A framework to detect digital changes in the mangrove forests

Document Type : Research Paper

Authors

1 Collage of Environment, Karaj, Iran.

2 Isfahan University of Technology, Isfahan, Iran.

Abstract

Despite the particular importance in marine ecosystems and food chain, mangrove forests are subject to destruction due to rapid population growth, poor planning, and inconsistent economic development. Identifying changes in these ecosystems is the first step in their sustainable management. Therefore, in this study, we have tried to determine the appropriate method for determining the changes in mangrove forests using satellite data and for identifying appropriate thresholds for revealing these changes. Based on the surveying of temporal variation of these forests, the tidal conditions are the first problem in determining the digital change of these forests. Accordingly, in order to determine the appropriate digital change detection method, OLI images in 2015 and ETM + images for 2001 were obtained in the same tidal conditions. The preprocessing operations included geometric, and radiometeric correction was done on these data. Then, to enhance spatial resolution, the fusion is done in a way that does not affect the output histogram. In the next step, first the images were enhanced by applying the spectral indexes, then the hybrid classification was applied to the images to extract the mangrove forest. At this stage, the changes in these forests were determined by post-classification comparison. In the next step, by combining the mangrove forest area at both time intervals, the total forest mask was obtained. Then, the NDVI spectral index was appropriately considered by analyzing the coefficients of variation of the spectral vegetation indices. Then, in the mask area of the mangrove forest, the NDVI was used to perform algebra change detection methods including image difference, image ratio, regression. Also, to determine the appropriate thresholds for algebra operations, the thresholds were applied based on deviation from the mean for all methods, then accordingly the changes were detected. Finally, by 120 sampling points, areas with the decrease, increase, and no change trends were visited and then overall accuracy and kappa coefficients were determined. Based on the results, the post-classification comparison has the highest accuracy in detecting changes. It was also found that the threshold of twice standard deviation showed the best accuracy in outputs.

Keywords

References

Afify, H. A. (2011). Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area. Alexandria Engineering Journal, 50(2), 187-195. doi:https://doi.org/10.1016/j.aej.2011.06.001
Al-Fares, W. (2013). Historical Land Use/land Cover Classification Using Remote Sensing: A case study of the Euphrates River Basin in Syria: Springer International Publishing.
Bahraminejad, M., Rayegani, B., Jahani, A., & Nezami, B. (2018). Proposing an early-warning system for optimal management of protected areas (Case study: Darmiyan protected area, Eastern Iran). Journal for Nature Conservation, 46, 79-88. doi:https://doi.org/10.1016/j.jnc.2018.08.013
Barati, S., Rayegani, B., Saati, M., Sharifi, A., & Nasri, M. (2011). Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Science, 14(1), 49-56. doi:https://doi.org/10.1016/j.ejrs.2011.06.001
Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. New York: Guilford Press.
Carney, J., Gillespie, T. W., & Rosomoff, R. (2014). Assessing forest change in a priority West African mangrove ecosystem: 1986–2010. Geoforum, 53, 126-135.
Chen, C.-F., Son, N.-T., Chang, N.-B., Chen, C.-R., Chang, L.-Y., Valdez, M., . . . Aceituno, J. (2013). Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model. Remote Sensing, 5(12), 6408.
Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices (2nd ed.). Boca Raton: CRC Press.
Eastman, J. (2012). IDRISI Selva Tutorial (Vol. 45).
Fung, T., & LeDrew, E. (1988). The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering and Remote Sensing, 54(10), 1449-1454.
Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). NDVI: vegetation change detection using remote sensing and GIS–a case study of Vellore District. Procedia Computer Science, 57, 1199-1210. doi:https://doi.org/10.1016/j.procs.2015.07.415
Giri, C., Pengra, B., Zhu, Z., Singh, A., & Tieszen, L. L. (2007). Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine, coastal and shelf science, 73(1-2), 91-100. doi:https://doi.org/10.1016/j.ecss.2006.12.019
Giri, C., Zhu, Z., Tieszen, L., Singh, A., Gillette, S., & Kelmelis, J. (2008). Mangrove forest distributions and dynamics (1975–2005) of the tsunami‐affected region of Asia. Journal of Biogeography, 35(3), 519-528. doi: https://doi.org/10.1111/j.1365-2699.2007.01806.x
Ilsever, M., & Unsalan, C. (2012). Two-dimensional change detection methods : remote sensing applications.
Jahari, M., Khairunniza-Bejo, S., Shariff, A. R. M., & Shafri, H. Z. M. (2011). Change detection studies in Matang mangrove forest area, Perak. Pertanika Journal of Science and Technology, 19, 307-327.
Jensen, J. R. (2005). Introductory digital image processing : a remote sensing perspective (3rd ed.). Upper Saddle River, N.J.: Prentice Hall.
Jong, S. M. d., & Meer, F. v. d. (2004). Remote sensing image analysis : including the spatial domain. Dordrecht ; London: Kluwer Academic.
Kennedy, R. E., Townsend, P. A., Gross, J. E., Cohen, W. B., Bolstad, P., Wang, Y. Q., & Adams, P. (2009). Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment, 113(7), 1382-1396. doi:https://doi.org/10.1016/j.rse.2008.07.018
Kerr, J. T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18(6), 299-305. doi:https://doi.org/10.1016/S0169-5347(03)00071-5
Koch, M., & Mather, P. (2013). Computer processing of remotely-sensed images : an introduction. Hoboken, N.J.: Wiley.
Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V., & Dech, S. (2011). Remote sensing of mangrove ecosystems: A review. Remote Sensing, 3(5), 878-928. doi:https://doi.org/10.3390/rs3050878
Lee, T.-M., & Yeh, H.-C. (2009). Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological engineering, 35(4), 487-496. doi:https://doi.org/10.1016/j.ecoleng.2008.01.007
Li, Z., Chen, J., & Baltsavias, E. (2008). Advances in photogrammetry, remote sensing and spatial information sciences: 2008 ISPRS congress book (1st ed.): CRC Press.
Liang, S., Li, X., & Wang, J. (2012). Advanced remote sensing:: Terrestrial Information Extraction and Applications (1st ed.). Amsterdam ; Boston: Academic Press.
Liu, K., Li, X., Shi, X., & Wang, S. (2008). Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands, 28(2), 336-346. doi:https://doi.org/10.1672/06-91.1
Lu, D., Batistella, M., & Moran, E. (2008). Integration of Landsat TM and SPOT HRG images for vegetation change detection in the Brazilian Amazon. Photogrammetric Engineering & Remote Sensing, 74(4), 421-430. doi:https://doi.org/10.14358/PERS.74.4.421
Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International journal of remote sensing, 25(12), 2365-2401.
Lyon, J. G., Yuan, D., Lunetta, R. S., & Elvidge, C. D. (1998). A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing, 64(2), 143-150.
Muchoney, D. M., & Haack, B. N. (1994). Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing, 60(10), 1243-1252.
Nguyen, H.-H., McAlpine, C., Pullar, D., Johansen, K., & Duke, N. C. (2013). The relationship of spatial–temporal changes in fringe mangrove extent and adjacent land-use: Case study of Kien Giang coast, Vietnam. Ocean & coastal management, 76, 12-22. doi:https://doi.org/10.1016/j.ocecoaman.2013.01.003
Pham, T. D., & Yoshino, K. (2015). Mangrove Mapping and Change Detection Using Multi-temporal Landsat imagery in Hai Phong city, Vietnam. Paper presented at the International Symposium on Cartography in Internet and Ubiquitous Environments.
Rayegani, B. (2016). Monitoring Hormozgan Mangrove forest changes in the past three decades and prioritizing of degraded ecosystems in order to carry out restoration projects.  Retrieved from Department of Environment www.doe.ir.
Rogan, J., Franklin, J., & Roberts, D. A. (2002). A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment, 80(1), 143-156. doi:https://doi.org/10.1016/S0034-4257(01)00296-6
Simard, M., Rivera-Monroy, V. H., Mancera-Pineda, J. E., Castañeda-Moya, E., & Twilley, R. R. (2008). A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia. Remote Sensing of Environment, 112(5), 2131-2144. doi:https://doi.org/10.1016/j.rse.2007.10.012
Vaiphasa, C., Skidmore, A. K., & de Boer, W. F. (2006). A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry and Remote Sensing, 61(1), 1-10. doi:https://doi.org/10.1016/j.isprsjprs.2006.05.005
Xiaolu, S., & Bo, C. (2011). Change detection using change vector analysis from Landsat TM images in Wuhan. Procedia Environmental Sciences, 11, 238-244. doi:https://doi.org/10.1016/j.proenv.2011.12.037

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