Incorporating CART algorithm and i for mapping Mangrove using Landsat 8 imagery

Document Type : Scientific article

Authors

1 Department of Natural ResourcesÙˆ Isfahan University of Technology

2 Associate Professor , Isfahan University of Technology Department of Natural Resources

3 Assistant Professor Isfahan University of Technology Department of Natural Resources

Abstract

The purpose of this study is to introduce a method for improving the accuracy of mapping mangrove covers by integrating CART algorithm and vegetation indices using Landsat 8 imagery. In this study, 7 vegetation indices were calculated including DVI, NDVI, NDII, IPVI, MNDWI, SAVI, and OSAVI. The important indicators and thresholds were identified by the CART algorithm in R-software and then the Mangrove cover besides surrounding areas was mapped using the decision tree technique. The results of accuracy assessment based on comparing control points which recorded by GPS with SPOT 6/7 images showed that the resulted map had a general accuracy of 80.97% and a kappa of 0.74. Using moderate resolution satellite imageries for mapping mangrove is difficult due to background reflectance effect (e.g. water and soil) and mixed pixels considering environmental conditions. Results demonstrated that this approach could produce information about mangroves for decision-makers in order to conservation and management planning.

Keywords

References

- Ahmadlou, M., M. R. Delavar & A. Tayyebi, 2016. Comparing ANN and CART to model multiple land use changes: A case study of Sari and Ghaem-Shahr cities in Iran, Journal of Geomatics Science and Technology, 6(1): 292-303.
- Alavi pahan, S. K., 2002. Application of Remote Sensing in Earth Sciences, Tehran University Press, Tehran, 240 p. (In Persian)
- Alavi pahan, S. K., 2011. Principles of Remote Sensing and Interpretation of Satellite Images and Aerial Photographs, Second Edition, Tehran University Press, Iran Tehran, 780 p. (In Persian)
- Alimohamadi, A., A. Motekan, P. Zeiaiean & H. Tabatabaei, 2009. Comparison of Basic Classification Pixel Classification, Base Object and Osim tree in Forest Types Maps Using Remote Sensing Data (Case Study: Astara Forest), Journal of Applied Research of Geographic Sciences, 13(10): 26-7. (In Persian)
- Arekhi, S. 2012. Assessment of the Effectiveness of Decision Tree Classification Method for Extracting Landuses Map by Using Sattellite Data in Cham Gardalan Catchment Area, Journal management system, 2(4): 17-26. (In Persian)
- Bagheri, R., Sh. Shataee & S. Y. Erfanifard, 2018. Efficiency of template matching algorithm on GeoEye-1 image for detecting wild pistachio trees and determining their spatial pattern (Case study: The Tag-03291-304. (In Persian)
- Breiman, L., J. Friedman, C. J. Stone & R. A. Olshen, 1984. Classification and regression trees, CRC press.
- Chen, C. F., N. T. Son, N. B. Chang, C. R. Chen, L. Y. Chang, M. Valdez & 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(12): 6408-6426.
- Dadrasisabzevar, A., A. M. Akhondali, F. Radmanesh & A. A. Noruzi, 2015. Modeling of Different Levels of Surface Soil Moisture in the Sphere of Thermal and Reflective Data, Quantitative geomorphological researches, 3(4): 31-49. (In Persian)
- Danehkar, A. & H. Majnonian, 2004. Proposed criteria for assessing coastal and maritime areas to determine areas under coastal protection Marine Iran (Case Study: Assessment of Protected Areas of the Caspian Sea), Journal of Environmental Studies, Journal of environmental studies, 35(3): 9-24. (In Persian)
- Danehkar, A. & S. GH. A. Jalali, 2005. Avicennia marina forest structure using line plot method, Pajouhesh va sazandegi, 18(2): 18-24. (In Persian)
- Dehghani, M., 2008, Potential of Mangrove forests of Qeshm Island Using GIS, Master Thesis, Forestry, Kurdistan University, Yasuj, 150 p. (In Persian)
- Fei, S. X., C. H. Shan & G. Z. Hua, 2011. Remote sensing of mangrove wetlands identification, Procedia Environmental Sciences, 10(1): 2287-2293.
- Giril, C., E. Ochieng, L. L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek & N. Duke, 2011. Status and distribution of mangrove forests of the world using earth observation satellite, Global Ecology Biogeography, 20(1): 154-159.
- Hamidi, S. K., M. Namiranian, J. Feghhi & M. Shabani. 2015. Comparison of land inventory and using of Ikonos image in Google Earth database to estimate quantity characteristics of urban forest (Case study: Iran; Sari city), Journal of Forest Research and Development, 1(1): 85-94. (In Persian)
- Hardisky, M., V. Klemas & R. Smart, 1983. The influence of soil salinity, growth form and leaf moisture on the spectral radiance of Spartina alterniflora canopies, Spartina alterniflora, 49: 77-84.
- Hoa, N. H. & T. D. Binh, 2016. Using Landsat Imagery and Vegetation Indices Differencing To Detect Mangrove Change: a Case in Thai Thuy District, Thai Binh Province, Management of Forest Resource and Environment, 5: 59-66.
- Huete, A. R., 1988. A soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment, 25(3): 295-309.
- Jafarnia, S., J. Oladi, S. Hoojati & K. Mir Akhor Loo, 2016. Status and change detection of Mangrove forest in Qeshm Island using satellite imagery from 1988 to 2008, Journal of Environmental Science and Technology, 18(1): 180-191. (In Persian)
- Kanniah, K. D., A. Sheikhi, A. Cracknell, H.C. Goh, K.P. Tan, C.S. Ho, & F.N. Rasli, 2015. Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia. Remote Sensing, 7(11), 14360–14385.
- Lawrence, R. L. & A. Wright, 2001. Rule-based classification systems using classification and regression tree (CART) analysis, Photogrammetric engineering and remote sensing, 67(10): 1137-1142.
- Lohrabi, Y., M. Abbasi, A. Soltani & H. R. Riyahi Bakhtyari, 2018. Determination of the most suitable method for forest type mapping in central Zagros using landsat-8 satellite Images, Journal of Forest Research and Development, 4(2): 191-205. (In Persian)
- Monsef, H. A. E. & S. E. Smith, 2017. A new approach for estimating mangrove canopy cover using Landsat 8 imagery, Computers and Electronics in Agriculture, 135(1): 183-194.
- Pham, T.D., K. Yoshino, N.N. Le & D.T. Bui, 2018. Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data. International Journal of Remote Sensing, 39(22), 7761–7788.
- Rahdari, V., A. Soffianian, S. J. Khajedin & S. Maleki, 2015. Investigating the Capability of Satellite Data in Mapping the Canopy Coverage of Arid and Semi-arid Regions (Case Study: Muteh Wildlife Refuge), Journal of Environmental Science and Technology, 15(4): 42-54. (In Persian)
- Safiari, S., 2005. Mangrove forests in Iran, Forestry and Rangeland Research Institute, Tehran, 540 p. (In Persian)
- Saleh, M. A., 2007. Assessment of mangrove vegetation on Abu Minqar Island of the RedSea, Journal of Arid Environments, 68(2): 331-336. (In Persian)
- Sari, S. P. & D. Rosalina, 2016. Mapping and Monitoring of Mangrove Density Changes on tin Mining Area, Procedia Environmental Sciences, 33: 436-442.
- Shi, T., J. Liu, Z. Hu, H. Liu, J. Wang & G. Wu, 2016. New spectral metrics for mangrove forest identification, Remote Sensing Letters, 7(9): 885-894.
- Temkin, N. R., R. Holubkov, J. E. Machamer, H. R. Winn & S. S. Dikmen, 1995. Classification and regression trees (CART) for prediction of the function at 1 year following head trauma, Journal of neurosurgery, 82(5): 764-771.
- Wang, L., W. P. Sousa, P. Gong & G. S. Biging, 2004. Comparison of IKONOS and Quick Bird images for mapping mangrove species on the Caribbean coast of Panama, RemoteSensing of Environment, 91(3-4): 432-440.
- Zhai, K., X. Wu, Y. Qin & P. Du, 2015. Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations, Geo-spatial Information Science, 18(1): 32-42.
Forest Research and Development
Volume 5, Issue 4
December 2019
Pages 557-569

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