Spatio-Temporal Assessment of Vegetation Dynamics Using NDVI in the Mann River Watershed, Maharashtra, India
Stanzin Angmo *
Department of Soil & Water Conservation Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth Akola, Maharashtra, India.
K.D. Gharde
Department of Soil & Water Conservation Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth Akola, Maharashtra, India.
R.S. Patode
Department of Soil & Water Conservation Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth Akola, Maharashtra, India.
N.G. Patil
National Bureau of Soil Survey and Land Use Planning, Nagpur, Maharashtra, India
A.R. Mhaske
Department of Soil & Water Conservation Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth Akola, Maharashtra, India.
M.M. Deshmukh
Department of Irrigation and Drainage Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth Akola, Maharashtra, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: To assess decadal vegetation dynamics in the semi-arid Mann River watershed, Maharashtra, by analysing changes in NDVI-based vegetation density between 2013 and 2022, and to evaluate the effectiveness of NDVI as a long-term vegetation monitoring tool.
Study design: A remote-sensing based comparative temporal analysis using satellite-driven NDVI classification for two time periods (2013 and 2022).
Place and Duration of Study: Mann River watershed, Maharashtra, India. The study utilized Landsat 8 imagery acquired for the years 2013 and 2022.
Methodology: Landsat 8 satellite images for 2013 and 2022 were processed to compute NDVI using the red and near-infrared (NIR) bands. NDVI values were reclassified into five vegetation density categories: water/non-vegetation (<0.00), very low (0.00–0.10), low (0.10–0.25), moderate (0.25–0.40), and dense vegetation (>0.40). Spatial distribution maps for both years were generated, followed by a change detection analysis to quantify transitions between vegetation classes. Class-wise areal extent (km²) and percentage cover were computed to identify temporal shifts in vegetation patterns.
Results: Sparse vegetation increased significantly from 1,723.64 km² (71.34%) in 2013 to 1,922.34 km² (79.57%) in 2022, indicating improved vegetation cover. Moderate vegetation decreased from 538.94 km² (22.31%) to 423.42 km² (17.53%), while dense vegetation showed a notable rise from 0.20 km² to 4.36 km². Bare land reduced from 125.45 km² (5.19%) to 49.93 km² (2.07%), and water bodies slightly declined from 27.78 km² (1.15%) to 15.96 km² (0.66%). Overall trends indicate a positive shift in vegetation vigor over the decade, likely supported by improved agricultural practices, watershed development interventions, and natural regeneration.
Conclusion: NDVI-based assessment proved effective for detecting long-term vegetation changes in semi-arid environments. The observed improvement in vegetation density from 2013 to 2022 highlights successful watershed and land management initiatives in the Mann River watershed. NDVI remains a cost-effective and reliable indicator for guiding sustainable ecological and land-use planning.
Keywords: Vegetation dynamics, remote sensing, semi-arid ecosystems, Spatio-temporal analysis