MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2

Samsul Arifin, Tatik Kartika

Abstract

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR < 0.0176 and to detect devegetation ΔNDVI < -0.0206 and ΔNBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

Keywords

Model, Monitoring. Land Cover, Sentinel-2, devegetation, revegetation

Full Text:

PDF

References

Barbosa, P.M., Pereira, J.M.C. and Grégoire, J. M. (1998). Compositing criteria for burned area assessment using multi-temporal low resolution satellite data. Remote Sensing of Environment 65 (1): 38-49.

Chuvieco, E., Martin, M.P. and Palacios, A. (2002). Assessment of different spectral indices in the red-nearinfrared spectral domain for burned land discrimination. International Journal of Remote Sensing 23: 5103–5110.

Escuin, S. Navarro, R.M., and Fernandez, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Journal of Remote Sensing. Vol. 29 (4): 1053–1073. DOI:10.1080/01431160701281072

Horning, N., Robinson, J.A., Sterling, E.J., Turner, W., Spector, S., (2010). Remote Sensing for Ecology and Conservation. Oxford University Press, New York.

Key, C.H. and Benson, N. (1999). Measuring and remote sensing of burn severity: the CBI and NBR. https://www.researchgate.net

Lillesand. T.M., Kiefer, R.W., Chipman, J.W. (2008). Remote sensing and image interpretation (Sixth Edition). John Wiley & Sons, Inc., New York.

Garcia, MJL, Caselles V (1991) Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International 6 (1): 31–37.

Martín, M.P., Díaz-Delgado, R. (2002). Burned land mapping using NOAA-AVHRR and TERRA-MODIS. In: Viegas (Ed.). Conference proceedings: Forest fire research & wildland fire safety. Millpress, Rotterdam.

Miettinen, J. (2007). Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery. Academic dissertation. Department of Forest Resource Management Faculty of Agriculture and Forestry University of Helsinki.

Nielsen, T.T., Mbow, C. & Kane, R. (2002). A statistical methodology for burned area estimation using multitemporal AVHRR data. International Journal of Remote Sensing 23 (6): 1181-1196.

Parwati, P., Zubaidah, A., Vetrita, Y., Yulianto, F., Ayu, K.D.S., Khomarudin, M.R. (2012), Usage of Spot-4, Normalized Burn Ratio(NBR) And Normalized Difference Vegetation Index(NDVI) to Identify Burnt Area, Jurnal Ilmiah Geomatika 18 (1): 29-41.

Ryan L. (1997). Creating a Normalized Difference Vegetation Index (NDVI) image Using MultiSpec. University of New Hampshire

Roy, D.P., Lewis, P. E., & Justice, C.O. (2002). Burned area mapping using Multi-temporal moderate spatial resolution data - a bi-directional reflectance model-based expectation approach. Remote Sensing and Environment 83: 263- 286.

Silva J.M.N, Cadima, J.F.C.L., Pereira, J.M.C. & Gré Goire J.-M. (2004). Assessing the feasibility of a global model for multitemporal burned area mapping using SPOTVEGETATION data. International Journal of Remote Sensing 25 (22): 4889-4913. DOI: 10.1080/01431160412331291251

Sugianto, Muhammad Rusdi, (2017), INTRODUCTION TO HYPERSPECTRAL REMOTE SENSING APPLICATION Universitas Syiah Kuala, Banda Aceh.

Kartika, T., Arifin, S., Sari, I.L., Tosiani,A., Firmansyah, R., Kustiyo, Carolita, I., Adi, K., Daryanto, A.F. and Said, Z. 2018. analysis of vegetation indices using metric landsat-8 data to identify tree cover change in Riau Province. Earth and Environmental Science 280. doi:10.1088/1755-1315/280/1/012013

https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2-comparison-sentinel-2-and-landsat

https://sentinel.esa.int/web/sentinel/missions/sentinel-2

https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php

http://un-spider.org/node/10959

DOI: http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3385

Refbacks

  • There are currently no refbacks.