In many domains, large amounts of time series data are being collected and analyzed in a semi-automatic manner. A prominent approach is the seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) technique, which has been applied extensively in the past. However, STL quickly becomes computationally very expensive when applied to large data sets. In this work, we propose the first parallel implementation for the STL decomposition approach, which is tailored to the specific needs of graphics processing units (GPU). Our experimental evaluation on two global-scale case studies in temperature and vegetation trend analysis exhibits at least three-to-four orders of magnitude speed-up, demonstrating the effectiveness of the overall approach and the immense potential of the implementation in spatio-temporal data analyses. The source code is publicly available at https://github.com/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://sid.erda.dk/sharelink/hOUrqJJ FfA.

In the past five years, an international team has been working towards improved detection, characterization and modeling of abrupt changes in dryland ecosystem functioning, EF. This paper collects the recent advances and lessons learnt from the U-TURN project (Belspo SR/00/339, SR/00/366). Specifically abrupt changes in EF were mapped and categorized over global drylands; new 30m resolution time series of land cover maps and cover fractions were created, validated and released open access for the Sahel region; and new physically-based insights into dryland vegetation response to extreme rainfall were derived based on dryland optimized LPJ-GUESS simulations.

Despite the relevance of historical land cover maps for scientists and policy makers, an accurate high resolution record is currently lacking over the Sudano-Sahel. In this study, 30m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. These include cropland expansion and the re-greening of the Sahel, forest degradation & the detection of fine-scale changes, such as smallholder or subsistence farming. The historical land cover / cover fraction maps are made available via an open-access platform.

Large amounts of satellite data are now becoming available, which, in combination with appropriate change detection methods, offer the opportunity to derive accurate information on timing and location of disturbances such as deforestation events across the earth surface. Typical scenarios require the analysis of billions of image patches/pixels. While various change detection techniques have been proposed in the literature, the associated implementations usually do not scale well, which renders the corresponding analyses computationally very expensive or even impossible. In this work, we propose a novel massively-parallel implementation for a state-of-the-art change detection method and demonstrate its potential in the context of monitoring deforestation. The novel implementation can handle large scenarios in a few hours or days using cheap commodity hardware, compared to weeks or even years using the existing publicly available code, and enables researchers, for the first time, to conduct global-scale analyses covering large parts of our Earth using little computational resources. From a technical perspective, we provide a high-level parallel algorithm specification along with several performance-critical optimizations dedicated to efficiently map the specified parallelism to modern parallel devices. While a particular change detection method is addressed in this work, the algorithmic building blocks provided are also of immediate relevance to a wide variety of related approaches in remote sensing and other fields.

The use of remote sensing images has been broadly employed over the past decades in order to detect and investigate temporal changes on the Earth surface. This is one of the main goals of the widely used Breaks For Additive Season and Trend (BFAST) method. In this paper, we introduce the BFAST Explorer, an effective open-source tool for time series analysis based on BFAST, which is ready to be used at a personal webpage. We present its functional and architectural design, as well as a common usage scenario. Moreover, this tool was well received by the target community, notably by its recently integration into a private cloud computing platform.

The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regarding both computation time and space requirements. In practice, the sheer data volumes render existing approaches too slow for processing and analyzing all the available data. This work aims at accelerating BFAST, one of the state-of-the-art methods for break detection given satellite image time series. In particular, we propose a massively-parallel implementation for BFAST that can effectively make use of modern parallel compute devices such as GPUs. Our experimental evaluation shows that the proposed GPU implementation is up to four orders of magnitude faster than the existing publicly available implementation and up to ten times faster than a corresponding multi-threaded CPU execution. The dramatic decrease in running time renders the analysis of significantly larger datasets possible in seconds or minutes instead of hours or days. We demonstrate the practical benefits of our implementations given both artificial and real datasets.

Empirical relations are frequently used to derive leaf area index (LAI). Such relations often make assumptions that make it hard to link the derived LAI to realistic trees and forest canopies. In previous work we developed a set of analytical expressions to describe LiDAR waveforms with only a limited number of assumptions based on radiative transfer. These expressions were a function of crown macro-structure and LAI. The expressions were successfully tested when applied on crown archetypes, but showed significant error when applied to more realistic crowns. In this study, we analyse the effect of clumping on inferring LAI from realistic trees. Despite the potential of the expressions to detect subtle changes in LAI, absolute inferred LAI values can be significantly off. However, the strong correlation between true and inferred LAI (R2>; 0.97) for the two test cases in this study, allows for calibration of the inferred LAI values.

Here, we test and optimise an approach to monitor and detect tropical deforestation in near-real time by comparing it with a seasonal-trend model fitted onto the historical time series. The method detects disturbances in near-real time by comparing newly acquired satellite data with a modelled stable period based on historic satellite time series data. The method allows the differentiation between normal and abnormal change in near-real time and is based on the Break For Additive Seasonal Trend (BFAST) concept [5, 6, 7]. Testing is done by analysis of 16-day MODIS satellite image time series (MOD13Q1) for areas in the Brazilian Amazon region for which optimised deforestation systems (e.g. PRODESDETER, IMAZON) are available.

Traditionally, spectral mixture analysis (SMA) fails to fully account for highly similar ground components or endmembers. The high similarity between weed and crop spectra hampers the implementation of SMA for steering weed control management practices. To address this problem, this paper presents an alternative SMA technique, referred to as Integrated Spectral Unmixing (InSU). InSU combines both magnitude (i.e., reflectance) and shape (i.e., derivative reflectance) related features in an automated waveband selection protocol. Analysis was performed on different simulated mixed pixel spectra sets compiled fromin situ-measured weed canopy,Citruscanopy, and soil spectra. Compared to traditional linear SMA, InSU significantly improved weed cover fraction estimations. An average decrease in fraction abundance error (Deltaf) of 0.09 was demonstrated for a signal-to-noise ratio (SNR) of 500 : 1, while for a SNR of 50 : 1, the decrease was 0.06.

Hierarchical image segmentation methodologies have the potential to integrate temporal information, spatial context and the hierarchical complexity of satellite image time series. The current methods, however, fail to identify the distinction between subsequences of time series, which can be essential for the interpretation of ecosystem processes. Therefore a novel conceptual methodology is introduced that allows an enhanced multi-temporal hierarchical image segmentation (EMTHIS) based on subsequences of time series (i.e., time series over a specified time window). The effect of using these subsequence windows is illustrated in an accuracy assessment approach that determines the accuracy of a classification based on subsequence windows versus the existent methodologies that do not take subsequence windows into account. Analysis of the accuracy assessment approach demonstrated the importance of considering image time series subsequences when the percentage of pixels that shows a land cover / land use change between consecutive years is above 0.5%.

Traditionally, Spectral Mixture Analysis (SMA) fails to fully account for highly similar ground components or endmembers. The high similarity between weed and crop spectra therefore hampers the implementation of SMA for steering weed control management practices. To address this problem, the current study presents an alternative SMA technique, referred to as Integrated Spectral Mixture Analysis (iSMA). iSMA combines both magnitude (~ reflectance) and shape (~ derivatives) related features in an automated waveband selection protocol and allows for an optimal separation between weed and crops, irrespective of the scenario considered. Compared to traditional approaches iSMA significantly improved weed cover fraction estimations (~ 17% increase). Analysis was performed on different simulated mixed pixel spectra sets compiled from in situ measured weed, Citrus canopy and soil spectra.

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation moisture monitoring tools. The normalized difference infrared index (NDII) derived from SPOT Vegetation satellite data and the Keetch-Byram drought index derived from temperature and rainfall data are both related to vegetation moisture dynamics. Autocorrelation of time series is a major issue when time series derived from remote sensing and meteorological variables are analyzed. Autocorrelation affects cross-correlation between variables measured in time, and violates the basic regression assumption of independence. Therefore, this study focuses on the extraction of independent drought metrics from seasonal time series to define quantitive relationships between remote sensing and meteorological time series. First, the correlation between time series of satellite- and climate-data based indices is investigated by cross-correlation analysis. Secondly, a novel method for extraction of drought metrics is optimized for satellite- and in-situ derived time series. The method is based on a nonlinear least squares fit of asymmetric Gaussian model functions. The smooth model functions are then used for defining key seasonality parameters. The hypothesis is that the `seasonal shapes' of satellite- and in-situ derived time series are correlated. Based on this hypothesis, the performance for parameter extraction from time series is explored