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Land Cover Classification Using Landsat 7 Data for Land Sustainability

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Abstract

Detection of land use/land cover with the help of satellite image data and extraction of geographical features is a challenging problem as it requires continuous monitoring and accurate processing of the images. For environment change management, Land Use Land Cover (LULC) information plays a vital part. This paper proposes a way for the detection of LULC types such as buildings, vegetation, water bodies, etc. with the use of Landsat 7 multispectral satellite images in Sabarmati Riverfront region, Ahmedabad, India. Landsat images have become valuable as well as a free resource as it provides the factors like high resolution, temporal distribution, and availability for the detection of LULC. Further, the use of the QGIS tool based Maximum/Minimum likelihood classification helps to explore and object-based image classification. Thus, Continuous monitoring over the same area will give the difference of LULC resulting in rapid or slower growth of land cover land use region. The proposed method gave a high rate of success and approximate 70–80% accurate results of LULC over period of 2014–2022.

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Data Availability

The data that support the findings of this study are processed and extracted from USGS Earth Explorer.

Code Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CAD:

Channel-attention-based densenet

CNN:

Convolutional neural networks

DHFF:

Deep homogeneous feature fusion

ETM+:

Enhanced thematic mapper

FCM:

Fuzzy C-means

GF:

Gaofen which means high-resolution in Chinese

IST:

Image style transfer

LiDAR:

Light detection and ranging

LULC:

Land use land cover

MLC:

Maximum likelihood classification

MLE:

Maximum likelihood estimation

NASA:

National aeronautics and space administration

NDII:

Normalized difference infrared index

NDVI:

Normalized difference vegetation index

OSM:

Open street map

PAN:

Panchromatic range

QGIS:

Quantum geographic information system

RBF-SVM:

Radial basic function support vector machine

RF:

Random forest

RGB:

Red green blue

ROI:

Region of interest

SAE:

Stacked auto encoder

SAR:

Synthetic aperture radar

SCP:

Semi-automatic classification plugin

SR:

Standard deviation of the structure

SWIR:

Shortwave infrared

TIFF:

Tagged image file format

TIR:

Thermal infrared range

UAV:

Unnamed aerial vehicle

USGS:

United States geological survey

VNIR:

Visible and near infrared

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Funding

The work includes no funding support.

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Authors and Affiliations

  1. SCOPE, Vellore Institute of Technology, Vellore, India

    K. Lavanya, Abhilasha Gondchar, Irene Maria Mathew, Sumitkumar Sarda & S. Ananda Kumar

  2. Laboratory of Theoretical Computer Science, HSE University, Moscow, Russia

    Anand Mahendran

  3. University of Colorado, CO Springs, USA

    Darshika G. Perera

Authors
  1. K. Lavanya
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  2. Abhilasha Gondchar
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  3. Irene Maria Mathew
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  4. Sumitkumar Sarda
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  5. S. Ananda Kumar
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  6. Anand Mahendran
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  7. Darshika G. Perera
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Correspondence to S. Ananda Kumar.

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Lavanya, K., Gondchar, A., Mathew, I.M. et al. Land Cover Classification Using Landsat 7 Data for Land Sustainability. Wireless Pers Commun 132, 679–697 (2023). https://doi.org/10.1007/s11277-023-10631-w

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