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Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS

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Abstract

Globally, land degradation becomes a serious environmental issue in the context of anthropogenic pressure and climate change in the twenty-first century. Modeling and assessment of land degradation vulnerability assume a greater importance especially in the semi-arid ecosystems for sustainable land resource management. Consistent and reliable earth observation satellite datasets, Analytic Hierarchy Process (AHP), and Geographic Information System (GIS) are the powerful tools to model and assess the land degradation vulnerability. The present study was aimed to model and assess the land degradation vulnerability through analysis of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), rainfall, terrain characteristics, and pedological parameters by using AHP and GIS in the semi-arid ecosystem of Rayalaseema region of southern India. The NDVI and LST products derived from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, rainfall products from Tropical Precipitation Measuring Mission (TRMM), terrain characteristics from Shuttle Radar Topography Mission (SRTM) (30 m), and pedological parameters derived from legacy soil datasets were used in the study. The AHP- and GIS-based modeling shows that about 25.3 and 9.9% of the study area were under high and very high vulnerability to land degradation, respectively. Inadequate rainfall and vegetative cover, high temperature, problematic soils, and lack of adequate conservation measures were found to be the main causative environmental factors for land degradation in the study area. The study clearly demonstrates the potential of AHP- and GIS-based modeling in the assessment of land degradation vulnerability by using the time-series MODIS NDVI- and TRMM-based rainfall products, terrain characteristics, and pedological parameters.

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References

  1. Han, W., Liu, G., Su, X., Wu, X., & Chen, L. (2019). Assessment of potential land degradation and recommendations for management in the south subtropical region, Southwest China. Land Degradation and Development, 30, 979–990.

    Article  CAS  Google Scholar 

  2. Tagore, G. S., Bairagi, G. D., Sharma, N. K., Sharma, R., Bhelawe, S., & Verma, P. K. (2012). Mapping of degraded lands using remote sensing and GIS techniques. Journal of Agricultural physics, 12, 29–36.

    Google Scholar 

  3. Masoudi, M., Joka, P., & Pradhan, B. (2018). A new approach for land degradation and desertification assessment using geospatial techniques. Natural and Hazards Earth System Sciences, 18, 1133–1140.

    Article  Google Scholar 

  4. Bai, Z. G., Dent, D. L., Olsson, L., & Schaepman, M. E. (2008). Proxy global assessment of land degradation. Soil Use and Management, 24, 223–234.

    Article  Google Scholar 

  5. Pacheco, F. A. L., Fernandes, L. F. S., Valle, R. F., Valera, C. A., & Pissarra, T. C. T. (2018). Land degradation: multiple environmental consequences and routes to neutrality. Current Opinion in Environmental Science, 5, 79–86.

    Article  Google Scholar 

  6. Adams, C. R., & Eswaran, H. (2000). Global land resources in the context of food and environmental security, 35-50. In S. P. Gawande (Ed.), Advances in land resources management for the 20th century (p. 655). New Delhi: Soil Conservation Society of India.

    Google Scholar 

  7. Maji, A.K., Reddy, G.P.O., & Sarkar, D. (2010). Degraded and wastelands of India, status and spatial distribution. ICAR and NAAS Publication, 1-158.

  8. NRSA. (1996). Mapping salt-affected soils of India on 1:250,000. NRSA, Hyderabad, India.

  9. Kosmas, C., Danalatos, N. G., & Gerontidis, S. (2000). The effect of land parameters on vegetation performance and degree of erosion under Mediterranean conditions. Catena, 40, 3–17.

    Article  Google Scholar 

  10. Salvati, L., & Zitti, M. (2005). Land degradation in the Mediterranean basin: linking bio-physical and economic factors into an ecological perspective. Biota, 5, 67–77.

    Google Scholar 

  11. Jong, R., Bruin, S., Schaepman, M., & Dent, D. (2011). Quantitative mapping of global land degradation using earth observations. International Journal of Remote Sensing, 32, 6823–6847.

    Article  Google Scholar 

  12. Higginbottom, T., & Symeonakis, E. (2014). Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sensing, 6, 9552–9575.

    Article  Google Scholar 

  13. Tueller, P. T. (1987). Remote sensing science applications in arid environment. Remote Sensing of Environment, 23, 143–154.

    Article  Google Scholar 

  14. Mascaro, J., Detto, M., Asner, G. P., & Muller-Landau, H. C. (2011). Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sensing of Environment, 115, 3770–3774.

    Article  Google Scholar 

  15. Reddy, G. P. O. (2018). Spatial data management, analysis, and modelling in GIS: principles and applications. In G. P. O. Reddy & S. K. Singh (Eds.), Geospatial technologies in land resources mapping, monitoring and management. Geotechnologies and the environment Vol. 21 (pp. 127–142). Cham: Springer.

  16. Essa, S. (2004). GIS modelling of land degradation in Northern-Jordan using Landsat image, In: Altan MO, editor. Geo-imagery bridging continents, the XXth ISPRS Congress; 2004 July 12–23; Vol. XXXV, Part B4. Istanbul, Turkey: ISPRS; p. 505–510.

  17. Reddy, G. P. O., Kumar, N., & Singh, S. K. (2018). Remote sensing and GIS in mapping and monitoring of land degradation. In G. P. O. Reddy & S. K. Singh (Eds.), Geospatial technologies in land resources mapping, monitoring and management, Geotechnologies and the environment, Vol. 21 (pp. 401–424). Cham: Springer.

  18. Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M., Neigh, C., & Reichstein, M. (2013). Trend change detection in NDVI time series: effects of inter-annual variability and methodology. Remote Sensing, 5, 2113–2144.

    Article  Google Scholar 

  19. Zhang, Y., Chen, B. Z., Zhu, X., Luo, Y., Guan, S., & Guo, & Nie, Y. (2008). Land desertification monitoring and assessment in Yulin of Northwest China using remote sensing and geographic information systems (GIS). Environmental Monitoring and Assessment, 147, 327–337.

    Article  Google Scholar 

  20. FAO, LADA. (2007). Technical report 2, biophysical indicator toolbox. Accessed 1 June 2018.

  21. Dubovyk, O., Menz, G., Conrad, C., Elena, K., Machwitz, M., & Khamzina, A. (2012). Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling. Environment Monitoring and Assessment, 185, 4775–4790.

    Article  Google Scholar 

  22. SAC. (2016). Desertification and land degradation atlas of India (based on IRS AWiFS data of 2011–13 and 2003–05). Ahmedabad: Space Applications Centre, ISRO.

    Google Scholar 

  23. Wang, G., Chen, J., Li, Q., & Ding, H. (2006). Quantitative assessment of land degradation factors based on remotely-sensed data and cellular automata: a case study of Beijing and its neighboring areas. Environmental Sciences, 3(4), 239–253.

    Article  Google Scholar 

  24. Khalil, A. A., Essa, Y. H., & Hassanein, M. K. (2014). Monitoring agricultural land degradation in Egypt using MODIS NDVI satellite images. Nature and Science, 12, 15–21.

    Google Scholar 

  25. Bai, Z., & Dent, D. (2009). Recent land degradation and improvement in China. Royal Swedish Academy of Sciences, 38, 150–156.

    Google Scholar 

  26. Li, Z., Deng, X., Yin, F., & Yang, C. (2015). Analysis of climate and land use changes impacts of land degradation in the North China plain. Advances in Meteorology, 1–11.

  27. Baroudy, A. A. E., & Moghanm, F. S. (2014). Combined use of remote sensing and GIS for degradation risk assessment in some soils of the Northern Nile Delta, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences, 17, 77–85.

    Article  Google Scholar 

  28. Ibrahim, Y. Z., Balzter, H., Kaduk, J., & Tucker, C. J. (2015). Land degradation assessment using residual trend analysis of GIMMS NDVI3g, soil moisture and rainfall in Sub-Saharan West Africa from 1982 to 2012. Remote Sensing, 7, 5471–5494.

    Article  Google Scholar 

  29. Pashaei, M., Rashki, A., & Sepehr, A. (2017). An integrated desertification vulnerability index for Khorasan- Razavi, Iran. Natural Resources and Conservation, 5, 44–55.

    Article  Google Scholar 

  30. Eckert, S., Hüsler, F., Liniger, H., & Hodel, E. (2015). Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113, 16–28.

    Article  Google Scholar 

  31. Weinzierl, T., Wehberg, J., Böhner, J., & Conrad, O. (2016). Spatial assessment of land degradation risk for the Okavango river catchment, southern Africa. Land Degradation and Development, 27, 281–294.

    Article  Google Scholar 

  32. Singh, R. B., & Ajai. (2019). A composite method to identify desertification hotspots and bright spots. Land Degradation and Development, 30, 1025–1039.

    Article  Google Scholar 

  33. Hassan, F., & Kader, A. (2018). Assessment and monitoring of land degradation in the northwest coast region, Egypt using Earth observations data. The Egyptian Journal of Remote Sensing and Space Sciences, 1–9.

  34. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1, 83–98.

    Article  Google Scholar 

  35. Saaty, T. L. (1980). The analytic hierarchy processes. New York: McGraw-Hill.

    Google Scholar 

  36. Wu, Q., & Wang, M. (2007). A framework for risk assessment on soil erosion by water using an integrated and systematic approach. Journal of Hydrology, 337, 11–21.

    Article  Google Scholar 

  37. Alexakis, D. D., Hadjimitsis, D. G., & Agapiou, A. (2013). Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of “Yialias” in Cyprus. Atmospheric Research, 131, 108–124.

    Article  Google Scholar 

  38. Sar, N., Chatterjee, S., & Adhikari, M. D. (2015). Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India. Modeling Earth Systems and Environment, 1–31.

  39. Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., & Eklundh, L. (2004). A simple method for reconstructing a high-quality NDVI time-series dataset based on the Savitzky-Golay filter. Remote Sensing of Environment, 91, 332–344.

    Article  Google Scholar 

  40. Jönsson, P., & Eklundh, L. (2002). Seasonality extraction by function-fitting to time series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1824–1832.

    Article  Google Scholar 

  41. Holben, B. N. (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7, 1417–1434.

    Article  Google Scholar 

  42. Sivakumar, M. V. K. (2007). Interactions between climate and desertification. Agricultural and Forest Meteorology, 142, 143–155.

    Article  Google Scholar 

  43. Gu, H., You, Z., Yang, C., Ju, Q., Lu, B., & Liang, C. (2010). Hydrological assessment of TRMM rainfall data over Yangtze River Basin. Water Science and Engineering, 3, 418–430.

    Google Scholar 

  44. Reddy, R. S., Shiva Prasad, C. R., & Harindranath, C. S. (1996). Soils of Andhra Pradesh for optimizing land use. NBSS Publication NBSS&LUP Nagpur, 69–94.

  45. Xue, R., Wang, C., Liu, M., Zhang, D., Li, K., & Li, N. (2019). A new method for soil health assessment based on analytic hierarchy process and meta-analysis. Science of the Total Environment, 650(2), 2771–2777.

    Article  CAS  Google Scholar 

  46. Kumar, K. M., Annadurai, R., Ravichandran, P. T., & Arumugam, K. (2015). Mapping of landslide susceptibility using analytical hierarchy process at Kothagiri Taluk, Tamil Nadu, India. International Journal of Applied Engineering Research, 10, 5503–5523.

    Google Scholar 

  47. Saaty, T. L., & Vargas, L. G. (2001). Models, methods, concepts and applications of the analytic hierarchy process. Norwell: Kluwer Academic Publishers.

    Book  Google Scholar 

  48. Carrion, J. A., Estrella, A. E., Dols, F. A., Torob, M. Z., Rodriguez, M., & Ridao, A. R. (2008). Environmental decision-support systems for evaluating the carrying capacity of land areas: optimal site-selection for grid-connected photovoltaic power plants. Renewable and Sustainable Energy Reviews, 12, 2358–2380.

    Article  Google Scholar 

  49. Chen, Y., Yu, J., & Khan, S. (2010). Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling and Software, 25, 1582–1591.

    Article  Google Scholar 

  50. Balasubramani, K., Veena, M., Kumaraswamy, K., & Saravanabavan, V. (2015). Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (RUSLE) model through GIS. Modeling Earth Systems and Environment, 1, 1–17.

    Article  Google Scholar 

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Acknowledgements

Authors of article are highly thankful to the US Geological Survey and NASA for providing free access of time-series MODIS (https://earthexplorer.usgs.gov) and TRMM data (http://trmm.gsfc.nasa.gov). Authors are also thankful to the Director, ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur, for providing the facilities to carry out the work. We sincerely thank anonymous reviewers whose constructive comments and suggestions greatly improved the overall quality of the manuscript.

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Authors and Affiliations

  1. ICAR-National Bureau of Soil Survey and Land Use Planning, Amravati Road, Nagpur-440033, India

    P. Sandeep, G. P. Obi Reddy & K. C. Arun Kumar

  2. Bharathidasan University, Tiruchirappalli, Tamil Nadu-620024, India

    P. Sandeep, R. Jegankumar & K. C. Arun Kumar

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  1. P. Sandeep
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  2. G. P. Obi Reddy
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  3. R. Jegankumar
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Contributions

P. Sandeep: conceptualization, methodology, software, validation, and writing—original draft, review, and editing. G.P. Obi Reddy: conceptualization, methodology, software, validation, and supervision. R. Jegankumar: validation. K.C. Arun Kumar: validation.

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Correspondence to G. P. Obi Reddy.

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Sandeep, P., Reddy, G.P.O., Jegankumar, R. et al. Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS. Environ Model Assess 26, 143–154 (2021). https://doi.org/10.1007/s10666-020-09739-1

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