Current Science

Open Access Open Access  Restricted Access Subscription Access

Automated Assessment of the Extent of Mangroves Using Multispectral Satellite Remote Sensing Data in Google Earth Engine


Affiliations
1 Central University of Jharkhand, Department of Geoinformatics, Brambe, Ranchi 835 222, India
2 Indian Institute of Space Science and Technology, Thiruvananthapuram 695 547, India
 

This study on the automatic assessment of mangroves uses geometric, textural parameters and vegetation indices derived from Landsat 8 images utilizing the Google Earth Engine. The extent of Indian mangroves is estimated as 5581 sq. km for 2019, with an overall accuracy (OA) of 86% and kappa coefficient (k) of 0.77. Among the five regions studied, maximum OA was obtained for Mumbai (94%; k = 0.89) and minimum for Godavari (81.625%; k = 0.66). Such automated mapping will benefit effective mangrove monitoring and management with a near real-time accurate estimation of mangroves.

Keywords

Automated Mapping, Cloud Platform, Mangrove Ecosystem, Satellite Data.
User
Notifications
Font Size

  • Duke, N., Ball, M. and Ellison, J., Factors influencing biodiversity and distributional gradients in mangroves. Global Ecol. Biogeogr. Lett., 1998, 7(1), 27–47.

  • Adeel, Z. and Pomeroy, R., Assessment and management of mangrove ecosystems in developing countries. Trees, 2002, 16(2–3), 235–238; https://doi.org/10.1007/s00468-002-0168-4.

  • Spalding, M., Blasco, F. and Field, C. (eds), World Mangrove Atlas, International Society for Mangrove Ecosystems, World Conservation Monitoring Centre, and International Tropical Timber Organization, Okinawa, Japan, 1997.

  • Kathiresan, K., Mangrove forests of India. Curr. Sci., 2018, 114(5), 976–981; https://doi.org/10.18520/cs/v114/i05/976-981.

  • Kandasamy, K., Mangroves in India and climate change: an overview. In Participatory Mangrove Management in a Changing Climate (eds DasGupta, R. and Shaw, R.), Disaster Risk Reduction Springer, Tokyo, Japan, 2017, pp. 31–57; https://doi.org/10.1007/978-4-431-56481-2_3

  • Colwell, R. N. (ed.), Manual of Remote Sensing, American Society of Photogrammetry, VA, USA, 1983, 2nd edn.

  • Nayak, S. and Bahuguna, A., Application of remote sensing data to monitor mangroves and other coastal vegetation of India. Indian J. Mar. Sci., 2001, 30(4), 195–213.

  • Selvam, V., Ravichandran, K. K., Gnanappazham, L. and Navamuniyammal, M., Assessment of community-based restoration of Pichavaram mangrove wetland using remote sensing data. Curr. Sci., 2003, 85, 794–798.

  • Chellamani, P. and Singh, C. P., Assessment of the health status of Indian mangrove ecosystems using multi temporal remote sensing data. Int. Soc. Trop. Ecol., 2014, 55(2), 245–253.

  • Guo, M., Li, J., Sheng, C., Xu, J. and Wu, L., A review of wetland remote sensing. Sensors, 2017, 17(4), 777.

  • Chen, B. et al., A mangrove forest map of China in 2015: analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sensing, 2017, 131, 104–120.

  • Li, W., El-Askary, H., Qurban, M. A., Li, J., ManiKandan, K. P. and Piechota, T., Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast. Ecol. Indi., 2019, 102, 734–745.

  • Petersen, L., Real-time prediction of crop yields from MODIS relative vegetation health: a continent-wide analysis of Africa. Remote Sensing, 2018, 10(11), 1726; https://doi.org/10.3390/rs10111726.

  • Fassnacht, F. E., Latifi, H. and Koch, B., An angular vegetation index for imaging spectroscopy data – preliminary results on forest damage detection in the Bavarian National Park, Germany. Int. J. Appl. Earth Obs. Geoinf., 2012, 1(19), 308–321.

  • Lendaris, G. G. and Stanley, G. L., Diffraction-pattern sampling for automatic pattern recognition. Proc. IEEE, 1970, 58, 198–216.

  • Trivedi, M., Segmentation of a high resolution urban scene using texture operation. Pattern Recogn., 1998, 25(8), 273–310.

  • Kayitakire, F., Hamel, C. and Defourny, P., Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing Environ., 2006, 102(3–4), 390–401.

  • Roslani, M. A., Mustapha, M. A., Lihan, T. and Wan Juliana, W. A., Classification of mangroves vegetation species using texture analysis on RapidEye satellite imagery. In AIP Conference Proceedings 1571, Selangor, Malaysia, 2013, 480–486; https://doi.org/10.1063/1.4858701.

  • Kumar, T., Mandal, A., Dutta, D., Nagaraja, R. and Dadhwal, V. K., Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans. Geocarto. Int., 2019, 34(4), 415–442.

  • Gnanappazham, L., Kumar, A. P. and Dadhwal, V. K., Geospatial tools for mapping and monitoring coastal mangroves. In Mangroves: Ecology, Biodiversity and Management, Springer, Singapore, 2021, pp. 475–551.

  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing Environ., 2017, 202, 18–27.

  • FBI, India State of Forest Report. Forest Survey of India, Dehrudan, 2019; http://fsi.nic.in/isfr19/vol1/chapter3.pdf

  • Giri, C. et al., Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecol. Biogeogr., 2011, 20(1), 154–159.

  • Selvam, V., Environmental classification of mangrove wetlands of India. Curr. Sci., 2003, 84(6), 757–765.

  • Thivakaran, G. A., Saravanakumar, A., Serebiah, J. S., Joshua, J., Sunderraj, W. and Vijayakumar, V., Vegetation structure of Kachchh mangroves, Gujarat, Northwest coast of India. Indian J. Mar. Sci., 2003, 32(1), 37–44.

  • Sukhdhane, K. S., Pandey, P. K., Vennila, A., Purushothaman, C. S. and Ajima, M. N. O., Sources, distribution and risk assessment of polycyclic aromatic hydrocarbons in the mangrove sediments of Thane Creek, Maharashtra, India. Environ. Monit. Assess., 2015, 187(5), 274; https://doi.org/10.1007/s10661-015-4470-1.

  • Haralick, R. M., Shanmugam, K. and Dinstein, I. H., Textural features for image classification. IEEE Trans. Syst., Man Cybernet., 1973, 6, 610–621.

  • Kaplan, G. and Avdan, U., Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification. Catena, 2019, 178, 109–119; https://doi.org/10.1016/j.catena. 2019.03.011.

  • Huete, A. K., Didan, T. M., Gao, E. P. R. X. and Ferreira, L. G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Environ., 2002, 83(1–2), 195–213.

  • Gao, B. C., Normalized difference water index for remote sensing of vegetation liquid water from space. In Imaging Spectrometry (eds Descour, M. R. et al.), SPIE, Orlando, Florida, USA, 1995, vol. 2480, pp. 225–236.

  • Dawson, T. P., The potential for estimating chlorophyll content from a vegetation canopy using the medium resolution imaging spectrometer (MERIS). Int. J. Remote Sensing, 2000, 21(10), 2043–2051.

  • Divya, Y., Sanjeevi, S. and Ilamparuthi, K., A study on the hyper-spectral signatures of sandy soils with varying texture and water content. Arab. J. Geosci., 2014, 7, 3537–3545.

  • Satheesh-Kumar, J. and Balaji, R., Tidal power potential assessment along the Gulf of Kutch, Gujarat, India. In Proceedings of the Asian Wave and Tidal Energy Conference, Singapore, 2016.

  • Ghosh, A., Schmidt, S., Fickert, T. and Nüsser, M., The Indian Sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity, 2015, 7(2), 149–169.


Abstract Views: 226

PDF Views: 78




  • Automated Assessment of the Extent of Mangroves Using Multispectral Satellite Remote Sensing Data in Google Earth Engine

Abstract Views: 226  |  PDF Views: 78

Authors

Rupsa Sarkar
Central University of Jharkhand, Department of Geoinformatics, Brambe, Ranchi 835 222, India
L. Gnanappazham
Indian Institute of Space Science and Technology, Thiruvananthapuram 695 547, India
A. C. Pandey
Central University of Jharkhand, Department of Geoinformatics, Brambe, Ranchi 835 222, India

Abstract


This study on the automatic assessment of mangroves uses geometric, textural parameters and vegetation indices derived from Landsat 8 images utilizing the Google Earth Engine. The extent of Indian mangroves is estimated as 5581 sq. km for 2019, with an overall accuracy (OA) of 86% and kappa coefficient (k) of 0.77. Among the five regions studied, maximum OA was obtained for Mumbai (94%; k = 0.89) and minimum for Godavari (81.625%; k = 0.66). Such automated mapping will benefit effective mangrove monitoring and management with a near real-time accurate estimation of mangroves.

Keywords


Automated Mapping, Cloud Platform, Mangrove Ecosystem, Satellite Data.

References





DOI: https://doi.org/10.18520/cs%2Fv125%2Fi3%2F299-308