Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Investigating the threats of mangrove forests with the help of remotely sensed data

Author
Behzad Rayegani
Doe
10.29252/jsaeh.6.2.53
Abstract
Investigating the threats of mangrove forests

with the help of remotely sensed data



Behzad Rayegani: Assistant Professor of College of Environment, Department of Environment





Mangroves are a group of trees and shrubs that live in the coastal intertidal zone. Mangrove forests are very important because they are known as natural heritage and crucial in protecting coastal ecosystems. Mangrove forests stabilize the coastline, reducing erosion from storm surges, currents, waves, and tides. The intricate root system of mangroves also makes these forests attractive to fish and other organisms seeking food and shelter from predators. So, they are ideal places to support the elements of seafood networks. However, these forests are in danger of degradation because of rapid population growth, poor planning and unsustainable economic development. In the process of regenerating an ecosystem, it is necessary to identify the precursors of the threat, to consider the means to eliminate these threats. Therefore, identifying the threatening factors of the mangrove forest ecosystem is the first step in the restoration and protection of the ecosystem.

This study aims to investigate the change and the destruction in Mangrove forests and to identify threatening forces in the Hara Protected Area. Remote sensing is now widely used in studies of ecosystem changes because its information is available for the past, and there are many highly-developed techniques for change detection through remote sensing. Therefore, in order to identify the threatening factors of mangrove forests, remote sensing techniques were used to identify changed areas during a 15-year period. Images of ETM+ and OLI sensor from 2001 to 2015 were collected in the Hara Protected Area (Khorekhoran International Wetland). Given that we have used the multiple-date remote sensor data in this study, it was necessary to use absolute atmospheric correction methods for radiometric harmonization of data. So, with the aid of the ERDAS IMAGINE 2014 software, the Atmospheric and Topographic Correction (ATCOR) model was applied to all data. Subsequently, due to the difference in radiometric resolution of the OLI sensor with the ETM+ sensor, the output of ATCOR of both sensors was stretched into 8-bit data in order to eliminate the existing divergence in radiometric resolution. Also, based on spatial information, one of the image of OLI sensor at the current time was corrected geometrically, and then other images were registered to this image to eliminate geometric errors. There are many ways to detect changes with the help of remote sensing data, but we used two widely used techniques in this study: 1) post-classification comparison; 2) Change detection techniques of Algebra. Totally four different change detection methods were applied to these images. Change detection techniques of Algebra image method include image difference, image ratio, regression and post-classification comparison were used. At first, with the knowledge of the studied area, by combining the two supervised and unsupervised classification (hybrid method), the pixels that were known as mangrove forests were identified in both time periods of study. Then pixels with decreasing trend were determined by post-classification comparison method. From the image of the mangrove forests with the logic of Boolean (OR), a mask of mangrove was obtained, which showed the areas of mangroves during the two periods. This mask was used to make the second group of methods for determining changes (Algebra method) applied to the data. By doing this, in all algebra methods, the histogram showed the normal distribution. Finally, the vegetation spectral indices were applied to the data and their coefficient of variation was obtained in the Boolean mask area. Among these indices, NDVI showed better performance, so the algebra operation was used for this index. Accordingly, areas with decrease, increase and no change trends were visited and then overall accuracy and kappa coefficients were determined.

The results showed that the method of post classification comparison has the highest accuracy in the monitoring of vegetation changes in mangrove forests. This method with a total accuracy of over 93% and a kappa of more than 0.9 showed the highest accuracy in the detection methods of the changes, therefore, in the final examination and prioritization of the regions, this method was used. The surveys showed that the smuggling of fuel due to pour gasoline into the water and camel grazing are the most important destructive factors in the mangrove forest. After determining the rate degradation in four regions, these regions were ranked in order to carry out reclamation and restoration projects.

In the case of intelligent use of the capabilities of remote sensing, one can easily identify the threatening factors of an ecosystem. In the case of mangroves, the only limiting factor is tidal conditions. It is therefore recommended that, as in this study, images are chosen to determine the changes that are in a same tidal state





Keywords: Remotely Sensed change detection, Image Algebra Change Detection, Post-classification comparison, Determination of thresholds






Keywords
Remotely Sensed change detection
Image Algebra Change Detection
Post-classification comparison
Determination of thresholds
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: 187-195.
Al-Fares, W. 2013. Historical land use/land cover classification using remote sensing : a case study of the Euphrates River Basin in Syria
Barati, S.; B. Rayegani; M. Saati; A. Sharifi, and M. Nasri. 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: 49-56.
Campbell, J.B., and R.H. Wynne. 2011. Introduction to remote sensing. Guilford Press, New York.
Carney, J.; T.W. Gillespie, and R. Rosomoff. 2014. Assessing forest change in a priority West African mangrove ecosystem: 1986–2010. Geoforum, 53: 126-135.
Chen, C.-F.; N.-T. Son; N.-B. Chang; C.-R. Chen; L.-Y. Chang; M. Valdez; G. Centeno; C. Thompson, and J. Aceituno. 2013. Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model. Remote Sensing, 5: 6408.
Congalton, R.G., and K. Green. 2008. Assessing the accuracy of remotely sensed data: principles and practices. CRC press,
Eastman, J. 2012. IDRISI Selva Tutorial
Eastman, J.R. 2015. TerrSet Tutorial. Clark Labs Clark University: Worcester, MA, USA
Falk, D.A.; M.A. Palmer, and J.B. Zedler. 2006. Foundations of restoration ecology. Island Press Washington, DC,
Gandhi, G.M.; S. Parthiban; N. Thummalu, and A. Christy. 2015. Ndvi: Vegetation Change Detection Using Remote Sensing and Gis–A Case Study of Vellore District. Procedia Computer Science, 57: 1199-1210.
Giri, C.; J. Long; S. Abbas; R.M. Murali; F.M. Qamer; B. Pengra, and D. Thau. 2015. Distribution and dynamics of mangrove forests of South Asia. Journal of environmental management, 148: 101-111.
Giri, C.; B. Pengra; Z. Zhu; A. Singh, and L.L. Tieszen. 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: 91-100.
Ilsever, M., and C. Unsalan. 2012. Two-dimensional change detection methods : remote sensing applications
Jahari, M.; S. Khairunniza-Bejo; A.R.M. Shariff, and H.Z.M. Shafri. 2011. Change detection studies in Matang mangrove forest area, Perak. Pertanika J. Sci. Technol, 19: 307-327.
Jensen, J.R. 2005. Introductory digital image processing : a remote sensing perspective. (3rd ed.). Prentice Hall, Upper Saddle River, N.J.
Jong, S.M.d., and F.v.d. Meer. 2004. Remote sensing image analysis : including the spatial domain. Kluwer Academic, Dordrecht ; London.
Kennedy, R.E.; P.A. Townsend; J.E. Gross; W.B. Cohen; P. Bolstad; Y. Wang, and P. Adams. 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: 1382-1396.
Kerr, J.T., and M. Ostrovsky. 2003. From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18: 299-305.
Koch, M., and P. Mather. 2013. Computer processing of remotely-sensed images : an introduction. Wiley, Hoboken, N.J.
Kuenzer, C.; A. Bluemel; S. Gebhardt; T.V. Quoc, and S. Dech. 2011. Remote Sensing of Mangrove Ecosystems: A Review. Remote Sensing, 3: 878.
Lee, T.-M., and H.-C. Yeh. 2009. Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological engineering, 35: 487-496.
Li, Z.; J. Chen, and E. Baltsavias. 2008. Advances in photogrammetry, remote sensing and spatial information sciences: 2008 ISPRS congress book. CRC Press,
Liang, S.; X. Li, and J. Wang. 2012. Advanced remote sensing:: Terrestrial Information Extraction and Applications. (1st ed.). Academic Press, Amsterdam ; Boston.
Liu, K.; X. Li; X. Shi, and S. Wang. 2008. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands, 28: 336-346.
Lu, D.; M. Batistella, and E. Moran. 2008. Integration of Landsat TM and SPOT HRG images for vegetation change detection in the Brazilian Amazon. Photogrammetric engineering and remote sensing, 74: 421.
Lu, D.; P. Mausel; E. Brondizio, and E. Moran. 2004. Change detection techniques. International Journal of Remote Sensing, 25: 2365-2401.
Lyon, J.G.; D. Yuan; R.S. Lunetta, and C.D. Elvidge. 1998. A change detection experiment using vegetation indices. Photogrammetric engineering and remote sensing, 64: 143-150.
Nguyen, H.-H.; C. McAlpine; D. Pullar; K. Johansen, and N.C. Duke. 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.
Pham, T.D., and K. Yoshino. Mangrove Mapping and Change Detection Using Multi-temporal Landsat imagery in Hai Phong city, Vietnam
Pham, T.D., and K. Yoshino 2015. Mangrove Mapping and Change Detection Using Multi-temporal Landsat imagery in Hai Phong city, Vietnam. In, International Symposium on Cartography in Internet and Ubiquitous Environments
Pohl, C., and J. van Genderen. 2016. Remote Sensing Image Fusion: A Practical Guide. CRC Press,
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. In (p. 280). Department of Environment: College of Environment
Rogan, J.; J. Franklin, and D.A. Roberts. 2002. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote sensing of environment, 80: 143-156.
Simard, M.; V.H. Rivera-Monroy; J.E. Mancera-Pineda; E. Castañeda-Moya, and R.R. Twilley. 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: 2131-2144.
Vaiphasa, C.; A.K. Skidmore, and W.F. de Boer. 2006. A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry and Remote Sensing, 61: 1-10.
Xiaolu, S., and C. Bo. 2011. Change detection using change vector analysis from Landsat TM images in Wuhan. Procedia Environmental Sciences, 11: 238-244.