Optimizing land use for climate mitigation using nature based solution (NBS) strategy: a study on afforestation potential and carbon sequestration in Rajasthan, India

Abstract

Abstract Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

Keywords

• Carbon sequestration
• Afforestation suitability
• Geospatial analysis
• Remote sensing
• Environmental dynamics
• Land cover classification