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<p>Assessing and monitoring vegetation diversity and biomass stocks using remote sensing (RS) and geographic information systems (GIS) are vital for effective forest resource management. The present study investigated herb, shrub, and tree diversity across different vegetation types, developed a habitat suitability model, and mapped forest biomass and carbon stocks using an artificial neural network (ANN) approach to support micro-level forest conservation. The study was conducted during 2021–2023 in eight districts of southern Odisha located in the north-central part of the Eastern Ghats (EaGh), India. A systematic sampling design was adopted, comprising 180 sub-plots of 31.62 × 31.62&#xa0;m² for trees, 720 sub-plots of 5 × 5&#xa0;m² for shrubs, and 900 sub-plots of 1 × 1&#xa0;m² for herbs. Habitat suitability for in situ conservation of plant species was assessed using the analytical hierarchy process (AHP) combined with weighted overlay analysis (WOA). In total, 213 plant species were recorded, including 96 trees, 40 shrubs, and 77 herbs. The estimated total vegetation carbon stock across the study area ranged from 12.74 to 206.26 Mg C ha⁻¹, with a mean value of 70.61 ± 48.78 Mg C ha⁻¹. The ANN-based biomass mapping showed high predictive accuracy, as evidenced by a strong agreement with the ESA global biomass product (R² = 0.73) for the selected forest patches. Assembled information of the present research will help in conserving plant diversity and retaining ecological sustainability.</p>

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Geospatial assessment of floristic treasures and carbon stocks in tropical forests at North-Eastern pockets of Eastern Ghats of India

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Prakash Paraseth,
  • Kakoli Banerjee

摘要

Assessing and monitoring vegetation diversity and biomass stocks using remote sensing (RS) and geographic information systems (GIS) are vital for effective forest resource management. The present study investigated herb, shrub, and tree diversity across different vegetation types, developed a habitat suitability model, and mapped forest biomass and carbon stocks using an artificial neural network (ANN) approach to support micro-level forest conservation. The study was conducted during 2021–2023 in eight districts of southern Odisha located in the north-central part of the Eastern Ghats (EaGh), India. A systematic sampling design was adopted, comprising 180 sub-plots of 31.62 × 31.62 m² for trees, 720 sub-plots of 5 × 5 m² for shrubs, and 900 sub-plots of 1 × 1 m² for herbs. Habitat suitability for in situ conservation of plant species was assessed using the analytical hierarchy process (AHP) combined with weighted overlay analysis (WOA). In total, 213 plant species were recorded, including 96 trees, 40 shrubs, and 77 herbs. The estimated total vegetation carbon stock across the study area ranged from 12.74 to 206.26 Mg C ha⁻¹, with a mean value of 70.61 ± 48.78 Mg C ha⁻¹. The ANN-based biomass mapping showed high predictive accuracy, as evidenced by a strong agreement with the ESA global biomass product (R² = 0.73) for the selected forest patches. Assembled information of the present research will help in conserving plant diversity and retaining ecological sustainability.