Vegetation Health and Forest Canopy Density Monitoring in The Sundarban Region Using Remote Sensing and GIS

##plugins.themes.academic_pro.article.sidebar##

Published Nov 28, 2023
https://doi.org/10.47164/ijngc.v14i4.1415
Download
Requires Subscription pdf
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 14, Issue 4, November 2023

##plugins.themes.academic_pro.article.main##

Soma Mitra
Brainware University, Kolkata, India
Samarjit Naskar
Maulana Abul Kalam Azad University of Technology, West Bengal
Dr. Saikat Basu

Abstract

The present study explores vegetation health and forest canopy density in the Sundarbans region using Landsat-8 images. This work analyzes changes in vegetation health using two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Forest Canopy Density (FCD) values of the Sundarbans, from 2014 to 2020. NDVI, comprising two bands, Red and Near-infrared (NIR), shows a declining trend during the period. Two NDVI land cover classification maps for 2014 and 2020 are produced, and the interest area is divided into five classes: Scanty, Low, Medium, and Densely Vegetated Regions and Water Bodies. A single-band linear gradient pseudo-color is used to assess the land cover difference between 2020 and 2014, showing marked changes in densely vegetative areas. The NDVI difference marks the coastal regions with a higher depletion rate of vegetation than the regions away from the seacoasts. FCD has been taken to compare the results of NDVI with it. FCD consists of another four models: AVI (advanced vegetative index), BI (Bare soil index), SSI (scaled shadow index), and TI (thermal index). FCD is also called crown cover or canopy coverage, which refers to the portion of an area in the field covered by the crown of trees. 2014 and 2015 FCD maps are produced with a single band linear gradient pseudocolor with five land cover classifications: bare soil, Bare Soil, Shrubs, Low, Medium, and Highly vegetated regions. Both maps bear a significant resemblance to NDVI land classification maps. Further, the FCD values of the two maps are scaled between 1 and 100, and the area of each class is calculated. To check the veracity of the NDVI and FCD analysis, a Deep Neural Network (DNN) model has been developed to classify each year’s image taken from Google Earth Engine (GEE). It classifies each year’s image with 99% accuracy. The calculation of the area of each class emphasizes the rapid decline of densely wooded vegetation. Almost 80% of the highly forested zone has been diminished and has become part of the medium-forested region. Area inflation in medium-forested regions corroborates the same. The study also analyzes the migration of vegetation density, i.e., where and how many areas are unchanged, growing, or deforested.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
Mitra, S., Naskar, S., & Basu, D. S. (2023). Vegetation Health and Forest Canopy Density Monitoring in The Sundarban Region Using Remote Sensing and GIS. International Journal of Next-Generation Computing, 14(4). https://doi.org/10.47164/ijngc.v14i4.1415
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Akike, S., Samanta, S., et al. 2016. Land use/land cover and forest canopy density monitoring of wafi-golpu project area, papua new guinea. Journal of Geoscience and Environment Protection 4, 08, 1. DOI: https://doi.org/10.4236/gep.2016.48001
  2. Azadeh, A., Dimitrios, P., and Peter, S. 2017. Forest canopy density assessment using different approaches–review. Journal of forest science 63, 3, 107–116. DOI: https://doi.org/10.17221/110/2016-JFS
  3. Blanco, A., Tamondong, A., Perez, A., Ang, M., and Paringit, E. 2015. The phil-lidar 2 program: national resource inventory of the philippines using lidar and other remotely sensed data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40, 7, 1123. DOI: https://doi.org/10.5194/isprsarchives-XL-7-W3-1123-2015
  4. Datta, D. and Deb, S. 2012. Analysis of coastal land use/land cover changes in the indian sunderbans using remotely sensed data. Geo-spatial Information Science 15, 4, 241–250. DOI: https://doi.org/10.1080/10095020.2012.714104
  5. Deka, J., Tripathi, O. P., and Khan, M. L. 2013. Implementation of forest canopy density model to monitor tropical deforestation. Journal of the indian society of remote sensing 41, 469–475. DOI: https://doi.org/10.1007/s12524-012-0224-5
  6. Falensky, M. A., Sulti, A. L., Putra, R. D., and Marko, K. 2020. Application of forest canopy density (fcd) model for the hotspot monitoring of crown fire in tebo, jambi province. Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments) 4, 1, 3. DOI: https://doi.org/10.7454/jglitrop.v4i1.76
  7. Ghosh, A., Schmidt, S., Fickert, T., and Nusser, M. 2015. The indian sundarban mangrove forests: history, utilization, conservation strategies and local perception. diversity 7, 149e169. DOI: https://doi.org/10.3390/d7020149
  8. iri, C., Pengra, B., Zhu, Z., Singh, A., and 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
  9. Joshi, C., De Leeuw, J., Skidmore, A. K., Van Duren, I. C., and Van Oosten, H. 2006. Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods. International Journal of Applied Earth Observation and Geoinformation 8, 2, 84–95. DOI: https://doi.org/10.1016/j.jag.2005.08.004
  10. Kundu, K., Halder, P., and Mandal, J. K. 2021a. Change detection and patch analysis of sundarban forest during 1975–2018 using remote sensing and gis data. SN Computer Science 2, 1–14. DOI: https://doi.org/10.1007/s42979-021-00749-8
  11. Kundu, K., Halder, P., and Mandal, J. K. 2021b. Detection and prediction of sundarban reserve forest using the ca-markov chain model and remote sensing data. Earth Science Informatics 14, 3, 1503–1520. DOI: https://doi.org/10.1007/s12145-021-00648-9
  12. Kundu, K., Halder, P., and Mandal, J. K. 2022. Estimation and analysis of change detection, forest canopy density, and forest fragmentation: A case study of the indian sundarbans. Journal of Sustainable Forestry, 1–16. DOI: https://doi.org/10.1080/10549811.2022.2059515
  13. Mahmudur Rahman, M. 2012. Time-series analysis of coastal erosion in the sundarbans mangrove. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 39, 425–429. DOI: https://doi.org/10.5194/isprsarchives-XXXIX-B8-425-2012
  14. Mitra, S. and Basu, S. 2022. Analysis of vegetation health of the sundarbans region using remote sensing methods. In Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021. Springer, 63–71. DOI: https://doi.org/10.1007/978-981-19-0105-8_7
  15. Mondal, I., Thakur, S., Juliev, M., and Kumar De, T. 2021. Comparative analysis of forest canopy mapping methods for the sundarban biosphere reserve, west bengal, india. Environment, Development and Sustainability, 1–26. DOI: https://doi.org/10.1007/s10668-021-01291-6
  16. Raha, A. et al. 2014. Sea level rise and submergence of sundarban islands: a time series study of estuarine dynamics. J Ecol Environ Sci ISSN , 0976–9900.
  17. Rikimaru, A., Roy, P., and Miyatake, S. 2002. Tropical forest cover density mapping. Tropical ecology 43, 1, 39–47.
  18. Samanta, S., Hazra, S., Mondal, P. P., Chanda, A., Giri, S., French, J. R., and Nicholls, R. J. 2021. Assessment and attribution of mangrove forest changes in the indian sundarbans from 2000 to 2020. Remote Sensing 13, 24, 4957. DOI: https://doi.org/10.3390/rs13244957
  19. Shit, P. K., Pourghasemi, H. R., Das, P., and Bhunia, G. S. 2020. Spatial Modeling in Forest Resources Management. Springer. DOI: https://doi.org/10.1007/978-3-030-56542-8
  20. Su Mon, M., Mizoue, N., Htun, N. Z., Kajisa, T., and Yoshida, S. 2012. Estimating forest canopy density of tropical mixed deciduous vegetation using landsat data: a comparison of three classification approaches. International Journal of Remote Sensing 33, 4, 1042–1057. DOI: https://doi.org/10.1080/01431161.2010.549851
  21. Sundararaju, V. 2019. Scientific management of mangroves is need of the hour. Down to Earth. New Delhi (India). https://www. downtoearth. org. in/blog/wildlife-biodiversity/scientific­management-of-mangroves-is-needof-the-hour-64007 .
  22. Thomas, J., Arunachalam, A., Jaiswal, R., Diwakar, P., and Kiran, B. 2014. Dynamic land use and coastline changes in active estuarine regions-a study of sundarban delta. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40, 8, 133. DOI: https://doi.org/10.5194/isprsarchives-XL-8-133-2014