Decline Of Vegetation Research Articles

Assessment of dyna clue model for prediction of spatio-temporal dynamics of urbanisation and green space in Jaipur district, Rajasthan (India)

Urbanisation in Jaipur, Rajasthan’s capital, has significantly impacted land use and land cover (LULC) over the past four decades. This study addresses the pressing issue of how rapid urban expansion affects green spaces and environmental balance. Utilizing satellite imagery and a support vector machine (SVM) algorithm, LULC maps for 1993, 2003, 2013, and 2023 were generated to analyze changes in land cover, including agriculture, barren land, built-up areas, vegetation, wasteland, fallow land, and water bodies. The Dyna-CLUE model, incorporating variables such as elevation, slope, aspect, and proximity to streams and roads, was employed to forecast LULC changes for 2033 and 2043. The findings reveal a marked increase in built-up areas at the expense of vegetative cover, driven by urbanization, infrastructural development, agricultural expansion, climate change, and policy decisions. With Kappa coefficients indicating high agreement between classified land cover and ground truth data (84–90% accuracy), the study projects continued urban growth and further decline in vegetation. This underscores the urgent need for sustainable land use planning and environmental conservation in Jaipur. Proposed strategies to address these issues include establishing ecological zones, enforcing green building regulations, promoting mixed-use development, and implementing a 15-min green space policy. These measures aim to enhance green space distribution and support Jaipur’s transition towards a greener urban environment.

Agriculture High-Quality Development and Nutrition

Modern Agricultural production wants to efficiently produce plant products and benefits to meet people's needs. However, because of better plant species and varieties, overuse of fertilizer and overdose of pesticide alters the plant resources relationship, resulting in soil degradation, vegetation decline, and crop failure or waste of resources, which influence the quality of fruit and benefit and are unfavorable for the sustainable utilization of nature resources and crops high-quality development. After a couple of years of study, the results showed that Agricultural development went into agricultural high-quality development in 2017, which is to use effective measures or methods to maximize yield and benefits and meet the need of people's need for better Agricultural produce and a better life. The theory foundation of the sustainable utilization of natural resources and Agriculture high-quality production is the resources use limit by plants, vegetation carrying capacity, and critical period of plant resources relationship regulation. The methods of Agriculture high-quality development are to select excellent plant species or varieties based on site conditions and market needs before planting crop young plants and sowing seeds, adopt appropriate initial planting density when planting crop young plants and sowing seeds, and regulate the relationship between plant growth and resources need in the process of crop production in a timely and appropriate amount, especially the chemical fertilizer and pesticide to obtain the maximum yield and benefits and realize the sustainable utilization of natural resources and achieve agricultural high-quality development.

FV-DLV3+: a light-weight flooded vegetation extraction method with attention-based DeepLabv3+

ABSTRACT Climate change and human activities have led to a decline in wetland vegetation and sharp reduction of wetland area, which has caused considerable damage to the hydrological cycle and ecological functions of many wetlands. Flooded vegetation (FV) is an important wetland landscape, and strengthening the research on methods of the extracting FV using remote sensing or aerial photos plays an important role in monitoring changes to wetlands. This paper used optical satellite and synthetic aperture radar satellite images as data sources, construct a set of feature variable sets suitable for FV extraction; in addition, a deep learning method based on the semantic segmentation model DeepLabv3+, FV-DLV3+, is proposed for FV extraction. The method adopts the lightweight MobileNetV2 model as the backbone feature extraction network, introduces the SegFormer network to make up for the feature information loss caused by MobileNetV2, and further captures more effective features and suppresses the interference of the background through an attention mechanism to enhance the ability to extract feature boundaries, thereby improving the accuracy of FV extraction. Taking the wetland of Poyang Lake as the experimental area, Landsat 8 and Sentinel-1A satellite data were selected as the data sources to produce a deep learning dataset, and U-Net, DeepLabv3+, Swin-Unet, Swin Transformer, and CVTNet were selected for comparison when conducting model training and validating experiments. The results show that FV-DLV3+ has an excellent effect on FV extraction of the Poyang Lake wetland, which effectively reduces the computational complexity of the process while improving extraction accuracy; the mean intersection over union of this paper’s method was higher than the five above-mentioned methods, which was 12.93% and 5.85% higher than that of DeepLabv3+ and CVTNet, respectively.

Assessing the impact of Land Use/Land Cover Change on Land Surface Temperatures and SUHI: Case of Pune, India

The emergence of urban heat islands and rising land surface temperatures in developing countries like India is becoming a crucial concern for urban planners and policymakers. This study attempts to assess the impact of Land use and land cover (LULC) change on the Land Surface Temperature (LST) and Surface Urban Heat Islands (SUHI) in Pune, India, using remote sensing and Geographic information systems (GIS). A spatiotemporal analysis of the Landsat satellite imagery from 2000 – 2023 has been used to trace the LULC trends and compute LST variations across the years in land cover types. The thermal band, 10 of the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), is used for computing LST. Spatial indicators such as normalized difference vegetation index (NDVI) and normalized differentiated built index (NDBI) are also calculated. Results indicate that there has been a 30% decline in vegetation across the city over the years, alongside an increase of 16.64°C in LST due to urbanization-induced LULC changes. Furthermore, this study attempts to identify the thermal hotspots in the city. The results of this study can enable the assessment of urbanization and the formulation of informed climate-sensitive urban planning strategies. Keywords: Land Surface Temperature, Pune, Remote Sensing, GIS, Local Climate Zone, SUHI

Spatio-temporal analysis of urbanization effects: unravelling land use and land cover dynamics and their influence on land surface temperature in Aligarh City

ABSTRACT Rapid urbanization causes significant changes in land use and land cover (LULC), contributing to Urban Heat Islands (UHIs) and environmental challenges. This study analyzes two decades of Landsat satellite imagery to examine LULC changes and their impact on Land Surface Temperature (LST) in Aligarh City, India. Supervised classification using maximum likelihood techniques produced LULC maps with 85% accuracy. The results show a 21.10% increase in built-up areas, from 2338.83 hectares in 2001 to 2832.66 hectares in 2021, alongside a 24.55% decline in vegetation, from 620.28 hectares to 467.91 hectares. LST increased by 11.35°C, with a total maximum rise of 10.70°C between 2001 and 2021. Regression analysis revealed strong correlations between LST and spectral indices, with NDVI showing a negative correlation (R² = 0.89, 0.83, 0.78) and NDBI a positive one (R² = 0.87, 0.89, 0.79). The Aligarh main city area consistently recorded higher LST values than the AMU campus, with 88.60% of the city area in the 40–45°C range by 2021, compared to 52.87% for the campus. This study underscores the need for sustainable urban strategies to mitigate UHI effects and promote climate resilience.

Analyzing the spatio-temporal pattern of urban growth and its influence on urban heat islands in the Sekondi-Takoradi metropolis, Ghana

The rapid urbanization in Sekondi-Takoradi, Ghana has significantly transformed the land cover, resulting in the proliferation of impervious surfaces and a decline in vegetation. However, the influence of this urban growth on the development of urban heat islands (UHIs) in the metropolis remains understudied. This study aimed to fill this research gap by employing Landsat images to explore the influence of urban growth on urban heat islands in the metropolis from 1991 to 2023. The supervised random forest technique was utilized to map the land cover changes. Furthermore, the computed normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), land surface temperature (LST), and urban thermal field variance index (UTFVI) were used to analyze the influence of urban expansion on UHIs. The findings revealed a 63.07 km2 increase in built-up areas and a 60.99 km2 decrease in vegetation cover during the study period. This dramatic land use change led to a 3.1°C rise in mean LST and a 19.38 km2 expansion of areas affected by the UHI effect. The UTFVI analysis further indicated a 33.63 km2 increase in the worst ecological zone due to the temperature rise. Statistical analysis between LST, NDVI, and NDBI revealed significant variability in explaining the intensity of LST and UHI in the metropolis over the study period. The study equips city authorities and planners with the fundamental knowledge needed to prepare a sustainable development plan that alleviates adverse effects of urban growth and elevated temperature-related issues. Also, the findings contribute to the global efforts in promoting more livable and climate-resilient urban environments.

Satellites Reveal Spatial Heterogeneity in Dryland Photovoltaic Plants' Effects on Vegetation Dynamics

AbstractLarge‐scale photovoltaic (PV) plants are growing rapidly in drylands because of the rich solar radiation and vast unutilized land. The transformation of landscapes in dryland has threatened local fragile vegetation. Existing studies have investigated the issue by field observations and satellite data, yet the spatial differences in vegetation changes due to dryland PV plants deployment and underlying driving mechanisms remain poorly understood. In this study, Landsat Normalized Difference Vegetation Index data were used to assess the vegetation changes disturbed by PV plants in China's drylands. We further identified environmental factors affecting vegetation changes based on random forest regression model. Our findings reveal the spatial heterogeneity in the impact of PV plants on vegetation dynamics—PV plant deployment promoted the growth of vegetation in the vast majority of arid and hyper‐arid drylands, while it tends to cause vegetation decline in the sub‐humid and semi‐arid drylands. The impact of PV plants on vegetation dynamics depends on local environmental conditions. We found that deploying PV plants in areas with sparse vegetation, low humidity, and long sunshine duration is more likely to promote vegetation restoration. The findings and data maps with highly detailed information can help guide solar energy operators in siting and ecological restoration to enhance techno–ecological synergies in the future.

Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

Spatio-temporal dynamics of agricultural and built-up areas in the Niaye area of Pikine urban wetland (Dakar, Senegal)

The Niaye area of Pikine, a wetland with favorable physical and climatic conditions, is a rich and varied ecosystem that guarantees a supply of water and fertile soil, conducive to market gardening. This plays an important socio-economic role in the Dakar region. However, this sensitive and fragile ecosystem is often affected by poor farming practices. The aim of the study was to analyze the spatial evolution of agricultural areas and built-up areas in order to identify related environmental problems. Interview guides, aerial photographs from 1966 and 1978 and Digital Globe Over view 2 satellite images from 2004 and 2022 were used. The interview guides were used to obtain data on the management of the market gardening activity, and also to identify the environmental problems it generates. Satellite data processing revealed, through cartographic analysis, a progressive market gardening dynamic of +5.07%. In addition, the built-up area around the lake has increased by over 200%, resulting in a 68.4% decline in upland vegetation and a migration of market gardeners to the lake. This situation leads to the destruction of market garden crops in the rainy season as the water rises. What's more, the intensification of market gardening in response to the population's growing demand for vegetables has not failed to encourage bad practices, including the use of pesticides and sewage, which is a public health problem and a threat to the future of this wetland.

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.

Foliar applied potassium nanoparticles (K-NPs) and potassium sulfate on growth, physiological, and phytochemical parameters in Melissa officinalis L. under salt stress.

Salinity stress significantly constrains agricultural productivity and vegetation decline worldwide, particularly in Iran. Potassium, the second most prevalent nutrient in plants, is well known to be essential for cell metabolism. Here, the effects of potassium fertilizer in two biogenic nanoparticles (K-NPs) and conventional (potassium sulfate) forms (0.1 mg/ml) on Melissa officinalis L. under salinity (0, 50, 100, and 150 mM) were investigated. The results demonstrated that stress markers (electrolyte leakage, malondialdehyde, and hydrogen peroxide) increased as salinity levels increased. Plant growth parameters (shoot and root length, fresh and dry weight of shoot and root) and physiological and photosynthetic parameters (stomatal conductance, relative water content, chlorophyll fluorescence, and photosynthetic pigments) were reduced in salinized plants. The highest reduction in fresh weight root, dry weight root, fresh weight shoot, dry weight shoot, root length, and shoot length was recorded under 150 mM NaCl by 30.2%, 51.6%, 30.5%, 24.7%, 26.4%, and 21%, respectively. In contrast, bulk potassium sulfate and K-NPs increased these parameters. Furthermore, K-NPs improved M. officinalis tolerance to NaCl toxicity by enhancing the content of osmolytes such as proline, soluble sugars, and antioxidant enzymes, improving antioxidant contents such as phenols, tannins, anthocyanins, and flavonoids; increasing total protein; and lowering stress markers in plant tissues. Given the results of the physiological, biochemical, and phytochemical parameters obtained from this study, it can be stated that K-NPs, in comparison to the conventional form of potassium fertilizer, exhibit a greater potential to mitigate damages caused by salinity stress in M. officinalis plants.

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

Sentinel-2 Perspective: Land Use and Land Cover Dynamics in East and West Karbi Anglong

This study delves into the dynamics of land use and land cover (LULC) in East and West Karbi Anglong using Sentinel-2 satellite imagery. In response to escalating environmental changes, remote sensing technologies, particularly Sentinel-2, offer crucial insights for sustainable land management. Through an analysis of LULC patterns, the study aims to inform evidence-based decisions for environmental conservation and resource management in the region. The present study highlights the utility of remote sensing in LULC assessments and acknowledges the necessity for customized approaches in regions with distinctive features such as Karbi Anglong. Methodologically, temporally stratified Sentinel-2 datasets were utilized, with meticulous pre-processing and accuracy assessments ensuring reliability. Results indicate notable shifts in LULC classes, including urban expansion and vegetation decline. Despite limitations, such as potential inaccuracies in data processing, the study emphasizes the importance of monitoring environmental changes and advocating for sustainable land management strategies.

Local Institutions Performance in Mangrove Forest Management on Small Islands: Case Study in Buano Island, Maluku Province, Indonesia

Mangrove forests on small islands play an essential role in the stability of island ecosystems. It is crucial to analyze the performance of local institutions in forest management to increase the community’s active role in preserving mangrove forests. This research aims to analyze the performance of local institutions managing mangroves on small islands. The research was conducted on Buano Island, Maluku, Indonesia. In-depth interviews and participant observation were used to obtain data. Data analysis uses a Situation, Structure, Behavior, and Performance (SSBP) approach. The research results show that the community is very dependent on mangrove forests. Resources are owned by the clan group (Soa) but managed by members of the clan group (family). Thus, the phenomenon occurs when common pool resources are managed by private (Dati/Nuru) proprietors. The community regulates the utilization of potential forest resources by dividing management areas controlled by each Soa/Nuru group. The preservation of natural potential on Buano Island is supervised by Kewang and Sasi culture. Applying customary rules increases the community’s active role in managing resources and supporting village management. However, mangrove forest logging activities continue to occur. This performance shows a very high decline in mangrove forest vegetation and regeneration of mangrove forests. Stakeholder support is needed to increase the knowledge and skills of the community, especially traditional leaders, in implementing sustainable forest management. Keywords: forest management, local institution, mangrove, small island

The Contributions of Climate and Human Activities to Water Use Efficiency in China’s Drylands

In the context of global warming, terrestrial ecosystems have undergone significant variations. China has implemented a variety of ecological engineering methods to enhance carbon stocks. However, understanding the spatial and temporal dynamics of carbon and water in drylands under climate change remains limited. Here, our research elucidates carbon and water dynamics in China’s drylands over the last two decades, with a focus on understanding spatial–temporal changes and the effects of ecological engineering on the carbon–water cycle. Furthermore, this study investigates the relationships among climate change, water use efficiency (WUE), and its components—Gross Primary Productivity (GPP) and Evapotranspiration (ET)—identifying key climatic drivers and assessing possible directions for enhancing WUE under changing climate conditions. Our research indicates that both GPP and ET have significantly increased over the past 20 years, with growth rates of 4.96 gC·m−2·yr−1 and 4.26 mm·yr−1, respectively. Meanwhile, WUE exhibited a slight declining trend, at a rate of −0.004 gC·mmH2O·yr−1. This confirms the positive impact of vegetation restoration efforts. We found that fluctuations in interannual WUE were influenced by human activities and climate change. Precipitation (Prec) was the key climatic factor driving the GPP increase. Both solar radiation (Solra) and Prec were crucial for the interannual variation of WUE. Interestingly, WUE was the main factor affecting GPP development. The decline in WUE in drylands is linked to interannual variability in WUE and increased Vapor Pressure Deficit (VPD) due to warming. Seasonal variations in how WUE responds to climatic factors were also observed. For instance, fall rainfall increased WUE, while spring rainfall decreased it. Fall WUE was highly sensitive to VPD. Spatially, we found higher WUE in China’s eastern and Xinjiang regions and lower in inland areas and the Tibetan Plateau. Geomorphologic factors and soil conditions were the main drivers of this spatial variability in WUE. Temperature (Tem), Solra, VPD, and relative humidity (Relah) also played significant roles. Our results show a generalized inverse persistence in WUE variability. This suggests a potential for increased WUE in the eastern regions and a risk of decreased WUE on the Tibetan Plateau. Addressing the threat of vegetation decline in arid regions, particularly within the Tibetan Plateau, is crucial. It is essential to adapt forestry practices to complement the carbon and water cycles in these landscapes.

Identifying the Climatic and Anthropogenic Impact on Vegetation Surrounding the Natural Springs of the Arava Valley Using Remote Sensing Methods

Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was to determine the factors that cause fluctuations in water availability to springs of the hyper-arid Arava Valley (Israel/Jordan). Using the Standard Precipitation Index, we statistically classified the historical record of yearly rainfall for the past four decades into clusters of dry and wet sub-periods. We assessed changes in vegetation cover around the springs using the Landsat-derived Normalized Difference Vegetation Index (NDVI) for each sub-period. To assess the anthropogenic effects, we examined the correlations between vegetation cover, water extraction from the aquifer, and the status of adjacent agricultural plots that share a hydrological connection with the springs. Our findings revealed fluctuations between wet and dry sub-periods over the last four decades. We observed high responsiveness of vegetation cover around the springs to these fluctuating sub-periods. Of the 25 studied springs, 12 were directly influenced by anthropogenic factors—7 experienced a decline in vegetation, which we attributed to water extraction from the aquifers, while vegetation increase in 5 springs was attributed to water seepage from agricultural areas upstream. In conclusion, addressing vital habitats such as natural springs in arid drylands requires a holistic approach that integrates long-term climatic, ecological, and anthropogenic observations.

Mapping multi-decadal wetland loss: Comparative analysis of linear and nonlinear spatiotemporal characterization

Wetlands provide critical habitat for birds and endangered species, and can be influenced by shifts in water availability and land use. The Landsat image archive can help both document historical wetland change and monitor current dynamics, but multi-decadal retrospective studies face two important challenges: 1) many important ecohydrological processes occur at scales finer than the field of view of a 30 m Landsat pixel, and 2) ecologically meaningful wetland response units frequently exhibit spatiotemporal complexity in both vegetation and surface water which may not be well-represented by a simple study area spatial mean. Here we explore a novel method for contending with these challenges with application to the rapidly changing Andrade Mesa wetlands along the US-Mexico border. Using a 26-year (1995–2021) Landsat image time series, we track changes in surface water and vegetation using green vegetation (V) and dark (D) fractions estimated from spectral mixture analysis (SMA). Spatiotemporal dynamics in both V and D fractions were characterized using two techniques: linear Principal Component Analysis (PCA) and nonlinear manifold learning (via Uniform Manifold Approximation and Projection; UMAP). Bounding temporal endmembers were identified from PCA and used to construct a temporal mixture model which quantified total area of surface water and vegetation loss, including an uncertainty estimate. UMAP clearly distinguished areas with differing onset of vegetation and water loss which were not identifiable using PCA. Change maps were then associated with different mechanisms and outcomes, including possible mechanical clearing of vegetation and unexpected greening of woody vegetation. The overall timing of surface water and vegetation decline follows the lining of the All-American Canal, suggesting a potential cross-border hydrologic association between loss of a wetland in Mexico and water efficiency measures implemented in the United States.

Land Use and Land Cover Trends and Their Impact on Streamflow and Sediment Yield in a Humid Basin of Brazil’s Atlantic Forest Biome

Understanding the trends in land use and land cover (LULC) is crucial for modeling streamflow and sediment yield, particularly in hydrological basins. This study examined the impact of LULC on the dynamics of streamflow and sediment yield within a humid tropical basin of the Atlantic Forest biome in Brazil, focusing on the period from 2000 to 2016. Changes in LULC were analyzed using annual MapBiomas data products for the same period. The Soil and Water Assessment Tool (SWAT) model was deployed to simulate streamflow and sediment yield based on LULC changes. To investigate temporal trends in LULC, a suite of non-parametric statistical tests, including the Mann–Kendall, Pettitt, and Sen’s slope estimator tests, was employed. Ecological diversity indices such as Shannon–Weaver, Simpson, and Pielou were applied to assess forest fragmentation, along with the Forest Fragmentation Index. The results revealed a growing trend in urban and sugarcane areas, coupled with a decline in dense vegetation, mangroves, and other forms of dense vegetation. With regard to the correlation between land uses and hydrological variables, the findings indicate minor variations in hydrological balance, attributable to the not-so-significant changes among the studied land-use scenarios, except for sediment yield estimates, which showed more considerable alterations. Notably, the estimates for 2000 and 2013–2016 were the most divergent. In a broader scientific context, this research conclusively establishes that the incorporation of dynamic LULC data into the SWAT model augments the precision and robustness of simulations pertaining to agricultural watersheds, thereby enabling a more comprehensive hydrological characterization of the study area.

An assessment of long-term and large-scale wetlands change dynamics in the Limpopo transboundary river basin using cloud-based Earth observation data

Significant progress has been made in monitoring and assessing the effects of land use and land cover (LULC) changes on wetland extent. However, our understanding of wetland within the transboundary basins has been limited by the scarcity of available data on their dynamic changes over time. This study aimed to address this gap by analyzing the long-term and large-scale spatio-temporal extent of wetland in the Limpopo transboundary river basin (LTRB) over a 20-year period (2000–2020). To achieve this, we utilized the Google Earth Engine (GEE) cloud-computing platform and various remotely sensed data. The study had two primary objectives; (1) to examine LULC changes over time using machine learning algorithms applied to multisource remotely sensed data in GEE, and (2) to assess the relationship between LULC changes and the extent of wetlands in the basin. A total of nine land cover classes were identified, including shrublands, croplands, bare-surface, wetlands, sparse vegetation, tree cover, built-up areas, and grasslands. Shrublands covered 76–82% of the LTRB. On the other hand, wetlands and sparse vegetation were the least dominant, with proportions ranging from 0.3 to 2%. The overall accuracy of the classification results was within acceptable ranges, ranging from 77 to 78%. The study further revealed a continuing decline in wetlands extent and sparse vegetation, with average rates of 19% and 44%, respectively. Conversely, shrublands, croplands, and tree cover showed an increase, with average rates of 0.4% and 12.4% respectively. A significant finding was the replacement of a substantial portion (40%) of wetland areas with built-up areas, indicating that urban expansion is a major driver of wetland shrinkage in the study area. These results provide valuable insights into the declining extent of wetlands in the LTRB. Such findings are crucial for environmental management efforts, as they provide information on which wetlands should be prioritized when implementing strategies to prevent the negative impacts of LULC changes on wetlands in the area. Therefore, contributing towards achieving sustainable development goals relating to freshwater ecosystems protection and management.

Grassland vegetation decline is exacerbated by drought and can be mitigated by soil improvement in Inner Mongolia, China

Grassland activity is highly susceptible to drought while drivers from climate and soil attributes can largely affect drought propagation. However, understanding how these drives regulate the risk of vegetation decline under drought conditions remains limited, potentially impeding the adoption of appropriate adaptation strategies. To address this knowledge gap, we conducted a case study focusing on grassland activity in Inner Mongolia, China. In this study, we applied copula theorem to estimate the conditional probabilities of vegetation decline under drought conditions. Additionally, we utilized a structural equation model and a machine learning approach to identify the relative contributions of external drivers to the risk of vegetation decline. Our findings demonstrated a positive correlation between anomalies in vegetation activity and the status of water balance, and grassland vegetation in drier regions exhibited a more rapid response to water deficit. Increasing water deficit continuously reduced vegetation activity with risks of 77.27 %, 83.83 %, and 88.35 % under moderate, severe, and extreme drought conditions, respectively. Furthermore, the risks of vegetation decline under drought conditions were primarily governed by climate attributes, followed by soil properties and topography. Soil with high soil organic carbon stock content contributed significantly to mitigating the adverse effects of drought on grassland vegetation. In addition, we detected nonlinear patterns among environmental drivers and vegetation decline risks caused by drought. These findings highlight the importance of climate, soil properties, topography, and their intricate interconnections in regulating vegetation decline. This knowledge provides valuable insights into drought risk management for vegetation in advance and offers potential solutions to enhance vegetation resistance in the face of extreme drought events.