Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia

作者: Santosh Bhandari , Stuart Phinn , Tony Gill

DOI: 10.3390/RS4061856

关键词:

摘要: … Time series of images are required to extract and separate information on vegetation … 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, …

参考文章(50)
F Maignan, F-M Bréon, C Bacour, J Demarty, A Poirson, None, Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications Remote Sensing of Environment. ,vol. 112, pp. 496- 505 ,(2008) , 10.1016/J.RSE.2007.05.011
R. B. Cleveland, STL : A Seasonal-Trend Decomposition Procedure Based on Loess Journal of Office Statistics. ,vol. 6, pp. 3- 73 ,(1990)
Junchang Ju, David P. Roy, The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally Remote Sensing of Environment. ,vol. 112, pp. 1196- 1211 ,(2008) , 10.1016/J.RSE.2007.08.011
Chengquan Huang, Samuel N Goward, Jeffrey G Masek, Nancy Thomas, Zhiliang Zhu, James E Vogelmann, None, An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks Remote Sensing of Environment. ,vol. 114, pp. 183- 198 ,(2010) , 10.1016/J.RSE.2009.08.017
S. S. Gillingham, N. Flood, T. K. Gill, R. M. Mitchell, Limitations of the dense dark vegetation method for aerosol retrieval under Australian conditions Remote Sensing Letters. ,vol. 3, pp. 67- 76 ,(2012) , 10.1080/01431161.2010.533298
John David Armston, Robert J Denham, Tim J Danaher, Peter F Scarth, Trevor N Moffiet, Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. Journal of Applied Remote Sensing. ,vol. 3, pp. 033540- 033540 ,(2009) , 10.1117/1.3216031
Egídio Arai, Yosio E. Shimabukuro, Gabriel Pereira, Nandamudi L. Vijaykumar, A Multi-Resolution Multi-Temporal Technique for Detecting and Mapping Deforestation in the Brazilian Amazon Rainforest Remote Sensing. ,vol. 3, pp. 1943- 1956 ,(2011) , 10.3390/RS3091943