Application of Satellite Data and Data Mining Algorithms in Estimating Coverage Percent (Case study: Nadoushan Rangelands, Ardakan Plain, Yazd, Iran)

Document Type : Research and Full Length Article


1 Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, Ardakan, Iran.

2 Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, Ardakan, Iran

3 Department of Reclamation of Arid and Mountainous Regions Engineering, Faculty of Natural Resources, Tehran University, Tehran, Iran


Assessing and monitoring rangelands in arid regions are important and essential tasks in order to manage the desired regions. Nowadays, satellite images are used as an approximately economical and fast way to study the vegetation in a variety of scales. This research aims to estimate the coverage percent using the digital data given by ETM+ Landsat satellite. In late May and early June 2018, the vegetation was measured in Ardakan plain, Yazd province, Iran. Information was obtained by 320 plots in 40 transects and also, the satellite images in terms of sampling time were downloaded and processed in USGS website. 16 indices involving NDVI, NIR, MSI, SS, IR1, MIRV1, NVI, TVI, RAI, SAVI, LWC, PD322, PD321, PD312, PD311 and IR2 were estimated. Through estimating the indices and extracting the values in order to conduct index-based predictions, six data mining models of Artificial Neural Network (ANN), the K Nearest Neighbor (KNN), Gaussian Process (GP), Linear Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT M5) have been applied. Model assessment results indicated high vegetation estimate efficiency based on the indices but the model KNN with Root Mean Square Error (RMSE= 2.520) and Coefficient of determination (R2= 0.94) and (RMSE= 2.872 and R2= 0.96) had the highest accuracy in the training and data sets, respectively. As well, to determine the weight and importance of parameters, and to estimate the coverage percent, the weighing process were conducted based on support vector machine. Weighing results indicated that the KNN model and the Simple Subtraction (SS) index had higher weight and importance in terms of vegetation percent.


Main Subjects

Alishah Aratboni, F., Arzani, H., Hosseini, S. Z., Babaie Kafaki, S. and Mirakhorlou, K., 2013. Rangelands vegetation cover mapping using IRS-LissIII data image processing (Case study: Sorkh Abad Basin, Mazandaran). Iranian Journal of Range and Desert Research, 20(3): 454- 462 (In Persian).
Arzani, H., and King, G. W., 2008. Application of remote sensing (landsat TM data) for vegetation parameters measurement in western division of NSW. International Grassland Congress. Hohhot, China. ID NO. 1083.
Asadi, M., Fatzhadeh, A. and Taghizadeh Mehrjerdi, R., 2017. Optimization suspended load estimation models by using geo-morphometric parameters and attribute reduction technique. Iranian Journal of Soil and Water Research, 48(3): 669- 678 (In Persian).
Azarnivand, H., Alizadeh, E., Sour, A. and Hajibeglo, A., 2012. The Effects of Range Management Plans of Soil Properties and Rangeland Vegetation (Case Study: Eshtehard Rangelands). Journal of Rangeland Science, 2(4): 625- 634.
Baugh, W. M. and Groeneveld, D. P., 2009. Broadband vegetation index performance evaluated for a low-cover environment. International Journal of Remote sensing, 27(21): 4715- 4730.
Bashiri, M., Ariapour, M. and Golkarian, A., 2018. Using Data Mining Algorithms in Separation of Sediment Sources in Nodeh Watershed, Gonabad. Desert Ecosystem Engineering Journal, 7(19): 81- 94 (In Persian).
Balouchi, B., Nikoo, M. R. and Adamowski, J. 2015. Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: application of different types of ANNs and the M5P model tree. Applied Soft Computing, 34: 51- 59.
Boyd, D. S., Foody, G. M., Curran, P. J., Lucas, R. M. and Honzak, M., 1996. An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration. International Journal of Remote Sensing. 17(2): 249-261.
Booth, D. T. and Tueller, P. T., 2003. Rangeland monitoring using remote sensing. Arid Land Research and Management, 17: 455- 467.
Chang, F. J., Tsai, Y. H., Chen, P. A., Coynel, A. and Vachaud, G., 2015. Modeling Water Quality in an Urban River Using Hydrological Factors– Data Driven Approaches. Journal of Environmental Management, 151: 87- 96.
Carpenter, G. A., Gopal, S., Macomber, S., Martens, S., Woodcock, C. E., and Franklin, J., 1999. A Neural Network method for efficient vegetation mapping. Remote Sensing of Environment 70(3): 326–338.
Darvishzadeh, R., Matkan, A. A., Hosseiniasl, A. and Ebrahimi Khusefi, M., 2012. Estimation of vegetation fraction in the Central arid region of Iran using satellite images (Case study: Sheitoor basin, Bafgh). Arid Biome Scientific and Research Journal, 2(1): 25-38 (In Persian).
David, S. K., Saeb. A. T. M., and Al Rubeaan, K., 2013. Comparative Analysis of Data Mining Tools and Classification Techniques using WEKA in Medical Bioinformatics. Computer Engineering and Intelligent Systems, 4(13): 28- 38.
Eastman, J. R., 1995. IDRISI for Windows: Student Manual, Version 1.0. Clark University Publication. USA.
Foody, G. M., Cutler, M., McMorrow, J., Pelz, D., Tangki, H., Boyd, D. S., and Douglas, I., 2001. Mapping the biomass of Bornean tropical rain forest from  remotely sensed data. Global Ecology and Biogeography. 10(4): 379–387.
Han, J., Pei, J. and Kamber, M., 2011, Data Mining: Concepts and Techniques (3nd Edition). Published by Elsevier, 744p.
Jabari, S., Khajedin, S. J., Jafari, R. and Soltani. S., 2016. Application of AWIFS digital data to determine vegetation cover (Case Study: Semirom- Isfahan). Journal of Rangeland. 9(4): 333- 343 (In Persian).
Jafari, H. R., Hamzeh, M., Nasiri, H. and Rafii, Y., 2011. Developing Decision Tree and Data Mining Based Conceptual Model for Detecting Land Cover Changes using TM images and Ancillary Data (Study area: Centeral section of Bouyerahmad County). Environmental Science. 8(3): 1-20 (In Persian). 
Kent, M., 2011. Vegetation Description and Data Analysis: A Practical Approach (2nd Edition). Wiley-Blackwell. 428p
Lawrence, R. L. and Ripple, W. J., 1998. Comparisons among Vegetation Indices and Bandwise Regression in a Highly Disturbed, Heterogeneous Landscape: Mount St. Helens, Washington. Remote Sensing of Environment, 64(1): 91- 102.
Leblon, B., 1993. Soil and vegetation optical properties. In: Applications in Remote Sensing, Volume 4, The International Center for Remote Sensing Education. Wiley Press, New York, USA.
Matkan, A. A., Darvishzadeh, R., Hosseiniasl, A. and Ebrahimi Khusefi, M., 2011. Capability using satellite images and neural network in estimating vegetation percentage in arid region. Journal of Environmental Erosion, 1: 7- 27 (In Persian).
Matsushita. B., Yang. W., Chen. J., Onda. Y. and Qiu. G., 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density Cypress forest. Sensors. 7(11): 2636-2651.
Mountrakis, G., Im, J. & Ogole, C., 2011, Support Vector Machines in Remote Sensing: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 66: 247 259.
Najafian, T., Fathianpour, N., Ranjbar, H. and Bakhshpour, R., 2012. Detecting Unknown Spectral Features from ALI, ASTER and Hyperion Images using Correlation Coefficient Method. Case Study: Sarcheshmeh copper mine, IRAN. Journal of Advanced Applied Geology 2(5): 59-68 (In Persian).
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J. and Stenseth, N. C., 2005. Using the satellite – derived NDVI to assess ecological responses to environmental change. Journal of Trends in Ecology and Evolution. 9(20): 503-510.
Pons, X., Pesquer, L., Cristobal, J. and Gonzalez-Guerrero, O., 2014. Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images. International Journal of Applied Earth Observation and Geoinformation. 33: 243- 254.
Rahdari, V., Maleki Najafabadi, S., Afsari, K.H., Abtin, E., Piri, H. and Fakhireh, A., 2012. Change detection of Hamoun wild life refuge using RS & GIS. Remote Sens. GIS Journal Iran. 3(2): 59-70 (In Persian).
Rajaee, T., Nourani, V., Zounemat-Kermani, M. and Kisi, O. 2011. River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model. Journal of Hydrologic Engineering, 16(8): 613- 627.
Rasmussen, C. E. and Williams, C. K. I., 2006. Gaussian Processes for Machine Learning. The MIT Press, ISBN 026218253X. c. 2006. Massachusetts Institute of Technology. USA.
Rasouli, K., Hsieh, W. W. and Cannon, A. J., 2012. Daily Streamflow Forecasting by Machine Learning Methods with Weather and Climate Inputs. Journal of Hydrology, 414: 284-293.
Rock, B.N., Vogelmann, J. E., Williams, D. L., Vogelmann, A. F., and Hoshizaki, T., 1986. Remote Detection of Forest Damage, 36(7): 439-445.
Sani Abade, M., Mahmoudi, S. and Taherparvar, D., 2017. Data Mining Applications (3nd Edition), Niaz-e-Danesh Publication. Tehran (In Persian).
Su, M. C., 1994. Use of neural networks as medical diagnosis expert systems. Computers in Biology and Medicine, 24(6): 419- 429.
Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer Verlag, New York, USA.
Wei, J., Lee, Z., Garcia, R., Zoffoli, L., Armstrong, R. A., Shang, Z., Sheldon, P. and Chen, R. F., 2018. An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters. Remote Sensing of Environment, 215: 18- 32.
Zarkogianni, K., Vazeou, A., Mougiakakou, S. G., Prountzou, A. and Nikita, K. S., 2011. An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Transaction Biomedical Engineering, 58(9): 2467- 2477.
Zhang, Y. X., Li, X. B. and Chen, Y. H., 2003. Overview of field and multi-scale remote sensing measurement approaches to grassland vegetation coverage. Advanced Earth science, 18: 85-93 (in Chinese with English abstract).
Volume 9, Issue 4
October 2019
Pages 333-350
  • Receive Date: 11 November 2018
  • Revise Date: 30 March 2019
  • Accept Date: 30 March 2019
  • First Publish Date: 01 October 2019