Simulation and variation pattern of vegetation phenology in Northeast China based on remote sensing

Chinese Journal of Ecology

Volumn 29, Issue 1, 2010, Pages 165-172

  Guo, Zhi Xing ^a,b   Zhang, Xiao Ning ^a,b,c   Wang, Zong Ming ^d   Fang, Weihua ^a,b  

^a BEIJING NORMAL UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c Ministry of Civil Affairs of China   (China)

^d NORTHEAST INSTITUTE OF GEOGRAPHY AND AGRICULTURAL ECOLOGY   (China)

Author keywords

Logistic function; NDVI; Northeast china; Phenology; Vegetation type

Indexed keywords

BROAD-LEAVED FOREST; COMPUTER SIMULATION; GIS; MEADOW; NDVI; PHENOLOGY; REMOTE SENSING; SPATIAL ANALYSIS; SWAMP; TIME SERIES ANALYSIS; TREND ANALYSIS; VEGETATION TYPE;

CHINA;

EID: 75449099971     PISSN: 10004890     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. Fischer A. 1994. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment, 48: 220-230. (Pubitemid 2099703)
2. Julien Y, Sobrinoa JA. 2009. The Yearly Land Cover Dynamics ( YLCD ) method: An analysis of global vegetation from NDVI and LST parameters. Remote Sensing of Environment, 113: 329-334.
3. Justice CO, Townshend JRG, Holben BN, et al. 1985. Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6: 1271-1318.
4. Li XB, Chen YH, Fan YD, et al. 2003. Detecting inter-annual variations of vegetation growth based on satellite-sensed vegetation index data from 1983 to 1999. Proceedings of IGARSS, 5: 3263-3265.
5. Markon CJ, Fleming MD, Binnian EF. 1995. Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Record, 31: 179-190.
6. Menzel A, Fabian P. 1999. Growing season extended in Europe. Nature, 397: 659.
7. Pettorelli N, Vik JO, Mysterud A, et al. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20: 503-510. (Pubitemid 41207086)
8. Piao SL, Fang JY, Zhou LM, et al. 2006. Variations in satellite-derived phenology in China s temperate vegetation. Global Change Biology, 12: 672-685.
9. Reed BC, Brown JF, VanderZee D, et al. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5: 703-714.
10. Suzuki R, Masuda K, Dye DG. 2007. Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia. Remote Sensing of Environment, 106: 387-398. (Pubitemid 46127790)
11. White MA, Thornton PE, Running SW. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles, 11: 217-234.
12. Yu FF, Kevin PP, James E, et al. 2003. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sensing of Environment, 87: 42-54.
13. Žalud Z, Dubrovsky M. 2002. Modeling climate change impacts on maize growth and development in the Czech Republic. Theoretical and Applied Climatology, 72: 85-102. (Pubitemid 34630106)
14. Zhang X, Friedl MA, Schaaf CB, et al. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84: 471-475. (Pubitemid 36290250)