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

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. OPEN ACCESSRemote Sens. 2012, 4 1857Comparison 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.

Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

“…As an example, Figure 1c shows the atmospherically-corrected B8a BoA reflectance (at 865 nm). Sen2Cor includes a scene classification module (example in Figure 1b) to map no data or defective pixels (pixel value = 0-1), four different cloud clover class probabilities (7)(8)(9)(10), and six different classes including shadows (2), cloud shadows (3), vegetation (4), soils and deserts (5), water (6), and snow (11).…”

“…As an example, Figure 1c shows the atmospherically-corrected B8a BoA reflectance (at 865 nm). Sen2Cor includes a scene classification module (example in Figure 1b) to map no data or defective pixels (pixel value = 0-1), four different cloud clover class probabilities (7)(8)(9)(10), and six different classes including shadows (2), cloud shadows (3), vegetation (4), soils and deserts (5), water (6), and snow (11). Other Sen2Cor outputs comprise (i) an estimation of the aerosol optical thickness (AOT) using the dense dark vegetation (DDV) algorithm [19] and the (ii) retrieval of water vapor (WV) using the pre-corrected differential absorption algorithm (APDA, [20]) analyzing Sentinel-2 bands B8a and B9.…”

“…To fill this gap, we developed a data service platform within the Earth Observation Data Centre (EODC) that allows access to atmospherically-corrected surface reflectance Sentinel-2 data (Level-2A) on-demand and globally. Level-2A data are required for numerous applications dealing with multi-temporal analysis (e.g., land surface phenology, land cover change) [8][9][10] as well as for accurate retrieval of land surface variables, such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), or albedo [11][12][13].…”

A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images

“…In Formulae (17) and (18), besides the mean vector, the other group of statistical parameters to be estimated are the cross-covariance matrices C…”

A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity

“…In [12], the STARFM was used to complete a time series that allowed improving the classification of tillage/no tillage fields. In [13], it was used to construct a Landsat image time series (2003)(2004)(2005)(2006)(2007)(2008) to monitor changes in vegetation phenology in Australia.…”