DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE

Anusha NALLAPAREDDY

doi:10.35784/acs-2022-4

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Published: Mar 30, 2022

DOI: https://doi.org/10.35784/acs-2022-4

DOI

https://doi.org/10.35784/acs-2022-4

Authors

Anusha NALLAPAREDDY

anusha.nallapareddy@gmail.com

Vidya Jyothi Institute of Technology, Department of Computer Science and Engineering

Abstract

Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive. In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possibilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified.

Keywords:

classification, remote sensing, soil adjusted vegetation index, normalized difference vegetation index, vegetation

References

Abburu, S., & Golla, S. B. (2015). An engineering evaluation on the glimpse of satellite image pre-processing utility tools. Article of Engineering Journal, 19(5), 1–10. https://doi.org/10.4186/ej.2015.19.2.129 DOI: https://doi.org/10.4186/ej.2015.19.2.129

Ahmad, A. M., Minallah, N., Ahmed, N., Ahmad, A. M., & Fazal, N. (2020). Remote sensing based vegetation classification using machine learning algorithms. 2019 International Conference on Advances in the Emerging Computing Technologies (AECT) (pp. 1–6). IEEE. https://doi.org/10.1109/AECT47998.2020.9194217 DOI: https://doi.org/10.1109/AECT47998.2020.9194217

Asokan, A., Anitha, J., Ciobanu, M., Gabor, A., Naaji, A., & Hemanth, D. J. (2020). Image processing techniques for analysis of satellite images for historical maps classification – an overview. Appied Sciences, 10(12), 1–21. https://doi.org/10.3390/app10124207 DOI: https://doi.org/10.3390/app10124207

Bouhennache, R., Bouden, T., & Taleb, A. A. (2014). Change Detection in Urban Land Cover Using Landsat Images Satellites, A Case Study in Algiers Town. 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems (pp. 622–628). IEEE. https://doi.org/10.1109/SITIS.2014.57 DOI: https://doi.org/10.1109/SITIS.2014.57

Chen, X., Giri, C., & Vogelmann, J. E. (2016). Land-Cover Change Detection. Remote Sensing of Land Use and Land Cover (pp. 152–176). CRC Press. https://doi.org/10.1201/b11964-14 DOI: https://doi.org/10.1201/b11964-14

Dalponte, M., Marzini, S., Solano-Correa, Y. T., Tonon, G., Vescovo, L., & Gianelle, D. (2020). Mapping forest wind throws using high spatial resolution multispectral satellite images. International Journal of Applied Earth Observation Geoinformatics, 93, 102206. https://doi.org/10.1016/j.jag.2020.102206 DOI: https://doi.org/10.1016/j.jag.2020.102206

Dutta, D., Rahman, A., & Kundu, A. (2015). Growth of Dehradun city: An application of linear spectral unmixing (LSU) technique using multi-temporal landsat satellite data sets. Remote Sensing Applications: Society and Environment, 1, 98–111. https://doi.org/10.1016/j.rsase.2015.07.001 DOI: https://doi.org/10.1016/j.rsase.2015.07.001

Fatihaa, B., Abdelkaderb, A., Latifac, H., & Mohamedd, E. (2013). Spatio temporal analysis of vegetation by vegetation indices from multi-dates satellite images: Application to a semi arid area in ALGERIA. TerraGreen 13 International Conference 2013 – Advancements in Renewable Energy and Clean Environment, Elsevier, Energy Procedia, 36, 667–675. https://doi.org/10.1016/j.egypro.2013.07.077 DOI: https://doi.org/10.1016/j.egypro.2013.07.077

Gandhi, G. M., Parthiban, S., & Christy, N. T. (2015). Ndvi: Vegetation change detection using remote sensing and gis – A case study of Vellore District. 3rd International Conference on Recent Trends in Computing 2015, Elsevier, Procedia Computer Science, 57, 1199–1210. https://doi.org/10.1016/j.procs.2015.07.415 DOI: https://doi.org/10.1016/j.procs.2015.07.415

He, B., Zhang, H., Feng, S., Liu, X., Zhou, Y., & Guan, Y. (2020). Improving land cover change detection and classification with BRDF correction and spatial feature extraction using Landsat Time Series: A case of urbanization in Tianjin, China. IEEE Journal of Selecteed Topics in Applied Earth Observation. and Remote Sensing (vol 13, pp. 4166–4177). IEEE. https://doi.org/10.1109/JSTARS.2020.3007562 DOI: https://doi.org/10.1109/JSTARS.2020.3007562

Jing, X., Wang, J., Huang, W., Liu. L., & Wang, J. (2009). Study on Forest Vegetation Classification Based on Multitemporal Remote Sensing Images. Computer and Computing Technologies in Agriculture II, Volume 1. CCTA 2008. IFIP Advances in Information and Communication Technology (vol 293). Springer. https://doi.org/10.1007/978-1-4419-0209-2_13 DOI: https://doi.org/10.1007/978-1-4419-0209-2_13

Landsat Missions. (n.d.). Retrieved February 13, 2022 from https://www.usgs.gov/landsat-missions/landsatcollection-2-level-1-data

Langendoen, D., Navarro, G., Willner, W., Keith, D. A., Liu, C., Guo, K., & Meidinger, D. (2020). Perspectives on Terrestrial Biomes: The International Vegetation Classification. Encyclopedia of the World's Biomes, 2020, 1–15. https://doi.org/10.1016/B978-0-12-409548-9.12417-0 DOI: https://doi.org/10.1016/B978-0-12-409548-9.12417-0

Li, A., Lei, G., Zhao, W., Nan, X., & Zhang, Z. (2017). Post-earthquake Landslides Mapping from Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method. IEEE Journal of Selected Topics in Applied. Earth Observation and Remote Sensing, 10, 1758–1768. https://doi.org/10.1109/JSTARS.2017.2661802 DOI: https://doi.org/10.1109/JSTARS.2017.2661802

Omar, M. S., & Kawamukai, H. (2021). Prediction of NDVI using the Holt-Winters model in high and low vegetation regions: A case study of East Africa. Scientific African, 14, e01020. https://doi.org/10.1007/s40808-018-0431-3 DOI: https://doi.org/10.1016/j.sciaf.2021.e01020

Persson, H. J., Ulander, L. M. H., & Soja, M. J. (2018). Modeling and Detection of Deforestation and Forest Growth in Multitemporal TanDEM-X Data. IEEE Journal of Selected Topic in Appied Earth Observation and Remote sensing, 11, 3548–3563. https://doi.org/10.1109/JSTARS.2018.2851030 DOI: https://doi.org/10.1109/JSTARS.2018.2851030

Porikli, F., Bennamoun, M., Khan, S. H., & He, X. (2017). Forest change detection in incomplete satellite images with deep neural networks. IEEE Tranactions on Geoscience and Remote sensing, 52, 5407–5423. https://doi.org/10.1109/TGRS.2017.2707528 DOI: https://doi.org/10.1109/TGRS.2017.2707528

Rhyma, P. P., Norizah, K., Hamdan, O., Faridah-Hanum, I., & Zulfa, A. W. (2019). Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sensing

Applications: Society and Environment, 17, 1–14. https://doi.org/10.1016/j.rsase.2019.100280 DOI: https://doi.org/10.1016/j.rsase.2019.100280

Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward,

A. S., Bindschadler, R., Cohen, W. B., Gao, F., Hipple, J. D., Hostert, P., Huntington, J., Justice, C. O., Kilic, A., Kovalskyy, V., Lee, Z. P., Lymburner, L., Masek, J. G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R. H., & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001 DOI: https://doi.org/10.1016/j.rse.2014.02.001

Ruiz, L. F. C., Guasselli, L. A., Simioni, J. P. D., Belloli, T. F., & Fernandes, P. C. B. (2021). Object-based Classification of Vegetation species in a subtropical wetland using Sentinel-1 and Sentinel-2A images. Science of Remote Sensing, 3, 1–10. https://doi.org/10.1016/j.srs.2021.100017 DOI: https://doi.org/10.1016/j.srs.2021.100017

Schmidt, G., Jenkerson, C. B., Masek, J., Vermote, E., & Gao, F. (2013). Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Algorithm Description (vi, 9p.). U.S. Geological Survey openfile report, U.S. Geological Survey. https://doi.org/10.3133/ofr20131057 DOI: https://doi.org/10.3133/ofr20131057

Sowmya, D. R., Deepa, P., & Venugopal, K. (2017). Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey. International Journal of Computer Applications, 161, 24–37. https://doi.org/10.5120/ijca2017913306 DOI: https://doi.org/10.5120/ijca2017913306

Xue, J., & Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Hindawi, Journal of Sensors, 2017, 1353691. https://doi.org/10.1155/2017/1353691 DOI: https://doi.org/10.1155/2017/1353691

Yu, M., Xie, Y., & Sha, Z. (2008). Remote sensing imagery in vegetation mapping. Journal of Plant Ecology, 1(1), 9–23. https://doi.org/10.1093/jpe/rtm005 DOI: https://doi.org/10.1093/jpe/rtm005

Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., Wang, M., Dai, S., & Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, 243–257.https://doi.org/10.1016/j.rse.2016.03.036 DOI: https://doi.org/10.1016/j.rse.2016.03.036

NALLAPAREDDY, A. (2022). DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE. Applied Computer Science, 18(1), 45–55. https://doi.org/10.35784/acs-2022-4

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