Vegetation Condition Index Research Articles

Implications of Ecological Drivers on Roan Antelope Populations in Mokala National Park, South Africa

Climate change has massive global impacts and affects a wide range of species. Threatened species such as the roan antelope (Hippotragus equinus) are particularly vulnerable to these changes because of their ecological requirements. Attempts to address concerns about the roan’s vulnerability have not been well documented in South African protected areas. This study identifies the landscape use and distribution of the roan as well as habitat and forage suitability changes to help inform management decisions for the conservation of roan. We used fine- and broad-scale data from Mokala National Park, South Africa that includes roan occurrence data, vegetation condition indices, vegetation (structure and plant species composition), elevation and temperature differences, and precipitation strata to construct a suitability framework using the Maximum Entropy (Maxent) and Random Forest statistical package. In Mokala National Park, roan occurred in the Schmidtia pappophoroides–Vachellia erioloba sparse woodland, Senegalia mellifera–Vachellia erioloba closed woodland, Senegalia mellifera–Vachellia tortilis open shrubland, Vachellia erioloba–V. tortilis closed woodland and Rhigozum obovatum–Senegalia mellifera open shrubland. The veld (vegetation) condition index (VCI) improved from 2019 (VCI < 50%) to 2021 (VCI > 60%), with the proportion of palatable grass species (Schmidtia pappophoroides and Eragrostis lehmanniana) also increasing. This study identified four key climatic conditions affecting roan distribution, namely annual mean daily temperature range, temperature seasonality, minimum temperatures of the coldest month, and precipitation of the wettest month. These results suggest that the conservation of roan antelope should consider these key variables that affect their survival in preferred habitats and foraging areas in anticipation of changing ecological conditions.

Влияние аэрозолей на лесорастительный покров в юго-западном регионе Азербайджана

Atmospheric pollution by anthropogenic emissions leads to an increase in the content of aerosols in the air. This causes distortions in the Earth remote sensing data in the visible ranges. Thus, the use of vegetation indices, in particular, the normalized differential vegetation index (NDVI), is difficult. In this regard, interest in the use of satellite images obtained in the short-wave infrared bands SWIR1 and SWIR2 has recently increased. Unlike NDVI, when calculating the AFRI-1600 and AFRI-2100 indices, the values of these bands are taken into account (instead of visible red). Therefore, these indices are called aerosol-free. The article addresses the issues of the NDVI and AFRI indices. The studies have been carried out in three regions located in the south-west of Azerbaijan. The initial data has been taken from 2000 and 2021 images obtained using the Landsat-5 and Landsat-8 satellites, respectively. The scanners of these satellites have ranges of 1600 and 2100 μm. The study has taken place in several stages. At the first stage, the AFRI-1600 indices have been calculated and the areas with high aerosol content for the indicated years have been identified. By studying the dynamics of the amount of aerosols, two types of sites have been identified. Type 1 sites are those where the aerosol content was high in 2000, but dropped in 2021. In type 2 sites, a high value of this indicator was not previously recorded, but was noted in 2021. The NDVI index value has been obtained for both categories, and the classification of the site coverage has been carried out. The results of superimposing index maps on each other have shown that an increase in aerosol content corresponds to a decrease in vegetation density, and vice versa, a decrease in aerosols corresponds to an increase in this indicator. Thus, the increased aerosol content has a negative effect on the condition of forest cover. This is confirmed by the VCI (vegetation condition index) maps.

Yield prediction models for some wheat varieties with satellite‐based drought indices and machine learning algorithms

AbstractIn recent years, frequent drought events in Konya, one of Türkiye's most important cereal production centres, have led to increased pressure on water and soil resources, resulting in yield losses, particularly in wheat production. Alternative yield prediction models, especially those that play a crucial role in agricultural import–export planning in the region, are important for economic contributions and the development of early warning systems. In this context, the aim of this study is to develop models that can be used in the yield prediction of wheat varieties widely grown in the Konya Altınova region. Agricultural drought indices obtained from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) products of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to obtain model inputs. These indices are the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) and Vegetation Supply Water Index (VSWI). In obtaining the input parameters for the models, the growth periods of the varieties in the region were also considered. Using various machine learning algorithms, 21 yield prediction models for Bayraktar‐2000, 12 for Kızıltan‐91 and 8 for Bezostaya‐1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively.

Spatial-Temporal Assessment of Drought in Hoa Vang district, Da Nang City, Vietnam Using Remote Sensing and Google Earth Engine

The objective of this research was to map the drought risk in Hoa Vang district, Da Nang city, and analyze the variability of drought events from 1991 to 2020 using remote sensing imagery. To achieve this, remote sensing and GIS methods were utilized to gather information on land surface temperature and the Normalized Difference Vegetation Index (NDVI). The study effectively employed the Google Earth Engine which is a cloud-based computing platform to analyze image sequences from Landsat 5 and OLI sensors spanning the period from 1991 to 2020. This analysis aimed to extract the remote sensing indices and applied them for drought assessment and monitoring. The study employed land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) to derive the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These indices were utilized to assess drought conditions in Hoa Vang district, Da Nang city, over the period from 1991 to 2020. The research findings reveal that the drought values in Hoa Vang district exhibited variations throughout the studied period. Notably, regions at risk of drought during the later periods exhibited an increasing trend compared to the initial periods. This suggests a potential escalation in drought severity in the study area over time. The outcomes of this study provide essential background for evaluating the impacts of drought on agricultural production in Hoa Vang district, Da Nang city.

Spatiotemporal Analysis of Drought Characteristics and Their Impact on Vegetation and Crop Production in Rwanda

In recent years, Rwanda, especially its Eastern Province, has been contending with water shortages, primarily due to prolonged dry spells and restricted water sources. This situation poses a substantial threat to the country’s agriculture-based economy and food security. The impact may escalate with climate change, exacerbating the frequency and severity of droughts. However, there is a lack of comprehensive spatiotemporal analysis of meteorological and agricultural droughts, which is an urgent need for a nationwide assessment of the drought’s impact on vegetation and agriculture. Therefore, the study aimed to identify meteorological and agricultural droughts by employing the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI). VHI comprises the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), both derived from the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). This study analyzed data from 31 meteorological stations spanning from 1983 to 2020, as well as remote sensing indices from 2001 to 2020, to assess the spatiotemporal patterns, characteristics, and adverse impact of droughts on vegetation and agriculture. The results showed that the years 2003, 2004, 2005, 2006, 2013, 2014, 2015, 2016, and 2017 were the most prolonged and severe for both meteorological and agricultural droughts, especially in the Southern Province and Eastern Province. These extremely dry conditions led to a decline in both vegetation and crop production in the country. It is recommended that policymakers engage in proactive drought mitigation activities, address climate change, and enforce water resource management policies in Rwanda. These actions are crucial to decreasing the risk of drought and its negative impact on both vegetation and crop production in Rwanda.

A Geospatial Analysis of Environmental Impacts of Climate Change on Osogbo and Environs

Climate change is one of the alarming global environmental changes likely to have deleterious effects on natural, social, cultural, and human systems. The risks associated with it call for a broad spectrum of policy responses and strategies at local, regional, national, and global levels. This research aims to carry out a geospatial analysis of the environmental impact of climate change on Osogbo and its environs with a view to providing information towards mitigating the negative impact of climate change in the study area. This study uses Landsat 4, Landsat 7, and Landsat 8 satellite data from 1982, 2002, and 2022, respectively, to evaluate land cover and temperature variation as a climatic change in the study area over four decades. The land survey method was carried out to collect ground truth data for validation of the satellite imageries. The drought map was produced based on the Vegetation Condition Index (VCI) map computed from Normalized Difference Vegetation Index (NDVI) datasets and was further classified into three categories: moderate, mild, and no drought. Analysis of the Land Use Land Cover (LULC) of the study area shows that the vegetation decreased from 84% in 1982 to 59% in 2002 and 56% in 2022. The built-up/exposed surface increased from 15.28% in 1982 to 38% in 2002 and 41.28% in 2022. The water bodies also increased from 0.17% in 1982 to 15.31% in 2022. LST analysis reveals that the mean temperature of the study area increased by 10.73 °C during the past four decades (from 1982 to 2022), with the most accelerated warming (11.41 °C) occurring during the last two decades (from 2002 to 2022). The Vegetation Condition Index (VCI) based drought map shows that there was no extreme or severe drought in the study area. However, moderate and mild droughts were observed mostly in the developed part of the study area, which confirmed the presence of drought. It is hereby recommended to introduce sustainable water management strategies to mitigate water shortage in the study area.

Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation

Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

The present study aimed to analyze the dynamics of vegetation within the Atrak catchment area, as well as its interplay with precipitation patterns. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover type. To achieve this objective, the Enhanced Vegetation Index (EVI) derived from MODIS data and the Comprehensive Historical and Real-Time Satellite-based Precipitation (CHRIPS) data were utilized for the period from 2003 to 2021. Additionally, the Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were employed to discern various degrees of drought and pluvial years within the Atrak basin. The study revealed that the years 2008, 2014, 2017, and 2021 exhibited the lowest vegetation coverage, while the years 2010, 2016, and 2019 showcased the most extensive vegetation extent. Notably, it was revealed from the VCI index that the year 2008 was the driest, and the year 2016 was the wettest. Furthermore, based on the SPI index findings, the years 2007, 2019, and 2020 were identified as pluvial years, while in the years 2008, 2014, and 2021 drought conditions occurred. All other years were classified as exhibiting normal conditions. Regarding seasonality, the observations ascertain that the spring season substantiates the most extensive vegetation cover, and a high correlation between spring precipitation and vegetation coverage was observed. Additionally, the anomaly detection outcomes indicate that the eastern regions of the basin have experienced an upward trend compared to the average of the first decade of the studied period.

Wetting or greening? Probing the global trends in Vegetation Condition Index (VCI)

Vegetation Condition Index (VCI), as a widely used drought index for monitoring vegetation drought stress and estimating drought trends, is constructed by normalizing the long-term satellite-based Normalized Difference Vegetation Index (NDVI) data. However, under global greening, vegetation across different regions has shown an increasing trend in greenness, which may cause VCI to inherit the greening trend in NDVI and further hamper its ability in drought trend analysis. Therefore, this study quantitatively explored the underlying relationship among VCI, global greening, and drought from 2001 to 2021 to examine the utility of VCI in a changing environment. Multi-source indicators were employed as surrogates for vegetation greenness and drought, respectively. Particularly, the Leaf Area Index (LAI) and Net Primary Production (NPP) were used as proxies for vegetation greenness while the Root-Zone Soil Moisture (RZSM), Palmer Drought Severity Index (PDSI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were used for drought indices. Based on Sen’s slope estimator, Mann-Kendall (MK) test, and partial correlation analysis, our results show that the proportion of pixels with an increasing trend in VCI is approximately 38.5%, which is similar to the greenness-related indices (i.e., NPP [26.47%] and LAI [59.14%]), and is significantly higher than the other drought indices (i.e., RZSM [19.83%], PDSI [10.38%], and SPEI [9.32%]). Furthermore, our results demonstrate that the VCI, as a drought index, exhibits a closer correlation with greenness-related indicators (LAI and NPP) than other drought indices (RZSM, PDSI, and SPEI). These results reveal the potential limitations in the practical applications of VCI and could enhance our understanding of vegetation dynamics and drought trends, especially in the current greening globe. Additionally, this study serves as a cautionary note for the scientific community involved in drought monitoring, emphasizing that the VCI may not be a suitable tool for evaluating long-term trends in drought impacts on vegetation.

Characteristics of propagation from meteorological drought to ecological drought in China: Lag and cumulative effects

The increasing frequency and severity of droughts will seriously impact agricultural production and the ecological environment. Studying the drought lag (DL) and drought cumulative (DC) effects of propagation between meteorological drought to ecological drought can help to enhance the capacity of ecological drought monitoring. Therefore, in this study, the meteorological drought index (non-stationary standardized precipitation evapotranspiration index, NSPEI) and ecological drought index (vegetation condition index, VCI) were obtained using ERA5 reanalysis data and MODIS vegetation index data, respectively. The purpose of this study was to reveal the DL and DC effects of the propagation of meteorological drought to ecological drought and investigate the spatial heterogeneity of drought propagation in different underlying surfaces and climate zones. The results indicated that: 1) Northern China is more vulnerable to severe and extreme droughts, and 68% of the region has significantly slower ecological droughts. 2) The western temperate zone, highland climate zone, and subtropical zone have shorter DL times for subtropical drought and longer DL times for eastern temperate zones. 3) DC and DL effects have similar spatial distributions. However, the DC duration of grassland and bare ground is shorter in the northwest and south, and longer in grassland and farmland in northern China. 4) China overall is more affected by the DC effect, while the DL effect is more significant in southern temperate zone.

Study loss of vegetative cover and increased land surface temperature through remote sensing strategies under the inter-annual climate variability in Jinhua-Quzhou basin, China.

The Jinhua-Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982-2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST).Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies.The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua-Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua-Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua-Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards.

Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators.

The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017-2021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production.

Time Series Analysis of Agricultural Drought and Desertification using Spectral Indices and Satellite Images

The aim of study to time series analysis of agricultural drought and desertification using Spectral Indices and Landsat Images. A time series of satellite images (TM and OLI) were coducted for the period 1990 to 2022. The located at coordinate 34°52'29.386"N and 43°26'15.703" E and the area study is (33.98) km2. The (18) eighteen of satellite images were selected and then image processing was carried out using ERDAS imagen Ver 15 and ArcGIS 10.6. The spectral indices (Normalized Difference Vegetation Index (NDVI), Land Surface Temprature (LST), Vegetation Condition Index (VCI), and Vegetation Health Idex (VHI) were calculated. The regression and correlation coefficient between rainfall and spectral indices were determined using SPSS programm. The result show that VHI at 1990, 2000 and 2010 are sever drought class and its area 63.66, 57.63 and 63.85% respectively. In addition, the simple linear regression and correlation coefficient were positive between a rainfall and spectral indices reach ≥ 0.70. The years 1998, 2008, 2013 and 2022 were suffering from sever drought and desertification compared with 2006, 2016 and 2019, respectively.

Unveiling and modelling: Land degradation, poverty nexus in Nigeria's semi-arid (jigawa state) using cloud-based geospatial data

Land degradation and multidimensional poverty represent global challenges whose interconnected dynamics are poorly understood and hinder effective interventions. The aim of this study was to address these issues by integrating the RULSE model, the Vegetation Condition Index (VCI), and the Multidimensional Poverty Index (MPI). Using high-resolution geospatial data from Sentinel-2 via the Google Earth Engine, land-use changes were identified as indicators of degradation, with VCI critical for assessing vegetation health. A holistic poverty assessment using MPI data from the Nigerian National Bureau found strong evidence of land degradation in these regions, with approximately 60–75% experiencing multidimensional poverty. Spatial overlap highlights the close connection between severe land degradation and high multidimensional poverty rates. The correlation analysis provided insights into the relationships between land degradation variables (vegetation status, soil loss, and digital elevation) and poverty patterns (population dynamics). This study highlights how soil erosion negatively impacts agriculture and perpetuates the vicious cycle of poverty. This study illuminates the complex relationship between land degradation and poverty, and argues for further exploration, technological integration, and a nuanced understanding of the components of poverty. Advanced assessments can guide targeted policies and interventions to break the cycle of land degradation and poverty.

Improving remote sensing based agricultural drought characterization in Saurashtra, Gujarat : A region-specific threshold approach

Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.

Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data

Climate change has led to various adverse consequences, with natural disasters being one of the most striking outcomes. Natural disasters negatively impact life, causing significant disruptions to the ecosystem. Prompt identification of affected areas and initiation of the rehabilitation process are imperative to address the disturbances in the ecosystem. Satellite imagery is employed for the rapid and cost-effective detection of damages caused by natural disasters. In this conducted study, the outputs of climate change wildfire, forest change detection, and drought analysis, have been examined, all of which worsens the impacts on the ecosystem. The analysis of drought involved using MODIS data, while Sentinel-2A satellite images were utilized to identify wildfire areas and changes in forested regions caused by windthrow. The research focused on Ganja, Azerbaijan, as the area for drought analysis. The driest June between 2005 and 2018 was assessed using the Vegetation Condition Index (VCI) in conjunction with data from the National Centers for Environmental Information (NOAA). At the Düzce Tatlıdere Forest Management Directorate, the Normalized Difference Red Edge Index (NDRE) was utilized between the years 2018 and 2019 to detect the changes occurring in forested areas due to windthrow. The NDRE synthetic band was added to satellite images for the years 2018 and 2019, and a Random Forest (RF) algorithm was employed to classify the data. The classification results were evaluated using Total Accuracy and Kappa statistics. The study includes the detection of the Normalized Burn Ratio (NBR) applied to determine the extent of the wildfire that occurred in the Solquca village of the Qabala region in Azerbaijan in 2021. According to the analysis of the VCI and NOAA, June 2014 was identified as the driest month in Ganja. In the Tatlıdere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that ~1007 hectares of land were burned in the Solquca village of Qabala. The analyses conducted provide information regarding the use of satellite imagery in relation to changes in forest areas due to drought, wildfire, and windthrow.

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

This study investigated the complex relationship among groundwater salinity, soil texture, and vegetation health in Al-Ahsa Oasis, Saudi Arabia. Utilizing vegetation condition index data from 5 years of satellite imagery, alongside ground measurements of groundwater salinity and soil analysis, the study unveiled significant spatial heterogeneity. The vegetation health and groundwater salinity in the study area ranged from 27 to 70% and from 1.7 to 9.6 dS m−1, respectively, and exhibited contrasting patterns, with a high vegetation condition index (healthy vegetation) in the north and east versus high salinity in the southwest. The applied cross-tabulation statistics revealed a strong negative correlation between the vegetation condition index and salinity, with “good” and “normal” vegetation health primarily coinciding with “slightly saline” groundwater. Soil texture further modulated the vegetation response, as “Silt loam” soils corresponded with “good” VCI, while “silty clay loam” only supported “dry” and “normal” conditions. The applied spatial autocorrelation analysis confirmed this, showing low vegetation clustering with “silt” texture and high salinity in the mid-west of the study area (p < 0.05). Outlier districts hint at local factors beyond simple salinity–vegetation health relationships. Finally, a conditional plot, applied as a spatial statistical approach, revealed that low vegetation health spatially correlates with high groundwater salinity (>5 dS m−1) on “silt loam” in the mid-west, while a higher vegetation condition index aligned with lower groundwater salinity in the east and north. These findings highlight the critical role of spatial variability and the need for advanced modeling and data integration to understand and predict the responses of vegetation to environmental challenges, and will be crucial for optimizing water management to ensure the long-term sustainability of Al-Ahsa Oasis.

An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques

Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.

Monitoring Drought Tolerance Mechanisms of Sorghum and Maize Under Unevenly Distributed Precipitation

The study examines the morpho-physiological differences in leaf characteristics between two species of C4 plants: sorghum and maize. The research was conducted in field conditions where plants were rainfed. Both species different varieties were cultivated at two distinct sites that exhibited variations in soil texture and drought stress incidence according to the vegetation condition index (VCI). Samples were collected during various growth stages to analyze the relative water content (RWC), proline levels, and stomatal density. Sorghum plants displayed higher RWC, proline levels, and stomatal density than maize plants. In sorghum, the biochemical traits, such as the proline content, may play a more critical role in withstanding water-limited conditions than in maize in our experiment. Under the same water restriction period, sorghum showed higher RWC levels. Sorghum plants reduced stomatal density under more water-limited conditions, which proves its plasticity. Additionally, early maturation played a crucial role in both species. The early sorghum variety KWS Kallisto and maize variety Walterinio KWS had a more stable yield at both sites. Nevertheless, the highest yields were found in the later varieties, KWS Hannibal and KWS Inteligens. The higher proline levels and the relative water content are drought-tolerant mechanisms and may be used to indicate drought intensity in field conditions. Our findings spotlight the influence of genetic diversity and genotype-environment interactions in determining crop responses to drought stress, providing valuable information for future breeding programs to enhance drought tolerance in crops.

Examination of the Effects of Climate Change on Türkiye through the Google Earth Engine Platform

The objective of this research is to assess the effects of climate change on Türkiye by utilizing data catalogues provided by the Google Earth Engine (GEE) cloud-computing platform. The utilized data catalogues encompassed precipitation, Land Surface Temperature (LST), EvapoTranspiration (ET), Potential EvapoTranspiration (PET), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI), and Forest Area Loss (FAL). Data spanning the years 2001-2022 were collected, and analyses were conducted separately for seven geographical regions of Türkiye on both an annual and seasonal basis. Initially, trend analysis techniques were applied to the individual data sets, followed by an examination of correlations among them. Notably, significant decreasing and increasing trends were observed in annual precipitation and LST data in the Eastern Anatolia region, respectively. Furthermore, a significant increasing trend was identified in annual ET data across all regions except Eastern Anatolia. Conversely, significant increasing trends were noted in annual PET data in Eastern Anatolia and the Aegean regions. Additionally, significant increasing trends were discerned in annual NDVI, EVI, and VCI data across all regions. Experiments revealed that the ET exhibited robust correlations with the NDVI (0.77), EVI (0.79) and VCI (0.81). Furthermore, the NDVI demonstrated strong correlations with EVI (0.99) and VCI (0.96).