Vegetation Health Research Articles

Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

Assessment of Waterlogging and Mitigation Strategies for Alanda Micro Watershed Using Remote Sensing in Akola District of Vidarbha Region, India

The socio-economic progress of Vidarbha, where 89% of the land is used for rainfed farming, is significantly influenced by water resources. Unfortunately, waterlogging, driven by insufficient drainage and erratic weather conditions, poses serious challenges to agricultural productivity and soil health. The Alanda micro watershed in Barshitakli tehsil, Akola district, Vidarbha region of Maharashtra, features hard basaltic terrain and clay soils primarily used for agriculture. Poorly drained soils have caused waterlogging and salinization, highlighting the need for assessment of mitigation measures for the waterlogging to improve agricultural productivity and address climate change impacts. Waterlogging can be effectively identified through remote sensing techniques like NDWI, NDMI and NDVI, using high-resolution Landsat 8 imagery processed in ArcGIS 10.8. These methods facilitate the analysis and management of waterlogged areas, which is critical for improving agricultural output. The NDVI, NDWI and NDMI maps of the Alanda micro watershed highlight vegetation health, moisture levels and waterlogging potential, with the northern regions showing higher moisture and increased waterlogging risk. Mitigating waterlogging in the area requires the implementation of efficient drainage systems, soil improvement through organic matter and deep tillage and precise water management practices, including levelling fields to prevent water accumulation and using drought-resistant crops.

AGRICULTURAL DROUGHT ANALYSIS BASED ON REMOTE SENSING: THE CASE OF AYDIN, TURKIYE

Agricultural drought is a phenomenon that arises when there is a deficiency of moisture in the soil, which has a detrimental impact on the productivity of agricultural crops. In Aydın, Turkey, particularly in the fertile Söke Plain region, agricultural drought is a major problem for farmers. The use of satellite data based on remote sensing and the indices derived from them allows for the timely and spatially detailed monitoring of vegetation health and moisture conditions over large areas. This enables the early detection and monitoring of agricultural drought. The present study evaluates the occurrence of agricultural drought in Aydın province between 1995 and 2020. For this purpose, satellite images captured by the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) in August 1995 and 2020 and land cover maps produced by the European Space Agency (ESA) at the same dates were utilized. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) indices were produced using Landsat satellite images. Then, the Vegetation Temperature Condition Index (VTCI) was obtained to detect agricultural drought. Finally, the relationship between the VTCI and land cover (LC) was evaluated, as well as the changes in the VTCI index between 1995 and 2020. The study found that agricultural drought increased with rising land surface temperature and declining NDVI values in Aydın province between 1995 and 2020.

Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index.

Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon's entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.

The RARCrec: A rapid riparian vegetation assessment method for use in river systems undergoing vegetative and geomorphic recovery

SummaryMonitoring the condition of returning vegetation over time is essential for effectively managing riparian vegetation. Rapid riparian assessment methods are standard tools for capturing a suite of variables relevant to river health and are accessible to a range of users with different levels of experience. Monitoring efforts are particularly important for rivers undergoing degrading impacts and subsequent management interventions. In coastal catchments of New South Wales, Australia, vegetation is returning to rivers that were historically cleared for agriculture and are now experiencing geomorphic recovery. We present a rapid riparian assessment method for rivers undergoing vegetative and geomorphic recovery in this region. It addresses a gap in available rapid assessments by providing a methodology that can be used at sites with no historical analogue. Our assessment method adapts and extends a widely used method called the Rapid Assessment of Riparian Condition (RARC) to capture the quality of returning riparian vegetation along recovering rivers (rec), including native species diversity and vegetation structure (hence the name RARCrec). The RARCrec incorporates four sub‐indices – vegetation cover, native species richness, proportion of native to exotic species, and features of vegetation health – to produce an overall vegetation condition score that classifies sites as poor, moderate, or good condition. Scores for each sub‐index are assessed for the vegetation strata (groundcover, graminoids, midstorey, vines, and canopy) on the landform units of river bars, benches, and floodplains. The RARCrec has been designed to be sufficiently general for use in the main Vegetation Formations in coastal New South Wales and has been trialled at 36 sites across this region. Assessments can be conducted within half a day by two technically competent users and can be used to monitor vegetation condition over time to inform where to prioritise implementation of management activities. Field methodology and data sheets, including a scoring system, are provided to support the implementation of the RARCrec.

Sentinel-2A Image Reflectance Simulation Method for Estimating the Chlorophyll Content of Larch Needles with Pest Damage

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management.

Integrating remote sensing modeling for drought assessment and vegetation monitoring in El Tarf, Algeria: a 40-year analysis

The escalating frequency and intensity of drought events, exacerbated by anthropogenic climate change, present critical challenges to hydrological resources, ecosystem resilience, and agricultural productivity. In North Africa, and specifically Algeria, comprehending the spatiotemporal patterns of drought is essential for sustainable water resource management and ecological conservation. This investigation focuses on the El Tarf region, located in northeastern Algeria, recognized for its ecological diversity and agricultural relevance. Employing a synergistic methodology, this study integrates the Standardized Precipitation Evapotranspiration Index (SPEI) for long-term drought severity evaluation with the Normalized Difference Vegetation Index (NDVI) to monitor vegetation phenology and health over time. Utilizing the computational power of Google Earth Engine, we process extensive temporal datasets of SPEI spanning from 1980 to 2023, complemented by high-resolution satellite imagery for NDVI computation. This comprehensive approach facilitates an in-depth analysis of drought dynamics, their severity, and impacts on vegetative cover in El Tarf over the past four decades. Our findings indicate significant long-term drought trends, with notable dry spells occurring between 1994 and 2003. Severe drought events were particularly pronounced in December 2022 (SPI = -2.50) and May 2002 (SPI = -2.2), correlating with substantial precipitation deficits. Although areas experiencing extreme drought have diminished, there has been an increase in moderate drought occurrences, suggesting an uptick in drought frequency, which poses risks to hydrological resources and ecosystem health. These observed patterns align with regional climate change projections, highlighting an urgent necessity for adaptive management strategies in agriculture and water resources.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm

Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts.

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

AbstractVegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.

A comparison of mustard, household detergent and formalin as vermifuges for earthworm sampling

The efficiencies of three vermifuges (household detergent, mustard and formalin) in extracting earthworms were compared in a pasture in western Norway. Relative efficiency of detergent and mustard compared to formalin with respect to number of earthworms extracted, was 79% and 66%, respectively. No significant differences were found among the three treatments in proportion of juveniles, earthworm size, number of earthworms or biovolume of emerging earthworms. Two genera of earthworms were represented in the samples, Allolobophora spp. and Lumbricus spp. Genus composition differed among the three treatments, formalin extracting the highest percentage of Allolobophora spp. and mustard the lowest. The relative efficiency of the three vermifuges differed from what has been found in an earlier comparable study. Both mustard and household detergent are less detrimental to health of vegetation, earthworms and field workers than formalin, and given their relatively high efficiency as vermifuges, they might in many cases be preferable to formalin.

Remote Sensing and Field Data Analysis to Evaluate the Impact of Stone Bunds on Rainfed Agriculture in West Africa

This study evaluates the effectiveness of stone bunds in enhancing soil moisture, vegetation health, and crop yields in Ghana’s semi-arid Upper East Region, an important area for agricultural productivity in West Africa. In this region, agricultural practices are heavily impacted by erratic rainfall and poor soil moisture retention, threatening food security. Despite the known benefits of traditional soil conservation practices like stone bunds, their effectiveness in this context has not been fully quantified. Field and remote sensing data were used to evaluate the influence of stone bunds on soil moisture dynamics, vegetation growth, and crop yield. Experimental plots with and without stone bunds were monitored for climate, soil water infiltration, and soil moisture and analyzed using the NDVI from Sentinel-2 satellite imagery over two growing seasons under sorghum production (2022–2023). The results indicated that stone bunds enhanced soil moisture retention and increased infiltration rates. The NDVI analysis consistently revealed higher vegetation health and growth in the plots with stone bunds, particularly during critical growth periods. The intermediate results of the conducted experiment indicated that stone bunds increased sorghum yields by over 35% compared to the control plots. The substantial agronomic benefits of stone bunds as a soil and water conservation strategy were evident, improving soil water infiltration, water retention, vegetation health, and crop yields. The findings support the broader adoption of stone bunds in semi-arid regions to enhance agricultural productivity and resilience against climate variability. Further research is recommended to explore the long-term impacts and the integration of stone bunds with other sustainable farming practices to optimize rainfed agricultural outcomes.

Remote Sensing-Based Drought Monitoring in Iran’s Sistan and Balouchestan Province

Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index (TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km3 Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km3. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km3. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future.

The Health and Nutritional Benefits of Fruits and Vegetables to Reduce the Risk of Obesity and High Blood Lipids

Background: Vegetables and fruits are an important part of a healthy diet and help with growth and support body functions and physical, mental, and social well-being at all ages. They can also help prevent all forms of malnutrition, such as undernutrition, micronutrient deficiencies, obesity, and overweight, and reduce the risk of non-communicable diseases. World Health Organization consumption of at least 400 grams of fruits and vegetables per day, or five servings of 80 grams each, and the reasons for the sad fact that most people do not eat enough of them to maintain a healthy life for the sake of obesity treatment and weight reduction. Objectives: The aim of the study was to investigate the effects of increasing fruit and vegetable intake rich in antioxidants and vitamins on people at risk of obesity and high levels of blood fat and to provide education about its benefits and the amount of daily rations. Methods: This a descriptive cross-sectional study, 160 obese women underwent to Aspetar ospital in Misurata city during The period (October–December 2022) and they were divided into two groups of 80 women per group. The first group was presented with a weight-loss diet devoid of vegetables and fruits. The second group was given a weight-loss diet rich in vegetables, including cucumber, lettuce, tomato, and fruits (kiwi, apple, and orange). For three consecutive months. Results: The findings were a decrease in weight with an average of 8 kg per woman for the first group and 15 kg per woman for the second group, and the average cholesterol and triglycerides were 280 mg/100 dL of blood and 230 mg/100 dL of blood, respectively, for the women of the first group. While it was 150 mg/100 dL blood and 90 mg/100 dL blood for the women of the second group, with some daily light exercise for all women in the two groups. Conclusion: This study indicates that consumption of three to five portions/day of fruit and vegetables is highly associated with prevention and a low risk of obesity, high blood fat, cholesterol, and cardiovascular disease. Therefore, we recommend relying on eating vegetables and fruits daily to reduce weight and blood fats, improve health, and nourish the body.

Environmental impacts of cement production in Ewekoro, Ogun State, southwestern Nigeria from 2005 to 2006 on surface water quality, vegetation, and workers' health

ABSTRACT The present study investigated the environmental impacts of cement production on surface water quality and vegetation in the vicinity of the industry. A 2-year study was conducted, covering both wet and dry seasons. Water samples were collected from the Akinbo River, where the cement industry discharges its liquid waste and effluents, and analysed for physical and chemical parameters using standard procedures. The vegetation around the factory was analysed for chlorophyll, plant density, basal areas, and heights of woody species. The study also assessed the health impacts of cement production on the factory employees and the residents living in the vicinity of the factory. The health assessment was based on hospital information obtained from two clinics in the villages near the factory. The water quality results revealed elevated concentrations of Ca and Fe. All the trees sampled around the study area had small basal areas and short heights. There was also a significant reduction in the chlorophyll contents of the vegetation. The health study showed a high incidence of upper-respiratory tract infections, cardiovascular diseases, arthritis, and dermatitis among factory workers. This study suggests a review of the existing dust-suppressing system put in place by the industry, to protect public health.

Climate variability through the lens of applied weather index insurance in Senegal-a novel perspective on the implications of decadal variation

IntroductionWeather-based index insurance is a financial instrument which allows smallholder farmers to protect themselves against climate shocks such as droughts and floods. In many cases, insurance indices are based on one or more earth observation datasets (e.g., rainfall, soil moisture, vegetative health) which are partly covering periods of more than 40 years. While remote sensing products and their associated data have improved over this time, understanding the historical climate variability and trends remains an essential piece in ensuring the development of indexes that best represent farmers’ risks. From a practical perspective, shortening time series to limit the risk of understudied climate variability, such as the Atlantic Multidecadal Variability, sometimes seems to be a quick solution. However, shorter time series jeopardize the overall robustness of the index. Therefore, understanding the links between climate variability, index design, and implications for farmers is key. Weather-based index insurance products in Sahelian West Africa usually face a challenge in robustly quantify underlying climatic decadal variation in seasonal rainfall.MethodsThis study analyzes the influence of decadal shifts in rainfall patterns in Sahelian West Africa, in particular Senegal, on index insurance calibration and design, concluding with practical recommendations for the next generation of drought risk finance instruments in the region.ResultsOur findings indicate that decadal variability has not led to a clear decrease in payouts in recent years compared to earlier years, despite an overall increase in seasonal rainfall. Rather, we find that interannual variability has increased which may be a more critical factor for assessing farmers’ agricultural risk than the increase in total rainfall.DiscussionFocusing on key moments of the cropping calendar in the design of an index shows that an increase in the total average rainfall per season does not result in fewer payouts.

Spatiotemporal dynamics of land use land cover change and its drivers in the western part of Lake Abaya, Ethiopia.

Understanding the dynamics of land use/land cover (LU/LC) changes and what drives these changes is essential for creating effective strategies for sustainable land management. It also helps to monitor the impact on ecosystems and biodiversity, which is crucial for policy-making. This study focused on assessing the trends, rates, and extent of LU/LC change and its causes in the western part of Lake Abaya in Ethiopia. To achieve this, we used a supervised classification method with a maximum-likelihood algorithm to map different land use land cover types. Additionally, we gathered information through field observations, focus group discussions (FGDs), and key informant interviews (KIIs) to identify the factors driving LU/LC change and its consequences between 1990 and 2022. The study findings revealed that vegetation and wetlands significantly decreased over this period, while water bodies, agricultural land, and settlements expanded at the expense of other land uses. The average normalized difference vegetation index (NDVI) values decreased from 0.368 in 1990 to 0.135 in 2022, indicating declining vegetation health. Local communities point to several factors responsible for these changes, including the expansion of agricultural land, increased settlement, firewood collection, and charcoal production (as proximate/immediate drivers), as well as population growth, poverty, unemployment, climate change, and policy-related issues (as underlying causes). Thus, it needs the development and implementation of an integrated and sustainable land management system, and strong land use and restoration policies in order to halt or reduce the rapid expansion of agricultural land and settlement areas at the expense of vegetation and wetlands.

Exploring Relationships Between Drought Indices and Ecological Drought Impacts Using Machine Learning and Explainable AI

Rangeland ecosystems in the United States hold great ecological, economic, and cultural value. However, the increasing frequency and severity of droughts pose potential threats to these ecosystems. This study used remotely sensed data, machine learning, and explainable artificial intelligence (XAI) to explore relationships between drought indices and vegetation health in the Cheyenne River Basin, USA. The study employed XGBoost Regressor and Extra Trees Regressor models in conjunction with SHapley Additive exPlanations (SHAP) to identify the most influential drought indices and environmental variables for predicting Normalized Difference Vegetation Index (NDVI), thereby uncovering indicators of vegetation stress in the basin. The XGBoost regressor was moderately successful at predicting NDVI, making the model suitable for subsequent XAI analysis using SHAP. SHAP results revealed that the Palmer Drought Severity Index (PDSI), the 90-day Standardized Precipitation Index (SPI), and snow water equivalent (SWE) were the most influential predictors of NDVI, indicating their strong association with changes in vegetation health in the Cheyenne River Basin. This study demonstrates the feasibility and value of applying XAI methods to investigate both the strength and directionality of ecological drought indicators—an approach that has been underutilized in drought research. These insights can inform future drought research, improve monitoring efforts, and help anticipate ecological drought impacts in the region.

Direct observations of evapotranspiration from three contrasting vegetation types on a coastal low-lying sub-tropical sand island

Coastal low-lying sand islands confront an imminent threat owing to global warming, primarily stemming from the confluence of rising sea levels and amplified precipitation variability. These islands harbour delicate freshwater reservoirs in unconfined aquifers, reliant significantly upon precipitation for replenishment. This investigation focuses on a sand island situated along Australia’s eastern coast, resembling islands prevalent across the Indian and Pacific Oceans. The study involves comprehensive instrumentation to monitor Actual Evapotranspiration (AET) concurrently with rainfall patterns and water table fluctuations. Eddy Covariance (EC) systems were employed across three distinct vegetation zones: a wetland, a swamp, and a commercially managed pine forest plantation. The aim was to gauge evapotranspiration dynamics, comparing computed reference evapotranspiration (ET0) at each site with EC-measured values to establish novel reference vegetation coefficients (Kv) tailored to these specific environments. Actual evapotranspiration was found the largest at the wetland, the swamp, and the pine plantation in decreasing order. Pine plantation evapotranspiration was found the most sensitive to water table depth, while the native swamp and wetland maintained high evapotranspiration rates through dry periods. A simple idealized water balance is proposed for the island for the dry and wet seasons. Large rainfall events during the wet season were found critical in providing recharge of the aquifer, highlighting the importance of such events in maintaining the freshwater resource and vegetation health on such islands.

Evaluating spectral properties of invasive plant species in Kentucky recreation areas

ABSTRACT This study examines the spectral characteristics of two invasive plant species, Amur honeysuckle (Lonicera maackii) and Callery ‘Bradford’ pear (Pyrus calleryana Decne), in central Kentucky’s recreational areas. Using a combination of spectrometer, satellite (National Agriculture Imagery Program (NAIP)) and unmanned aerial vehicle (UAV) data, this study investigated the correlation between leaf locations and normalized difference vegetation index (NDVI), as well as the impact of terrain features on vegetation indices. Significant variations in NDVI and normalized difference red-edge (NDRE) index values across study areas and leaf positions were observed. Terrain attributes, particularly slope and aspect direction, significantly influenced vegetative indices in one study site. Weak to moderate correlations were found between the spectrometer and UAV-derived NDVI and NDRE values. Additionally, the study highlighted the feasibility of upscaling UAV data for NAIP imagery comparison, reaffirming the superior accuracy of UAV imagery in assessing vegetation health. This integration of remote sensing techniques offers valuable insights into the spectral characteristics of invasive plants, essential for their effective mapping and management.