labs Clark University: Worcester, MA, USA, 2009. [Google Scholar]
- Food Agriculture Organization (FAO). Forest Resources Assessment 1990. Tropical Countries; FAO: Rome, Italy, 1993. [Google Scholar]
- Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35–46. [Google Scholar] [CrossRef]
- Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC Press/Taylor & Francis: Boca Raton, FL, USA, 2009. [Google Scholar]
- Tokmakian, R.; Strub, P.T.; McClean-Padman, J. Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images. J. Atmos. Ocean. Technol. 1990, 7, 852–865. [Google Scholar] [CrossRef]
- Evans, A.N. Glacier surface motion computation from digital image séquences. IEEE Trans. Geosci. Remote Sens. 2000, 38, 1064–1072. [Google Scholar] [CrossRef] [Green Version]
- Malila, W.A. Change vector analysis: An approach for detecting forest changes with Landsat. In Proceedings of the LARS Symposia, Purdue University, Lafayette, IN, USA, 3–6 June 1980. [Google Scholar]
- Baker, C.; Lawrence, R.; Montagne, C.; Patten, D. Change detection of wetland ecosystems using Landsat imagery and change vector analysis. Wetlands 2007, 27, 610–619. [Google Scholar] [CrossRef]
- Munro, M.A.; Blenkinsop, T.G. MARD—A moving average rose diagram application for the geosciences. Comput. Geosci. 2012, 49, 112–120. [Google Scholar] [CrossRef]
- Dewan, A.; Corner, R.; Saleem, A.; Rahman, M.M.; Haider, M.R.; Rahman, M.M.; Sarker, M.H. Assessing channel changes of the Ganges-Padma River system in Bangladesh using Landsat and hydrological data. Geomorphology 2017, 276, 257–279. [Google Scholar] [CrossRef]
- Midha, N.; Mathur, P.K. Channel characteristics and planform dynamics in the Indian Terai, Sharda River. Environ. Manag. 2014, 53, 120–134. [Google Scholar] [CrossRef] [PubMed]
- Güneralp, İ.; Rhoads, B.L. Empirical analysis of planform curvature–migration relation of meandering rivers. Water Resour. Res. 2009, 45, W09424. [Google Scholar] [CrossRef]
- Güneralp, İ.; Rhoads, B.L. Spatial autoregressive structure of meander evolution revisited. Geomorphology 2010, 120, 91–106. [Google Scholar] [CrossRef]
- Gupta, N.; Atkinson, P.M.; Carling, P.A. Decadal length changes in the fluvial planform of the River Ganga: Bringing a mega-river to life with Landsat archives. Remote Sens. Lett. 2013, 4, 1–9. [Google Scholar] [CrossRef]
- Yang, C.; Cai, X.; Wang, X.; Yan, R.; Zhang, T.; Zhang, Q.; Lu, X. Remotely sensed trajectory analysis of channel migration in Lower Jingjiang Reach during the period of 1983–2013. Remote Sens. 2015, 7, 16241–16256. [Google Scholar] [CrossRef]
- Foody, G.M. Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogramm. Eng. Remote Sens. 2004, 70, 627–633. [Google Scholar] [CrossRef]
- Foody, G.M. Harshness in image classification accuracy assessment. Int. J. Remote Sens. 2008, 29, 3137–3158. [Google Scholar] [CrossRef]
Accuracy (%) | Year | |||||
---|---|---|---|---|---|---|
1987 | 1990 | 2006 | ||||
Overall | 90.17 | 90.33 | 90.00 | |||
By land cover class | Producer’s | User’s | Producer’s | User’s | Producer’s | User’s |
Forest | 90.91 | 90.00 | 85.32 | 93.00 | 86.11 | 93.00 |
Non-forest vegetation | 84.31 | 86.00 | 87.76 | 86.00 | 83.00 | 83.00 |
River and oxbow lake | 86.21 | 100.00 | 94.34 | 100.00 | 95.24 | 100.00 |
Sand | 100.00 | 90.00 | 95.88 | 93.00 | 100.00 | 92.00 |
Dry soil | 88.46 | 92.00 | 91.58 | 87.00 | 90.70 | 78.00 |
Wet soil | 93.26 | 83.00 | 87.37 | 83.00 | 78.90 | 86.00 |
Kappa coefficient | 0.882 | 0.884 | 0.880 |
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You, M.; Filippi, A.M.; Güneralp, İ.; Güneralp, B. What is the Direction of Land Change? A New Approach to Land-Change Analysis. Remote Sens. 2017, 9, 850. https://doi.org/10.3390/rs9080850
You M, Filippi AM, Güneralp İ, Güneralp B. What is the Direction of Land Change? A New Approach to Land-Change Analysis. Remote Sensing. 2017; 9(8):850. https://doi.org/10.3390/rs9080850
Chicago/Turabian StyleYou, Mingde, Anthony M. Filippi, İnci Güneralp, and Burak Güneralp. 2017. "What is the Direction of Land Change? A New Approach to Land-Change Analysis" Remote Sensing 9, no. 8: 850. https://doi.org/10.3390/rs9080850