Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning

• Published in: Environmental and sustainability indicators Vol. 24; p. 100485
• Main Authors: Anees, Shoaib Ahmad, Mehmood, Kaleem, Rehman, Akhtar, Rehman, Nazir Ur, Muhammad, Sultan, Shahzad, Fahad, Hussain, Khadim, Luo, Mi, Alarfaj, Abdullah A., Alharbi, Sulaiman Ali, Khan, Waseem Razzaq
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2024; Elsevier
• Subjects: Driving forces analysis; Fractional vegetation cover; Machine learning; Remote sensing; Fractional vegetation cover; Driving forces analysis; Remote sensing; Machine learning
• ISSN: 2665-9727; 2665-9727
• DOI: 10.1016/j.indic.2024.100485

Understanding the dynamics of Fractional Vegetation Cover (FVC) is crucial for effective environmental monitoring and management, especially in regions like Pakistan that are sensitive to climate change. This study employs an innovative approach using MODIS NDVI data and the Pixel Dichotomy Model (PDM) to analyze the spatiotemporal dynamics of FVC across Pakistan from 2003 to 2020. Our findings reveal an overall increasing trend in FVC, with the highest value recorded in 2017 (0.37) and the lowest in 2004 (0.26). The Hurst exponent analysis (R/S ratio = 0.718) indicates a degree of long-term memory in the FVC time series. Rainfall was found to positively correlate with FVC (r = 0.6), while Land Surface Temperature (LST) and the Compounded Night Light Index (CNLI) exhibited negative correlations (r = −0.59 and r = −0.43, respectively). The Random Forest regression model highlighted CNLI as the most influential predictor (importance = 62.4%), emphasizing the need to consider human-induced factors in environmental management. These results provide critical insights for sustainable land management and contribute to understanding vegetation-climate interactions in arid and semi-arid environments." •FVC Trends: 18-year analysis of Fractional Vegetation Cover (FVC) in Pakistan using MODIS NDVI.•Vegetation Memory: Long-term persistence detected through Hurst exponent.•Key Drivers: Significant impact of rainfall, temperature, and CNLI on FVC.•Modeling Insights: Random Forest model identifies CNLI as a crucial predictor.•Management Implications: Findings are vital for sustainable land and resource management.