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Technical Note

Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022

by
Yunxia Dong
1,2,
Guimin Liu
1,*,
Xiaodong Wu
2,3,4,
Lin Wang
1,
Haiyan Xu
1,
Sizhong Yang
2,
Tonghua Wu
2,
Evgeny Abakumov
5,
Jun Zhao
1,
Xingyuan Cui
1 and
Meiqi Shao
1
1
College of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Ministry of Education Engineering Research Center of Water Resource Comprehensive Utilization in Cold and Arid Regions, Lanzhou 730070, China
2
Cryosphere Research Station on the Qinghai-Tibet Plateau, Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3
Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin 150025, China
4
International Research Center for China-Mongolia-Russia Cold and Arid Regions Environment and Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
5
Department of Applied Ecology, Saint Petersburg State University, Saint Petersburg 199178, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(1), 169; https://doi.org/10.3390/rs17010169
Submission received: 12 November 2024 / Revised: 28 December 2024 / Accepted: 2 January 2025 / Published: 6 January 2025
(This article belongs to the Special Issue Remote Sensing in Applied Ecology (Second Edition))
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Abstract

:
The northern permafrost regions are increasingly experiencing frequent and intense extreme events, with a rise in the occurrence of compound extreme events. Many climate-related hazards in these areas are driven by such compound events, significantly affecting the stability and functionality of vegetation ecosystems. However, the cumulative and lagged effects of compound extreme events on vegetation remain unclear, which may lead to an underestimation of their actual impacts. This study provides a comprehensive analysis of the spatiotemporal variations in compound extreme events and the vegetation response to these events in the northern permafrost regions from 1982 to 2022. The primary focus of this study is on examining the cumulative and lagged effects of compound extreme climate events on the Kernel Normalized Difference Vegetation Index (kNDVI) during the growing seasons. The results indicate that in high-latitude regions, the frequency of extreme high temperature–precipitation compound events and high temperature–drought compound events have increased in 58.0% and 67.0% of the areas, respectively. Conversely, the frequency of extreme low temperature–drought compound events and extreme low temperature–precipitation compound events has decreased in 70.6% and 57.2% of the areas, with the high temperature–drought compound events showing the fastest increase. The temporal effects of compound extreme events on kNDVI vary with vegetation type; they produce more cumulative and lagged effects compared with single extreme high-temperature events and fewer effects compared with single extreme precipitation events, with compound events significantly affecting forest and grassland ecosystems. Notably, extreme high temperature–precipitation compound events exhibit the strongest cumulative and lagged effects on vegetation, while extreme low temperature–drought compound events influence wetland and shrubland areas within the same month. This study underscores the importance of a multivariable perspective in understanding vegetation dynamics in permafrost regions.

1. Introduction

In recent years, climate warming has accelerated, leading to intensified global cycles of water, energy, and carbon. This change has significantly increased the frequency and intensity of extreme events, including heavy rainfall, snowstorms, heat waves, cold waves, and hailstorms [1]. Such intensification may lead to a rise in the frequency and impacts of compound events like summer heat waves coupled with droughts, strong winds, and extreme precipitation [2,3,4]. “Compound events” refer to the simultaneous occurrence of multiple disasters or climate-driven incidents that affect ecosystems and societies across different temporal and spatial dimensions [5,6,7]. The characteristic of compound extreme events is the simultaneous influence of multiple weather and climate drivers, leading to impacts on society and the environment that are greater than those of individual extreme events [8,9,10]. Compound extreme events can potentially trigger large-scale collapses of vegetated ecosystems, pushing the ecosystem into an irreversible state. For example, the 2003 European heatwave led to a 30% reduction in vegetation productivity [11], and the 2010 Russian heatwave occurred alongside a drought [12], resulting in a record decline in plant productivity [13]. Compound extreme events can be classified as four types: preconditioned (such as the impacts of summertime vegetation from springtime weather), multivariate, temporally compounding, and spatially compounding [7]. The interactions of these compound extreme events across different temporal and spatial scales may exacerbate the vulnerability of ecosystems, leading to more profound and unpredictable impacts. In high-latitude regions, common compound extreme events involve combinations of heat waves and precipitation [6], driven by abundant moisture and elevated summer temperatures [14]. Although individual extreme events have been widely studied, research on the mechanisms, quantification, and forecasting of various compound events remains limited, especially concerning compound extremes in northern permafrost regions [15].
Permafrost regions in the Northern Hemisphere are primarily found at high latitudes and altitudes, typically in remote, cold climates. The rates of climate change vary across different permafrost regions. The vegetation ecosystems in these areas are highly sensitive to climate fluctuations [16,17], making them susceptible to extreme climate events. Climate change-induced declines in vegetation function are primarily driven by the downregulation of physiological processes, such as stomatal conductance and light use efficiency [18]. The physiological and ecological responses of vegetation to extreme climate events vary across different ecosystem types and climatic conditions [19]. For example, the inherent capacity of vegetation to withstand external disturbances enables forests to better resist the stresses of heat or drought compared with grasslands due to the relatively higher resistance of forests [20,21]. When climate changes surpass the tolerance limits of vegetation, plant growth typically adjusts in response [22]. The lag effect refers to vegetation responding to past rather than current climate factors [16], while the cumulative effect involves the influence of continuous climate dynamics on vegetation [23]. The diversity of vegetation ecosystems across seasons makes the relationships between climate factors and cumulative and lagged effects on plant growth highly complex. For instance, in Siberia, precipitation has the longest cumulative effect and the shortest lag effect on vegetation growth [24], while in the Tibetan Plateau, lag effects from extreme droughts are more pronounced in higher altitude regions with lower precipitation and temperature [22]. Existing studies predominantly utilize the NDVI to examine the impacts of extreme climate events on vegetation [25,26,27], and the effects of drought or heatwaves are well documented [28,29,30]. Compound temperature and precipitation anomalies—such as hot and dry conditions occurring simultaneously—have substantial impacts on vegetation productivity. Although several studies have analyzed and predicted the effects of droughts or heatwaves on vegetation [31], the cumulative and lagged responses of vegetation productivity under compound climate conditions and the regional asymmetries of these responses have yet to receive sufficient attention. To implement effective ecological protection measures and support the sustainable development of ecosystems, it is essential to examine the cumulative and lag effects of compound extreme climate changes on vegetation. This understanding can help reveal the complex mechanisms of ecosystem responses to climate change.
The NDVI has advantages, including extensive spatiotemporal coverage, high sensitivity for vegetation detection, low noise, and strong comparability [32]. However, in cold and arid regions with sparse vegetation cover, as well as widespread snow, bare land, and glaciers, issues like reflectance and atmospheric moisture content increase the uncertainty in data quality [33,34,35]. A novel vegetation index, the kernel normalized difference vegetation index (kNDVI), was introduced [36]. Compared with the NDVI, this index can overcome saturation effects, mixed pixels, and complex climate change cycles, reducing uncertainty [37]. kNDVI has been shown to outperform NDVI and NIRv in high-latitudinal regions [36,37,38]. This index can sensitively reflect vegetation responses to environmental changes, especially in harsh environments such as permafrost regions. Therefore, we chose kNDVI as the indicator for analyzing vegetation dynamics in the northern permafrost regions. We used ArcGIS 10.8 to spatially map different types of extreme climate events and vegetation changes, subsequently analyzing their spatial distribution patterns and trends, providing strong spatial support for the study. Furthermore, ERA5 data are an important data source for studying extreme climate events [39]. ERA5 offers high spatiotemporal resolution climate data covering global climate conditions from 1982 to 2022, making it particularly suitable for studying long-term climate trends and extreme events. In summary, the combination of kNDVI and ERA5 data can provide high-precision remote sensing data for analyzing the impacts of extreme climate events on vegetation dynamics in permafrost regions [40].
Under the warming climate, changes in permafrost are expected to feed back into the climate system via the carbon cycle [41]. This study applies the kNDVI index and compound extreme climate indices to investigate the spatiotemporal variations in compound extreme events across high-latitude permafrost regions from 1982 to 2022, as well as the cumulative and lagged effects of these events on vegetation productivity during the growing season. The findings enhance our understanding of climate change impacts on vegetation growth and provide critical insights for enhancing the model of the carbon cycle and vegetation dynamics in permafrost regions.

2. Materials and Methods

2.1. Study Area and Date

The study area was the high-latitude permafrost region of the Northern Hemisphere (45°–90°N) (Figure 1a). The permafrost regions stretch across multiple countries, including Russia, Canada, the United States, Norway, Sweden, and Finland, with Russia and Canada holding the largest share of global permafrost coverage [42]. Daily temperature and precipitation data were obtained from the ERA5 reanalysis dataset, with a resolution of 0.1° × 0.1°. Land cover data were sourced from the MCD12Q1 product, employing the widely used IGBP classification method and using 2020 data resampled to 0.1° × 0.1° to align with the other datasets. Forests were categorized into evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, and mixed forests. Shrublands were classified as closed and open shrublands, while savannas, woody savannas, and grasslands were grouped together as grasslands (Figure 1b). The NDVI data were derived from GIMMS (1982–1999) and MODIS13Q1 (2000–2022) products. Given that these datasets originate from different satellite sensors, we calibrated the GIMMS NDVI data to match the MODIS dataset by establishing correlations of monthly average NDVI values over the entire study area from 2001 to 2013. The results indicated a strong correlation (R2 = 0.99, n = 156) between the original MODIS NDVI and the corrected GIMMS NDVI values. The kNDVI is a vegetation index derived from remote sensing data to assess vegetation cover and growth conditions. Building on the NDVI, the kNDVI uses kernel density estimation to smooth NDVI values, reducing noise, enhancing spatial continuity, and improving the accuracy of the vegetation index [36].

2.2. Methods

2.2.1. Compound Extreme Climate Indices

To characterize compound extreme events, this study uses the 25th and 75th percentiles to identify compound extremes in temperature and precipitation [43,44]. Based on temperature and precipitation thresholds, four types of compound extreme events are defined: extreme high temperature–precipitation (HP) events, extreme high temperature–drought (HD) events, extreme low temperature–precipitation (CP) events, and extreme low temperature–drought (CD) events [45].

2.2.2. Cumulative and Lagged Effects

Previous studies have shown that the cumulative and lag effects of climate variables on vegetation generally range between zero and three months [46,47]. In this study, the Pearson correlation coefficient was used to represent the linear correlation between kNDVI and climate variables, allowing for the analysis of the cumulative and lagged effects of climate variables on kNDVI. We determined the lag and accumulation periods of climate variables that most influence kNDVI by calculating the Pearson correlation coefficient between kNDVI and climate variables across various combinations of lag and accumulation months. The climate parameters with the highest absolute correlation values were identified as the key factors affecting kNDVI [47]. For instance, a one-month lag implies that the kNDVI from June to October correlates with temperature or precipitation from May to September; and a one-month accumulation means that the kNDVI from June to October correlates with the average temperature or precipitation in May and each subsequent month up to October. A one-month accumulation with a one-month lag indicates that the kNDVI from June to October is linked to the average temperature or precipitation in April and each month through September. Similar principles apply for other cases [48].

3. Results

3.1. The Spatiotemporal Variation of Compound Extreme Events

In the northern permafrost region, the number of days with HP events ranges from 20 to 60 days, and the number of days decreases with increasing latitude (Figure 2a). In 58% of the area, there is an increasing trend, with an average increase rate of 0.08 days/year, mostly observed in higher latitude regions. However, in the southern parts of Canada, Central Alaska, Southern Russia, and Northern Mongolia, the number of HP days shows a decreasing trend (Figure 3a). The number of days for CD events is concentrated between 30 and 80 days, with a higher occurrence in Eastern and Southern Russia, as well as Mongolia (Figure 2b). Among these, 70.6% of the area shows a decreasing trend, with an average decrease rate of 0.18 days/year, and the reduction rate is faster at higher latitudes (Figure 3b). The number of days for HD events ranges from 30 to 40 days, with higher occurrences in Central Russia and Northern Mongolia (Figure 2c). In 67% of the area, an increasing trend is observed, with an average increase rate of 0.14 days/year, with Siberia showing the fastest increase rate (Figure 3c). CP events last for 5 to 15 days, with higher occurrences in parts of Western Russia and Alaska (Figure 2d). In 57.2% of the area, a decreasing trend is observed, with an average decrease rate of 0.06 days/year, mainly concentrated in higher latitude regions (Figure 3d). In summary, in the Arctic high-latitude regions, temperatures are rising, and precipitation is increasing. In Northern Siberia, Northern Mongolia, and Northeastern China, temperature rise and drought intensification are most pronounced, with an increasing trend in compound events related to high temperatures. In Western Russia, temperatures are decreasing, precipitation is decreasing, and drought intensifies, whereas in Eastern Russia, Alaska, and Southern Canada, temperatures are decreasing, and precipitation is increasing, showing a decreasing trend in compound events related to low temperatures.

3.2. Cumulative and Lagged Effects of Compound Extreme Events on Growing Season Vegetation in Permafrost Regions

The temporal effects of compound extreme events on kNDVI vary across different vegetation types. The regions with no temporal effects on vegetation from different compound extreme climate events are mainly located in the Arctic. Although current-month climate exerts the most substantial impact on vegetation across all types, there are notable differences depending on the specific compound event. For instance, under HP events, current-month conditions influence 40% of the region, while a one-month cumulative effect is observed in 24.7% of the area, particularly affecting shrubland, followed by wetlands. HP events also show a complex cumulative and lag effect on forests, with varying impacts: 18.3% of the area shows no temporal effect, 19% a one-month cumulative effect, 16.9% a one-month lag effect, 15.5% a two-month lag effect, and 16.5% a two-month lag with a one-month cumulative effect. During CD events, current-month climate effects are dominant, especially in shrubland and wetlands. For HD events, current-month conditions primarily affect vegetation, with 17.4% of the region showing a one-month cumulative effect, again with the strongest impact on shrubland, followed by grasslands. Under CP events, wetlands and shrubland are most affected, with 59.6% of the area showing no temporal effect, 10.3% displaying a one-month cumulative and one-month lag effect, and 13.4% exhibiting a three-month lag effect (Figure 4 and Figure 5).

4. Discussion

4.1. Spatiotemporal Analysis of Compound Extreme Events in Northern Permafrost Regions

Extreme events occurring independently can cause significant losses, but compound extreme events tend to amplify these impacts. Consequently, focusing only on individual extreme events may underestimate the effects of compound events [49]. This study finds that in the northern permafrost regions, compound extreme events associated with extreme high temperatures exhibit an increasing trend, while those associated with extreme low temperatures show a decreasing trend. This observation aligns with the overall trends in extreme temperature changes in the northern permafrost regions [50], as well as with previous findings on compound extreme events [51,52].
In permafrost regions, compound events involving extreme high temperatures and extreme precipitation have increased [51,53]. Although the frequency of compound events involving extreme high temperatures and extreme drought is relatively low, the trend has risen significantly, particularly in Russia [6]. In these areas, strong connections exist among temperature, drought (precipitation), and soil moisture [54]. Climate warming, which leads to rising extreme temperatures, often triggers cascading effects, exacerbating other extreme events such as droughts or wildfires [55]. Additionally, as soil moisture decreases, evaporation and transpiration are reduced, while increased solar radiation and sensible heat raise surface temperatures. This process can lead to or intensify heatwave events that may persist for extended periods [55,56]. In contrast, precipitation-related extreme climate events are generally shorter in duration, typically driven by atmospheric low-pressure systems and fronts [57,58,59]. Under hot conditions, high temperatures and humidity create favorable conditions for moisture convergence, making strong winter rainfall events more common [60]. Furthermore, high temperatures and humidity are closely linked to convective available potential energy, which induces atmospheric instability, leading to convection and stormy weather [61]. As a result, in high-latitude regions, the likelihood of extreme rainfall occurring after heatwaves is approximately four times higher [14,62,63]. Global warming exacerbates heatwaves and synchronizes the occurrence of extreme precipitation and heatwaves, significantly increasing the frequency of compound events involving extreme high temperatures and extreme precipitation [64,65]. Currently, we lack a robust methodological framework for assessing the risks of cascading disasters, especially when primary drivers are difficult to identify using statistical data (e.g., extreme rainfall in areas burned by wildfires a year earlier) [55]. As climate change intensifies, further understanding and recognition of the connections and interdependencies between extreme events are crucial.

4.2. Cumulative and Lagged Effects of Compound Extreme Events on Vegetation Growth During the Growing Season

By analyzing the cumulative and lagged effects of different compound extreme events on vegetation growth during the growing season, this study confirms the existence of an asymmetrical temporal relationship between compound extreme events and vegetation in the northern permafrost regions. Considering both lagged and cumulative effects can improve the prediction accuracy of vegetation growth under the influence of compound extreme events. The results indicate that compound extreme events exert significant lagged effects on vegetation, with the primary cumulative effect occurring over a one-month period. Compound extreme events associated with high temperatures have a greater impact on vegetation than those associated with low temperatures. These findings contribute to understanding the varied vegetation responses to compound extreme events across different land cover types and climatic conditions.
In the colder northern high-latitude permafrost regions, individual extreme temperature events impact vegetation within the same month, without cumulative or lag effects. Under compound dry heat conditions, the likelihood of vegetation decline is significantly reduced [66]. Higher temperatures may lead to the melting of snow and glaciers, increasing surface runoff and promoting vegetation growth in lower catchments [67]. However, when extreme temperatures combine with extreme precipitation, cumulative and lag effects on vegetation occur, indicating that vegetation growth does not directly respond to precipitation but rather to soil moisture. Therefore, accumulated heat and moisture over time may have a more pronounced effect on plants, soils, and ecosystem carbon exchange than current climate conditions [46].
Different land cover types exhibit various time accumulation or lag effects in response to compound extreme climates. The cumulative effect of extreme high-temperature compound events across all land cover types shows a one-month period. Forests typically exhibit a longer lag time in responding to compound extreme climates, primarily due to the distinct growth cycles and physiological characteristics of trees [68]. Grasslands and shrublands are more affected by extreme high temperatures and extreme drought, while forests and wetlands are relatively less impacted [31]. Drought is one of the most destructive environmental stresses, severely altering plant yield, growth rates, biomass, and productivity [69]. Grasslands and shrublands, with shallower root systems, are more sensitive to changes in water and temperature, whereas deeper-rooted forests can access water from deeper soil layers, making them less susceptible to drought and heat events [70]. Therefore, the responses of vegetation growth to compound extreme events are closely related to vegetation physiological characteristics, with varying sensitivity and vulnerability to dry heat conditions [66].

4.3. Future Outlook

There are some potential uncertainties in the results of this study. Firstly, there are several standards and methods to define and classify extreme climate events, and the choice of definitions and classifications may influence the interpretation and applicability of the findings. Secondly, the response of different vegetation types to climate change may exhibit heterogeneities. While we analyzed the responses of various land cover types, the same land cover type may still respond differently to climate change in different regions. Thirdly, using the empirical copula method, the joint probability and return period of these compound events can be estimated, and the correlation between different variables can be evaluated. Although the statistical methods used in this study are widely applied and can reveal the importance of complex extreme events [2,5,6], we emphasize that additional analytical methods should be explored in the future to quantitatively describe the effects of extreme events on vegetation growth.

5. Conclusions

This study systematically analyzed the spatiotemporal distribution and trends of compound extreme events in the Northern permafrost regions from 1982 to 2022 and explored the cumulative and lag effects of these compound extreme events on vegetation growth. Our findings revealed that the four types of compound extreme events in the Northern Hemisphere’s permafrost regions exhibit distinct trends, with significant differences in their impacts on vegetation. The effects of compound extreme high-temperature events on vegetation are more complex than those of individual extreme events, especially for the extreme high-temperature-precipitation compound event, which has the most pronounced cumulative and lag effects on vegetation. In contrast, extreme low-temperature-drought compound events have an immediate impact on wetlands and shrubs, indicating that compound events associated with precipitation have more complex effects on vegetation. These results fill the knowledge gap regarding the impacts of compound extreme events on vegetation in permafrost regions. In the future, more attentions should be paid on the impacts of precipitation on vegetation in permafrost regions. Overall, our findings provide new insights into the mechanisms of vegetation growth in the context of climate warming and increasing compound extreme events.

Author Contributions

Y.D.: Writing—review and editing, Writing—original draft, Visualization, Validation, Data curation. G.L.: Writing—review and editing, Supervision, Resources. X.W.: Writing—review and editing, Supervision, Investigation, Funding acquisition. L.W.: Writing—review and editing. H.X.: Writing—review and editing. S.Y.: Writing—review and editing. T.W.: Writing—review and editing. E.A.: Writing—review and editing, Funding acquisition. J.Z.: Writing—review and editing. X.C.: Writing—review and editing. M.S.: Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2020YFA0608500) for Tonghua Wu, National Natural Science Foundation of China (42430412, 32061143032, W2412013) for Xiaodong Wu, National Natural Science Foundation of China (42261025) for Guimin Liu, National Natural Science Foundation of China (32361133551) for Sizhong Yang, Russian Science Foundation (24-44-00006) for Evgeny Abakumov, Gansu Provincial Science and Technology Program (22ZD6FA005) for Tonghua Wu, and West Light Foundation of the Chinese Academy of Sciences for Tonghua Wu.

Data Availability Statement

The data can be found in [71,72,73,74].

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution map of permafrost in the Northern Hemisphere (a) and vegetation type distribution map (b).
Figure 1. Distribution map of permafrost in the Northern Hemisphere (a) and vegetation type distribution map (b).
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Figure 2. Spatial distribution of compound extreme events in the permafrost regions from 1982 to 2022. (a) HP, extreme high temperature–precipitation compound event; (b) CD, extreme low temperature–drought compound event; (c) HD, extreme high temperature–drought compound event; (d) CP, extreme low temperature–precipitation compound event.
Figure 2. Spatial distribution of compound extreme events in the permafrost regions from 1982 to 2022. (a) HP, extreme high temperature–precipitation compound event; (b) CD, extreme low temperature–drought compound event; (c) HD, extreme high temperature–drought compound event; (d) CP, extreme low temperature–precipitation compound event.
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Figure 3. Trends of compound extreme events in the permafrost regions from 1982 to 2022. (a) HP, extreme high temperature–precipitation compound event; (b) CD, extreme low temperature–drought compound event; (c) HD, extreme high temperature–drought compound event; (d) CP, extreme low temperature–precipitation compound event.
Figure 3. Trends of compound extreme events in the permafrost regions from 1982 to 2022. (a) HP, extreme high temperature–precipitation compound event; (b) CD, extreme low temperature–drought compound event; (c) HD, extreme high temperature–drought compound event; (d) CP, extreme low temperature–precipitation compound event.
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Figure 4. Cumulative and lag effects of compound extreme events on kNDVI in permafrost regions from 1982 to 2022. This figure illustrates the cumulative and lag effects of compound climate indices on kNDVI across permafrost areas, categorized by the following events: (a) HP, extreme high temperature–precipitation compound events, (b) CD, extreme low temperature–drought compound events, (c) HD, extreme high temperature–drought compound events, and (d) CP, extreme low-temperature–precipitation compound events. The a represents months of lag, and the b represents months of cumulative effects. For example, “0–0” indicates no time effect, and “1–1” indicates one-month lag and one-month cumulative effects.
Figure 4. Cumulative and lag effects of compound extreme events on kNDVI in permafrost regions from 1982 to 2022. This figure illustrates the cumulative and lag effects of compound climate indices on kNDVI across permafrost areas, categorized by the following events: (a) HP, extreme high temperature–precipitation compound events, (b) CD, extreme low temperature–drought compound events, (c) HD, extreme high temperature–drought compound events, and (d) CP, extreme low-temperature–precipitation compound events. The a represents months of lag, and the b represents months of cumulative effects. For example, “0–0” indicates no time effect, and “1–1” indicates one-month lag and one-month cumulative effects.
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Figure 5. Area proportion of cumulative and lag effects of compound extreme climate indices on kNDVI in permafrost regions from 1982 to 2022. This figure shows the distribution of area proportions for the cumulative and lag effects of various compound extreme events on kNDVI. (a) Forest, (b) wetland, (c) shrubbery, and (d) grassland. HP, extreme high temperature–precipitation compound events. CD, extreme low temperature–drought compound events. HD, extreme high temperature–drought compound events. and CP, extreme low–temperature-precipitation compound events. The a represents months of lag, and the b represents months of cumulative effects. For example, “0–0” indicates no time effect, and “1–1” indicates one-month lag and one-month cumulative effects.
Figure 5. Area proportion of cumulative and lag effects of compound extreme climate indices on kNDVI in permafrost regions from 1982 to 2022. This figure shows the distribution of area proportions for the cumulative and lag effects of various compound extreme events on kNDVI. (a) Forest, (b) wetland, (c) shrubbery, and (d) grassland. HP, extreme high temperature–precipitation compound events. CD, extreme low temperature–drought compound events. HD, extreme high temperature–drought compound events. and CP, extreme low–temperature-precipitation compound events. The a represents months of lag, and the b represents months of cumulative effects. For example, “0–0” indicates no time effect, and “1–1” indicates one-month lag and one-month cumulative effects.
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Dong, Y.; Liu, G.; Wu, X.; Wang, L.; Xu, H.; Yang, S.; Wu, T.; Abakumov, E.; Zhao, J.; Cui, X.; et al. Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022. Remote Sens. 2025, 17, 169. https://doi.org/10.3390/rs17010169

AMA Style

Dong Y, Liu G, Wu X, Wang L, Xu H, Yang S, Wu T, Abakumov E, Zhao J, Cui X, et al. Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022. Remote Sensing. 2025; 17(1):169. https://doi.org/10.3390/rs17010169

Chicago/Turabian Style

Dong, Yunxia, Guimin Liu, Xiaodong Wu, Lin Wang, Haiyan Xu, Sizhong Yang, Tonghua Wu, Evgeny Abakumov, Jun Zhao, Xingyuan Cui, and et al. 2025. "Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022" Remote Sensing 17, no. 1: 169. https://doi.org/10.3390/rs17010169

APA Style

Dong, Y., Liu, G., Wu, X., Wang, L., Xu, H., Yang, S., Wu, T., Abakumov, E., Zhao, J., Cui, X., & Shao, M. (2025). Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022. Remote Sensing, 17(1), 169. https://doi.org/10.3390/rs17010169

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