Detection Of Abrupt Changes Research Articles

Satellite remote sensing reveals overwhelming recovery of forest from disturbances in Asia

Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake; however, frequently occurring climate extremes and human activities have considerably threatened the stability of forests. At the same time, detailed accounts of disturbances and forest responses are not yet well quantified in Asia. This study employed the Breaks For Additive Seasonal and Trend method—an abrupt-change detection method—to analyze the Enhanced Vegetation Index time series in East Asia, South Asia, and Southeast Asia. This approach allowed us to detect forest disturbance and quantify the resilience after disturbance. Results showed that 20 % of forests experienced disturbance with an increasing trend from 2000 to 2022, and Southeast Asian countries were more severely affected by disturbances. Specifically, 95 % of forests had robust resilience and could recover from disturbance within a few decades. The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude. In summary, this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades. The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022. The findings of this study underscore the complex relationship between disturbance and resilience, contributing to comprehension of forest resilience through satellite remote sensing.摘要目前对于亚洲森林在应对气候变化和人类活动干扰方面的研究相对较少. 本研究利用BFAST突变检测方法, 分析了东亚, 南亚和东南亚的增强植被指数 (EVI) 长时间序列中检测到的森林扰动和恢复情况. 结果显示, 2000年至2022年期间, 约20%的森林经历了扰动, 且受到扰动的森林面积呈增加趋势, 东南亚国家受扰动的影响更为严重. 在扰动事件发生后, 95%的森林具有较好的恢复能力, 能够在受到扰动后的一段时间后恢复过来, 其中约有三分之二的森林在2022年时已经恢复到了较扰动前更好的状态.

Incorporating small‐scale disturbances in models of forest structure and aboveground biomass of tropical mountains

AbstractSeveral efforts have been made to model forest structure and estimate global aboveground biomass (AGB) magnitude and distribution. However, AGB models still show large uncertainties, especially over tropical mountains that are difficult to survey and understudied. Although forest disturbance and land use greatly influence forest structure and AGB accumulation, they are rarely considered in forest structure and AGB models. Including the effect of forest disturbance and land use could improve model performance. In this study, we used Landsat time series analysis and forest inventory data to assess and model the effect of small‐scale disturbance and land use on tropical montane forest structure and AGB. We explored two approaches: abrupt forest change detection, using the algorithm Breaks For Additive Seasonal Trend (BFAST), and gradual vegetation change by calculating the variation of remote sensing vegetation indices over time. We found that tropical montane landscapes are very dynamic and heterogenous. Out of the 284 forest plots analyzed, 27% had abrupt disturbances at least once between 1993 and 2014, and some of them showed abrupt disturbances several times. Forest plots with abrupt disturbances exhibit statistically significant lower basal area, tree height, and AGB than plots without abrupt disturbances. Using linear mixed‐effects models, we found that the number of breakpoints, annual Normalized Difference Vegetation Index SD, and Normalized Difference Water Index minimum values over time were the most informative variables for predicting forest structure, particularly basal area (adjusted R2 = 0.61). These variables are easy to calculate and could add significant power to forest structure and AGB models. In conclusion, incorporating small‐scale disturbance to model forest structure and AGB at regional scales is feasible through the identification of recent abrupt and gradual forest change using remote sensing time series. Including recent forest disturbance variables as predictors in forest structure models can greatly improve biomass predictability and mapping in dynamic landscapes.

Detection of abrupt changes in East Asian monsoon from Chinese loess and speleothem records

Detection of abrupt changes in East Asian monsoon from Chinese loess and speleothem records

BFASTm-L2, an unsupervised LULCC detection based on seasonal change detection – An application to large-scale land acquisitions in Senegal

In the context of Global Change Research, detection, monitoring and characterization of land use/land cover (LULC) changes are of prime importance. The increasing availability of dense satellite image time series (SITS) has led to a shift in the change detection paradigm, with algorithms able to exploit the full temporal information laid down in SITS. So far, most of these algorithms have focused on the detection of abrupt and gradual changes, and thus developed breakpoint detection based on significant deviations from the mean. However, LULC changes may manifest themselves in other patterns, particularly changes in seasonality (amplitude, number and length of the growing seasons) that are harder to detect. In this paper, we propose a simple method to automatically select the breakpoint linked to the biggest seasonal change in long and dense SITS with multiple breakpoints. This approach - BFASTm-L2 - relies on linking a high-speed algorithm (BFAST monitor) with a time series similarity metric (Euclidian distance L2) sensitive to seasonal changes. The capacity of BFASTm-L2 to identify the date of change in different situations was tested on two data sets, and compared to the performances of three other algorithms (BFAST monitor, BFAST lite, and Edyn). The data sets are 1. a published benchmark data set composed of 25 200 simulated SITS with different change types and change magnitudes, and 2. the 2000–2020 MODIS NDVI SITS over a 200x200 pixels area in Senegal including different study sites which have undergone recent LULC changes due to agricultural large-scale land acquisitions (LSLAs) (as reported in the ground field database used in this study). The results show that BFASTm-L2 is efficient in accurately detecting in time most of the changes, and, in contrast with BFAST Lite and BFASTmonitor, to spatially highlight LSLAs-induced changes without the need of any prior knowledge. The automatic proposed approach, faster than BFAST Lite and Edyn, and with very few tuneable parameters, may thus be easily implemented in unsupervised pipelines to map and analyse generic LULC changes at regional scale.

Three-dimensional distribution of mass movement signals in disturbed Picea abies (L.) Karst.

Dendrogeomorphologic approaches enable annual absolute dating of various potentially hazardous geomorphic processes. This study tested the dating precision of three commonly used dendrogeomorphic approaches used for the extraction of geomorphic signals from the tree ring series of disturbed trees. The first approach is based on the onset of reaction wood occurrence, and the other two approaches are based on the detection of abrupt changes in tree ring eccentricity. Nine 165 cm long stems of maximally 27 year old Norway spruces (Picea abies (L.) Karst.) growing on unstable slope affected by mass movement events of known age were analysed. Several sampling directions on stems were analysed to reveal the best sampling strategy for each tested approach. The relationships between stem curvature and the dating precision of the approaches were also analysed. The results of dating precision showed crucial differences between the tested approaches. Tree stem curvature did not influence dating precision. Overall, we concluded that the approaches based on eccentric increments are less precise than those based on reaction wood analysis when juvenile P. abies trees that grow on landslide slope are sampled. The importance of this study is in providing new insight into dating precision by analysing the whole tree stems of trees influenced by mass movements. This is unique since the approaches based on eccentricity increments have not been previously tested in such a detail.

Spatiotemporal detection of abrupt change in trends of rainfall and dry and wet periods at different time scales: The case of the Medjerda basin in northeast Algeria

Spatiotemporal detection of abrupt change in trends of rainfall and dry and wet periods at different time scales: The case of the Medjerda basin in northeast Algeria

Identifying the true drivers of abrupt changes in ecosystem state with a focus on time lags: Extreme precipitation can determine water quality in shallow lakes

A better understanding of abrupt ecosystem changes is needed to improve prediction of future ecosystem states under climate change. Chronological analysis based on long-term monitoring data is an effective way to estimate the frequency and magnitude of abrupt ecosystem changes. In this study, we used abrupt-change detection to differentiate changes of algal community composition in two Japanese lakes and to identify the causes of long-term ecological transitions. Additionally, we focused on finding statistically significant relationships between abrupt changes to aid with factor analysis. To estimate the strengths of driver–response relationships underlying abrupt algal transitions, the timing of the algal transitions was compared to that of abrupt changes in climate and basin characteristics to identify any synchronicities between them. The timing of abrupt algal changes in the two study lakes corresponded most closely to that of heavy runoff events during the past 30–40 years. This strongly suggests that changes in the frequency of extreme events (e.g., heavy rain, prolonged drought) have a greater effect on lake chemistry and community composition than do shifts in the means of climate and basin factors. Our analysis of synchronicity (with a focus on time lags) could provide an easy method to identify better adaptative strategies for future climate change.

Corrigendum to “A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network”

Corrigendum to “A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network”

MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM-BASED ABRUPT CHANGES DETECTION FOR HEART SOUNDS SEGMENTATION

The aim of this paper is cardiac sound segmentation in order to extract significant clinical parameters that can aid cardiologists in diagnosis, through maximal overlap discrete wavelet transform (MODWT) and abrupt changes detection. After reconstruction of the fifth to seventh level of decomposition of the pre-processed phonocardiogram (PCG), we can correctly measure the time duration of Fundamental heart sounds (S1, S2), while the third and fourth levels localize murmurs and clicks. From this scope, it is possible to establish the time interval between clicks and fundamental heart sounds or evaluating murmur severity through energetic ratio. We have tested this approach on several phonocardiography records. Results show that this method performs greatly on long and short PCG records and gives the precise duration of fundamental heart sounds; we have achieved an accuracy of 88.6% in cardiac sounds segmentation.

Detection Framework of Abrupt Changes and Trends in Rainfall Erosivity in Three Gorges Reservoir, China

Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this study, a detection framework is introduced to identify temporal changes in rainfall erosivity time series as follows: (i) The significance of time series variation of rainfall erosivity is assessed based on the Hurst coefficient and divided into three levels: None, medium, and high. (ii) The detection of abrupt changes (Mann–Kendall, Moving T, and Bayesian tests) and trends (Spearman and Kendall rank correlation tests) of variate series and the correlation coefficient between the variation component and the original series is calculated. (iii) The modified series is obtained by preferentially eliminating the variation component (trend or change point) with larger correlation coefficients. (iv) We substituted the modified series into steps i to iii until the correlation coefficient was not significant. This framework is used to analyze the variation of rainfall erosivity in the Three Gorges Reservoir, China. The results showed that by using traditional methods, both an increasing trend and an upward change point were observed in Zigui station. However, after the upward change point was deducted from the annual rainfall erosivity series R(t), the resultant Rm(t) showed no statistically significant trend. Trend analysis should be performed considering the existence of an abrupt change to assess the long-term changes in rainfall erosivity series; otherwise, it would result in the wrong conclusion. In addition, the change points detected in the Rm(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty.

Bayesian multiple changepoint detection with missing data and its application to the magnitude‐frequency distributions

AbstractThe detection of abrupt changes in an evolving pattern of time series in the presence of missing data still poses a challenge to real applications. We formulate the multiple changepoint problem into a latent Markov model on a countably infinite state space. For efficiency‐enhancing, we propose a partially collapsed Gibbs sampler for the inference of the joint posterior of the number of changepoints and their locations. Variants of Viterbi algorithms are suggested for obtaining the MAP estimates of random changepoints in the presence of missing data, which provides better performances in these varying‐dimensional problems. The method is generally applicable for multiple changepoint detection under a variety of missing data mechanism. The method is applied to a case study of the magnitude‐frequency distribution of the 2010 Darfield M7.1 earthquake sequence in New Zealand. We find out some unusual features of the seismic b‐value in the Darfield earthquake sequence. It is noted that two changepoints are detected and in contrast to the background seismic b‐value, relatively low b‐values in the early aftershock propagation period are identified. We suggest that this might be a forewarning of potentially devastatingly strong aftershocks. The advance in the method of b‐value changepoint detection will enhance our understanding of earthquake occurrence and potentially lead to improved risk forecasting.

Interannual variation trend of hail days in hail prone areas in Xinjiang

Hail causes significant agricultural losses in Xinjiang every year, and the study of interannual variation of hail days and its causes can provide a climatic background for hail warning and defense. This study analyzes the trends and abrupt changes in the interannual variability of hail days in the areas with frequent hail using linear trend analysis and abrupt climate change detection methods, and explains the climatic causes of these trends using atmospheric stability. The results show that areas with frequent hails in Xinjiang are located on both sides of the Tianshan Mountains and their adjacent areas, and area with the most to the fewest hail days are in the following order: Zhaosu and Bayinbruck on the Alpine Platform, Aksu region on the south side of the Tianshan Mountains, and Bortala and Kuy-Ma Basin on the north side of the Tianshan Mountains. The interannual variation of hail days in Aksu region, Bortala and Kuy-Ma Basin from 1961 - 2016 shows a linear increasing trend, but the increase is small, only 0.2 - 0.4 d per 10 a, which is contrary to the conclusion of previous studies where the number of hail days in the Chinese region showed a decreasing trend; whereas the Alpine Platforms shows a linear decreasing trend with a larger decrease of 2.5 d per 10 a. The annual number of hail days on the Alpine Platform changed abruptly in 1993 and in the Aksu region in 1978. The linear increase (decrease) in the interannual variation of the warm season modified total index leads to a linear increase (decrease) in the number of hail days on both sides of the Tianshan Mountains and on the Alpine Platform. The trend of interannual variation in the number of hail days in the hail-prone areas in Xinjiang depends on the altitude of the elevation, with a decreasing trend at higher elevations and an increasing trend at lower elevations.

Land cover classification of the Alps from InSAR temporal coherence matrices

Land cover mapping is of great interest in the Alps region for monitoring the surface occupation changes (e.g. forestation, urbanization, etc). In this pilot study, we investigate how time series of radar satellite imaging (C-band single-polarized SENTINEL-1 Synthetic Aperture Radar, SAR), also acquired through clouds, could be an alternative to optical imaging for land cover segmentation. Concretely, we compute for every location (using SAR pixels over 45 × 45 m) the temporal coherence matrix of the Interferometric SAR (InSAR) phase over 1 year. This normalized matrix of size 60, ×, 60 (60 acquisition dates over 1 year) summarizes the reflectivity changes of the land. Two machine learning models, a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN) have been developed to estimate land cover classification performances of 6 main land cover classes (such as forests, urban areas, water bodies, or pastures). The training database was created by projecting to the radar geometry the reference labeled CORINE Land Cover (CLC) map on the mountainous area of Grenoble, France. Upon evaluation, both models demonstrated good performances with an overall accuracy of 78% (SVM) and of 81% (CNN) over Chambéry area (France). We show how, even with a spatially coarse training database, our model is able to generalize well, as a large part of the misclassifications are due to a low precision of the ground truth map. Although some less computationally expensive approaches (using optical data) could be available, this land cover mapping based on very different information, i.e., patterns of land changes over a year, could be complementary and thus beneficial; especially in mountainous regions where optical imaging is not always available due to clouds. Moreover, we demonstrated that the InSAR temporal coherence matrix is very informative, which could lead in the future to other applications such as automatic detection of abrupt changes as snow fall or landslides.

The dynamic interaction between COVID-19 and shipping freight rates: a quantile on quantile analysis

This study determines the impact of the coronavirus disease (COVID-19) that has been prevalent since the year 2019, on the shipping freights. This task has been undertaken by using the wavelet quantile on the quantile approach. The results of the study affirm that the pandemic has in fact affected the shipping freight costs, primarily due to the lower demand for energy and raw materials, and the unavailability of the vessels. In addition to this, the spread of COVID-19 has had a positive impact on the Baltic Dry Index in the high quantiles and is deemed to be more responsive in the long run. Also, the COVID-19 infection has had a negative effect on the Baltic Dry Tanker Index and the Baltic Clean Tanker Index in the medium to high quantiles, particularly in the short and the medium run. The positive impact of COVID-19 on the Baltic Clean Tanker Index has been recognized in the long term in the high quantiles. These findings support the theoretical model which states that the spread of COVID-19 and the shipping freights are closely related. The results suggest that the degree of the effect is more causal in the short. Therefore, the shipping industry must ideally pay special attention to the detection of abrupt changes in the freight rate dynamics, and the specific regulations regarding these intricacies are critical.

Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement

Long-term land surface temperature (LST) variation is vital for the study of climate change and environmental monitoring. Change detection methods provide access to recovery trajectories of trend and seasonality and detect abrupt changes in LST time series, but a comprehensive evaluation of the published methods is lacking. In this study, simulated LST data with a temporal resolution of 8 days under different scenarios were used to evaluate the performance of three commonly used methods: Detecting Breakpoints and Estimating Segments in Trend (DBEST), Breaks for Additive Seasonal and Trend (BFAST), and Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). The results obtained using the simulated data indicated that BEAST was the best method for decomposing LST time series into trend and seasonality (mean RMSEs were 0.28 K and 0.27 K, respectively) and for detecting abrupt changes in these two components (mean F1 scores were 0.83 and 0.95, respectively). BFAST was less robust to high-complexity data (F1: 0.56 and 0.52, RMSE: 1.34 K and 1.46 K). 0.91 K and 1.29 K). DBEST is recommended to capture component details because it yields the least generalized output (F1 for trend: 0.37, RMSE: 0.64 K and 1.37 K). Both BFAST and DBEST exhibited reduced accuracy when the time-series data has long-lasting continuous missing data. An application using the 20-year MODIS LST time series supports the results obtained using the simulated data. BEAST exhibited the highest detection accuracy for land cover change (13 correct detections among 15 true changes), followed by DBEST (9) and BFAST (7). All three methods were ineffective for detecting low-magnitude disturbances: wildfires, heatwaves, and cold spells due to their low intensity or short duration. To reduce the non-negligible commission error of BEAST, this study proposes an improved BEAST, which eliminates the false breakpoints in BEAST using a set of thresholds. Compared with BEAST, the user accuracy of the improved BEAST was significantly increased by 13.9% in the simulated data, resulting in an F1 increase of 0.04, and 15 false breakpoints were eliminated among 53 detected disturbances in the MODIS LST time series. This study outlines commonly used change detection methods and offers guidance for choosing the optimal method to detect changes in LST time series. Furthermore, suggestions on the determination of parameters and false breakpoints elimination in the improved BEAST enable it more practical.

Labeled optimal partitioning

In data sequences measured over space or time, an important problem is accurate detection of abrupt changes. In partially labeled data, it is important to correctly predict presence/absence of changes in positive/negative labeled regions, in both the train and test sets. One existing dynamic programming algorithm is designed for prediction in unlabeled test regions (and ignores the labels in the train set); another is for accurate fitting of train labels (but does not predict changepoints in unlabeled test regions). We resolve these issues by proposing a new optimal changepoint detection model that is guaranteed to fit the labels in the train data, and can also provide predictions of unlabeled changepoints in test data. We propose a new dynamic programming algorithm, Labeled Optimal Partitioning, and we provide a formal proof that it solves the resulting non-convex optimization problem. We provide theoretical and empirical analysis of the time complexity of our algorithm, in terms of the number of labels and the size of the data sequence to segment. Finally, we provide empirical evidence that our algorithm is more accurate than the existing baselines, in terms of train and test label error.

Determination of indium melting curve at high pressure by picosecond acoustics

Picosecond acoustics combined with a diamond anvil cell is used to study liquid indium and to determine with high accuracy both the sound velocity and the melting curve over an extended pressure and temperature range. The sound velocities, determined by phonon surface imaging, complement previous inelastic x-ray scattering determinations and are in good agreement with estimations according to a thermodynamic model. Based on exact thermodynamic relations, the equation of state of the liquid phase is obtained using the isothermal bulk modulus ${B}_{T,0}$ and its first pressure derivative ${B}_{T}^{\ensuremath{'}}$. These quantities are derived from the precise experimental determination of the variation of the sound velocity as a function of pressure. Melting is determined via the detection of abrupt changes in the elastic properties between solid and liquid phases and through the monitoring of the solid-liquid coexistence. The melting curve constrained up to 6 GPa and 673 K is shown to be well described by the Simon-Glatzel equation in the full $(p,T)$ range explored.

Detection of abrupt change in trends of rainfall and rainy day’s pattern of Uttarakhand

Detection of abrupt change in trends of rainfall and rainy day’s pattern of Uttarakhand

Spatiotemporal variations of global solar radiation: Case Study Egypt

The spatio-temporal characteristic of the available global solar radiation (GSR) variability is essential for effective grid integration of photovoltaic (PV) power systems, particularly in developing countries such as Egypt. Thus, the long-term spatio-temporal variability of GSR and its trend assessment, fluctuations, and abrupt change detection during the climate period 1985-2018 based on eight ground-based stations in Egypt were statistically analyzed. The missing records in the observed GSR data were completed using the SoDa (Solar radiation data) database after its validation at the selected eight stations, which produced high accuracy and slight bias compared to the available observations. The results showed that the northern coast, particularly the Nile Delta region, has lower GSR values than the southern region. At all stations, winter had the largest coefficient of variation (COV) values while summer had the lowest COV values. Furthermore, the trend analysis for GSR using Mann-Kendall (MK) rank statistical test revealed that autumn, followed by winter and spring has a positive trend across Egypt, while the annual and summer GSR have positive trends over middle and northern Egypt. The annual GSR long-term variability measured by Gaussian and Binomial low-path filters shows a positive trend at all stations except Arish, Asyut and Kharga. Moreover, the detected first abrupt change point for the annual GSR data occurred during the 1990s at all stations except for Matrouh in 2002. This first abrupt change point is positive (increasing GSR values) at all stations except a negative trend for Bahtim and Kharga. The second and third abrupt change points are positive at all stations except Arish during 2003-2009 in the second change point and returns to its positive trend in the third change point in 2010.

Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa

While cropland expansion and demand for woodfuel exert increasing pressure on woody vegetation in East Africa, climate change is inducing woody cover gain. It is however unclear if these contrasting patterns have led to net fractional woody cover loss or gain. Here we used non-parametric fractional woody cover (WC) predictions and breakpoint detection algorithms driven by satellite observations (Landsat and MODIS) and airborne laser scanning to unveil the net fractional WC change during 2001–2019 over Ethiopia and Kenya. Our results show that total WC loss was 4-times higher than total gain, leading to net loss. The contribution of abrupt WC loss (59%) was higher than gradual losses (41%). We estimated an annual WC loss rate of up to 5% locally, with cropland expansion contributing to 57% of the total loss in the region. Major hotspots of WC loss and degradation corridors were identified inside as well as surrounding protected areas, in agricultural lands located close to agropastoral and pastoral livelihood zones, and near highly populated areas. As the dominant vegetation type in the region, Acacia-Commiphora bushlands and thickets ecosystem was the most threatened, accounting 69% of the total WC loss, followed by montane forest (12%). Although highly outweighed by loss, relatively more gain was observed in woody savanna than in other ecosystems. These results reveal the marked impact of human activities on woody vegetation and highlight the importance of protecting endangered ecosystems from increased human activities for mitigating impacts on climate and supporting sustainable ecosystem service provision in East Africa.