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.
Highlights
Landsat, one of the longest running satellite programs has been acquiring images of the Earth’s surface since 1972
Data from low spatial-resolution sensors like Advanced Very High Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre (SPOT)—VEGETATION, Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS) with spatial resolution ranging from 250 m to 1,000 m are acquired at high temporal-resolutions, making them a suitable source for time series analysis to monitor vegetation change [10,11,12]
As the registration accuracy of the predicted images remains similar to the input Landsat Thematic Mapper (TM) images, the registration error of all images in the Landsat Image Time Series (LITS) was considered to be within a half of a Landsat TM pixel in each dimension
Summary
One of the longest running satellite programs has been acquiring images of the Earth’s surface since 1972. Separating changes taking place due to phenological cycles, inter-annual climatic variability, human activities and long-term trends is challenging unless a data set of sufficient duration and short enough period between image acquisition dates exists It is not possible with commonly used multi-temporal remote sensing applications, such as simple bi-temporal change detection and multi-date image analysis to extract this type of information [5,6,7]. Data from low spatial-resolution sensors like Advanced Very High Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre (SPOT)—VEGETATION, Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS) with spatial resolution ranging from 250 m to 1,000 m are acquired at high temporal-resolutions, making them a suitable source for time series analysis to monitor vegetation change [10,11,12]. Many processes of interest in terrestrial ecosystems operate at spatial scales below the spatial resolution of those sensors and are more suited to detection at Landsat TM and ETM+ scales [13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
