Evaluating Biophysical Impacts of Watershed Interventions Using Time-Series Satellite Data: A Study in Semi-Arid Andhra Pradesh
DOI:
https://doi.org/10.54536/ijsrd.v2i1.4152Keywords:
Bandlapalli, GIS, Impact Analysis, MGNREGA, Remote Sensing, WatershedAbstract
This study demonstrates the critical role of satellite data in monitoring and evaluating NRM interventions in semi-arid, rain-fed agricultural regions. This study evaluates the biophysical impacts of Watershed level interventions in Bandlapalle village, Anantapur district, Andhra Pradesh, using time-series satellite data. Landsat-TM data (2006–2022) was utilized for seamless temporal analysis, with 2006 as the base year due to the commencement of MGNREGA and IWMP projects during this period. The study focused on the analysis of Land Use Land Cover (LULC), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), and Soil Moisture Index (SMI) across three cropping seasons - Kharif, Rabi, and Zaid. LULC analysis shows an increase in agricultural land from 1839.21 ha in 2006 to 2041.45 ha in 2022, alongside a decrease in scrubland from 464.39 ha to 268.79 ha, indicating shifts in land use patterns. VCI values improved significantly, reflecting healthier vegetation over time, particularly in the Rabi season, where the high vegetation class increased from 287.12 ha in 2007 to 1539.11 ha in 2022. NDWI analysis shows an overall improvement in water availability, with high NDWI class areas expanding notably during the Kharif season, from 214.78 ha in 2006 to 2046.13 ha in 2021. Similarly, SMI analysis indicates enhanced soil moisture levels, especially during the Rabi and Zaid seasons, with medium and high moisture areas showing considerable growth. The results reflects the positive influence of watershed management and water harvesting structures, such as check dams and farm ponds, on improving land use patterns, vegetation health, water availability, and soil moisture retention.
References
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Grégoire, J. M. (2002). Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77(1), 22–33.
Crews-Meyer, K. A. (2004). Agricultural landscape change and stability in northeast Thailand: Historical patch-level analysis. Agriculture, Ecosystems & Environment, 101(2-3), 155–169.
Gao, B. C. (1996). NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Gu, Y., Brown, J. F., Verdin, J. P., & Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34(6).
Halder, S., Sharma, R. K., Mahapatra, S., Mishra, S. N., & Dhorde, A. (2021). Impact assessment of Narwa Programme through remote sensing (RS) and geographical information system (GIS).
Hamilton, L. S., & Pearce, A. J. (1986). Biophysical aspects in watershed management. In Watershed resources management: An integrated framework with studies from Asia and the Pacific (pp. 33–52). Westview Press.
Janani, N., Kannan, B., Nagarajan, K., Thiyagarajan, G., & Duraisamy, M. R. (2024). Soil moisture mapping for different land-use patterns of lower Bhavani river basin using vegetative index and land surface temperature. Environment, Development and Sustainability, 26(2), 4533–4549.
Janga, B., Asamani, G. P., Sun, Z., & Cristea, N. (2023). A review of practical AI for remote sensing in earth sciences. Remote Sensing, 15(16), 4112.
Kerr, J., Pangare, G., Pangare, V., & George, P. J. (2002). Watershed development projects in India: An evaluation. International Food Policy Research Institute.
Kogan, F. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8), 1405–1419.
Kumar, G., Sena, D. R., Kurothe, R. S., Pande, V. C., Rao, B. K., Vishwakarma, A. K., ... & Mishra, P. K. (2014). Watershed impact evaluation using remote sensing. Current Science, 107(8), 1369–1378.
Kundu, A., Patel, N. R., Denis, D. M., & Dutta, D. (2020). An estimation of hydrometeorological drought stress over the central part of India using geo-information technology. Journal of the Indian Society of Remote Sensing, 48(1), 1–9.
Li, X., Wu, C., Meng, Z., Hu, Y., Li, W., & Zhang, H. (2020). Comparing Landsat and Sentinel-2 data for mapping crop type and cropping intensity in the North China Plain. Remote Sensing, 12(19), 3200. https://doi.org/10.3390/rs12193200
Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401.
Mall, R. K., Gupta, A., Singh, R., Singh, R. S., & Rathore, L. S. (2017). Water resources and climate change: An Indian perspective. Current Science, 93(8), 1000–1008.
Maurya, P. K., Ali, S. A., Alharbi, R. S., Yadav, K. K., Alfaisal, F. M., Ahmad, A., ... & Jeon, B. H. (2021). Impacts of land use change on water quality index in the upper Ganges River near Haridwar, Uttarakhand: A GIS-based analysis. Water, 13(24), 3572.
Mekuriaw, T. (2019). Evaluating impact of land-use/land-cover change on surface runoff using Arc SWAT model in Sore and Geba Watershed, Ethiopia. Journal of Environment and Earth Science, 9(10), 6–17.
Mirzabaev, A., Ahmed, M., Werner, J., Pender, J., & Louhaichi, M. (2016). Rangelands of Central Asia: Challenges and opportunities. Journal of Arid Land, 8(1), 93–108.
Munoth, P., & Goyal, R. (2020). Impacts of land use land cover change on runoff and sediment yield of Upper Tapi River Sub-Basin, India. International Journal of River Basin Management, 18(2), 177–189
Naga Rajesh, A., Abinaya, S., Purna Durga, G., & Lakshmi Kumar, T. V. (2023). Long-term relationships of MODIS NDVI with rainfall, land surface temperature, surface soil moisture, and groundwater storage over the monsoon core region of India. Arid Land Research and Management, 37(1), 51–70.
Oyler, J. W., Ballantyne, A. P., Jencso, K., Sweet, M., & Running, S. W. (2016). Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature. International Journal of Climatology, 36(7), 2257–2275.
Palanisami, K., & Kumar, D. Suresh. (2014). Impacts of watershed development programs: Experiences and lessons from India. Agricultural Economics Research Review, 27(1), 99–109.
Riley, W. D., Potter, E. C., Biggs, J., Collins, A. L., Jarvie, H. P., Jones, J. I., ... & Siriwardena, G. M. (2018). Small water bodies in Great Britain and Ireland: Ecosystem function, human-generated degradation, and options for restorative action. Science of the Total Environment, 645, 1598–1616.
Rapport, D. J., Fyfe, W. S., Costanza, R., Spiegel, J., Yassi, A., Böhm, G. M., ... & Horwitz, P. (2001). Ecosystem health: Definitions, assessment, and case studies. In Our fragile world: Challenges and opportunities for sustainable development—Volume I (Vol. 3, pp. 21). Eolss Publishers.
Reddy, P., Kona, S., Reddy, C. P., Reddy, R. V., & Reddy, J. (2022). Scientific evaluation of integrated watershed development programme projects in Kurnool district of Andhra Pradesh, India. International Journal of Plant & Soil Science, 34(24), 492–499.
Rockström, J., Barron, J., & Fox, P. (2003). Water productivity in rainfed agriculture: Challenges and opportunities for smallholder farmers in drought-prone tropical agro-ecosystems. In Water productivity in agriculture: Limits and opportunities for improvement (pp. 145–162). CABI Publishing.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Third ERTS-1 Symposium (NASA SP-351 I, pp. 309–317). NASA.
Stroosnijder, L. (2007). Rainfall and land degradation. In Climate and land degradation (pp. 167–195). Springer Berlin Heidelberg.
Sharma, B. R., Rao, K. V., Vittal, K. P. R., Ramakrishna, Y. S., & Amarasinghe, U. (2010). Estimating the potential of rainfed agriculture in India: Prospects for water productivity improvements. Agricultural Water Management, 97(1), 23–30.
Singh, D., Gautam, N. N., & Gupta, S. (2021). Assessment of crop parameters and groundwater level in a policy-implemented micro-watershed environment using geospatial technology. Arabian Journal of Geosciences, 14(22), 2439.
Sreenivas, K., Sujatha, G., Mitran, T., Suresh, J. R., Ravisankar, T., & Rao, P. V. N. (2021). Decadal changes in land degradation status of India. Current Science, 121(4), 25.
Thakkar, H., Khepar, S. D., & Sondhi, S. K. (2017). Impact of watershed management programs on land use/land cover dynamics using remote sensing and GIS. Journal of Soil and Water Conservation, 16(1), 21–31.
Venkateswarlu, B. (2010). Agricultural drought management in India. In SAARC workshop on drought risk management in South Asia (pp. 1–13). Kabul, Afghanistan.
Wang, L., & Qu, J. J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2), 237–247.
Wang, L., & Qu, J. J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2), 237–247.
Wani, S. P., Singh, P., Boomiraj, K., & Sahrawat, K. L. (2009). Climate change and sustainable rain-fed agriculture: Challenges and opportunities. Agricultural Situation in India, 66(5), 221–239.
Wani, S. P., Singh, P., Boomiraj, K., & Sahrawat, K. L. (2009). Climate change and sustainable rain-fed agriculture: Challenges and opportunities. Agricultural Situation in India, 66(5), 221–239.
Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1353691.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Stutee Gupta, T. Dharmaraj, Harika Chikkala, K. M. Reddy
This work is licensed under a Creative Commons Attribution 4.0 International License.
