Seasonal spatio-temporal NDVI analysis for vegetation monitoring in Satara district of Maharashtra from 2014 to 2023

Introduction

Vegetation plays a vital role in maintaining ecological balance, supporting agricultural productivity, and regulating the hydrological cycle. Accurate monitoring of vegetation dynamics is essential for assessing ecosystem health, managing natural resources, and planning agricultural activities. Remote sensing technology has emerged as a reliable, cost-effective, and consistent approach for vegetation monitoring over large spatial and temporal scales. Satellitebased observations allow continuous assessment of vegetation health, crop conditions, and land cover changes, thereby supporting decision-making in agriculture, forestry, and environmental management. Digital image processing of satellite data offers instruments for image analysis using various mathematical indices and algorithms. Indexes have been created to draw attention to the image's salient aspects, which are based on reflectance properties (Shikhar and Akansha., 2014) [8] . Among the various vegetation indices developed, the Normalized Difference Vegetation Index (NDVI) is the most widely adopted due to its simplicity, robustness, and strong correlation with vegetation vigour (Sahebjalal and Dashtekian, 2013) [7] . NDVI, introduced by Deering (1978) [2] and Tucker (1979) [9] , ranges from -1 to +1, where values approaching +1 indicate dense and healthy vegetation, values near zero represent bare soil or built-up areas, and negative values correspond to water bodies or non-vegetated surfaces. Satara district in western Maharashtra exhibits diverse agro-climatic zones, ranging from high-rainfall regions in the west to droughtprone areas in the east. This variability, coupled with seasonal cropping patterns, makes it an ideal location for studying vegetation changes over time. This study aims to conduct a spatiotemporal analysis of NDVI from 2014 to 2023 for Satara district, assessing seasonal variations (rabi, summer and kharif) and their relationship with rainfall.

The findings will contribute to a better understanding of vegetation patterns, identify drought-prone zones, and support sustainable agricultural and resource management strategies in the region.

Materials and Methods

Study Area

Satara district is located in the western part of Maharashtra state, situated between 17°05′ to 18°11′ N latitudes and 73°33′ to 74°54′ E longitudes. The western part, particularly Mahabaleshwar, receives an average annual rainfall of over 6000 mm, while the eastern tahsils of Man, Phaltan, Khandala, and Khatav receive as little as 500 mm or less (Barakade, 2012) [1] . The eastern part of the district is particularly drought-prone. According to Pravin and Hemalata (2020) [6] , out of the 11 tahsils, four (Khandala, Phaltan, Man, and Khatav) exhibit dominant drought characteristics, and one tahsil (Koregaon) is partially affected by drought. In this study, Landsat 8 Collection 2 Level 2 data, obtained from USGS Earth Explorer, for the period from 2014 to 2023 were used to find out NDVI using the software QGIS. NDVI is based on the principle that vegetation health reflects water availability or stress. NDVI is calculated as the ratio of the difference between the NIR and Red bands to their sum. Expressed as: NDVI= (NIR -RED) / (NIR + RED) . . . [Deering (1978) [2] , Tucker (1979)] [9+] Where, NIR and RED are the reflectance in the near infrared and red bands. For Landsat 8, NDVI = (Band 5 -Band 4) / (Band 5 + Band 4) The NDVI quantifies variations in chlorophyll content (via absorption of visible red radiation) and spongy mesophyll (via reflected NIR radiation) within the vegetation canopy. Consequently, higher NDVI values typically signify greater vitality and photosynthetic activity, or "greenness," of the vegetation canopy (Tucker, 1979) [9] . Healthy vegetation reflects a significant amount of NIR radiation and absorbs much of the red light. When there is a substantial difference between reflected NIR and red radiation, the NDVI value is higher, indicating healthy, vigorous vegetation. In contrast, when the difference between NIR and red reflection is minimal, the NDVI value is lower, suggesting unhealthy vegetation or a surface that is partially or not vegetated at all. The NDVI values range from -1 to +1 and are typically divided into four classes based on vegetation health and density, as described by Maher Milad Aburas et al. (2015) [4] and Sahebjalal et al. (2013) [7] . These classes are as follows; 3 and illustrated in Figure 6. 4 and illustrated in Figure 7. Rainfall and NDVI showed a positive association across all three seasons (Table 7), with the strongest correlation during kharif (r = 0.73, p< 0.05, significant at 5% or 0.05), followed by rabi (r = 0.59, p< 0.1, significant at 10% or 0.1) and summer (r = 0.50, not significant even at 10%). This pattern highlights the critical role of monsoonal precipitation in vegetation development and aligns with the findings of Himanshu et al. (2015) [3] , who reported a strong linear relationship between rainfall and NDVI.

Summary

This study focuses on analyzing the spatial and temporal vegetation conditions in Satara district situated in the western region of Maharashtra using vegetation indices derived from satellite imagery. Landsat 8 Collection 2 Level-2 data, known for its accurate surface reflectance corrections, was employed for a 10-year period (2014-2023). The analysis was conducted for three major crop-growing seasons: rabi, summer, and kharif, with NDVI maps of January, April and October representing these seasons, respectively. NDVI is computed to assess vegetation health. Thematic maps illustrated the inter-annual and seasonal variations, and correlation analysis was performed with rainfall to understand its influence on vegetation. The seasonal mean variation showed that NDVI was highest during kharif and lowest in summer, indicating better vegetation in the monsoon and poor cover in dry months. Across all seasons and years, the eastern region of Satara consistently showed lower NDVI values, identifying it as a vulnerable and drought-prone zone requiring targeted intervention. NDVI analysis revealed that kharif seasons consistently exhibited higher vegetation cover, driven by adequate monsoon rainfall. In contrast, rabi seasons showed reduced NDVI in certain years due to dependence on residual moisture and irrigation, with 2019 indicating lower vegetation and 2022 showing significantly better vegetation health. During the summer season, the eastern part of Satara persistently recorded low NDVI values, indicating recurring drought-prone conditions. Years like 2016 showed poor vegetation, while 2020 and 2022 indicated improved vegetation, likely due to effective irrigation practices. NDVI showed a substantial positive association with precipitation in all three seasons, especially in kharif (r = 0.73), followed by rabi (r = 0.59) and summer (r = 0.50), highlighting the critical role of precipitation in vegetation development.

Conclusion

The analysis of mean NDVI across rabi, summer, and kharif seasons over a ten-year period (2014-2023) clearly illustrates the seasonal dynamics of vegetation health in Satara district. The kharif season consistently exhibited higher mean NDVI values, indicating dense and healthy vegetation cover due to favourable monsoon rainfall. In contrast, the summer season recorded the lowest NDVI values, reflecting scarce vegetation and dry soil conditions. Analysis of the NDVI maps revealed that the eastern part of Satara district consistently experienced low vegetation cover and higher drought severity throughout the study period. This pattern was particularly noticeable in the summer, indicating that this region is particularly vulnerable to seasonal drought stress and may require focused water conservation and agricultural management interventions. From the NDVI maps and area classification, rabi 2019 was marked by the least vegetation cover, while 2022 had the most extensive vegetation. In the summer season, 2014 showed very low vegetation density, indicating severe drought and 2023 recorded high vegetation density, suggesting improved conditions. During kharif, NDVI maps highlighted 2015 as a drought-impacted year, while 2019 demonstrated strong vegetation growth. Across all seasons, NDVI exhibited a positive correlation with rainfall, with the strongest association observed during the kharif season, indicating greater vegetation responsiveness to precipitation in this period.

References

1. Barakade AJ. Growth of population in Satara district of Maharashtra. World Res J Geol. 2012;1(2):17-20.
2. Himanshu SK, Singh G, Kharola N. Monitoring of drought using satellite data. International Research Journal of Earth Sciences. 2015;3(1):66-72.
3. Maher Milad Aburas MMA, Sabrina Ho Abdullah SHA, Mohammad Firuz Ramli MFR, Zulfa Hanan Ash'aari ZHA. Measuring land cover change in Seremban, Malaysia using NDVI index. Procedia Environmental Sciences. 2015;30:238-43.
4. Patil PP, Jagtap MP, Khatri N, Madan H, Vadduri AA, Patodia T. Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study. Case Studies in Chemical and Environmental Engineering. 2024;9:100573.
5. Pravin J, Hemlata P. Rainfall variability in the droughtprone area of Satara district (Maharashtra State). Peer Reviewed International Research Journal of Geography. 2020;37(2):81-7.
6. Sahebjalal E, Dashtekian K. Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods. African Journal of Agricultural Research. 2013;8(37):461422.
7. Shikhar Deep, Akansha Saklani. Urban Sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Sciences. 2014;17:179-8.
8. Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment. 1979;8(2):127-50.