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Trend and Time Series Analysis of Vegetation Dynamics Using Satellite Data: A Case Study of Uttarakhand, India

  • Govind Verma
  • Ashish Mehta
  • Shikha Goswami

Current Journal of Applied Science and Technology, Page 14-26
DOI: 10.9734/cjast/2022/v41i333947
Published: 15 September 2022

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Abstract


In this work, aim is to collect time series data for Normalized Difference Vegetation Index (NDVI) band using google earth engine (GEE) and MOD13A1 V6.1 product for the region of Uttarakhand, Uttarakhand districts and Himachal Pradesh. Thereafter investigation and comprehension of the viability of using MODIS NDVI satellite data time series to identify trends and give a forecast model. Time series data was collected using Google Earth Engine for NDVI indices for the period of the year 2010 to 2022. Trend analysis and time series analysis were performed for collected data. NDVI time series data set is collected using GEE for Uttarakhand state, its districts and Himachal Pradesh State. The Mann- Kendell (MK) method is used to find trend analysis of above regions. NDVI time series data is divided into train and test dataset. Five forecasting models are used to forecast NDVI time series dataset i.e., Long short-term memory (LSTM), Bidirectional Long short-term memory (BiLSTM), Support vector regression (SVR), Autoregressive Integrated Moving (ARIMA), Adaptive Neuro fuzzy interference system (ANFIS) models are trained using train data and are used to generate the predicted value. The predicted value is then compared with test data using various metrics for forecasting NDVI times series. Trend analysis of NDVI shows an increasing trend in NDVI values for Uttarakhand and its districts as well as Himachal Pradesh. ANFIS model resulted R2 value of 0.6702, Stacked LSTM model resulted R2 value of 0.7541, Bidirectional LSTM model resulted with highest R2 value of 0.8365, Autoregressive Integrated Moving Average (ARIMA) model resulted with lowest R2 value of 0.153, SVR model resulted with R2 value of 0.6719.


Keywords:
  • NDVI
  • LSTM
  • ARIMA
  • ANFIS
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How to Cite

Verma, G., Mehta, A., & Goswami, S. (2022). Trend and Time Series Analysis of Vegetation Dynamics Using Satellite Data: A Case Study of Uttarakhand, India. Current Journal of Applied Science and Technology, 41(33), 14-26. https://doi.org/10.9734/cjast/2022/v41i333947
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References

Bai Y. Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data. Ecological Indicator. 2021;129:108029.

Vasilakos C, Tsekouras GE, Kavroudakis D. LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data. Land. 2022;11:923.

Amani M. et al. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020;13:5326-5350.

Mutanga O, Kumar L. Google Earth Engine Applications. Remote Sensing, v. 11, 2019. ISSN ISSN: 2072-4292.

Available: https://www.mdpi.com/2072-4292/11/5/591.

Dobbs C, Nitschke C, Kendal D. Assessing the drivers shaping global patterns of urban vegetation landscape structure. Science of the Total Environment. 2017;592:171-177.

Xue J, Su B. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors; 2017.

Eckert S. et al. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments. 2015;113:16-28,.

Guo M. et al. Detecting global vegetation changes using mann-kendal (MK) trend test for 1982--2015 time period. Chinese Geographical Science. 2018;28:907-919.

LI, Z. et al. Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series MODIS NDVI with climate and stocking data. Remote Sensing of Environment. 2013;138:232-244.

Gandhi GM. et al. Ndvi: Vegetation change detection using remote sensing and gis--A case study of Vellore District. Procedia Computer Science. 2015;57:1199-1210.

Ferchichi A. et al. Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review. Ecological Informatics. 2022;101552.

Hussain M, Mahmud I, pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software. 2019;4:1556. ISSN ISSN: 2475-9066.

Available:http://dx.doi.org/10.21105/joss.01556

Cao J. et al. Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches. Agricultural and Forest Meteorology. 2021;297:108275. ISSN ISSN: 0168-1923.

Available:https://www.sciencedirect.com/science/article/pii/S0168192320303774

Warszawski L. et al. Center for International Earth Science Information Network—CIESIN—Columbia University. Gridded population of the World, Version 4 (GPWv4): Population density. Palisades. NY: NASA Socioeconomic Data and Applications Center (SEDAC). doi: 10. 7927/H4NP22DQ. Atlas of Environmental Risks Facing China Under Climate Change. 2016:228.

Ahmad R. et al. A machine-learning based ConvLSTM architecture for NDVI forecasting. International Transactions in Operational Research; 2020.

Reddy DS, Prasad P. Prediction of vegetation dynamics using NDVI time series data and LSTM. Modeling Earth Systems and Environment. 2018;4: 409-419.

Hussain M, Mahmud I. Pymannkendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software. 2019; 4:1556.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation. 1997;9;1735-1780.

Lim B, Zohren S. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A. 2021;379:20200209.

Omar MS, Kawamukai H. Prediction of NDVI using the Holt-Winters model in high and low vegetation regions: a case study of east Africa. Scientific African. 2021;14:e01020,.

Saini T, Chaturvedi P, Dutt V. Modelling particulate matter using multivariate and multistep recurrent neural networks. Frontiers in Environmental Science. 2021:614.

Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on Systems, Man, and Cybernetics. 199;323:665-685.
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