Abstract
Forests are one of the most important components to balance and regulate the terrestrial ecosystem on the Earth in protecting the environment. Accurate forest above-ground biomass (AGB) assessment is vital for sustainable forest management to recognize climate change and deforestation for mitigation processes.
In this study, Sentinel 2 remote sensing image has been used to calculate the fraction of vegetation cover (FVC) in order to accurately estimate the forest above-ground biomass of Tundi reserved forest in the Dhanbad district located in the Jharkhand state, India. The FVC is calculated in four steps: first, vegetation index image generation; second, vegetation index image rescaled between 0 to 1; third, the ratio of vegetated and non-vegetated areas was calculated with respect to the total image area, and finally, FVC image is generated.
In this paper, three vegetation indices have been calculated from the Sentinel 2 image, namely: normalized difference vegetation index (NDVI), normalized difference index 45 (NDI45), and inverted red-edge chlorophyll index (IRECI). Then, the FVC images were generated from the above vegetation indices individually. The ground FVC values were estimated from 22 different locations from the study area. Finally, the image based FVC estimates were compared with the ground estimated FVC. The results show that the IRECI based FVC provided the best approximation to the ground FVC among the different vegetation indices tested.
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Agata, H., Aneta, L., Dariusz, Z., Krzysztof, S., Marek, L., Christiane, S., Carsten, P.: Forest aboveground biomass estimation using a combination of sentinel-1 and sentinel-2 data. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 9026–9029. IEEE, July 2018
Chen, L., Ren, C., Zhang, B., Wang, Z., Xi, Y.: Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery. Forests 9(10), 582 (2018)
Frampton, W.J., Dash, J., Watmough, G., Milton, E.J.: Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J. Photogramm. Remote Sens. 82, 83–92 (2013)
Nuthammachot, N.A., Phairuang, W., Wicaksono, P., Sayektiningsih, T. Estimating Aboveground biomass on private forest using sentinel-2 imagery. J. Sens. (2018)
Zhang, Y., Liang, S., Yang, L.: A review of regional and global gridded forest biomass datasets. Remote Sensing 11(23), 2744 (2019)
Timothy, D., Onisimo, M., Cletah, S., Adelabu, S., Tsitsi, B.: Remote sensing of aboveground forest biomass: a review. Tropical Ecol. 57(2), 125–132 (2016)
Lu, X.T., Yin, J.X., Jepsen, M.R., Tang, J.W.: Ecosystem carbon storage and partitioning in a tropical seasonal forest in Southwestern China. Ecol. Manage. 260, 1798–1803 (2010)
Qureshi, A., Pariva, B.R., Hussain, S.A.: A review of protocols used for assessment of carbon stock in forested landscapes. Environ. Sci. Policy 16, 81–89 (2012)
ESA: Copernicus, Overview. ESA. 28 October 2014. Accessed 26 Apr 2016
Gomez, M.G.C.: Joint use of Sentinel-1 and Sentinel-2 for land cover classifi-cation: A machine learning approach. Lund University GEM thesis series (2017)
Zhang, S., Chen, H., Fu, Y., Niu, H., Yang, Y., Zhang, B.: Fractional vegetation cover estimation of different vegetation types in the Qaidam Basin. Sustainability 11(3), 864 (2019)
Kumar, P., Krishna, A.P.: Forest biomass estimation using multi-polarization SAR data coupled with optical data. Curr. Sci. 119(8), 1316–1321 (2020)
Kumar, P., Krishna, A.P.: InSAR based Tree height estimation of hilly forest using multi-temporal Radarsat-1 and Sentinel-1 SAR data. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 12(12), 5147–5152 (2019)
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Kumar, P., Krishna, A.P., Rasmussen, T.M., Pal, M.K. (2021). An Approach for Fraction of Vegetation Cover Estimation in Forest Above-Ground Biomass Assessment Using Sentinel-2 Images. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_1
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DOI: https://doi.org/10.1007/978-981-16-1086-8_1
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