Mangroves are amongst the richest ecosystems in the world providing valuable goods and services to millions of people while enhancing the resilience of coastal communities against climate change ...induced hazards, especially island nations. However, these mangroves are severely affected by many anthropogenic activities. Therefore, understanding the spatial variability of mangroves in island nations is highly essential in the events of ongoing climatic change. Thus, this study assessed the use of remote sensing techniques and GIS to map and monitor mangrove cover change at selected sites, namely Le Morne and Ferney, on the tropical island of Mauritius. Freely available 2013 SPOT-5 and 2023 Sentinel 2A images were retrieved and processed using ArcGIS Pro tools and SNAP; mangroves were mapped based on Google Earth Pro historical imagery and ground truthing at the respective sites. Following the application of selected vegetation indices, GLCM and PCA analysis, mosaicked images were classified using the Random Trees algorithm. Kappa values of all the classified images were in the 90 s; Le Morne showed a significant increase in mangrove cover over the decadal scale with main class change from mudflat to mangroves. This study demonstrates how geo-spatial tools are crucial for monitoring mangroves as they provide spatially explicit and time sensitive information. Decision makers, researchers, and relevant stakeholders can utilize this data to bolster tailored mitigation and adaptation strategies at specific sites, thereby enhancing resilience to climate change.
Abstract In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) ...models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R 2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.
Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food ...sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.
The establishment and alteration of any coastal feature is largely dependent upon complex hydrological and geomorphologic processes. Therefore, understanding hazard factors and threat risk level is ...crucial for mitigating risk in coastal zones. This study examines coastal vulnerability factors and their influence along the Coastal Andhra Pradesh (CAP) region in India. CAP has been exposed to frequent hydrological and meteorological hazards due to variations in the geographical, geological, and bathymetric characteristics. Despite substantial vulnerabilities, the risk to the coastline of Andhra Pradesh has not been rigorously evaluated. The current research systematically reviews the drivers and effects of hazards and vulnerabilities in CAP. Findings indicate that urban cities have a considerably higher risk of cyclones and floods due to their locations on the Bay of Bengal tectonic plate, the topology of this coastal region, and higher population density. The study revealed that the Coastal Vulnerability Index (CVI) data along CAP is mostly gathered using low-resolution satellite data and/or field observation surveys. The study further revealed that there are very few existing mitigation strategies developed or discussed within the obtained results. However, more accurate data gathering techniques for coastal vulnerability factors are available such as Unmanned Aerial Vehicles (UAVs): Air-borne and LiDAR sensors, which provide very high resolution data and low-cost accessibility to physically inaccessible places, making them suitable for vulnerability data collection in coastal locations. These findings are useful for stakeholders seeking to reduce or ameliorate the impact of coastal disasters and their impacts on the CAP economy, environment, and population. The study further helps to reduce the existing shortcomings in the assessment techniques used previously.
ABSTRACT This study evaluates the rainfall erosivity (RE) and erosivity density (ED) over the Kelani River basin, Sri Lanka for a period of 31 years (1990–2020). The river basin is well known for its ...annual floods during the southwestern monsoon season and severe erosion including landslides can be observed. The catchment was analyzed for its RE using the Wischmeier and Smith algorithm and for its ED using Kinnel's algorithm. The monthly rainfall data spreading over the river basin were used to analyze the monthly, seasonal, and annual RE and ED. Interestingly, the annual RE showed a linear increasing trend line over 31 years, and a maximum value of 2,831.41 MJ mm ha−1 h−1 yr−1 was able to be observed in the year 2016. The RE peaks in May which is in the southwestern monsoon season. This reveals that the risk of soil erosion in the basin is high in the southwestern monsoon season. In addition, land use and land cover changes over the years have adversely impacted the erosion rates. Therefore, it is highly recommended to investigate soil erosion in-depth and then implement relevant regulations to conserve the soil layers upstream of the river basin.
The significance of coastal regions to the infrastructure and the need to protect such assets are crucial to the economy of countries. Therefore, there is a real need to enhance the understanding of ...coastal infrastructure susceptibility as well as to develop methodologies to estimate vulnerability. A review of the literature regarding coastal vulnerability reveals that the focus has been on geomorphological and physical parameters but not infrastructure and the associated fiscal factors. In order to address this knowledge gap, an innovative model is developed, i.e., the Coastal Infrastructure Vulnerability Index (CIVI). Then the model is applied to the case of the Aberystwyth coast demonstrating how the model estimates the vulnerability of the coastal infrastructure (comprising population, commercial and residential properties). Subsequently, the CIVI scores were used to rank coastal sections into five classes, ranging from extremely low to extremely high, based on the relative magnitude of the vulnerability. The rankings for each parameter were combined, and then an index value was calculated. Results revealed that Aberystwyth contains more than £40 billion of coastal infrastructure vulnerability and more than 10,000 inhabitants are at the high coastal risk posed by flooding, erosion, storm surges, and strong winds.
Turmeric cultivation primarily thrives in India, with significant presence in Bangladesh, Thailand, Cambodia, China, Indonesia, Philippines, and Malaysia. India leads globally in both area (0.19 Mha) ...and production (0.844 MT) of turmeric. Despite this, there's a recognized gap in research regarding the combined effects of mulching and drip fertigation on turmeric growth in Tamil Nadu conditions. Therefore, this study aims to assess how mulching and drip fertigation impact water usage, turmeric growth, productivity, and post-harvest soil health via field experiments. The treatments comprise of different mulching techniques (M1-25 μm Plastic Mulching, M2-50 μm Plastic Mulching, M3- Organic Mulching, M4-No Mulching) as the main plot, coupled with various irrigation regimes as the sub plot (S1-100% of pan evaporation, S2- 80% of pan evaporation, S3- 60% of pan evaporation), in a split plot design. Findings show that 50-μm Plastic Mulching (M2) notably enhances turmeric growth parameters, including plant height, biomass, leaf count, and yield attributes such as tillers and rhizomes, compared to no mulch. Significantly, when 80% of pan evaporation is utilized in drip irrigation, it showcases the most pronounced plant growth and yield characteristics, with plastic mulch at this level significantly improving water and nutrient use efficiency while increasing beneficial compounds like Curcumin and oleoresin. The highest fresh rhizome yield is observed with 50-μm plastic mulch and 80% pan evaporation (M2S2), displaying a 39.79% increase compared to the control. Additionally, the study notes effects on microbial populations and mulch degradation. Economically, M2S2 exhibits the highest profitability with a benefit-cost ratio of 3.23 compared to other treatments. Implementing these practices not only enhances yields but also conserves water (estimated at 9.15 mm3) while emphasizing the importance of drip irrigation, fertilizer application, and mulching in boosting turmeric productivity, optimizing resource efficiency, and ensuring economic and environmental sustainability.
•Drip irrigation with plastic mulching evaluated for two years with field experiments.•50 μm Mulching registered maximum growth and yield attributes.•Irrigation as 80% pan evaporation through drip irrigation performed better than control.•Higher content of Curcumin and oleoresin were also observed in the same treatment.•Drip irrigation with mulching results in higher efficiency of water, nutrients.
The COVID‐19 health crisis has imposed extensive shocks to many global systems, particularly the UK food production chains, further challenging Eurocentric development discourses and stereotypes. ...Thus, this paper investigates how the pandemic has challenged the UK's development status by analysing how the pandemic has impacted the country's food industry. A literature review was conducted and used to identify, select and critically appraise publications between 2000 and 2021 discussing the challenges in the UK food system. The findings reveal that the UK's food industry is unsustainable as there are significant flaws in the system, that is food insecurity and food waste that go unaddressed. The impact of the pandemic has exacerbated the social and economic impacts of operating with such a system. Compounded with the geopolitical adjustments caused by Brexit, the UK is faced with the challenge of restructuring and developing new frameworks such as policies, regulations, schemes and partnerships to support the food industry's sustainability. Lastly, the findings reinforce that ‘developed’ and ‘developing’ nations encounter similar food challenges, which manifest differently in various landscapes and contexts. Therefore, the world (not just the UK) needs to shift away from Eurocentrism, moving towards a universal but equally personalised development outlook. This review provides an outline of the major problem areas in the UK food system and presents potential solutions aimed at helping guide the government's decision‐making process.
The COVID‐19 pandemic reinforced the reality that the UK's Food system is unsustainable The findings provide a unique insight on the UK food system from a Global development lens. Bridges the gap between pre‐pandemic literature and emerging post‐pandemic research. The research encourages new innovative ways to conceptualise and measure Development.
This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven parameters: ...land use, temperature, slope, proximity to roads and settlements, elevation, and aspect. All these parameters were derived using GIS techniques with ArcGIS10.4 and QGIS3.16. Data from Remote Sensing sources, particularly the MODIS hotspot real-world dataset, were employed to gather fire count information for the year 2020. Validation was conducted through the merging hotspot technique and kernel density estimation (KDE). The research findings highlight the districts in the Central and Uva provinces, such as NuwaraEliya (10.3 km2), Kandy (2.74 km2), and Badulla (10.41 km2), as having a “very low risk" of forest fire potential. Conversely, districts like Hambanthota (0.1 km2), Kaluthara (0.04 km2), and Kurunegala (0.2 km2) exhibit a “very high risk" of forest fire potential, although it is negligible compared country's total area. Overall, the study suggests that Sri Lanka is not currently facing a significant threat of forest fires and is a “medium risk" of forest fires as 49.49% of land falls under this category. These results are of immense value to relevant authorities, including the Ministry of Wildlife and Forest Resources Conservation, in formulating effective strategies to manage and mitigate forest fire risks in the country.
The Saudi construction industry is going through a process of acclimatizing to a shifting fiscal environment. Due to recent fluctuations in oil prices, the Saudi construction sector decided to adjust ...to current trade-market demands and rigorous constitutional regulations because of competitive pressures. This quantitative study assesses and compares existing flat design vs. mid-terrace housing through cost estimation and design criteria that takes family privacy into consideration and meets the needs of Saudi Arabian families (on average consisting of seven members). Five pilot surveys were undertaken to evaluate the property preference type of Saudi families. However, Existing models did not satisfy the medium range family needs and accordingly a 5D (3D + Time + Cost) Building Information Modelling (BIM) is proposed for cost benefiting houses. Research results revealed that mid-terrace housing was the best option, as it reduced land usage and construction costs. While, 5D BIM led to estimate accurate Bill of Quantities (BOQ) and the appraisal of construction costs.
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