Vegetation Condition Index Research Articles

An Analysis of Spatial–Temporal Evolution and Propagation Features of Vegetation Drought in Different Sub-Zones of China

The vegetation drought phenomenon will reduce the amount of water available to the vegetation system, change the ecological and hydrological cycles of plants, and affect the aquatic and terrestrial ecosystem in various forms. Therefore, research on the dynamic variation and driving mechanism of vegetation drought will help us recognize and predict the response of vegetation under drought stress conditions, implement appropriate policy measures to deal with the drought crisis, and provide technical support for implementing vegetation protection and alleviating the increasing risk of vegetation drought. However, the dynamic variation of vegetation drought and its dynamic propagation mechanism are still undefined across China. In this study, the spatio-temporal evolutions and pixel-scaled trends of vegetation drought were analyzed during the period between 1999 and 2020. Additionally, the propagation features were investigated between vegetation drought and meteorological drought. Finally, the relationships between vegetation drought and atmospheric teleconnection were explicitly clarified using multivariate cross wavelet transform technology. The results highlighted five key findings: (1) the vegetation drought presented an overall decreasing trend across China in 1999–2020; (2) the most serious vegetation drought occurred in the year 2000, with the average vegetation condition index (VCI) values ranging from 0.36 to 0.46; (3) vegetation droughts were alleviating at the pixel scale for each season; (4) the propagation time from meteorological drought to vegetation drought was shorter in summer (1.26 months) and longer in winter (2.26 months); and (5) the three-factors combination of Pacific North American (PNA), El Niño-Southern Oscillation (ENSO), and Trans Polar Index (TPI) can satisfactorily explain the variations of vegetation drought. This study sheds new viewpoints into the identification of vegetation drought variation across China, which can also be applied in other areas.

Spectral Drought Indices: A Methodology for Detecting Basin-Wide Drought

Drought is a normal disaster that profoundly impacts several factors, including the economy, agriculture, environment, and society. Using spectral indices from Landsat, the current research evaluated droughts' magnitude and frequency in the Diyala River Catchment, Iraq, during the growing season for 2013 through 2022. Fifteen mosaics were created over ten years using forty images in TM and ETM+ (167/36 and 168/37) that had been gathered from 2013 to 2022. The scenes are from two Landsat time series. The drought situation was assessed using the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index, and Normalized Difference Water Index. The research's findings revealed a rise in the DRC's occurrence and severity of drought over the past ten years, especially in 2000 and 2008. However, between 2021 and 2022, the total vegetation covered based on NDVI decreased by 20.67% and 36.33%, respectively. In 2021 and 2022, the vegetation cover significantly decreased (54.3% and 29.5%, respectively). Derbendekan and Hemrin lakes, conversely, experienced declines of 15.1%, 7.12%, 108.38%, and 143.33% in 2021 and 2022, respectively. Between 2013 and 2022, the DRC experienced an increase in drought, a decline in water body surface area, and a decrease in precipitation averages.

Investigating the effectiveness of Landsat-8 OLI and Sentinel-2 MSI satellite data in monitoring the effects of drought on surface water resources in the Western Cape Province, South Africa

Drought frequency and magnitude are increasing, posing significant risks to surface water bodies, particularly in arid and semi-arid regions. Leveraging on moderate resolution satellite data, such as Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), offers unique opportunities for monitoring surface water availability and drought impacts. In this study, we assessed the extent to which Landsat-8 OLI and Sentinel-2 MSI satellite data can be used to characterise and monitor the impacts of drought on water resources in the Western Cape, South Africa. Multispectral indices including Normalised Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), and the Land Surface Water Index (LSWI+5) were computed to determine the most suitable method for surface water detection and drought monitoring. Sentinel-2-derived NDVI emerged as the most suitable index for mapping surface waterbodies, demonstrating an overall accuracy of 77.27%. However, LSWI+5 exhibited limitations, misclassifying built-up and mountainous areas as surface waterbodies. SPI, VCI, and WRSI effectively identified drought periods and correlated well with climate data, revealing low rainfall and high evapotranspiration rates during the 2016–2018 drought period. These findings provide valuable insights into surface water variability and the impacts of drought on water resources. Overall, our study underscores the feasibility of utilizing moderate resolution datasets to assess drought impacts on surface water resources, enabling improved water resource management, drought detection, and preparedness efforts in resource limited environments.

Resposta da vegetação semiárida à seca agrícola determinada por índices derivados do satélite MODIS

ABSTRACT Indices based on data from remote sensors play an important role in the characterization, mapping and monitoring of a drought event. Thus, the objective of this study was to monitor the dynamics of agricultural drought, through the response of vegetation, in the semi-arid region that comprises the state of Paraíba, Brazil, through MODIS satellite products applied to the VCI (vegetation condition index), TCI (temperature condition index) and VHI (vegetation health index) indices, and to evaluate the correspondence of VHI index with standardized precipitation index (SPI) and agricultural standardized precipitation index (aSPI). In this study, values of NDVI (normalized difference vegetation index) and LST (land surface temperature) - covering the period between 2010 and 2020 - were used to estimate VCI, TCI and VHI. In addition, the correspondence of VHI with SPI and aSPI was evaluated at the 12-month time scale, conducted using Pearson’s correlation analysis. Characteristics of a stressed vegetation predominated in the study region, due to irregularity of precipitation and high temperatures, confirming the possibility of detection of droughts through VHI, VCI and TCI, and that the indices detected remotely and through local data are strongly correlated in drought detection.

Evaluation of the Relationship between Spatio-Temporal Variability of Vegetation Condition Index (VCI), Fire Occurrence and Burnt Area in Mount Kenya Forest Reserve and National Park

Climate change, vegetation dynamics, human activities and forest management influence the occurrence of fires. This study investigated the spatio-temporal variability of the Vegetation Condition Index (VCI) and its influence on fire occurrence in three different land use types in Mount Kenya Forest Reserve and National Park (MKFRNP): National Park (NP), Forest Stations (FS) and Farmlands (FL). The study used MODIS satellite data to obtain the Normalized Difference Vegetation Index (NDVI), the VCI, the number of fires and the burnt area. The specific objectives of this research were (i) to examine the spatio-temporal variability of VCI, fire occurrence and burnt area in MKFRNP from 2003 to 2018 and (ii) to explore the relationship between VCI, fire occurrence and burnt area in different areas of the MKFRNP (NP, FS and FL). The findings show that even though fires occur throughout the year in MKFRNP, most of the fires occur during dry seasons. The relationship between spatio-temporal fire occurrence and VCI distribution is different for each land use type. In the FL, the probability of fire ignition and the number of fires per month was more or less the same irrespective of the VCI because of the traditional use of fire as a land management tool. However, the probability of fire ignition and the number of fires per month is high in the NP and FS when the VCI is below 50% (drought), especially in the dry seasons, when and where the impact of meteorological conditions and climate have much more impact than human activities. In addition to the efforts already made by communities, KFS and KWS in the fire fighting and monitoring system, satellite data can be useful to acquire accurate and timely information on the VCI and the likely spatio-temporal occurrence of fires in order to be prepared in the most fire-prone periods and improve fire management, the planning of resources and fire suppression activities in MKFRNP.

Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan

ContextCrop yield is a major agriculture sector affected by climate change; especially agrometeorological droughts experienced by south Asian countries in past decades. Research objectiveThe main goals of this research were to explore the spatiotemporal characteristics of seven agrometeorological drought indices at a regional scale in Pakistan. Secondly, to forecast the wheat yield loss risk (YLR) due to droughts under current and future climate scenarios by employing three machine learning (ML) methods; random forest (RF), gradient boosting machine (GBM), and generalized additive model (GAM). MethodThe relationship between detrended wheat yield and a combination of five remote sensing indices Normalized Difference Water Index (NDWI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and Drought Severity Index (DSI)), and two meteorological drought indices (Palmer’s Drought Severity Index (PDSI), and Standardized Precipitation Evapotranspiration Index (SPEI)) was analyzed. Mann-Kendall trend (MK), Sens’s slope, and Sequential Mann-Kendall (SQMK) tests are applied to explore the trend and trend-changing years for all indices over the historical time of 20 years.The YLR and all indices were projected (2021–2050) from the baseline period (2001–2020) using PROPHET time series forecasting and CMIP6 climatic models. YLR was forecasted on present and future projected time series by employing three non-linear ML regression models. ResultsThe output of the drought analysis revealed that the study area was hit by high to severe drought events in 2001–2004, 2006, 2008, 2010, 2012, and 2017. Trend analysis revealed intersection years breaking the rising trend of drought indices. All drought indices are significantly correlated with meteorological wheat yield with a sequence of NDWI>DSI>VCI>VHI>PDSI>SPEI>TCI. Future projections under high emission scenarios revealed a rise in YLR associated with frequent projected droughts from VHI, DSI, SPEI, and PDSI. YLR forecasting from agrometeorological indices is best predicted by random forest with the lowest RMSE = 0.005314. NDWI (26%) and VCI (19%) are found to be significant relative predictors associated with 51% high YLR in the baseline period and SPEI (20%) and NDWI (17%) as the most important relative predictors associated with 39% high YLR in future. ConclusionThe region is vulnerable to agrometeorological droughts with more susceptibility to less rain and high temperature affecting crop health and a high risk of yield loss in the future. ImplicationThe study provides a direction to stakeholders and policymakers to develop and adapt better strategies to mitigate and prevent drought-related yield loss risk in the future.

Agricultural drought conditions over mainland Southeast Asia: Spatiotemporal characteristics revealed from MODIS-based vegetation time-series

Drought is a reoccurring slow-onset hazard event and has a tremendous impact on agricultural production and ecosystem health. Mainland Southeast Asia is a major rainfed crop-producing area of the world, and this region is increasingly vulnerable to drought hazards. However, the monitoring and characterization of agricultural and vegetative droughts in this region remains understudied. This paper presented the first comprehensive analysis of spatiotemporal characteristics and trends of agricultural and vegetative droughts across the region using the Vegetation Condition Index (VCI) based on the reconstructed MODIS-based vegetation time-series data between 2000 and 2021. This study also developed an approach for identifying severe drought years using annual extreme pixels. Results showed that vegetation-based drought characteristics and trends varied in space and time across the region. Central Myanmar suffered from the most frequent droughts (nearly 60%), but prolonged events were less common. By contrast, the Lower Mekong area suffered from frequent and prolonged drought conditions. Most of the recent severe droughts were observed in Cambodia, and this area is characterized by a drying trend. Regionally, the severe drought years were detected in 2000, 2004, 2005, 2010, 2016, 2019, and 2020, and they were commonly found in Central Myanmar, Thailand, and Cambodia. The analysis of drought years indicated that the temporal change pattern of drought has shifted from the northern regions (e.g., Central Myanmar) in 2010 to the Lower Mekong region in recent years. The findings of this study provide valuable information for drought early warning management and agricultural planning across mainland Southeast Asia.

Investigating the vegetation's temporal–spatial response to meteorological and hydrogeological drought in drylands

AbstractAs the most constraining environmental factor of vegetation development in arid areas, soil moisture is mainly supplied by precipitation and groundwater resources. Considering the varying sensitivity of different plant communities to precipitation and groundwater‐induced water shortages, the communities' management requires the study of the effects of both meteorological and hydrogeological droughts on vegetation. Therefore, this study sought to model the effects of meteorological and hydrogeological droughts on vegetation indices obtained from MODIS satellite images in the Sirjan plain from 2000 to 2019. To this end, vegetation communities were first identified and separated based on extensive field operations, taking into account the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) at the plant communities' scale. Then, meteorological drought was calculated using Standardized Precipitation Index (SPI) and the hydrogeological drought was also measured by Groundwater Resource Index (GRI) via the Kriging technique. Finally, the relationship was modelled using Multivariate Linear Regression (MLR). The results revealed that SPI at a 6‐month time scale (as an important index) played a significant role in elaborating the changes in VCI in eight communities out of 18 ones at the 6‐month scale, acting as a strong and reliable estimator. Moreover, 61.6% of VCI changes in Artemisia sieberi‐Seidlitzia rosmarinus community were explained with GRI and SPI at a 6‐month time scale, indicating the dependence on groundwater and precipitation. Nonetheless, some communities (i.e. six cases) were unresponsive. These findings draw our attention to the importance of considering the special reaction of each plant community in the management.

Spatio-temporal distribution and prediction of agricultural and meteorological drought in a Mediterranean coastal watershed via GIS and machine learning

Drought is a complex and devastating natural disaster that needs to be constantly investigated. In this study, standardized precipitation indexes (SPI-3 and SPI-6) were computed using daily precipitation data collected from six meteorological stations in a Mediterranean coastal basin (Northwestern Morocco) during the period from 1984 to 2021. Subsequently, we examined the spatio-temporal distribution of three agricultural indices namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, to predict SPI at two scales (SPI-3 and SPI-6), we investigated the ability of four machine learning (ML) models such as random subspace, random forest, M5P, and REPTree. ML model's performance was evaluated using statistical metrics such as R2, MAE, RMSE, RAE, and RRSE. As per SPI results, 1995, 1999, 2005, 2015, and 2017 were observed as severe driest years. Agricultural drought’ magnitude differs over time and space from 1984 to 2021. Besides, findings showed that the REPTree model achieved the best performance during the testing and validation phases, with R2 (0.64–0.85), MAE (0.37–0.58), RMSE (0.51–0.74), RAE (48.10–75.34%) and RRSE (53.24–76.68%). In contrast, RF was found to offer the lowest performance accuracy, despite outperforming during the training phase. Moreover, SPI-6 has higher prediction accuracy than SPI-3. Furthermore, our findings offer a reliable model for drought prediction, which may further assist policymakers and authorities in developing better adaptation and mitigation strategies to reduce drought-related losses. In future research, we suggest exploring alternative ensemble or hybrid ML algorithms to further improve prediction accuracy and capability.

Association of farmers’ wellbeing in a drought-prone area, Thailand: applications of SPI and VCI indices

Drought has major consequences for agricultural activity, economy, and the environment. For improved drought management, it is necessary to assess drought severity, frequency, and the potential of drought occurrence. The purpose of this study is to use drought indices, standardized precipitation index (SPI), and vegetation condition index (VCI) to characterize the severity of drought and investigate the association between drought severity and subjective well-being among local farmers. The SPI was used to quantify precipitation deficits at various time scales, while the VCI was utilized to monitor crop and vegetation drought conditions. Throughout the period 2000-2017, satellite data were incorporated, as well as a household survey of rice farmers in the dry zone research region in northeastern Thailand. The findings suggest that extreme droughts occur more frequently in the central part of the northeastern region of Thailand than in the rest of the region. The influence of drought on farmers' wellbeing was evaluated at various drought severity levels. Drought and overall wellbeing are strongly linked at the household level. Thai farmers in drought-prone areas are dissatisfied with their livelihoods more than farmers in less-affected area. It is intriguing that farmers who live in drought-prone areas are more content with their lives, their communities, and their occupations than farmers who live in less drought-prone areas. In this context, using proper drought indices could potentially improve the utility of governmental interventions and community-based programs targeted at assisting drought-affected people.

A geo-spatial assessment of drought impacts on forest cover in yabello forest, in the semi-arid region of Ethiopia

Drought is a key challenge for third-world countries whose economies are based on agriculture. Also, lack of rainfall plays a major role in accelerating climate change. Drought is resulting in a decline in vegetation greenness and plant mortality specifically in the semi-arid region. In this study, satellite-based drought assessment was used to characterize drought and its impact on forests in the semi-arid regions of Ethiopia. Drought severity and forest cover change were detected over the period between 2014 and 2022 Using geographic information systems and remote sensing techniques. Vegetation condition index (VCI) and Normalized difference vegetation index (NDVI) were used to detect drought severity in the present study. Results of this study showed that 2017, 2015, 2022 and 2019 were the most drought years. The forest resource is significantly decreased during severe drought years. The Minimum dense forest was observed in 2017 and 2015 over a spatial extent of 193 ha and 217 ha respectively during drought years. To evaluate how precipitation deficit affects forest function, simple linear regression is performed. Results of this linear regression “R2 = 0.49 and P = 0.05” indicated that nearly 50% of forest greenness is influenced by precipitation deficit. This study suggested that remote sensing indices are very crucial to characterize drought patterns at a small “scale”, particularly in the assessment of drought impact on forest resources.Article HighlightsSatellite indices like NDVI and VCI help to monitor the drought impact on forest cover.When severe drought is occurred along with precipitation deficit, the forest cover is decreased.Remote sensing data provide clear information for decision-making regarding drought's impact on the forest.

Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia

Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature, rainfall, and normalized difference vegetation index (NDVI). These factors are linked to excesses drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change study. East Asia is very sensitive and susceptible to climate change. In this study, we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk; and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019. The data were studied using a series of several drought indexes, and the data were then classified using a heat map, box and whisker plot analysis, and principal component analysis. The various drought indexes from January to August improved rapidly, except for vegetation health index (VHI) and temperature condition index (TCI). While these indices were constant in September, they increased again in October, but in December, they showed a descending trend. The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984, 1993, 2007, and 2012 among the study years. The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia. The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region. The correlations among monthly precipitation anomaly percentage (NAP), NDVI, TCI, vegetation condition index (VCI), temperature vegetation drought index (TVDI), and VHI indicated considerably positive correlations, while considerably negative correlations were found among the three pairs of NDVI and VHI, TVDI and VHI, and NDVI and TCI. This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within the East Asian region. This study is a step forward in monitoring the seasonal variation of vegetation and variations in drought dynamics within the East Asian region, which will serve and contribute to the better management of vegetation, disaster risk, and drought in the East Asian region.

DROUGHT MONITORING FROM 2001–2019 IN NORTHEAST THAILAND USING MODIS NDVI IMAGE TIME SERIES AND Savitzky-Golay APPROACH

Abstract. Drought directly threatens food security and livelihoods, thereby increasing socioeconomic risks and remains a challenge for natural resource management, particularly in frequently affected regions. Earth observation (EO) satellites provide extensive spectral and temporal data for long-term drought monitoring. This study monitored droughts in Northeast Thailand from 2001 to 2019 using the MODIS normalised difference vegetation index (NDVI) image time series. The Savitzky-Golay (S-G) method was used to remove noise and fill gaps in the image datasets. Optimal indicators as the vegetation condition index (VCI) and the standard vegetation index (SVI) were used to monitor drought distribution patterns over the previous 19 years. S-G filtering effectively reduced the impact of undetected clouds and water vapour, while VCI had the highest accuracy coefficient of determination (R2) for rainfall data at 0.85. Long-term droughts occurred frequently in 2005, 2004, 2007, and 2001 with the northern and central regions most severely affected. Severe drought primarily impacted agricultural land, forest and miscellaneous areas. Inter-annual drought variability for one and three time steps was clearly demonstrated in May and April to June from 2001 to 2019. Overall, the VCI provided a high level of satisfaction for drought monitoring in this region and clearly displayed the spatial distribution of long-term drought regions. Our findings provide a valuable resource for drought mitigation planning and warning systems.

Geo-spatial analysis of drought in The Gambia using multiple models

Climate change has made The Gambia vulnerable to drought hazard. Variability and negative trends in rainfall quantity and mid-season dry spells mainly attributed to the impacts of climate change. The inadequacy in hydrometeorological information puts the agricultural sector at a high risk which employs over 70% of the population. The aim of this study was to establish the intensity and spatiotemporal pattern of drought in The Gambia from 2000 to 2020 using multiple drought indices. Rainfall data, satellite images, and government policy documents were analyzed to determine the state of drought in The Gambia. Rainfall data, using Standardized Precipitation Index (SPI) and Precipitation Anomaly Percentage (PAP) were calculated and interpolated, and satellite images were processed using Vegetation Condition Index (VCI) to determine drought intensity and spatial distribution. The findings revealed that drought exists in The Gambia at moderate levels of SPI values (− 1.00 to − 1.49), (35% of PAP), and VCI of no drought intensity of more than 35%. The most drought prone areas in The Gambia are North Bank Region and Eastern parts of country in both north and south of The Gambia River banks. Recommendations of adaptation practice both on-farm and off-farm such as damming and economic diversification was drawn from other parts of the world, to reduce the negative effects of drought hazard in The Gambia.

A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data

Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R2 = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R2 = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R2 = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data.

Characterization of Evapotranspiration in the Orange River Basin of South Africa-Lesotho with Climate and MODIS Data

Evapotranspiration (ET) is crucial to the management of water supplies and the functioning of numerous terrestrial ecosystems. To understand and propose planning strategies for water-resource and crop management, it is critical to examine the geo-temporal patterns of ET in drought-prone areas such as the Upper Orange River Basin (UORB) in South Africa. While information on ET changes is computed from directly observed parameters, capturing it through remote sensing is inexpensive, consistent, and feasible at different space–time scales. Here, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spectral indices within Google Earth Engine (GEE) to analyze and characterize patterns of ET over the UORB from 2003 to 2021, in association with various climatic parameters. Our results show spatially consistent ET patterns with the Vegetation Condition Index (VCI), with lower values in the west, increasing toward the eastern section of the basin, over the Lesotho highlands. We noted that the UORB faced significant variability in ET and VCI during pronounced drought episodes. The random forests (RF) model identified precipitation, temperature, Standardized Precipitation Index (SPI)-6, Palmer Drought Severity Index (PDSI), and VCI as variables of high importance for ET variability, while the wavelet analysis confirmed the coherence connectivity between these variables with periodicities ranging from eight to 32 months, suggesting a strong causal influence on ET, except for PDSI, that showed an erratic relationship. Based on the sequential Mann–Kendall test, we concluded that evapotranspiration has exhibited a statistically downward trend since 2011, which was particularly pronounced during the dry periods in 2015–2016, 2019, and 2021. Our study also confirmed the high capacity of the GEE and MODIS-derived indices in mapping consistent geo-temporal ET patterns.

Biophysical and biochemical features’ feedback associated with a flood episode in a tropical river basin model

Global climate change scenarios such as frequent and extreme floods disturb the river basins by destructing the vegetation resulting in rehabilitation procedures being more costly. Thus, understanding the recovery and regeneration of vegetation followed by extreme flood events is critical for a successful rehabilitation process. Spatial and temporal variation of biochemical and biophysical features derived from remote sensing technology in vegetation can be incorporatedto understand the recovery and regeneration of vegetation. The present study explores the flood impact on vegetation caused by major river basins in Sri Lanka (a model tropical river basin) by comparing pre-flood and post-flood cases. The study utilized enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), the fraction of photosynthetically active radiation (FPAR), and gross primary productivity (GPP) of the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A remarkable decline in EVI, LAI, FPAR, GPP, and vegetation condition index was observed in the post-flood case. Notably, coupled GPP-EVI and GPP-LAI portrayed dependency of features and showed a significant impact triggered by the flood episode by narrowing the feature in post-flood events. EVI depicted the highest regeneration (0.333) while GPP presented the lowest regeneration (0.093) after the flood event. Further, it was revealed that 1.18years have been on the regeneration. The regeneration of GPP and LAI remained low comparatively justifying the magnitude and impact of the flood event. The study revealed successful implications of vegetation indices on flood basin management of small to large tropical river basins.

Application of the Vegetation Condition Index in the Diagnosis of Spatiotemporal Distribution of Agricultural Droughts: A Case Study Concerning the State of Espírito Santo, Southeastern Brazil

We applied a robust framework for agricultural drought identification in the State of Espírito Santo, Brazil, by employing the Vegetation Condition Index (VCI) based on data obtained through the Enhanced Vegetation Index (EVI). By doing so, we analyzed the interrelationships between the VCI and anomalies in the Land Surface Temperature (LST), along with connections between the VCI and data considering water deficits in vulnerable areas. When it came to image processing, we focused on the use of analytics and GIS algorithms, while the Scott–Knott method elucidated the statistical analyses. Consequently, we identified drought areas followed by periods susceptible to their occurrence, indicating 2016 as the driest year. The North macroregion presented the lowest average values regarding VCI values in the most vulnerable periods, followed by the Central one. We also call attention to the highest LST averages observed in 2015 and 2016, as strong El Niño events marked the same timeframe periods. The methodological approach was efficient for the identification, analysis, and characterization of agricultural drought occurrences, enabling mitigation actions, as well as the management of the exploitation and protection of water resources. Moreover, further research should be conducted by incorporating other indices to enhance the understanding of agricultural drought and its effects on vegetation.

Dynamic Assessment of Drought Risk of Sugarcane in Guangxi, China Using Coupled Multi-Source Data

Globally, drought is an increasing threat to agricultural ecosystems, resulting in impaired crop yields, high food prices, and low incomes for farmers. Fluctuations in crop production and prices can exert a negative transboundary effect on food exporting and importing countries across the world through international trade. Therefore, it is important to regionally assess agricultural drought risk to reduce crop yield reduction by adapting existing systems. In this study, from the perspective of Chinese sugar security, a comprehensive assessment index of drought risk of sugarcane was constructed by considering the atmosphere–soil–crop continuum. Based on disaster-causing factors (hazards) and exposure, vulnerability, and mitigation capabilities of disaster subjects (disaster bearers), a risk assessment model of drought disaster of sugarcane in the growing season was established. Results of this study were three-fold. First, the maximum entropy model accurately reflected the reliability and relative importance of the disaster-causing factors of vegetation condition index (VCI), soil moisture condition index (SMCI), and standardized precipitation evapotranspiration index (SPEI), with the area under the curve value of the comprehensive drought risk of sugarcane being greater than 0.75. Second, the drought frequency and impact range in four growth stages of sugarcane significantly declined with the increasing drought severity. Light drought was prevalent in each growth stage, and the occurrence frequency of severe drought was relatively low. The drought frequency was significantly higher in the seedling and maturity stages than in the tillering and stem elongation stages, and the drought distribution was mainly concentrated in the southwest and central regions. Finally, the spatial distribution characteristics of drought risk significantly differed among the four growth stages of sugarcane. The risk level in the seedling stage declined from the southwest to the northeast. The high risk in the tillering stage was mainly concentrated in the southwest and northeast of the study region. In the stem elongation stage, the southwest became a low- risk area. In the maturity stage, the risk level was higher in the southeast than in the other areas. As sugarcane is majorly planted on dry slopes with uneven rainfall, a lack of good infrastructure, and the further intensification of global warming, sugarcane areas that were highly exposed to drought stress were highly vulnerable to drought risk, which in turn weakens farmers’ willingness to plant, thus threatening the security of sugar and biofuel production.

Inter-annual drought monitoring in northern mixed grasslands by a revised vegetation health index from historical Landsat imagery

Drought is defined as a natural phenomenon with comprehensive influences on ecosystems, economies, and the livelihood and health of people resident in the affected regions. Severe drought has affected the grassland ecosystem in the Canadian prairie region with increased frequency. Thus, it is essential to monitor the drought effects. With various temporal, spectral, and spatial resolutions, remote sensing data can detect vegetation's dynamic responses to drought. The objective of this study was to monitor inter-annual drought in the northern mixed grassland, over the last thirty-six years by using remote sensing approach through a revised vegetation health index (VHIr), which combined the vegetation condition index (VCI), the temperature condition index (TCI), and the dead fuel condition index (DCI) together. Historical Landsat imagery was explored to establish the VHIr and this index was tested in the Grasslands National Parks (GNP), a typical conserved northern mixed grasslands region. The results suggested that the VHIr can distinguish dry and wet years effectively and a VHIr map was able to depict the spatial distribution pattern of the drought severity. Compared with the original vegetation health index (VHI), VHIr was more correlated with the dead-to-green biomass ratio. Meanwhile, the spatial pattern indicated that the valley grasslands had relatively low drought sensitivity; the upland grasslands experienced severe to extreme drought severity during dry years, due to a large amount of dead vegetation existed; and the drought effect on sloped grasslands was due to relatively sparse vegetation cover. In addition, VHIr was significantly correlated with greenness and wetness components derived from the tasseled cap (K-T) transformation but the correlation coefficients (r) varied between dry and wet years and among different grassland landscapes. This study expanded our understanding of the spatial and temporal distribution of drought severity in the northern mixed grasslands, which can facilitate natural resource management in the arid and semi-arid regions.