A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Meteorological Data
2.3. Remote Sensing Data and Preprocessing
3. Methodology
3.1. Simulation of NDVI Using Vegetation-Type-Based BiLSTM (NDVI–BiLSTM)
3.1.1. An Overview of Vegetation-Type-Based NDVI–BiLSTM
3.1.2. Modeling NDVI with Vegetation-Based BiLSTM
3.1.3. Training Details
3.2. VCI for Monitoring Vegetation Stress
3.3. Trend Analysis of Vegetation Activity in China
3.4. Performance Assessment
4. Results
4.1. NDVI Predicted Using Vegetation-Type-Based BiLSTM
4.2. Assessment of the NDVI Simulation
4.3. Vegetation Activity and Stress Monitoring via Vegetation Condition Index
4.4. Trends in Vegetation drought in China across 2008–2017
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Xu, L.; Abbaszadeh, P.; Moradkhani, H.; Chen, N.; Zhang, X. Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index. Remote Sens. Environ. 2020, 250, 112028. [Google Scholar] [CrossRef]
- Gao, J.; Jiao, K.; Wu, S.; Ma, D.; Zhao, D.; Yin, Y.; Dai, E. Past and future effects of climate change on spatially heterogeneous vegetation activity in China. Earth’s Future 2017, 5, 679–692. [Google Scholar] [CrossRef]
- Peng, S.-S.; Piao, S.; Zeng, Z.; Ciais, P.; Zhou, L.; Li, L.Z.X.; Myneni, R.B.; Yin, Y.; Zeng, H. Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci. USA 2014, 111, 2915–2919. [Google Scholar] [CrossRef] [Green Version]
- Piao, S.; Tan, K.; Nan, H.; Ciais, P.; Fang, J.; Wang, T.; Vuichard, N.; Zhu, B. Impacts of climate and CO2 changes on the vegetation growth and carbon balance of Qinghai–Tibetan grasslands over the past five decades. Glob. Planet. Change 2012, 98–99, 73–80. [Google Scholar] [CrossRef]
- Reddy, D.S.; Prasad, P.R.C. Prediction of vegetation dynamics using NDVI time series data and LSTM. Model. Earth Syst. Environ. 2018, 4, 409–419. [Google Scholar] [CrossRef]
- Chen, B.; Xu, G.; Coops, N.C.; Ciais, P.; Innes, J.L.; Wang, G.; Myneni, R.B.; Wang, T.; Krzyzanowski, J.; Li, Q.; et al. Changes in vegetation photosynthetic activity trends across the Asia–Pacific region over the last three decades. Remote Sens. Environ. 2014, 144, 28–41. [Google Scholar] [CrossRef]
- Stepchenko, A.; Chizhov, J. NDVI Short-Term Forecasting Using Recurrent Neural Networks. Environment. Technology. Resources. Proc. Int. Sci. Pract. Conf. 2015, 3, 180. [Google Scholar] [CrossRef]
- Ali, S.; Zhang, H.; Qi, M.; Liang, S.; Ning, J.; Jia, Q.; Hou, F. Monitoring drought events and vegetation dynamics in relation to climate change over mainland China from 1983 to 2016. Environ. Sci. Pollut. Res. 2021, 28, 21910–21925. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium- Volume I: Technical Presentations, Greenbelt, MD, United States; NASA: Washington, DC, USA, 1974; p. 309. [Google Scholar]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
- Delegido, J.; Verrelst, J.; Meza, C.M.; Rivera, J.P.; Alonso, L.; Moreno, J. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur. J. Agron. 2013, 46, 42–52. [Google Scholar] [CrossRef]
- Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Chu, H.; Venevsky, S.; Wu, C.; Wang, M. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Sci. Total Environ. 2019, 650, 2051–2062. [Google Scholar] [CrossRef]
- Aban, J.; Tateishi, R.; Tsolmon, R. The Polynomial Least Squares Operation (PoLeS): A Method for Reducing Noise in NDVI Time Series Data. In Proceedings of the Asian Conference on Remote Sensing (ACRS 2002), Kathmandu, Nepal, 25–29 November 2002. [Google Scholar]
- Petitjean, F.; Inglada, J.; Gancarski, P. Assessing the quality of temporal high-resolution classifications with low-resolution satellite image time series. Int. J. Remote Sens. 2014, 35, 2693–2712. [Google Scholar] [CrossRef]
- Zhao, S.; Cong, D.; He, K.; Yang, H.; Qin, Z. Spatial-Temporal Variation of Drought in China from 1982 to 2010 Based on a modified Temperature Vegetation Drought Index (mTVDI). Sci. Rep. 2017, 7, 17473. [Google Scholar] [CrossRef] [Green Version]
- Jiang, Y.; Ranghui, W.; Peng, Q.; Wu, X.; Ning, H.; Li, C. The relationship between drought activity and vegetation cover in Northwest China from 1982 to 2013. Nat. Hazards 2018, 92, 145–163. [Google Scholar] [CrossRef]
- Kogan, F.N. Global Drought Watch from Space. Bull. Am. Meteorol. Soc. 1997, 78, 621–636. [Google Scholar] [CrossRef]
- Liang, L.; Qiu, S.; Yan, J.; Shi, Y.; Geng, D. VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China. Int. J. Environ. Res. Public Health 2021, 18, 7967. [Google Scholar] [CrossRef]
- Liu, L.; Liao, J.; Chen, X.; Zhou, G.; Su, Y.; Xiang, Z.; Wang, Z.; Liu, X.; Li, Y.; Wu, J.; et al. The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sens. Environ. 2017, 199, 302–320. [Google Scholar] [CrossRef]
- Nay, J.; Burchfield, E.; Gilligan, J. A machine-learning approach to forecasting remotely sensed vegetation health. Int. J. Remote Sens. 2018, 39, 1800–1816. [Google Scholar] [CrossRef]
- Mazza, A.; Gargiulo, M.; Scarpa, G.; Gaetano, R. Estimating the NDVI from SAR by Convolutional Neural Networks. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 1954–1957. [Google Scholar]
- Wright, C.K.; de Beurs, K.M.; Henebry, G.M. Combined analysis of land cover change and NDVI trends in the Northern Eurasian grain belt. Front. Earth Sci. 2012, 6, 177–187. [Google Scholar] [CrossRef]
- Wu, Y.; Tang, G.; Gu, H.; Liu, Y.; Yang, M.; Sun, L. The variation of vegetation greenness and underlying mechanisms in Guangdong province of China during 2001–2013 based on MODIS data. Sci. Total Environ. 2019, 653, 536–546. [Google Scholar] [CrossRef] [PubMed]
- Wang, T.; Lin, X.; Liu, Y.; Dantec-Nédélec, S.; Ottlé, C. Causes of uncertainty in China’s net primary production over the 21st century projected by the CMIP5 Earth system models. Int. J. Climatol. 2016, 36, 2323–2334. [Google Scholar] [CrossRef] [Green Version]
- Zheng, Y.; Han, J.; Huang, Y.; Fassnacht, S.R.; Xie, S.; Lv, E.; Chen, M. Vegetation response to climate conditions based on NDVI simulations using stepwise cluster analysis for the Three-River Headwaters region of China. Ecol. Indic. 2018, 92, 18–29. [Google Scholar] [CrossRef]
- Zhou, Z.; Ding, Y.; Shi, H.; Cai, H.; Fu, Q.; Liu, S.; Li, T. Analysis and prediction of vegetation dynamic changes in China: Past, present and future. Ecol. Indic. 2020, 117, 106642. [Google Scholar] [CrossRef]
- Barrett, A.; Duivenvoorden, S.; Salakpi, E.; Muthoka, J.; Mwangi, J.; Oliver, S.; Rowhani, P. Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya. Remote Sens. Environ. 2020, 248, 111886. [Google Scholar] [CrossRef]
- Han, J.-C.; Huang, Y.; Zhang, H.; Wu, X. Characterization of elevation and land cover dependent trends of NDVI variations in the Hexi region, northwest China. J. Environ. Manag. 2019, 232, 1037–1048. [Google Scholar] [CrossRef]
- Forkel, M.; Dorigo, W.; Lasslop, G.; Teubner, I.; Chuvieco, E.; Thonicke, K. A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1). Geosci. Model Dev. 2017, 10, 4443–4476. [Google Scholar] [CrossRef] [Green Version]
- Adede, C.; Oboko, R.; Wagacha, P.W.; Atzberger, C. A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring. Remote Sens. 2019, 11, 1099. [Google Scholar] [CrossRef] [Green Version]
- Kamilaris, A.; Prenafeta-Boldú, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 2018, 147, 70–90. [Google Scholar] [CrossRef] [Green Version]
- Das, M.; Ghosh, S.K. Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data. IEEE Geosci. Remote Sens. Lett. 2016, 13, 1984–1988. [Google Scholar] [CrossRef]
- Su, B.; Lu, S. Accurate Recognition of Words in Scenes without Character Segmentation Using Recurrent Neural Network. Pattern Recognit. 2017, 63, 397–405. [Google Scholar] [CrossRef]
- Bahdanau, D.; Cho, K.; Bengio, Y. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv 2016. [Google Scholar] [CrossRef]
- Cheng, J.; Dong, L.; Lapata, M. Long Short-Term Memory-Networks for Machine Reading. arXiv 2016. [Google Scholar] [CrossRef]
- Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef]
- Venugopalan, S.; Hendricks, L.A.; Mooney, R.; Saenko, K. Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text. arXiv 2016, arXiv:1604.01729. [Google Scholar]
- Zhong, L.; Hu, L.; Zhou, H. Deep learning based multi-temporal crop classification. Remote Sens. Environ. 2019, 221, 430–443. [Google Scholar] [CrossRef]
- Lai, D.; Qi, Y.; Liu, J.; Dai, X.; Zhao, L.; Wei, S. Ventilation behavior in residential buildings with mechanical ventilation systems across different climate zones in China. Build. Environ. 2018, 143, 679–690. [Google Scholar] [CrossRef] [Green Version]
- Chen, J.; Cao, X.; Peng, S.; Ren, H. Analysis and Applications of GlobeLand30: A Review. ISPRS Int. J. Geo-Inf. 2017, 6, 230. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.; Park, T.; Wang, X.; Piao, S.; Xu, B.; Chaturvedi, R.K.; Fuchs, R.; Brovkin, V.; Ciais, P.; Fensholt, R.; et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2019, 2, 122–129. [Google Scholar] [CrossRef]
- Amthor, J. Calculation of daylength. Comput. Appl. Biosci. CABIOS 1997, 13, 479–480. [Google Scholar] [CrossRef] [Green Version]
- Vermote, E. MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid V061; NASA EOSDIS Land Processes DAAC: Missoula, MT, USA, 2021. [Google Scholar] [CrossRef]
- Chen, J.; Jönsson, P.; Tamura, M.; Gu, Z.; Matsushita, B.; Eklundh, L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 2004, 91, 332–344. [Google Scholar] [CrossRef]
- Gray, J.; Sulla-Menashe, D.; Friedl, M.A. User Guide to Collection 6 MODIS Land Cover Dynamics (MCD12Q2) Product; NASA EOSDIS Land Processes DAAC: Missoula, MT, USA, 2019. [Google Scholar]
- Loveland, T.R.; Belward, A.S. The International Geosphere Biosphere Programme Data and Information System global land cover data set (DISCover). Acta Astronaut. 1997, 41, 681–689. [Google Scholar] [CrossRef]
- Unganai, L.S.; Kogan, F.N. Drought Monitoring and Corn Yield Estimation in Southern Africa from AVHRR Data. Remote Sens. Environ. 1998, 63, 219–232. [Google Scholar] [CrossRef]
- Graves, A.; Mohamed, A.-r.; Hinton, G. Speech recognition with deep recurrent neural networks. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26–31 May 2013; pp. 6645–6649. [Google Scholar]
- Norman, S.P.; Koch, F.H.; Hargrove, W.W. Review of broad-scale drought monitoring of forests: Toward an integrated data mining approach. For. Ecol. Manag. 2016, 380, 346–358. [Google Scholar] [CrossRef] [Green Version]
- Ghaeini, R.; Hasan, S.A.; Datla, V.; Liu, J.; Lee, K.; Qadir, A.; Ling, Y.; Prakash, A.; Fern, X.Z.; Farri, O. Dr-bilstm: Dependent reading bidirectional lstm for natural language inference. arXiv 2018. [Google Scholar] [CrossRef]
- Chollet, F.; Lorenzen, K. Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek; MITP: Frechen, Germany, 2018. [Google Scholar]
- Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014, 15, 1929–1958. [Google Scholar]
- Pei, F.; Wu, C.; Liu, X.; Li, X.; Yang, K.; Zhou, Y.; Wang, K.; Xu, L.; Xia, G. Monitoring the vegetation activity in China using vegetation health indices. Agric. For. Meteorol. 2018, 248, 215–227. [Google Scholar] [CrossRef]
- Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- Mann, H.B. Nonparametric Tests Against Trend. Econometrica 1945, 13, 245–259. [Google Scholar] [CrossRef]
- Kendall, M.G. Rank Correlation Methods; Griffin: London, UK, 1973; p. 399. [Google Scholar]
- Gocic, M.; Trajkovic, S. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob. Planet. Change 2013, 100, 172–182. [Google Scholar] [CrossRef]
- Linderholm, H.W. Growing season changes in the last century. Agric. For. Meteorol. 2006, 137, 1–14. [Google Scholar] [CrossRef]
- Li, X.; Li, Y.; Chen, A.; Gao, M.; Slette, I.; Piao, S. The impact of the 2009/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China. Agric. For. Meteorol. 2019, 269, 239–248. [Google Scholar] [CrossRef]
- Song, L.; Li, Y.; Ren, Y.; Wu, X.; Guo, B.; Tang, X.; Shi, W.; Ma, M.; Han, X.; Zhao, L. Divergent vegetation responses to extreme spring and summer droughts in Southwestern China. Agric. For. Meteorol. 2019, 279, 107703. [Google Scholar] [CrossRef]
Year | NDVI | Vegetation Types | NDVI |
---|---|---|---|
2009 | 0.66 ± 0.29 | Croplands | 0.73 ± 0.24 |
2010 | 0.70 ± 0.28 | Deciduous | 0.87 ± 0.16 |
2011 | 0.71 ± 0.27 | Evergreen | 0.46 ± 0.18 |
2012 | 0.75 ± 0.25 | Grasslands | 0.67 ± 0.28 |
2013 | 0.71 ± 0.27 | Mixed | 0.61 ± 0.23 |
2014 | 0.67 ± 0.29 | Savannas | 0.59 + 0.24 |
2015 | 0.69 ± 0.28 | --- | --- |
2016 | 0.68 ± 0.28 | --- | --- |
2017 | 0.66 ± 0.29 | --- | --- |
March | April | May | June | July | August | September | October | Average | |
---|---|---|---|---|---|---|---|---|---|
GT | 27 ± 17 | 35 ± 18 | 45 ± 18 | 54 ± 18 | 65 ± 18 | 76 ± 16 | 75 ± 14 | 63 ± 14 | 55 ± 18 |
Prediction | 25 ± 15 | 33 ± 15 | 42 ± 16 | 50 ± 16 | 59 ± 17 | 70 ± 16 | 70 ± 14 | 58 ± 14 | 51 ± 16 |
Year | 2009 | 2010 | 2011 | 2012 | 2013 |
R2 | 0.67 ± 0.29 | 0.70 ± 0.28 | 0.72 ± 0.27 | 0.75 ± 0.25 | 0.72 ± 0.27 |
Year | 2014 | 2015 | 2016 | 2017 | |
R2 | 0.68 ± 0.29 | 0.70 ± 0.28 | 0.69 ± 0.28 | 0.67 ± 0.29 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sun, Y.; Lao, D.; Ruan, Y.; Huang, C.; Xin, Q. A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data. Sustainability 2023, 15, 6632. https://doi.org/10.3390/su15086632
Sun Y, Lao D, Ruan Y, Huang C, Xin Q. A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data. Sustainability. 2023; 15(8):6632. https://doi.org/10.3390/su15086632
Chicago/Turabian StyleSun, Ying, Dazhao Lao, Yongjian Ruan, Chen Huang, and Qinchuan Xin. 2023. "A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data" Sustainability 15, no. 8: 6632. https://doi.org/10.3390/su15086632
APA StyleSun, Y., Lao, D., Ruan, Y., Huang, C., & Xin, Q. (2023). A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data. Sustainability, 15(8), 6632. https://doi.org/10.3390/su15086632