Reserch to establis mangrove forests map in Viet Nam using time series Landsat 8 Oli and Sentinel - 1 in google earth engine cloud computing platform
Keywords:
GEE, mangrove forests,, canopy frequency,, inundation frequency,, greeness frequencyAbstract
Due to anthropogenis disturbances and climate change, accurate and
contemporary maps of mangrove forests are needed to management, protection and establish plans for sustainable management. In this study, a classification algorithm was developed using the biophysical characteristics of mangrove forests in Viet Nam. Specifically, mangrove forests distribution maps were mapped by: (1) Greeness frequency; (2) Canopy frequency; (3) Inundation frequency from time series Landsat 8 OLI and some other datas: elevation, slop... The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of Greeness frequency, Canopy frequency, Inundation frequency to determined
areas mangrove forests distribution. In addition, the intergration of Sentinel 1 VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresth water bodies, which can not distribute of mangrove forests. Mangrove forests distribution maps were mapped at 30 m spatial resolution have accuracy greater than 93% when validated with ground reference data. This study has demonstrated the potential of using time series Landsat 8 OLI and Sentinel - 1 imagery in Google earth engine cloud computing platform to identify and map mangrove forests along the coastal zones in Viet Nam
References
1. Chen. B, Xiao. X, Li. X, Pan. L, Doughty. R, Ma. J, Dong. J, Qin. Y, Zhao. B, Wu. Z, Sun. R, Lan. G, Xie. G, Clinton. N, Giri. C, 2017. A mangrove forest map of China in 2015: Analysis of time siries Landsat 7/8 and Sentinel - 1A imagery in Google Earth Engine cloud computing platform. Journal of Photogrammetry and Remote Sensing, No 131, pp104 - 120.
2. Dong. J, Xiao. X, Menarguez. MA, Zhang. G, Qin. Y, Thau. D, Biradar. C, Moore. B, 2016. Mapping paddy rice planting area in Northeastern Asia with Landsat 8 images, phenology - based algorithm and Google Earth Engine. Remote Sensing. Environ, No 185, pp 142 - 154.
3. Gao. B, 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58 (1996), 257 - 266.
4. Giri. C, Long. J, Abbas. S, Murali. RM, Qamer. FM, Pengra. B, Thau. D, 2015. Distribution and dynamics of mangrove forests of South Asia. Environ Manage, No 148, pp 101 - 111.
5. Giri. C, Ochieng. E, Tieszen. LL, Zhu. Z, Singh. A, Loveland. T, Masek. J, Duke. N, 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecol. Biogeogr, No 20, pp 154 - 159.
6. Hamilton. SE, Casey. D, 2016. Creation of a high spatio - temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC - 21). Global Ecol. Biogeogr, No 25, pp 729 - 738.
7. Hansen. MC, Potapov. PV, Moore. R, Hancher. M, Turubanova. SA, Tyukavina. A, Thau. D, Stehman. SV, Goetz. SJ, Loveland. TR, Kommareddy. A, Egorov. A, Chini. L, Justice. CO, Townshend. JRG, 2013. High -resolution global maps of 21st - century forest cover change. Science, No 342, pp 850 - 853.
8. Huete. A, Didan. K, Miura. T, Rodriguez. EP, Gao. X, Ferreira. LG, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. No 83, pp 195 - 213.
9. Kirui. KB, Kairo. JG, Bosire. J, Viergever. KM, Rudra. S, Huxham. M, Briers. RA, 2013. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean Coast Manage, No 83, pp 19 - 24.
10. Long. JB, Giri. C, 2011. Mapping the Philippines’ mangrove forests using landsat imagery. Sensors No 11, pp 2972 - 2981.
11. Nayak. S, Bahuguna. A, 2001. Application of remote sensing data to monitor mangroves and other coastal vegetation of India. Indian Journal of Marine Sciences. No 30, pp 195 - 213.
12. Qin. Y, Xiao. X, Dong. J, Zhou. Y, Zhu. Z, Zhang. G, Du. G, Jin. C, Kou. W, Wang. J, Li. X, 2015. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. ISPRS J. Photogramm, No 105, pp 220 - 233.
13. Rahman. AF, Dragoni. D, Didan. K, Barreto - Munoz. A, Hutabarat. JA, 2013. Detecting large scale conversion of mangroves to aquaculture with change point and mixed - pixel analyses of high - fidelity MODIS data. Remote Sens. Environ, No 130, pp 96 - 107.
14. Spalding. M, Kainuma, M, Collins. L, 2010. World atlas of mangroves. In: World Atlas of Mangroves. Routledge.
15. Tucker. CJ, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ, No 8, pp 127 - 150.
16. Wu. P, Zhang. J, Ma. Y, Li. X, 2013. Remote sensing monitoring and analysis of the changes of mangrove resource in China in the past 20 years. Adv. Mar. Sci., 406 - 414.
17. Xiao. X, Biradar. CM, Czarnecki. C, Alabi. T, Keller. M, 2009. A simple algorithm for large - scale mapping of evergreen forests in tropical America, Africa and Asia. Remote Sens. 1, 355 - 374.
18. Xiao. X, Boles. S, Liu. J, Zhuang. D, Frolking. S, Li. C, Salas. W, BM, 2005. Mapping paddy rice agriculture in Southern China using multi - temporal MODIS images. Remote Sens Environ. 95, 480 - 492.
19. Xu. H, 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025 - 3033.
20. Zhou. Y, Xiao. X, Qin. Y, Dong. J, Zhang. G, Kou. W, Jin. C, Wang. J, Li. X, 2016. Mapping paddy rice planting area in rice - wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. Int. J. Appl. Earth Obs. 46, 1 - 12
