Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 409–419 | Cite as

Prediction of vegetation dynamics using NDVI time series data and LSTM

  • D. Sushma Reddy
  • P. Rama Chandra Prasad
Original Article


Understanding and analyzing the changes in vegetation cover is very important in several aspects including climatic changes, water budget, ecological balance and specially to undertake necessary conservation measures. The concept of neural network has gained much significance in the analysis of vegetation dynamics using remote sensing satellite data. In the current study an attempt has been made to predict the vegetation dynamics using MODIS NDVI time series data sets and long short term memory network, an advanced technique adapted from the artificial neural network. The dataset of 861 NDVI images from January 2000 to June 2016 is used for making the time series. The data is segregated into three sets which comprises of training set (70%), validation set (20%), and testing set (10%). To check the reliability of the experiment we have finalised two different regions after extensive research for investigation. These include different terrains in the Great Nicobar Islands, one region along the coast where vegetation has severe ecological damage due to 2004 Indian Ocean tsunami and the other, an interior region which remained imperturbable during the tsunami. Long short term memory network, an advanced neural network is trained with these NDVI values for both the regions separately to predict the future vegetation dynamics. To measure the accuracy of the LSTM network, root mean square error is calculated. The resulting plots from both the experiments indicate that the long short-term memory neural network follows the series in addition to coinciding with the required time series. Also, an unanticipated change in the trend of the NDVI series were well adapted by the network and was able to predict the future NDVI values with good accuracy maintaining RMSE less than 0.03 without providing any supplementary data. By adopting the prescribed method in the paper, anticipation of vegetation changes can be done accurately much ahead of time and take proactive measures accordingly to safeguard and improve the vegetation in any area.


Time series Normalized Difference Vegetation Index Long short term memory Great Nicobar Neural networks 



We are thankful to University of Natural Resources and Life Sciences (BOKU), Vienna for providing us with the MODIS NDVI dataset for free. Also, grateful to anonymous reviewers for their constructive suggestions in revising the manuscript.


  1. Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificialneural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727CrossRefGoogle Scholar
  2. Agone V, Bhamare SM (2012) Change detection of vegetation cover using remote sensing and GIS. J Res Dev 2(4)Google Scholar
  3. Anderson JR (1976) A land use and land cover classification system for use with remote sensor data. US Government Printing Office, Washington, D.C. ).Google Scholar
  4. Anonymous (2000) Understanding the Normalized Difference VegetationIndex (NDVI). Accessed 03 June 2017Google Scholar
  5. Anonymous (2003) Biodiversity characterization at landscape level in Andaman and Nicobar islands using remote sensing and geographic information system. Indian Institute of Remote Sensing, Dehra Dun, p 304Google Scholar
  6. Atkinson PM, Tatnall ARL (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18(4):699–709CrossRefGoogle Scholar
  7. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult IEEE Trans Neural Netw, 5(2):pp 157–166CrossRefGoogle Scholar
  8. Budama N (2015) A deep drive into recurrent neural nets. Accessed 19 Jun 2017
  9. Cai S, Liu D (2015) Detecting change dates from dense satellite time series using a sub-annual change detection algorithm. Remote Sens 7:8705–8727CrossRefGoogle Scholar
  10. Chen J, Jonsson P, Tamura M et al (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ 91:332–344CrossRefGoogle Scholar
  11. Colah (2015) Understanding LSTM Networks. Accessed 19 Apr 2017
  12. Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Review ArticleDigital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25(9):1565–1596CrossRefGoogle Scholar
  13. Cordeiro C, Cristina S, Goela PC, Danchenko S, Icely J, Lavender S, Newton A (2016) Time series analysis of remote sensing data. Accessed 19 Jan 2017
  14. De Beurs KM, Henebry GM (2005) A statistical framework for the analysis of long image time series. Int J Remote Sens 26(8):1551–1573CrossRefGoogle Scholar
  15. Eilers PH (2003) A perfect smoother. Anal Chem 75(14):3631–3636CrossRefGoogle Scholar
  16. Eslamian S, Eslamian FA (2017) Handbook of drought and water scarcity: principles of drought and water scarcity. CRC Press, Florida, p 674CrossRefGoogle Scholar
  17. Foody GM (2006) Pattern recognition and classification of remotely sensed images by artificial neural networks. In: Ecological informatics. Springer, Berlin, pp 459–477Google Scholar
  18. Gamboa JCB (2017) Deep Learning for Time-Series Analysis. arXiv preprint arXiv:1701.01887Google Scholar
  19. Geng L, Ma M, Wang X, Yu W, Jia S, Wang H (2014) Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river Basin, China. Remote Sens 6:2024–2049CrossRefGoogle Scholar
  20. Gershenson C (2003) Artificial neural networks for beginners. arXiv preprint cs/0308031Google Scholar
  21. Gómez C, Wulder MA, White JC, Montes F, Delgado JA (2012) Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain. Int J Remote Sens 33(17):5546–5573CrossRefGoogle Scholar
  22. Gómez C, White JC, Wulder MA, Alejandro P (2014) Historical forest biomass dynamics modelled with Landsat spectral trajectories. ISPRS J Photogramm Remote Sens 93:14–28CrossRefGoogle Scholar
  23. Gómez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: a review. ISPRS J Photogramm Remote Sens 116:55–72CrossRefGoogle Scholar
  24. Gopal S, Woodcock C (1996) Remote sensing of forest change using artificial neural networks. IEEE Trans Geosci Remote Sens 34(2):398–404CrossRefGoogle Scholar
  25. Haas EM, Bartholomé E, Combal B (2009) Time series analysis of optical remote sensing data for the mapping of temporary surface water bodies in sub-Saharan western Africa. J Hydrol 370(1):52–63CrossRefGoogle Scholar
  26. Ivits E, Cherlet M, Sommer S, Mehl W (2013) Addressing the complexity in non-linear evolution of vegetation phenological change with time-series of remote sensing images. Ecol Ind 26:49–60CrossRefGoogle Scholar
  27. Jana A, Maiti S, Biswas A (2016) Seasonal change monitoring and mapping of coastal vegetation types along Midnapur-Balasore Coast, Bay of Bengal using multi-temporal landsat data Model. Earth Syst Environ 2:7. CrossRefGoogle Scholar
  28. Jeevalakshmi D, Reddy SN, Manikiam B (2016) Land cover classification based on NDVI using LANDSAT8 time series: a case study Tirupati region. In: Communication and Signal Processing (ICCSP), 2016 International Conference on IEEE, pp 1332–1335Google Scholar
  29. Jia Y, Wu Z, Xu Y, Ke D, Su K (2017) Long Short-Term Memory Projection recurrent neural networkarchitectures for Piano’s continuous note recognition. J RobotGoogle Scholar
  30. Jose E, Ryutaro T, Renchin T (2002) The polynomial least squares operation (PoLeS): a method for reducing noise in NDVI time series data. In: Proceedings of the 23rd Asian conference on remote sensing, section of data processing, algorithm and modelling, Kathmandu, NepalGoogle Scholar
  31. Kang L, Di L, Deng M, Yu E, Xu Y (2016) Forecasting vegetation index based on vegetation-meteorological factor interactions with artificial neural network. In: Agro-Geoinformatics (Agro-Geoinformatics), 2016 Fifth International Conference on IEEE, pp 1–6Google Scholar
  32. Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series:1. LandTrendr—Temporal segmentation algorithms. Remote Sens Environ 114(12):2897–2910CrossRefGoogle Scholar
  33. Khorasani M, Ehteshami M, Ghadimi H et al (2016) Simulation and analysis of temporal changes of groundwater depth using time series modeling Model. Earth Syst Environ 2:90. CrossRefGoogle Scholar
  34. Kriminger E, Latchman H (2011) Markov chain model of homeplug CSMA MAC for determining optimal fixed contention window size. In: Power Line Communications and Its Applications (ISPLC), 2011 IEEE International Symposium on IEEE, pp 399–404Google Scholar
  35. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019Google Scholar
  36. Lyu H, Lu H, Mou L (2016) Learning a transferable change rule from a recurrent neural network for land cover change detection. Remote Sens 8(6):506CrossRefGoogle Scholar
  37. Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2016). Fully convolutional neural networks for remote sensing image classification. In:Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp 5071–5074). IEEEGoogle Scholar
  38. Manobavan M, Lucas NS, Boyd DS, Petford N (2002) Forecasting the interannual trends in terrestrial vegetation dynamics using time series modelling techniques. ForestSAT Symposium Heriot Watt UniversityGoogle Scholar
  39. Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29(3):617–663CrossRefGoogle Scholar
  40. Mehal Z, Van Ryn L, Jess K, Hamilton (2009) Accessed on 22 Jan 2018
  41. Miller DM, Kaminsky EJ, Rana S (1995) Neural network classification of remote-sensing data. Comput Geosci 21(3):377–386CrossRefGoogle Scholar
  42. Nay JJ, Burchfield E, Gilligan J (2016) A machine learning approach to forecasting remotely sensed vegetation health. arXiv preprint arXiv:1602.06335Google Scholar
  43. Obitko M (1999) Prediction using neural networks. tutorials/neural-network-prediction/introduction.html. Accessed 20 May 2017
  44. Ordóñez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115CrossRefGoogle Scholar
  45. Peckham RJ, Gyozo J (2007) Digital terrain modelling. Springer, BerlinCrossRefGoogle Scholar
  46. Petitjean F, Inglada J, Gancarski P (2014) Assessing the quality of temporal high-resolution classifications with low-resolution satellite image time series. Int J Remote Sens 35(7):2693–2712CrossRefGoogle Scholar
  47. Porwal MC, Padalia H, Roy PS (2012) Impact of tsunami on the forest and biodiversity richness in Nicobar Islands (Andaman and Nicobar Islands), India. Biodivers Conserv 21(5):1267–1287CrossRefGoogle Scholar
  48. Prabakaran N, Paramasivam B (2015) Littoral forest composition and influence of soil characteristics on vegetation succession in the Tsunami impacted coastal habitats of Nicobar Islands, India. J Isl Ecol 1:1–17Google Scholar
  49. Prasad PRC, Nagabhatla N, Reddy CS, Gupta S, Rajan KS, Raza SH, Dutt CBS (2009) Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia. Environ Monit Assess 160:541–553CrossRefGoogle Scholar
  50. Prasad PRC, Mamtha Lakshmi P, Rajan KS, Bhole V, Dutt CBS (2012) Tsunami and tropical Island ecosystems—a meta-analysis of the studies in Andaman and Nicobar Islands. Biodivers Conserv 21(2):309–322CrossRefGoogle Scholar
  51. Pratap K, Shelja (2013) Artificial Neural Network (ANN) inspired from biological nervous system. Int J Appl Innovation Eng Manag 2Google Scholar
  52. Rembold F, Meroni M, Urbano F, Royer A, Atzberger C, Lemoine G, Haesen D (2015) Remote sensing time series analysis for crop monitoring with the spirits software:New functionalities and use examples. Front Environ Sci 3:46CrossRefGoogle Scholar
  53. Rußwurm M, Körner M (2017) Multi-temporal land cover classification with long short-term memory neural networks.International archives of the photogrammetry, remote sensing and spatial information sciences, p 42Google Scholar
  54. Shimabukuro YE, Beuchle R, Grecchi RC, Achard F (2014) Assessment of forest degradation in Brazilian Amazon due to selective logging and fires using time series of fraction images derived from Landsat ETM + images. Remote Sens Lett 5(9):773–782CrossRefGoogle Scholar
  55. Silva PR, Acerbi Júnior FW, Carvalho LMTD., Scolforo JRS (2014) Use of artificial neural networks and geographic objects for classifying remote sensing imagery. Cerne 20(2):267–276CrossRefGoogle Scholar
  56. Singh (2011) Geography, Tata McGraw Hill Series. TataMcGraw-Hill Education, USAGoogle Scholar
  57. Skymind (2016) A beginners guide to recurrent networks and LSTMs. Accessed 25 Oct 2016
  58. Sridhar R, Thangaradjou T, Kannan L, Ramachandran A, Jayakumar S (2006) Rapid assessment on the impact of tsunami on mangrove vegetation of the Great Nicobar Island. J Indian Soc Remote Sens 34(1):89–93CrossRefGoogle Scholar
  59. Stepchenko A (2016) Ndvi index forecasting using a layer recurrent neural net-work coupled with stepwise regression and the pca. The 5th International Virtual Scientific Conference on Informatics and Management Sciences, pp 130–135Google Scholar
  60. Stepchenko A, Chizhov J (2015a) Applying markov chains for NDVI time series forecasting of latvian regions. Inf Technol Manag Sci 18(1):57–61Google Scholar
  61. Stepchenko A, Chizhov J (2015b) NDVI Short-Term Forecasting Using Recurrent Neural Networks. In Environment. Technology.Resources. Proceedings of the International Scientific and Practical Conference 3:180–185Google Scholar
  62. Tulbure MG, Broich M (2013) Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS J Photogramm Remote Sens 79:44–52CrossRefGoogle Scholar
  63. Valtonen A, Molleman F, Chapman CA, Carey JR, Ayres MP, Roininen H (2013) Tropical phenology: Bi-annual rhythms and interannual variation in an Afrotropical butterfly assemblage. Ecosphere 4:36CrossRefGoogle Scholar
  64. Velmurugan A, Dam Roy S, Krishnan P, Swarnam TP, Jaisankar I, Singh AK, Biswas TK (2015) Climate change and Nicobar islands: impacts and adaptation strategies. J Andaman Sci Assoc 20(1):7–18Google Scholar
  65. Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114:106–115CrossRefGoogle Scholar
  66. Viovy N, Arino O, Belward AS (1992) The Best Index Slope Extraction (BISE): a method for reducing noise in NDVI time-series. Int J Remote Sens 13(8):1585–1590CrossRefGoogle Scholar
  67. Vuolo F, Mattiuzzi M, Klisch A, Atzberger C (2012) Data service platform for MODIS Vegetation Indices time series processing at BOKU Vienna: current status and future perspectives. In: SPIE Remote Sensing. International Society for Optics and Photonics, 85380A-85380AGoogle Scholar
  68. Wagh VM, Panaskar DB, Muley AA et al (2016) Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India Model. Earth Syst Environ 2:196. CrossRefGoogle Scholar
  69. Wang S, Yang B, Yang Q, Lu L, Wang X, Peng Y (2016) Temporal trends and spatial variability of vegetation phenology over the northern hemisphere during 1982–2012. PLoS One 11(6):e0157134. CrossRefGoogle Scholar
  70. Wildml (2015) Recurrent neural networks tutorial, Part 3 Backpropagation Through Time And Vanishing Gradients. Accessed 19 Oct 2016
  71. Wu C-Y, Ahmed A, Beutel A, Smola AJ, Jing H (2017) Recurrent recommender networks. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 495–503Google Scholar
  72. Xu Y, Lili M, Ge L, Yunchuan C, Hao P, Zhi J (2015) Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1785–1794Google Scholar
  73. Xue J, Su B (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sens, 2017Google Scholar
  74. Zhang SL, Chang TC (2015) A study of image classification of remote sensing based on back-propagation neural network with extended delta bar delta. Math Problems EngGoogle Scholar
  75. Zhou L, Yang X (2008) Use of neural networks for land cover classification from remotely sensed imagery.the international archivesof the photogrammetry, remote sensing and spatial information sciences, p 37Google Scholar
  76. Zhou GB, Wu J, Zhang CL, Zhou ZH (2016) Minimal gated unit for recurrent neural networks. Int J Autom Comput 13(3):226–234CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Lab for Spatial InformaticsInternational Institute of Information TechnologyHyderabadIndia

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