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ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data

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Abstract

Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD) algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposed algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012–2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20–0.05%) and fallow land (1–0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping.

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Dataset availability

The Landsat series dataset can be downloaded from the online web portal (https://earthexplorer.usgs.gov/).

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Acknowledgements

The authors would like to thank the United States Geological Survey (USGS), and the National Aeronautics and Space Administration (NASA) for providing Landsat series datasets.

Funding

This research work is financially supported by Women Scientist Scheme-A (WOS-A) Project (Grant no. SR/WOS-A/ET-55/2019) by Department of Science and Technology (DST), Govt. of India and Teachers Associateship for Research Excellence (TARE) Project (Grant no. TAR/2019/000354) by the Science and Engineering Research Board (SERB), Govt. of India.

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Authors and Affiliations

  1. School of Sciences, Noida International University, Sector-17A, Noida, Uttar Pradesh, 203201, India

    Gurwinder Singh

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, 140401, India

    Neelam Dahiya

  3. Department of Civil Engineering, Indian Institute of Technology, Ropar, 140001, India

    Vishakha Sood

  4. Department of Computer and Engineering, University Institute of Engineering, Chandigarh University, Chandigarh, Punjab, 140413, India

    Sartajvir Singh & Apoorva Sharma

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Contributions

Gurwinder Singh, as the first author, had responsibility for conducting the research, including a writing task. Neelam Dahiya and Vishakha Sood performed re-writing tasks and visualization. Sartajvir Singh and; Apoorva Sharma revised the manuscript, respectively. All authors have read and approved the final manuscript.

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Correspondence to Sartajvir Singh.

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Singh, G., Dahiya, N., Sood, V. et al. ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data. Environ Monit Assess 196, 233 (2024). https://doi.org/10.1007/s10661-024-12394-8

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