Normalized Difference Vegetation Index Anomalies Research Articles

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Spatial-temporal dynamics of paddy productivity on the north coast of Java Island, Indonesia based on the principal component analysis of MODIS NDVI anomaly data

Comprehending the dynamics of paddy productivity is imperative for enhancing the efficacy of agricultural land developments. This study provides the application of principal component analysis (PCA) as a method for visualizing the spatial-temporal changes in paddy productivity. The analysis is conducted using the 8-day NDVI (normalized difference vegetation index) anomaly data of MODIS (Moderate Resolution Imaging Spectroradiometer) data spanning the period from 2000 to 2020. The regencies of Karawang, Subang, and Indramayu on the north coast of Java island are chosen as the study area because of their top rice production areas in Indonesia. The results show that the first leading PCA of the NDVI anomaly is related to the interannual variability of paddy productivity with 3-4 year cycles. The spatial and temporal dynamics of the first mode of eigenvectors and principal component time series can generally be grouped into nine categories. Two important categories to note are category-1 (1 January – 19 March) and category-8 (12 September – 16 December). In category-1, the NDVI anomalies move from north to middle and middle to north areas in Karawang and Subang regencies, respectively. In Indramayu Regency, the NDVI anomalies relatively remain in almost all areas. In contrast, in category-8 the NDVI anomalies move from the middle to northern areas in Karawang, Subang, and Indramayu regencies.

Drought thresholds that impact vegetation reveal the divergent responses of vegetation growth to drought across China.

Identifying droughts and accurately evaluating drought impacts on vegetation growth are crucial to understanding the terrestrial carbon balance across China. However, few studies have identified the critical drought thresholds that impact China's vegetation growth, leading to large uncertainty in assessing the ecological consequences of droughts. In this study, we utilize gridded surface soil moisture data and satellite-observed normalized difference vegetation index (NDVI) to assess vegetation response to droughts in China during 2001-2018. Based on the nonlinear relationship between changing drought stress and the coincident anomalies of NDVI during the growing season, we derive the spatial patterns of satellite-based drought thresholds (T SM ) that impact vegetation growth in China via a framework for detecting drought thresholds combining the methods of feature extraction, coincidence analysis, and piecewise linear regression. The T SM values represent percentile-based drought threshold levels, with smaller T SM values corresponding to more negative anomalies of soil moisture. On average, T SM is at the 8.7th percentile and detectable in 64.4% of China's vegetated lands, with lower values in North China and Jianghan Plain and higher values in the Inner Mongolia Plateau. Furthermore, T SM for forests is commonly lower than that for grasslands. We also find that agricultural irrigation modifies the drought thresholds for croplands in the Sichuan Basin. For future projections, Earth System Models predict that more regions in China will face an increasing risk for ecological drought, and the Hexi Corridor-Hetao Plain and Shandong Peninsula will become hotspots of ecological drought. This study has important implications for accurately evaluating the impacts of drought on vegetation growth in China and provides a scientific reference for the effective ecomanagement of China's terrestrial ecosystems.

Decoupling of Ecological and Hydrological Drought Conditions in the Limpopo River Basin Inferred from Groundwater Storage and NDVI Anomalies

Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources.

Assessment of vegetation variation and its response to ENSO and IOD in the semi-arid ecosystem of Western India

Abstract The variation in the vegetation pattern reflects the change in the regional environment. Normalized Difference Vegetation Index (NDVI) data from 2000 to 2022 for the Upper Bhima sub-basin in Western India has been used to identify the response of vegetation to the El Niño-Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) events. As a novelty, the present study identifies the ENSO-sensitive and IOD-sensitive vegetation areas within the watershed using vegetation mean to difference anomalies. Monthly NDVI anomalies are used to determine sensitive pixels of vegetation using mean monthly NDVI. Local spatial autocorrelation (LISA) is performed to analyze the pattern of the NDVI anomalies and cluster maps are generated. The results of spatial variation show that NDVI is adversely affected in El Niño years. During La Niña years, the percentage area covered by dense vegetation is more than 80%, which is significantly higher than that of El Niño years in the monsoon and post-monsoon periods. Positive IOD years show significantly more sparse vegetation cover than negative IOD years. The results of LISA analysis show that the rainfall shadow zone in the study area has a cluster of negative sensitive pixels even in the monsoon and post-monsoon period except in negative IOD year.

Monitoring vegetation sensitivity to drought events in China

The frequency and severity of drought events have increased over the decades under the influence of global warming. Continued drought increases the risk of vegetation degradation. Many studies have investigated the responses of vegetation to drought but rarely from the perspective of drought events. Moreover, the spatial distributions of vegetation sensitivity to drought events are not well understood in China. Thus, the spatiotemporal patterns of drought events were quantified based on the run theory at different time-scales in this study. The relative importance of drought characteristics for vegetation anomalies during drought events were calculated by using the BRT model. Then, the sensitivity of vegetation anomalies and vegetation phenology was quantified by dividing standardized anomalies of vegetation parameters (NDVI and phenological metrics) and SPEI during drought events for different regions in China. The results show that Southern Xinjiang and Southeast China experienced relatively higher values of drought severity, especially at the 3-month and 6-month scales. Most arid areas experienced more drought events but of low severity, while some humid zones underwent few drought events but of high severity. Notable negative NDVI anomalies appeared in the Northeast China and Southwest China, while positive NDVI anomalies were observed in Southeast China and Northern central region. Drought interval, intensity and severity contributed approximately 80 % of the model's explained vegetation variance in most regions. The sensitivity of vegetation anomalies to drought events (VASD) varied regionally in China. The Qinghai-Tibet Plateau and Northeast China tended to exhibit higher sensitivity to drought events. Vegetation in these regions with high sensitivity faced a high risk of degradation and could function as warning signals of vegetation degradation. Drought events at high timescales had a greater impact on vegetation sensitivity in dry zones, while they had a smaller impact on humid areas. With the increase in drought degree of climate zones and the decrease in vegetation coverage, VASD showed a gradual increase. Furthermore, a strong negative correlation between VASD and the aridity index (AI) was observed in all vegetation types. The change in VASD for sparse vegetation was the largest with the change in AI. For vegetation phenology, drought events in most regions delayed the end of the growing season and extended the length of growing season, especially for sparse vegetation. The start of the growing season was advanced in most humid areas, while being delayed in most dry areas during drought events. Knowledge of vegetation sensitivity to drought events will be beneficial to provide decision-making references for the prevention and control of vegetation degradation, especially in the ecological fragile regions.

Andean peatlands at risk? Spatiotemporal patterns of extreme NDVI anomalies, water extraction and drought severity in a large-scale mining area of Atacama, northern Chile

Andean peatlands at risk? Spatiotemporal patterns of extreme NDVI anomalies, water extraction and drought severity in a large-scale mining area of Atacama, northern Chile

Remote Sensing Assessment and Modeling of the Spatial Dynamics of Tree Stand Disturbance after the Impact of Siberian Silk Moth (Dendrolimus sibiricus)

In this study, we have analyzed tree stand disturbance by hthe Siberian Silk Moth (Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae)) in Central Siberia (Krasnoyarsk region, Russia) in 2015–2020. We considered two plots that experienced silk moth outbreaks in 2015–2018 and 2018–2020 and used satellite data (Terra/MODIS, Landsat/ETM/OLI), field forest inventory data, a meteorological data set, and a vegetation cover vector layer. Silk moth-disturbed areas were classified using NDVI, which was calculated for each 15-day period during the growing season (April–September). We obtained formalized descriptions of the temporal dynamics of the disturbed area. Next, we classified the degree of disturbance of the forest stand after the impact of the silk moth by the threshold method according to the ranges of NDVI anomalies. Based on the generalized data from the forest inventory, we performed a correlation analysis of the relationship between the main characteristics of forests and the classes of disturbance. Finally, using a series of regression equations, we described a procedure for predicting the degree of impact on the stand during the time of silk moth outbreaks in the dark-needle coniferous stands of Central Siberia.

How long is the memory of forest growth to rainfall in asynchronous climates?

The out-of-phase rainfall and temperature and deep root system make the sequential connection between past rainfall events, soil water storage, and forest growth response complicated and temporally extended in asynchronous climates with Mediterranean-type settings. Unfortunately, these location-specific deep-soil water stores are rarely measured due to logistic and financial constraints, especially in the forest. Therefore, at a large spatio-temporal scale, forest growth relationship to growth drivers is still unknown in these ecosystems, limiting our knowledge to understand the functioning of these forests and their links with hydrological processes. Although process-based water balance models can analyze vegetation growth response to the input climate forcing, they rely upon some significant assumptions regarding plants access to deep soil water storage. Thus, this study aims to understand how the out-of-phase rainfall events affect the current ecosystem growth response, represented by the observed Normalized Difference Vegetation Index (NDVI), across the landscape. We have used an empirical approach on long term observed data without any assumption on access to deep-soil water stores. We estimated time lags between forest growth and rainfall events using a lagged correlation analysis applied to monthly anomalies of NDVI and rainfall against their climatological averages over 2002–2018. The study found that the forests in asynchronous climates exhibit unexpectedly long (10–25 months) memory to rain, and this memory has a systematic pattern across the landscape, which we contend has highlighted three things: 1) the forest in the middle aridity (∼3–4.5) range are relatively more sensitive to changes in the short-term rainfall than the forest in lower (<3) and higher (>5) aridity regions, could be due to rapid depletion of relatively small soil water storage in between the storms, 2) the variable memory of forest to rain across the landscape can be an indicator of soil depth/rooting depth, and 3) the variable sized location specific antecedent rainfall windows can explain significant variability in forest growth status in asynchronous climates, thus these rainfall windows can be employed to forecast forest growth with a lead time (>4 months).

Contrasting 20-year trends in NDVI at two Siberian larch forests with and without multiyear waterlogging-induced disturbances

The fate of a boreal forest may depend on the trend in its normalized difference vegetation index (NDVI), such as whether the NDVI has been increasing significantly over the past few decades. In this study, we analyzed the responses of two Siberian larch forests at Spasskaya Pad and Elgeeii in eastern Siberia to various waterlogging-induced disturbances, using satellite-based NDVI and meteorological data for the 2000–2019 period. The forest at Spasskaya Pad experienced waterlogging (i.e. flooding events caused by abnormal precipitation) during 2005–2008 that damaged canopy-forming larch trees and increased the abundance of water-resistant understory vegetation. By contrast, the forest at Elgeeii did not experience any remarkable disturbance, such as tree dieback or changes in the vegetation community. Significant increasing NDVI trends were found in May and June–August at Elgeeii (p < 0.05), whereas no significant trends were found at Spasskaya Pad (p > 0.05). NDVI anomalies in May and June–August at Elgeeii were significantly associated with precipitation or temperature depending on the season (p < 0.05), whereas no significant relationships were found at Spasskaya Pad (p > 0.05). Thus, the 20 year NDVI trend and NDVI–temperature–precipitation relationship differed between the two larch forests, although no significant trends in temperature or precipitation were observed. These findings indicate that nonsignificant NDVI trends for Siberian larch forests may reflect waterlogging-induced dieback of larch trees, with a concomitant increase in water-resistant understory vegetation.

Reconstruction of summer NDVI over the past 339 years based on the tree-ring width of Picea schrenkiana in Bayinbuluke, Central Tianshan, China

The normalized difference vegetation index (NDVI) is widely used in various fields of vegetation research. Due to the short observation time, however, it is difficult to meet the research needs at long time scale. Here, we established a tree-ring width chronology (STD) based on Picea schrenkiana in Bayinbuluke, and calculated the correlation coefficient of chronology and NDVI with meteorological data. The results showed that both tree-ring width index and NDVI were significantly correlated with meteorological data. Combined with the significant positive correlation between width chronology and NDVI in June-August (r=0.7, P<0.01, n=38), summer NDVI (from June to August) was reconstructed over the past 339 years using a regression model. During 1680-2018, the reconstruction series had four dense vegetation periods (1738-1765, 1786-1798, 1964-1973 and 2000-2018) and five sparse vegetation periods (1690-1714, 1825-1834, 1850-1880, 1895-1920 and 1945-1955). The reconstruction reflected the hydrological signals in the central Tianshan Mountains. The comparison with the surrounding reconstructions revealed that when the runoff of Kaidu River increased and the local environment was humid, the vegetation coverage was high; otherwise the vegetation coverage was low. The extreme value of the reconstruction series also captured a series of natural disasters recorded in historical documents. Results of HYSPLT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) backward trajectory model and wind field analysis showed that NDVI anomalies were affected by the precipitation from Westerlies.

Dust storm index anomaly for sand-dust events monitoring in western Iran and its association with the NDVI and LST anomalies

Sand-dust events (SDE) are an increasing concern in many arid and semi-arid regions of the world, which have severely damaged air quality and human health in recent years. This study was conducted to monitor the SDE in western Iran using the dust storm index anomaly (DSIA) during 2000-2018. The spatio-temporal change detection and statistical analysis were used to understand the impacts of normalized difference vegetation cover anomaly (NDVIA) and land surface temperature anomaly (LSTA) on the SDE activities. The area has suffered from the highest dust pollution in 2004, 2009, and 2012 (DSIA>+40) while it experienced the lowest dust pollution in 2002 and 2017 (DSIA<-40). Approximately 48% of western Iran experienced decreasing changes and 52% of the total area experienced increasing changes in dust pollution during 2010-2018 compared to the previous years. Incremental changes in NDVIA and LSTA were observed in 73.2% and 7.5% of the study area while their decreasing changes were observed in 26.8% and 92.5% of the total area, respectively. Spatially, regions affected by the increase in dust pollution are mainly distributed in the eastern and southern regions of the study area. Significant effects of changes in anomalies of both terrestrial parameters on DSIA were observed throughout the study period ((RLSTA-DSIA= +0.52; RNDVIA-DSIA= -0.41); P<0.05). It was also found that spatial correlation between LSTA and DSIA, as well as NDVIA and DSIA in many parts of the study area, was significant at the 95% confidence level (|R| > 0.45). These findings can be useful for decision-makers to assess the risks of dust pollution and reduce its negative consequences in western Iran.

A landscape assessment of frost damage in the northmost Neotropical dry forest

Extreme climatic and weather events are inducing widespread tree mortality in many forest ecosystems worldwide. A severe frost related to the historic cold wave that affected much of North America in early February 2011 triggered extensive tree mortality in the lowland tropical dry forest in northwestern Mexico. In this landscape, we assessed frost-induced vegetation damage using anomalies of the widely used NDVI spectral index calculated with a 12-year pre-disturbance reference period. In addition, NDVI anomalies were related to ground-based measures of frost-disturbance severity (density of frost-killed trees) and forest recovery (post-frost recruits and resprouting of surviving trees) obtained in 48 1-ha study plots distributed across the landscape. We found negative NDVI anomalies in 84% of our study area, indicating widespread frost-induced vegetation damage. The relationship between NDVI anomalies and density of frost-killed trees confirmed the level of frost damage across the lowland vegetation, suggesting that even small deviations from long-term mean NDVI values may hint to severe disturbance on forest structure and function. Higher frost-disturbance severity stimulated more new recruitment and less tree resprouting. Our results demonstrate the reliability of spectral indices for rapid assessment of climate-induced vegetation change, the high vulnerability of tropical dry forests to extreme frost and the predominant mechanism of vegetation recovery after severe frost disturbance.

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study.

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands.

Climate drivers of Arctic tundra variability and change using an indicators framework

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Anomalous arctic polar vortex-induced spring vegetation variability and lagged productivity responses in China

The Arctic polar vortex (APV) system plays an important role in controlling winter and spring atmospheric circulation pattern in China. Here, we evaluate the anomalous APV-induced spring vegetation variability and lagged productivity responses in China. We found that both strong and weak APV conditions have almost equally negative impacts on spring vegetation growth in China, e.g., negative NDVI anomalies occurred in 48.6 and 53.2% of China’s vegetated areas under strong and weak APV conditions, respectively. However, large seasonal compensation effects were associated with weak APV conditions, and beneficial lagged vegetation productivity responses occurred in 67.2% of China’s vegetated areas, whereas adverse responses occurred in only 32.8% of vegetated areas. Under a strong APV, adverse lagged vegetation productivity responses occurred in 54.5% of China’s vegetated areas, whereas beneficial responses occurred in only 45.5% of vegetated areas. The temperature, precipitation, and solar radiation changes caused by anomalous APV-induced changes in circulation patterns are the main reasons for spring vegetation variability. The lagged vegetation productivity responses were attributed to anomalous APV-related precipitation and air temperature anomalies in the following summer and autumn. This improved understanding of the strong links between APV anomalies and vegetation dynamics in China should facilitate early warning of vegetation productivity reductions under anomalous APV conditions.

Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.

Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSIPM) and 0.2 mm/decade (scPDSITH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies.

Abiotic disturbances affect forest short-term vegetation cover and phenology in Southwest China

Forest disturbances significantly affect the global carbon cycle by, for example, vegetation loss or changing forest phenology. However, the lack of historical disturbance events constitutes a challenge for in-depth temporal and spatial analysis. Available remote sensing time series and combined climate data may have great potential to quickly and consistently detect and identify forest disturbances events. We employed time-series data (2001–2014) of a vegetation index (normalized difference vegetation index, NDVI) and a change detection algorithm (the breaks for additive seasonal and trend, BFAST) to detect forest disturbances in a sub-tropical area located in Southwest China. Remote sensing and meteorological data were combined to distinguish among the typical forest disturbances: fires, extreme cold events in winter (ECE), and droughts. With the reconstructed historical disturbance events, post-disturbance vegetation loss, short-term vegetation cover, and phenology changes were analyzed. Our results show that fires and droughts caused severe damage to forest cover (NDVI anomalies can reach up to −1.84 and −1.11, respectively). Fire changed the regular phenological periods which last 3–4 years, and it also took 1–2 years for vegetation greenness to recover after ECE and droughts, which triggered carbon emissions and reduced forest stocks. Warmer areas were vulnerable to ECE effects as well and should be paid more attention. Post-disturbance effects show complex patterns: characteristics of disturbances, climatic conditions, and multiple events overlaying contribute to modifying forest vegetation. Hence, forest disturbances cannot be neglected but should be emphasized in future forest ecosystem modeling or analyzing. The approach used in the study can be a crucial step in detecting and assessing the effects of various disturbances on forest vegetation and phenology and, thereby, contributes to improved risk analysis and management in forestry.

A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa.

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the “Crisis” phase of AFI—requiring emergency food assistance—at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.