Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain

Abstract

Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000–2018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.

Shorter blooms expected with longer warm periods under climate change: an example from a shallow meso-eutrophic Mediterranean lake

“…For phenological analysis, calculation of the start of season (SOS), end of season (EOS), and length of season (LOS), a spline interpolation was used as this smoothed the seasonal patterns. Interpolation and smoothing is a standard and essential step in phenological analysis in order to identify the timing of seasonal change and this technique has been used to determine the changes in land cover phenology (Forkel et al, 2013Garcı ´a et al, 2019). For phenological analysis, we used 15 mg m -3 of Chl-a as a level that could be considered as a starting point for the summer bloom for Lake Trasimeno, and this concentration has been used as a trigger value for instigating enhanced bloom monitoring (Touchette et al, 2007).…”

A Time-Frequency Joint Time-Delay Difference Estimation Method for Signal Enhancement in the Distorted towed Hydrophone Array

“…Thus, it is reasonable to model the change of time-delay difference as a first-order hidden Markov process. The HMM is characterized by λ = (A, B, Π), where A, B, and Π represent the state transition probability matrix, observation probability matrix, and initial state probability vector, respectively [44,45]. Let u = {u 1 , u 2 , • • • , u L } denote the set of L hidden states (time-delay differences).…”

Impact of Land Cover Changing on Wetland Surface Temperature Based on Multitemporal Remote Sensing Data

“…Temporal series analysis can be carried out through the extraction of data and its subsequent aggregation into statistical parameters [13], through the application of harmonic analysis [7], or by utilizing change detection algorithms and unsupervised classification of the changes [7,14]. In order to address these issues, methods such as Breaks For Additive Seasonal and Trend (BFAST) [15], and greenbrown [16,17] have been developed. They examine data by breaking it down into three components: seasonal fluctuation, trend, and a residual portion of the data [18].…”