Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia

Bhandari, Santosh, Phinn, Stuart and Gill, Tony (2012) Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sensing, 4 6: 1856-1886. doi:10.3390/rs4061856

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Author Bhandari, Santosh
Phinn, Stuart
Gill, Tony
Total Author Count Override 3
Title Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2012-06
Sub-type Article (original research)
DOI 10.3390/rs4061856
Open Access Status DOI
Volume 4
Issue 6
Start page 1856
End page 1886
Total pages 31
Place of publication Basel, Switzerland
Publisher MDPI
Collection year 2013
Language eng
Abstract Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM) sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS) is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR) product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1) assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC) estimates using ground-measured values; and (2) comparing the LITS-generated normalized difference vegetation index (NDVI) and MODIS NDVI (MOD13Q1) time series. The predicted image-derived FPC products (value ranges from 0 to 100%) had an RMSE of 5.6. Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used) difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf) forest environment and also demonstrated that the approach can be used to form a time series of Landsat TM images to study vegetation phenology over a number of years.
Keyword Vegetation phenology
Time series
Synthetic image
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2013 Collection
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Citation counts: TR Web of Science Citation Count  Cited 30 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 36 times in Scopus Article | Citations
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Created: Wed, 10 Apr 2013, 09:41:28 EST by Helen Smith on behalf of School of Geography, Planning & Env Management