Abstract
Changes in land use and land cover play an important role in understanding the interactions of human activities with the environment. This makes it necessary to monitor and detect the changes happening over a period of time to maintain a sustainable environment. In this paper, an attempt has been made to study the changes in land use and land cover of Aurangabad district in Maharashtra, India. The study is carried out using multitemporal satellite images from Landsat-8 sensors for the period from 2013 to 2019. The results show changes occurring in different covers including green vegetation cover, settlement, bare land and water bodies. Different classification algorithms are applied to observe and record changes happening in different land covers. The classified maps provide information which can be used by district authorities and other government agencies to take future decisions for development and sustaining environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
http://earthexplorer.usgs.gov. Accessed on 22 Oct 2018
Mather PM (2004) Computer processing of remotely-sensed images: an introduction, 3rd edn. Wiley, pp 149–169, 203–245
Aung HPP, Aung ST (2019) Analysis of land cover change detection using satellite images in Patheingyi township. In: Zin T, Lin JW (eds) Big data analysis and deep learning applications. ICBDL 2018. Advances in intelligent systems and computing, vol 744. Springer, Singapore
Radhika K, Varadarajan S, Li Z (2018) A neural network based classification of satellite images for change detection applications. Cogent Eng 5:1. https://doi.org/10.1080/23311916.2018.1484587
Grivei A, Radoi A, Datcu M (2017) Land cover change detection in satellite image time series using an active learning method. In: 9th international workshop on the analysis of multitemporal remote sensing images (MultiTemp), Brugge, pp 1–4
Adam HE, Csaplovics E, Elhaja ME (2016) A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan. In: 8th IGRSM international conference and exhibition on remote sensing & GIS (IGRSM), pp 1–10
Birdi PK, Kale KV (2019) Accuracy assessment of classification on Landsat-8 data for land cover and land use of an urban area by applying different image fusion techniques and varying training samples. In: LNEE, vol 521. Springer, Singapore, pp 189–197
Birdi PK, Kale KV (2018) Enhancement of land cover and land use classification accuracy using spectral and textural features of fused images. In: CCIS, vol 876. Springer, Singapore, pp 317–325
Richards JA, Jia X (2006) Remote sensing digital image analysis: an introduction. Springer-Verlag, Heidelberg, pp 193–238
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20:213–220
Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. CRC, Lewis Publishers, pp 47–65
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Birdi, P.K., Ajith, V. (2021). Study of Changes in Land Use and Land Covers Using Temporal Landsat-8 Images. In: Chowdary, P., Chakravarthy, V., Anguera, J., Satapathy, S., Bhateja, V. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 655. Springer, Singapore. https://doi.org/10.1007/978-981-15-3828-5_34
Download citation
DOI: https://doi.org/10.1007/978-981-15-3828-5_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3827-8
Online ISBN: 978-981-15-3828-5
eBook Packages: EngineeringEngineering (R0)