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Updated: Jan 15, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Evaluating urbanization effects on biomass density using a hybrid AI model: a case study.

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  • 1Department of Software Engineering, Istanbul Topkapi University, 34087, Istanbul, Turkey. buketisler@topkapi.edu.tr.

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Urban expansion impacts vegetation. Advanced AI models like DWT-LSTM predict vegetation changes, aiding sustainable urban planning and environmental management.

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Discrete wavelet transformLSTMRemote sensingUrbanization

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Artificial Intelligence

Background:

  • Urbanisation significantly alters socioeconomic structures and ecological systems globally.
  • Sustainable urban planning requires integrated environmental management approaches.
  • Fethiye, Turkey, faces challenges balancing urban development with ecological preservation.

Purpose of the Study:

  • To investigate the impact of urban expansion on natural vegetation cover in Fethiye, Turkey.
  • To project future vegetation dynamics using AI-driven models.
  • To provide data-driven insights for sustainable land-use strategies.

Main Methods:

  • Utilized time series data from 2013-2023, including Land Surface Temperature (LST) and Normalised Difference Built-up Index (NDBI).
  • Employed Long Short-Term Memory (LSTM) networks to model and project Normalised Difference Vegetation Index (NDVI) values up to 2032.
  • Developed and validated a hybrid Discrete Wavelet Transform with LSTM (DWT-LSTM) model, achieving a 9.1% improvement in NDVI accuracy.

Main Results:

  • The DWT-LSTM model demonstrated enhanced predictive accuracy for vegetation dynamics.
  • Validated against CORINE land cover data, confirming model robustness and generalisability.
  • Quantitatively linked urbanisation indicators to ecological degradation patterns.

Conclusions:

  • Advanced AI models are effective tools for forecasting ecological changes due to urbanisation.
  • Findings support informed decision-making for mitigating adverse environmental impacts of urban development.
  • The study provides a foundation for developing sustainable land-use strategies in rapidly urbanizing coastal regions.