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This study focuses on assessing the effectiveness of fractal dimension (FD) in characterizing mangrove ecosystems and its significance in studying natural object dynamics. An enhanced FD computing technique is proposed, surpassing the conventional box-counting approach, with application in mangrove dynamics exploration. Traditional limitations of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST)-based inferences are emphasized, stemming from computations based on emissivity and proportion of vegetation, which are dependent on NDVI. The proposed method employs an AND gate circuit, shift registers, and Digital Signal Processing modules for improved pixel manipulation. The Saptamukhi Reserve Forest in the Sun-arbans, West Bengal, India, serves as the study location. The study challenges the effectiveness of NDVI in characterizing mangrove dynamics before the Amphan cyclone from January 1, 2020, to April 30, 2020. Following the cyclone from June 1 to August 31, 2020, NDVI dropped to 0.136, indicating a 54.67% decline in mangrove health, while LST increased by 39.02%. LST-based inferences face challenges from seasonal fluctuations, cloud cover, and water dynamics. To address these issues, the study proposes incorporating fractal dimension-based inferences. After the cyclone, FD analysis shows a decline of 1.84 to 1.83 in the region demarcated as 1, and a decline of 1.93 to 1.92 for region 2. The studypromotes the use of FD-based approaches to get around the shortc om\ings in NDVI and LST evaluations, for better monitoring of mangrove ecosystems.

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Exploring Mangrove Complexity with Gate-Based Fractal Analysis Through AND Circuitry

  • 会议论文

  • 发表日期：

    2025年

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  • Anindita Das Bhattacharjee,
  • Somdatta Chakravortty,
  • Veena Venugopal,
  • Sumedha Basu,
  • Debi Majumdar

摘要

This study focuses on assessing the effectiveness of fractal dimension (FD) in characterizing mangrove ecosystems and its significance in studying natural object dynamics. An enhanced FD computing technique is proposed, surpassing the conventional box-counting approach, with application in mangrove dynamics exploration. Traditional limitations of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST)-based inferences are emphasized, stemming from computations based on emissivity and proportion of vegetation, which are dependent on NDVI. The proposed method employs an AND gate circuit, shift registers, and Digital Signal Processing modules for improved pixel manipulation. The Saptamukhi Reserve Forest in the Sun-arbans, West Bengal, India, serves as the study location. The study challenges the effectiveness of NDVI in characterizing mangrove dynamics before the Amphan cyclone from January 1, 2020, to April 30, 2020. Following the cyclone from June 1 to August 31, 2020, NDVI dropped to 0.136, indicating a 54.67% decline in mangrove health, while LST increased by 39.02%. LST-based inferences face challenges from seasonal fluctuations, cloud cover, and water dynamics. To address these issues, the study proposes incorporating fractal dimension-based inferences. After the cyclone, FD analysis shows a decline of 1.84 to 1.83 in the region demarcated as 1, and a decline of 1.93 to 1.92 for region 2. The studypromotes the use of FD-based approaches to get around the shortc om\ings in NDVI and LST evaluations, for better monitoring of mangrove ecosystems.