بررسی روند‌ تغییرات سرسبزی پوشش گیاهی ( Greening و Browning ) با استفاده از سری زمانی MODIS-NDVI در استان مازندران

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
21^st century was caused jointly by warming and anthropogenic activities. J. Ecological Indicators. 125: 107479.