Skip to main content

Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data

Abstract

Detailed analysis of Land Use/Land Cover (LULC) using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three different techniques: error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coefficients (K). The average overall accuracies for rabi and kharif are 82.83% and 78.21%, respectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation between cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.

This is a preview of subscription content, access via your institution.

References

  • Agricultural Outlook Forum, 2012. The world and United States cotton outlook. United States Department of Agriculture.

    Google Scholar 

  • Anderson J R, 1977. Land use and land cover changes: A framework for monitoring. Journal of Research by the Geological Survey, 5: 143–153.

    Google Scholar 

  • Barraza V, Grings F, Salvia M et al., 2013. Monitoring and modelling land surface dynamics in Bermejo River Basin, Argentina: Time series analysis of MODIS NDVI data. International Journal of Remote Sensing, 34(15): 5429–5451. doi: 10.1080/01431161.2013.791759.

    Article  Google Scholar 

  • Bastiannssen W G M, 1998a. Remote sensing in water resources management: The state of the art. International Water Management Institute, Colombo, Sri Lanka.

    Google Scholar 

  • Bastiaanssen W G M, Menenti M, Feddes R A et al., 1998b. A remote sensing surface energy balance algorithm for land (SEBAL) formulation. J. Hydrol., 212/213: 198–212.

    Article  Google Scholar 

  • Black A, Stephen H, 2014. GIScience & remote sensing relating temperature trends to the normalized difference vegetation index in Las Vegas. GIScience and Remote Sensing, 51(4): 468–482.

    Article  Google Scholar 

  • Campbell J B, 2002. Introduction to Remote Sensing. New York: The Guilford Press.

    Google Scholar 

  • Cheema M J M, Bastiaanssen W G M, 2010. Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis. Agricultural Water Management, 97(10): 1541–1552. doi: 10.1016/j.agwat.2010.05.009.

    Article  Google Scholar 

  • Congalton R, Green K, 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton: CRC/Lewis Press, FL. 137 p.

    Google Scholar 

  • Congalton R G, 1996. Accuracy assessment: A critical component of land cover mapping in gap analysis: A landscape approach to biodiversity planning. A Peer-Reviewed Proceedings of the ASPRS/GAP Symposium, February 27–March 2, 1995, Charlotte, N.C. 119–131.

    Google Scholar 

  • Dappen Patti R, Ratcliffe I C, Robbins C R et al., 2008. Mapping agricultural land cover for hydrologic modeling in the Platte River Watershed of Nebraska. Great Plains Research: A Journal of Natural and Social Sciences, Paper 926, http://digitalcommons.unl.edu/greatplainsresearch/926.

    Google Scholar 

  • Ding H, Shi W, 2013. Land-use/land-cover change and its influence on surface temperature: A case study in Beijing City. International Journal of Remote Sensing, 34(15): 5503–5517. doi: 10.1080/01431161.2013.792966.

    Article  Google Scholar 

  • Douglas K B, Mark A F, 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173: 74–84.

    Article  Google Scholar 

  • Fang W, Chen J, Shi P, 2005. Variability of the phenological stages of winter wheat in the North China Plain with NOAA/AVHRR NDVI data (1982–2000). IEEE International Geoscience and Remote Sensing Symposium Proceedings, 5: 3124–3127.

    Google Scholar 

  • Fisher P F, 2010. Remote sensing of land cover classes as type 2 fuzzy sets. Remote Sensing of Environment, 114: 309–321.

    Article  Google Scholar 

  • Foody G M, 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80: 185–201.

    Article  Google Scholar 

  • Gao X, Huete A R, Ni W et al., 2000. Optical-biophysical relationships of vegetation spectra without back-ground contamination. Remote Sensing of Environment, 74: 609–620.

    Article  Google Scholar 

  • Giri, Chandra, Jenkins C, 2005. Land cover mapping of greater Mesoamerica using MODIS data. Remote Sensing, 31(4): 274–282. Retrieved at http://thepimmgroup.org/wpcontent/uploads/2007/11/remotesensing2.pdf.

    Google Scholar 

  • Gong P, Wang J, Yu L et al., 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7): 2607–2654. doi: 10.1080/01431161.2012.748992.

    Article  Google Scholar 

  • Gumma M K, Nelson A, Thenkabail P S et al., 2011. Mapping rice areas of South Asia using MODIS multitemporal data. J. Applied Remote Sensing, 5(1): 53547. doi: 10.1117/1.3619838.

    Article  Google Scholar 

  • Jensen J R, 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd ed. New Jersey: Prentice-Hall, 316p.

    Google Scholar 

  • Jeong S, Jang K, Hong S et al., 2011. Detection of irrigation timing and the mapping of paddy cover in Korea using MODIS images data. Korean Journal of Agricultural and Forest Meteorology, 13: 69–78.

    Article  Google Scholar 

  • Julien Y, Sobrino J A, 2009. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing, 30: 3495–3513.

    Article  Google Scholar 

  • Kim, Y. 2013. Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems. International Journal of Remote Sensing, 34(14): 4889–4899. doi: 10.1080/2150704X.2013.781700.

    Article  Google Scholar 

  • Kimaro T A, Tachikawa Y, Takara K, 2005. Distributed hydrologic simulations to analyze the impacts of land use changes on flood characteristics in the Yasu River Basin in Japan. Journal of Natural Disaster Sciences, 27(2): 85–94.

    Google Scholar 

  • Latifovic R, Olthof I, 2004. Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sensing of Environment, 90: 153–165.

    Article  Google Scholar 

  • Leff B, Ramankutty N, Foley J A, 2004. Geographic distribution of major crops across the world. Global Biogeochem. Cycles, 18, GB 1009. doi: 10.1029/203GB002108.

    Google Scholar 

  • Liang L, Gong P, 2013. Evaluation of global land cover maps for cropland area estimation in the conterminous United States. International Journal of Digital Earth: 1–16. doi: 10.1080/17538947.2013.854414.

    Google Scholar 

  • Lorencov A E, Fr Elichov A J, Nelson E et al., 2013. Past and future impacts of land use and climate change on agricultural ecosystem services in the Czech Republic. Land Use Policy, 33: 183–194.

    Article  Google Scholar 

  • Lu D, Li G, Moran E et al., 2013. Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon. International Journal of Remote Sensing, 34(16): 5953–5978. doi:10.1080/01431161.2013.802825.

    Article  Google Scholar 

  • Matthews E, 1983. Global vegetation and landuse: New high resolution data bases for climate studies. Journal of Climate and Applied Meteorology, 22: 474–487.

    Article  Google Scholar 

  • Mitrakis N E, Mallinis G, Koutsias N et al., 2011. Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier. International Journal of Image and Data Fusion, 1–20.

    Google Scholar 

  • Molden D, 1997. Accounting for water use and productivity. SWIM paper 1. Colombo, Srilanka.

    Google Scholar 

  • Morton D C, DeFries R S, Shimabukuro Y E et al., 2006. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon. Proceedings of the National Academy of Sciences of the United States of America, 103(39): 14637–14641.

    Article  Google Scholar 

  • Niu Z, Zhang H, Wang X et al., 2012. Mapping wetland changes in China between 1978 and 2008. Chinese Science Bulletin, 57(22): 2813–2823. doi: 10.1007/s11434-012-5093-3.

    Article  Google Scholar 

  • Osborne P, Alonso J, Bryant R, 2001. Modelling landscape-scale habitat use using GIS and remote sensing: A case study with great bustards. Journal of Applied Ecology, 38: 458–471.

    Article  Google Scholar 

  • Oslon J S, 1994. Global ecosystem framework definitions. USGS EROS Data Center Internal Report, Sioux Falls, SD, 37p.

    Google Scholar 

  • Peng D, Huete A R, Huang J et al., 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. International Journal of Applied Earth Observation and Geoinformation, 13: 13–23.

    Article  Google Scholar 

  • Pettorelli N, 2013. The Normalized Difference Vegetation Index. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Portmann F T, Siebert S, Döll P, 2010. MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Global Biogeochemical Cycles, 24: GB1011.

    Article  Google Scholar 

  • Prakasam C, 2010. Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil nadu. International Journal of Geomatics and Geosciences, 1(2): 150–158.

    Google Scholar 

  • Reed B C, Brown J F, VanderZee D et al., 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5: 703–714.

    Article  Google Scholar 

  • Reis S, 2008. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors, 8(10): 6188–6202. doi: 10.3390/s8106188.

    Article  Google Scholar 

  • Schilling K E, Jha M K, Zhang Y et al., 2008. Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7): 1–12. Available at: http://doi.wiley.com/10.1029/2007WR006644 [Accessed October 8, 2014].

    Article  Google Scholar 

  • Shao Y, Fan X, Liu H et al., 2001. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sensing of Environment, 76(3): 310–325. doi: 10.1016/S0034-4257(00)00212-1.

    Article  Google Scholar 

  • Shi J, Huang J, Zhang F, 2013. Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data. Journal of Zhejiang University (Science B), 14(10) (October): 934–946. doi: 10.1631/jzus.B1200352.

    Article  Google Scholar 

  • Thi T, Nguyen H, De-Bie C A J M et al., 2012. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis. International Journal of Remote Sensing, 33(2): 415–434.

    Article  Google Scholar 

  • Tou J T, Gonzalez R C, 1974. Pattern Recognition Principles. London: Addison-Wesley, 1974.

    Google Scholar 

  • Tucker C J, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127–150.

    Article  Google Scholar 

  • Tucker C J, Vanpraet C L, Sharman M J et al., 1985. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sensing of Environment, 17: 233–249.

    Article  Google Scholar 

  • Usman M, Liedl R, Awan U K, 2015a. Spatio-temporal estimation of consumptive water use for assessment of irrigation system performance and management of water resources in irrigated Indus Basin, Pakistan. J. Hydrol. doi: 10.1016/j.jhydrol.2015.03.031.

    Google Scholar 

  • Usman M, Liedl R, Kavousi A, 2015b. Estimation of distributed seasonal net recharge by modern satellite data in irrigated agricultural regions of Pakistan. Environ. Earth Sciences. doi: 10.1007/s12665-015-4139-7.

    Google Scholar 

  • Usman M, Liedl R, Shahid M A M, 2014. Managing irrigation water by yield and water productivity assessment of a rice-wheat system using remote sensing. Journal of Irrigation and Drainage Engineering. doi: 10.1061/(ASCE)IR.1943-4774.0000732.

    Google Scholar 

  • Wajid A, Ahmad A, Khaliq T et al., 2010. Quantification of growth, yield and radiation use efficiency of promising cotton cultivars at varying nitrogen levels. Pakistan Journal of Botany, 42(3): 1703–1711.

    Google Scholar 

  • Wajid A, Hussain K, Maqsood M et al., 2007. Simulation modeling of growth, development and grain yield of wheat under semi arid conditions of Pakistan. Pakistan Journal of Agricultural Sciences, 44(2): 194–199.

    Google Scholar 

  • Wardlow B D, Egbert S L, Kastens J H, 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Drought Mitigation Center Faculty Publications. Paper 2. http://digitalcommons.unl.edu/droughtfacpub/2.

    Google Scholar 

  • Wegehenkel M, 2009. Modeling of vegetation dynamics in hydrological models for the assessment of the effects of climate change on evapotranspiration and groundwater recharge. Adv. Geosci., 21: 109–115. doi: 10.5194/adgeo-21-109-2009.

    Article  Google Scholar 

  • Wilson M, Henderson-Sellers A, 1985. A global archive of land cover and soils data for use in general circulation models. Journal of Climatology, 5: 119–143.

    Article  Google Scholar 

  • Xiao X, Boles S, Frolking S et al., 2006. Mapping paddy rice agriculture in South and South-east Asia using multi-temporal MODIS images. Remote Sensing of Environment, 100: 95–113. http://dx.doi.org/10.1016/j.rse.2005.10.004.

    Article  Google Scholar 

  • Yu L, Wang J, Gong P, 2013. Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach. International Journal of Remote Sensing, 34(16): 5851–5867. doi: 10.1080/01431161.2013.798055.

    Article  Google Scholar 

  • Zhao L, Xia J, Xu C et al., 2013. Evapotranspiration estimation methods in hydrological models. J. Geogr. Sciences, 23(2): 359–369. doi: 10.1007/s11442-013-1015-9.

    Article  Google Scholar 

  • Zheng P Q, Baetz B W, 1999. GIS-based analysis of development options from a hydrology perspective. Journal of Urban Planning and Development, 125: 164–180.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Usman.

Additional information

Author: M Usman, IGW, Faculty of Environmental Sciences, TU Dresden, Helmholtzstrasse 10, 01069 Dresden, Germany.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Usman, M., Liedl, R., Shahid, M.A. et al. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. J. Geogr. Sci. 25, 1479–1506 (2015). https://doi.org/10.1007/s11442-015-1247-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-015-1247-y

Keywords

  • land use/land cover
  • remote sensing
  • normalized difference vegetation index
  • accuracy assessment
  • change detection
  • hydrological modeling