Abstract
Biomass monitoring, specifically detecting changes in the biomass or vegetation of
a geographical region, is vital for studying the carbon cycle of the system and has significant
implications in the context of understanding climate change and its impacts. Recently, several time
series change detection methods have been proposed to identify land cover changes in temporal
profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we
adapt Gaussian process regression to detect changes in such time series in an online fashion. While
Gaussian process (GP) has been widely used as a kernel based learning method for regression and
classification, their applicability to massive spatio-temporal data sets, such as remote sensing data,
has been limited owing to the high computational costs involved. In this paper we address the
scalability aspect of GP based time series change detection. Specifically, we exploit the special
structure of the covariance matrix generated for GP analysis to come up with methods that
can efficiently estimate the hyper-parameters associated with GP as well as identify changes in
the time series while requiring a memory footprint which is linear in the size of input data,
as compared to traditional method which involves solving a linear system of equations for the
Choleksy decomposition of the quadratic sized covariance matrix. Experimental results show
that our proposed method achieves significant speedups, as high as 1000, when processing long
time series, while maintaining a small memory footprint. To further improve the computational
complexity of the proposed method, we provide a parallel version which can concurrently process
multiple input time series using the same set of hyper-parameters. The parallel version exploits the
natural parallelization potential of the serial algorithm and is shown to perform significantly better
than the serial version, with speedups as high as 10. Finally, we demonstrate the effectiveness of
the proposed change detection method in identifying changes in Normalized Difference Vegetation
Index (NDVI) data. Moreover, we show that the scalable solution is able to process NDVI data
for the entire Iowa region significantly faster than the standard method.