Investigating the trend of vegetation changes (greening and browning) using MODIS-NDVI time series in Mazandaran province

Document Type : Complete scientific research article

Authors

1 PhD. Student, Faculty of Natural Resources, University of Tehran, Karaj, I.R. Iran.

2 Professor, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

Abstract

Background and objectives: Vegetation is one of the main components of the biosphere and a vital element in the climate system, and knowing its changes and activity trends can affect the optimal productivity of agricultural land, natural ecosystems, climate change, and biodiversity. Vegetation indices derived from satellite data have provided a powerful tool for monitoring and investigating vegetation dynamics in large temporal and spatial scales. The aim of this study is to investigate the long-term trend of vegetation in pixels to pixels in Mazandaran province using the NDVI time series of MODIS sensor.
Materials and methods: The current research was conducted in the province of Mazandaran, which includes 53% of Hyrcanian forests and 12050 square kilometers of rangeland. In this study, the vegetation index products of the MODIS sensor with a spatial resolution of 250 meters and an interval of 16 days, named MOD13Q1 and MYD13Q1, have been used. The NDVI index images available in these two products were combined and a time series of 828 images with an 8-day interval and a spatial resolution of 250 meters was created from the NDVI index for the period from 2003 to 2021.In order to increase the quality of the time series, the pixels affected by clouds, snow and ice in each image of the time series based on MODIS quality assurance information and the seasonal component within the time series that may make long-term trend investigation difficult, using the Anomaly method were removed. Then the time series was analyzed using parametric ordinary least squares (OLS) regression and non-parametric Theil-Sen and Mann-Kendall in Google Earth Engine (GEE).
Results: The results of this study showed that the output of OLS, Theil-Sen and Mann-Kendall methods were close to each other and Greening and Browning occurred in about 87% and 13% of the province, respectively. Also, the significance of Man-Kendall trends shows that 77.21% of trends in the province, include 70.65% of positive trends (Greening) and 6.56% of negative trends (Browning) at ρ < 0.01, 6.48% and 2.95% of trends at ρ < 0.05 and ρ < 0.10 are significant, respectively. The spatial distribution of the trends also showed that a large part of the Browning trends occurred in the plains of the north of the province and most of the Greening trends occurred in forest lands and rangeland.
Conclusion: In general, this study showed that greening occurred on a large scale, especially in natural vegetation including forests and rangeland. Changes in natural vegetation are affected by climate change, because human activities are limited in them. Therefore, the process of global warming can be the most important factor of Greening in Mazandaran province.

Keywords

Main Subjects

References

1.Foley, J.A., Levis, S., Costa, M.H., Cramer, W., and Pollard, D. 2000. Incorporating dynamic vegetation cover within global climate models. J. Ecological Applications, 10: 6. 1620-1632.
2.Parida, B.R., Pandey, A.C., and Patel, N.R. 2020. Greening and browning trends of vegetation in India and their responses to climatic and non-climatic drivers. J. Climate. 8: 8. 92-107.
3.De Jong, R., de Bruin, S., de Wit, A., Schaepman, M.E., and Dent, D.L. 2011. Analysis of monotonic greening and browning trends from global NDVI time-series. J. Remote Sensing of Environment, 115: 2. 692-702.
4.Li, D., Lu, D., Wu, M., Shao, X., and Wei, J. 2018. Examining land cover and greenness dynamics in Hangzhou Bay in 1985–2016 using Landsat time-series data. J. Remote Sensing. 10: 1. 32-45.
5.Guan, Q., Yang, L., Pan, N., Lin, J., Xu, C., Wang, F., and Liu, Z. 2018. Greening and browning of the Hexi Corridor in Northwest China: Spatial patterns and responses to climatic variability and anthropogenic drivers. J. Remote Sensing, 10: 8. 1270-1290.
6.Alcaraz‐Segura, D., Chuvieco, E., Epstein, H.E., Kasischke, E.S., and Trishchenko, A. 2010. Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets. J. Global Change Biology. 16: 2. 760-770.
7.Kuenzer, C., Dech, S., and Wagner, W. 2015. Remote sensing time series revealing land surface dynamics: Status quo and the pathway ahead. J. In Remote Sensing Time Series. (pp. 1-24). Springer, Cham.
8.Zhang, Y., Song, C., Band, L.E., Sun, G., and Li, J. 2017. Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening? J. Remote Sensing of Environment. 191: 145-155.
9.Mishra, N.B., and Mainali, K.P. 2017. Greening and browning of Himalayasalaya: Spatial patterns and the role of climatic change and human drivers. J. Science of The Total Environment. 587: 326-339.
10.Pan, N., Feng, X., Fu, B., Wang, S., Ji, F., and Pan, S. 2018. Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. J. Remote Sensing of Environment. 214: 59-72.
11.Zhang, Y., and Ye, A. 2020. Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China. J. Remote Sensing. 41: 11. 4238-4268.
12.Liu, L., Liang, L., Schwartz, M.D., Donnelly, A., Wang, Z., Schaaf, C.B., and Liu, L. 2015. Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest. J. Remote Sensing of Environment. 160: 156-165.
13.Deka, J., Kalita, S., and Khan, M.L. 2019. Vegetation phenological characterization of alluvial plain shorea robusta-dominated tropical moist deciduous forest of northeast India using MODIS NDVI time series data. J. the Indian Society of Remote Sensing. 47: 8. 1287-1293.
14.Padhee, S.K., and Dutta, S. 2019. Spatio-temporal reconstruction of MODIS NDVI by regional land surface phenology and harmonic analysis of time-series. J. GIScience & Remote Sensing. 56: 8. 1261-1288.
15.Masihpour, M., Darvishsefat, A.A., and Rahmani, R. 2019. Long-term trend analysis of vegetation changes using MODIS-NDVI time series during 2000-2017 (Case study: Kurdistan province). J. Forest and Wood Products. 72: 3. 193-204.
16.Burrell, A.L., Evans, J.P., and Liu, Y. 2017. Detecting dryland degradation using time series segmentation
and residual trend analysis (TSS-RESTREND). J. Remote Sensing of Environment. 197: 43-57.
17.Yu, L., Yan, Z., and Zhang, S. 2020. Forest phenology shifts in response to climate change over China–Mongolia–Russia international economic corridor. J. Forests. 11: 7. 757-768.
18.Miles, V.V., and Esau, I. 2016. Spatial heterogeneity of greening and browning between and within bioclimatic zones in northern West Siberia. J. Environmental Research Letters. 11: 11. 115002.
19.Kiapasha, K., Darvishsefat, A.A., Zargham, N., Attarod, P., Nadi, M., and Schaepman, M. 2017. Greening trend in the Hyrcanian forests using NOAA NADVI time series during 1981-2012. J. Forest and Wood Products. 70: 3. 409-420.
20.Ghelichnia, H., Arzani, H., Akbarzadeh, M., Farahpour, M., and Azimi, M. 2010. Investigation on variation trends of vegetation and yield in rangelands of Mazandaran province (2001-2005), J. Range and Desert Research, 19: 2. 203-220.
21.Mahmoodi, B., Marvie Mohadjer, M.R., Daneh Kar, A., and Feghhi, J. 2014. Estimation of forest level changes in topographic zones of Mazandaran province. J. Natural Environment. 67: 3. 333-341.
22.Li, Z., Huffman, T., McConkey, B., and Townley-Smith, L. 2013. Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series MODIS NDVI with climate and stocking data. J. Remote Sensing of Environment. 138: 232-244.
23.Rankine, C., Sánchez-Azofeifa, G.A., Guzmán, J.A., Espirito-Santo, M.M., and Sharp, I. 2017. Comparing
MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers. J. Environmental Research Letters. 12: 10. 105007.
24.Fagua, J.C., and Ramsey, R.D. 2019. Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: the Chocó-Darien of South America and the Great Basin of North America. J. GIScience & Remote Sensing. 56: 7. 1046-1064.
25.Antonio, C., Ovando, G.G., and Díaz, G. J. 2019. Interannual variability of seasonal rainfall in Cordoba, Argentina, evaluated from ENSO and ENSO Modoki signals and verified with MODIS NDVI data. J. SN Applied Sciences. 1: 12. 1-21.
26.Kong, D., Zhang, Y., Gu, X., and Wang, D. 2019. A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. J. Photogrammetry and Remote Sensing. 155: 13-24.
27.Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A., and Hostert, P. 2015. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. J. Remote Sensing of Environment.
163: 312-325.
28.Luintel, N., Ma, W., Ma, Y., Wang, B., Xu, J., Dawadi, B., and Mishra, B. 2021. Tracking the dynamics of paddy rice cultivation practice through MODIS time series and PhenoRice algorithm. J. Agricultural and Forest Meteorology. 307: 108538.
29.Hirsch, R.M., Slack, J.R., and Smith, R.A. 1982. Techniques of trend analysis for monthly water quality data. J. Water resources research. 18: 1. 107-121.
30.Theil, H. 1950. A rank-invariant method of linear and polynomial regression analysis. J. Indagationes Mathematicae. 12: 85. 173-194.
31.Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall's tau. J. American statistical association. 63: 324. 1379-1389.
32.Fensholt, R., and Proud, S.R. 2012. Evaluation of earth observation based global long-term vegetation trends-Comparing GIMMS and MODIS global NDVI time series. J. Remote sensing of Environment. 119: 131-147.
33.Carslaw, D.C., and Ropkins, K. 2012. Openair-An R package for air quality data analysis. J. Environmental Modelling & Software. 27: 52-61.
34.Sayemuzzaman, M., and Jha, M.K. 2014. Seasonal and annual precipitation time series trend analysis in North Carolina, United States. J. Atmospheric Research. 137: 183-194.
35.Chaudhuri, S., and Dutta, D. 2014. Mann–Kendall trend of pollutants, temperature, and humidity over an
urban station of India with forecast verification using different ARIMA models. J. Environmental Monitoring and Assessment. 186: 8. 4719-4742.
36.Neeti, N., and Eastman, J.R. 2011. A contextual mann‐kendall approach for the assessment of trend significance in image time series. J. Transactions in GIS. 15: 5. 599-611.
37.Emmett, K.D., Renwick, K.M., and Poulter, B. 2019. Disentangling climate and disturbance effects on regional vegetation greening trends. J. Ecosystems. 22: 4. 873-891.
38.Li, H., Jiang, J., Chen, B., Li, Y., Xu, Y., and Shen, W. 2016. Pattern of
NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming. J. Environmental monitoring and assessment. 188: 3. 1-10.
39.Tian, F., Liu, L.Z., Yang, J.H., and Wu, J.J. 2021. Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the
21st century was caused jointly by warming and anthropogenic activities. J. Ecological Indicators. 125: 107479.
Journal of Wood and Forest Science and Technology
Volume 30, Issue 1
March 2023
Pages 125-140

Files

Share

How to cite

Statistics