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Urbanization dramatically alters landscapes and ecological conditions. This study examines the environmental conditions within Dar es Salaam, Tanzania, using the Settlement Surface Ecological Index (SSEI). The SSEI combines biophysical parameters from satellite imagery to assess ecological health, revealing disparities across diverse urban typologies. This study’s methodology demonstrates advancements in using the Calculate Composite Index tool for streamlined index creation and greater flexibility in urban analysis. Statistical analysis reveals crucial relationships between ecological conditions and variables like tree cover, impervious surfaces, vegetation moisture, and Land Surface Temperature (LST). Multivariate clustering highlights distinct areas of high and Low SSEI, emphasizing disparities in urban ecological health. This study demonstrates how composite indices like the SSEI can inform targeted urban planning, promote green infrastructure, and mitigate the negative impacts of urbanization, ensuring both equitable and sustainable development in the face of climate change. [ABSTRACT FROM AUTHOR]

Background: Informing local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance. Methods: We leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees. Results: Using OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making. Conclusions: Gathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need. [ABSTRACT FROM AUTHOR]

Satellite imagery is a potent tool for estimating human wealth and poverty, especially in regions lacking reliable data. This study compares a range of poverty estimation approaches from satellite images, spanning from expert-based to fully machine learning-based methodologies. Human experts ranked clusters from the Tanzania DHS survey using high-resolution satellite images. Then expert-defined features were utilized in a machine learning algorithm to estimate poverty. An explainability method was applied to assess the importance and interaction of these features in poverty prediction. Additionally, a convolutional neural network (CNN) was employed to estimate poverty from medium-resolution satellite images of the same locations. Our analysis indicates that increased human involvement in poverty estimation diminishes accuracy compared to machine learning involvement, exemplified with the case of Tanzania. Expert defined features exhibited significant overlap and poor interaction when used together in a classifier. Conversely, the CNN-based approach outperformed human experts, demonstrating superior predictive capability with medium-resolution images. These findings highlight the importance of leveraging machine learning explainability methods to identify predictive elements that may be overlooked by human experts. This study advocates for the integration of emerging technologies with traditional methodologies to optimize data collection and analysis of poverty and welfare. [ABSTRACT FROM AUTHOR]

The extraction of building footprints from aerial photos and satellite imagery plays a crucial role in change detection, urban development, and detecting encroachments on agricultural land. Deep neural networks offer the capability of extracting features and provide accurate methods for detecting and extracting building footprints from satellite imagery. Image segmentation, the process of dividing an image into coherent parts, can be accomplished using two types: semantic segmentation and instance segmentation. Convolutional neural networks (CNN) are commonly used for both instance and semantic segmentation tasks. In this paper, we propose a hybrid approach to extracting building footprints from low-resolution satellite imagery using instance segmentation techniques. Our analysis demonstrates that the mask region-based CNN (R-CNN) architecture with a ResNet-34 backbone and PointRend head to improve the bounding-boxes and mask prediction achieves the highest performance, as evidenced by various metrics, including an average precision (AP) score of 83.39% and an F-1 score of 85.71%. This approach holds promise for developing automated tools to process satellite imagery, benefiting fields such as agriculture, land use monitoring, and disaster response. [ABSTRACT FROM AUTHOR]

The Salish Sea, a dynamic system of straits, fjords, and channels in southwestern British Columbia, is home to ecologically and culturally important bull kelp (Nereocystis luetkeana) forests. Yet the long-term fluctuations in the area and the persistence of this pivotal coastal marine habitat are unknown. Using very high-resolution satellite imagery to map kelp forests over two decades, we present the spatial changes in kelp forest area within the Salish Sea, before (2002 to 2013) and after (2014 to 2022) the 'Blob,' an anomalously warm period in the Northeast Pacific. This analysis was spatially constrained by local environmental conditions. Based on nearshore sea surface temperatures (SSTs) from four decades (1984-2022), we found two periods of distinct increases in SST, one starting in 2000 and another in 2014. Further, the highest SST anomalies occurred on warmer coastlines in the enclosed inlets and the Strait of Georgia, while smaller anomalies were found on colder coastlines near the Strait of Juan de Fuca and the Discovery Passage. The total area of bull kelp forests from 2014 to 2022 has decreased compared to 2002 to 2013, particularly in the northern sector of the Salish Sea. Using the satellite-derived kelp data, we also present an analysis of kelp persistence compared with historical distribution of kelp forests depicted on British Admiralty Nautical Charts from 1858 to 1956. This analysis shows that warm, sheltered areas experienced a considerable decrease in persistence of kelp beds when compared to satellite-derived distribution of modern kelp, confirming a century-scale loss. In particular, the presence of kelp forests in the Strait of Georgia and on the warmest coasts has decreased considerably over the century, likely due to warming temperatures. While the coldest coasts to the south have maintained their centennial persistence, the northern Salish Sea requires further research to understand its current dynamics. This research contributes to a wider understanding of temporal and spatial factors for kelp from the regional perspective of the Salish Sea. [ABSTRACT FROM AUTHOR]

In today's world, the need for higher levels of security in storing and transferring data has become a key concern. It is essential to safeguard data from any potential information leaks to prevent threats that may compromise data confidentiality. Therefore, to protect critical and confidential satellite imagery, this paper proposes a novel encryption method based on the combination of image bands scrambling with chaos and the advanced encryption standard (AES). The proposed approach aims to enhance the security of satellite imagery while maintaining efficiency and robustness against various attacks. It possesses several appealing technical characteristics, notably a high level of security, a large key space, and resilience to single event upsets (SEUs) and transmission errors. To evaluate the performance of the proposed encryption technique, extensive experiments have been conducted by considering factors such as security level, resistance to SEUs, and computational efficiency. Our results demonstrate that the proposed method achieves a high level of security and a large key space, ensuring the confidentiality and integrity of satellite imagery data. Furthermore, the method exhibits resilience against SEUs and transmission errors, and offers efficient processing, making it suitable for real-world applications. [ABSTRACT FROM AUTHOR]

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods. [ABSTRACT FROM AUTHOR]

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects. Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height. Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species. Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts. [ABSTRACT FROM AUTHOR]

To test the potential of high‐resolution satellite image analysis for assessing and predicting the mesoscale (<10 km in this study) effects of climate and environmental change on temperature and primary productivity in fishing grounds, we conducted satellite image analysis around an island in a coastal strait west of Japan from 2018 to 2023. We observed a distinct north–south gradient in sea surface temperature (SST) and chlorophyll a concentration (CHL) over approximately 20 km of the transect, which was likely affected by the current system. The model configuration suggests that the frequency of southward currents during winter–spring can control the magnitude of spring phytoplankton blooms. In the study region, an increase in SST at a rate of 0.06–0.13°C y−1 occurred during the study period, accompanied by a decrease in CHL. The north–south gradient in the rate of change suggests that the variation in the temperature and flow rate of the Kuroshio Current into the study area was due to these abrupt changes. The relationship between the annual mean SST and CHL was also spatially heterogeneous, showing a higher sensitivity of CHL to SST in the southwest of the island than in the north. In addition to the intrusion of warm and oligotrophic Kuroshio waters, the spread of less saline and more eutrophic coastal waters likely influenced this spatial heterogeneity. The satellite image analysis in the present study successfully revealed mesoscale mosaics of environmental conditions in coastal fishery grounds. [ABSTRACT FROM AUTHOR]

The world has a considerable portion of its population living in vulnerable conditions, with about 47% of people living in poverty and approximately 9.3% in extreme poverty. To understand and face this problem, it is essential to have access to updated data on poverty and trace the socioeconomic profile of the population. In this context, Censuses and demographic surveys play a crucial role. However, conducting a Census in certain regions faces significant challenges, both in terms of cost and logistics. It is a complex process that demands significant resources and an extensive data collection effort throughout the national territories. Given the difficulties of collecting data in the traditional way, passively collected data sources, such as satellite imagery, may be an alternative way to measure these results. Therefore, we use computational techniques to combine nighttime and daytime satellite imagery to predict socioeconomic indicators. Our results demonstrate the efficacy of combining these features in predicting the average income and demographic density of cities across Brazil. In particular, the use of neural networks for inferring socioeconomic indicators has been demonstrated highly effective. This technique holds the potential to enhance our comprehension of the socioeconomic landscape in Brazil and provide technical means for the analysis of other countries that face difficulties in conducting Censuses. Our method has convincingly demonstrated that satellite images can be used and applied for socioeconomic purposes. [ABSTRACT FROM AUTHOR]

With the rise of forced displacement, attention has turned to the economic impact of refugees. However, few studies investigate long-term impacts. We use data for Tanzania for the period 1985–2015 to examine the effect of camps on urbanisation and local development, exploiting a unique satellite-derived dataset of high spatial resolution and temporal frequency. We show a modest but significant effect of refugee camps on built-up area up to a 100 km distance. We then match camp locations to regional gross domestic product, local consumption spending and employment patterns. Output in areas with camps grew at a faster rate during camp operation, but closure of camps was associated with change in economic activity. Activity induced by camps is largely in non-tradeable goods and services rather than inducing longer run structural transformation. [ABSTRACT FROM AUTHOR]

There is an urgent need for the development of cutting-edge port infrastructure monitoring solutions that exploit different multimodal data towards timely optimal port management strategies and decision-making. Advanced monitoring applications allow for optimising maintenance, rehabilitation, and upgrade actions by assessing the structural integrity of port structures that is affected by a vast variety of stressors such as aging, imposed loads, inadequate maintenance treatments, human-induced factors, natural hazards, and the ever-changing climate. The purpose of this research is to enhance Remote Sensing (RS) port monitoring practices by investigating the potential of combining different types of RS methods to record and assess infrastructure condition. Two RS types of data, a) satellite imagery and b) aerial imagery from Unmanned Aerial Vehicle (UAV), were considered for structural monitoring at Lavrio port, located in northeastern Attica, Greece. In particular, the applied monitoring program was focused on its windward rubble mound structure where temporal changes in the armour layer were detected. Significant parameters regarding spatial resolution and UAV flight characteristics were further investigated aiming at ensuring high-quality data. The overall research indicated that RS synergies proved to be a promising practice for acquiring advanced spatial and temporal information on port infrastructure condition. [ABSTRACT FROM AUTHOR]

The dawn of mass plastic production in the early 20th century has accelerated the penetration of microplastics into the environment, making them known to be an insidious threat to diverse ecosystems. The current scenario for microplastics is dire, as they pervade living organisms and disrupt vital functions while also affecting the environment by altering soil quality. They pose an inherent risk to human health, making their elimination a multifaceted challenge. Due to factors such as small size, low biodegradability, and ubiquity, microplastics are particularly challenging to detect, and methods for their elimination from terrestrial and marine environments are an ever‐evolving field of research. The detection of such microplastics has necessitated the use of various biotechnological techniques to monitor microplastic pollution. The control of microplastic pollution in natural ecosystems can be met by replacing mass‐produced single‐use plastics with viable bio‐based alternatives. The main objectives of this review are to discuss how microplastic pollution is currently monitored, assessed, and controlled using satellite‐generated imagery complemented with a range of biotechnological and bioremedial techniques. This review also traces the development of a range of bio‐based plastic alternatives that are observed to potentially replace single‐use plastics. Further discussion on the various challenges and prospects for the mitigation of microplastics will also be conducted, stressing the importance of future research. [ABSTRACT FROM AUTHOR]

ABSTRACT Conflict, manifesting as riots and protests, is a common occurrence in urban environments worldwide. Understanding their likely locations is crucial to policymakers, who may (for example) seek to provide overseas travelers with guidance on safe areas, or local policymakers with the ability to pre‐position medical aid or police presences to mediate negative impacts associated with riot events. Past efforts to forecast these events have focused on the use of news and social media, restricting applicability to areas with available data. This study utilizes a ResNet convolutional neural network and high‐resolution satellite imagery to estimate the spatial distribution of riots or protests within urban environments. At a global scale (N = 18,631 conflict events), by training our model to understand relationships between urban form and riot events, we are able to predict the likelihood that a given urban area will experience a riot or protest with accuracy as high as 97%. This research has the potential to improve our ability to forecast and understand the relationship between urban form and conflict events, even in data‐sparse regions. [ABSTRACT FROM AUTHOR]

On 8 June 2017, a heavy storm struck the parallel ridge-valley area of western Chongqing, resulting in serious urban waterlogging and landslides, which led to severe impacts on infrastructure and damage to private property. Based on high-resolution optical satellite images, this paper comprehensively identified the landslides triggered by this rainfall event, and established a corresponding landslide database. The database takes the landslide area density and landslide number density as the main indicators, and combines the lithology characteristics to analyze the spatial distribution of landslides. The results show that this event triggered 487 landslides in an area of 583 km2, involving an area of about 485,587 m2, accounting for about 0.083% of the study area. The average landslide number density is 0.84 num/km2, the highest value of landslide number density can reach 55.6 num/km2, and the maximum landslide area density is about 6.4%. These landslides are mainly distributed in the southern foothills of the Huaying Mountain, especially in the weak interlayer lithology area. The database provides scientific reference and data support for exploring the mechanism of landslides in western Chongqing and reducing the risk of landslide disasters under the background of rapid development of local society. [ABSTRACT FROM AUTHOR]

High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2–7) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder–decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model. [ABSTRACT FROM AUTHOR]

In this study, the emphasis is on assessing how satellite-derived vegetation indices respond to drought stress characterized by meteorological observations. This study aimed to understand the dynamics of grassland vegetation and assess the impact of drought in the Wielkopolskie (PL41) and Podlaskie (PL84) regions of Poland. Spatial and temporal characteristics of grassland dynamics regarding drought occurrences from 2020 to 2023 were examined. Pearson correlation coefficients with standard errors were used to analyze vegetation indices, including NDVI, NDII, NDWI, and NDDI, in response to drought, characterized by the meteorological parameter the Hydrothermal Coefficient of Selyaninov (HTC), along with ground-based soil moisture measurements (SM). Among the vegetation indices studied, NDDI showed the strongest correlations with HTC at r = −0.75, R2 = 0.56, RMSE = 1.58, and SM at r = −0.82, R2 = 0.67, and RMSE = 16.33. The results indicated drought severity in 2023 within grassland fields in Wielkopolskie. Spatial–temporal analysis of NDDI revealed that approximately 50% of fields were at risk of drought during the initial decades of the growing season in 2023. Drought conditions intensified, notably in western Poland, while grasslands in northeastern Poland showed resilience to drought. These findings provide valuable insights for individual farmers through web and mobile applications, assisting in the development of strategies to mitigate the adverse effects of drought on grasslands and thereby reduce associated losses. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa subsp. pauca ST53 (XFP), the causal agent of olive quick decline syndrome (OQDS), was thoroughly investigated after a 2013 outbreak in the Salento region of Southern Italy. Some trees from Ogliarola Salentina and Cellina di Nardò, susceptible cultivars in the Gallipoli area, the first XFP infection hotspot in Italy, have resprouted crowns and are starting to flower and yield fruits. Satellite imagery and Normalized Difference Vegetation Index analyses revealed a significant improvement in vegetation health and productivity from 2018 to 2022 of these trees. Lipid molecules have long been recognized as plant defense modulators, and recently, we investigated their role in XFP-positive hosts and in XFP-resistant as well as in XFP-susceptible cultivars of olive trees. Here, we present a case study regarding 36 olive trees (12 XFP-positive resprouting, 12 XFP-positive OQDS-symptomatic, and 12 XFP-negative trees) harvested in 2022 within the area where XFP struck first, killing millions of trees in a decade. These trees were analyzed for some free fatty acid, oxylipin, and plant hormones, in particular jasmonic and salicylic acid, by targeted LC-MS/MS. Multivariate analysis revealed that lipid markers of resistance (e.g., 13-HpOTrE), along with jasmonic and salicylic acid, were accumulated differently in the XFP-positive resprouting trees from both cultivars with respect to XFP-positive OQDS symptomatic and XFP-negative trees, suggesting a correlation of lipid metabolism with the resprouting, which can be an indication of the resiliency of these trees to OQDS. This is the first report concerning the resprouting of OQDS-infected olive trees in the Salento area. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems. [ABSTRACT FROM AUTHOR]

Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for large-scale LST retrieval. It explores the impact of Spectral indices of the surface parameters, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to reduce uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST estimations during both summer and winter seasons. All the ML models gave an R-square of above 0.8 and RF with slightly higher R-square during both summer (0.93) and winter (0.85). Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems. [ABSTRACT FROM AUTHOR]

This study first utilizes four well-performing pre-trained convolutional neural networks (CNNs) to gauge the intensity of tropical cyclones (TCs) using geostationary satellite infrared (IR) imagery. The models are trained and tested on TC cases spanning from 2004 to 2022 over the western North Pacific Ocean. To enhance the models performance, various techniques are employed, including fine-tuning the original CNN models, introducing rotation augmentation to the initial dataset, temporal enhancement via sequential imagery, integrating auxiliary physical information, and adjusting hyperparameters. An optimized CNN model, i.e., visual geometry group network (VGGNet), for TC intensity estimation is ultimately obtained. When applied to the test data, the model achieves a relatively low mean absolute error (MAE) of 4.05 m s−1. To improve the interpretability of the model, the SmoothGrad combined with the Integrated Gradients approach is employed. The analyses reveal that the VGGNet model places significant emphasis on the distinct inner core region of a TC when estimating its intensity. Additionally, it partly takes into account the configuration of cloud systems as input features for the model, aligning well with meteorological principles. The several improvements made to this model's performance offer valuable insights for enhancing TC intensity forecasts through deep learning. [ABSTRACT FROM AUTHOR]

Background: Wildfire fuels are commonly mapped via manual interpretation of aerial photos. Alternatively, RGB satellite imagery offers data across large spatial extents. A method of individual tree detection and classification is developed with implications to fuel mapping and community wildfire exposure assessments. Methods: Convolutional neural networks are trained using a novel generational training process to detect trees in 0.50 m/px RGB imagery collected in Rocky Mountain and Boreal natural regions in Alberta, Canada by Pleiades-1 and WorldView-2 satellites. The workflow classifies detected trees as 'green-in-winter'/'brown-in-winter', a proxy for coniferous/deciduous, respectively. Key results: A k-fold testing procedure compares algorithm detections to manual tree identification densities reaching an R 2 of 0.82. The generational training process increased achieved R 2 by 0.23. To assess classification accuracy, satellite detections are compared to manual annotations of 2 cm/px drone imagery resulting in average F 1 scores of 0.85 and 0.82 for coniferous and deciduous trees respectively. The use of model outputs in tree density mapping and community-scale wildfire exposure assessments is demonstrated. Conclusion & Implications: The proposed workflow automates fine-scale overstorey tree mapping anywhere seasonal (winter and summer) 0.50 m/px RGB satellite imagery exists. Further development could enable the extraction of additional properties to inform a more complete fuel map. High-resolution satellite imagery is collected around various communities in Alberta, Canada. Machine learning algorithms are used to detect and classify individual trees from collected imagery in an automated fashion. Use of the algorithm in fuel mapping and community wildfire exposure assessments is explored. [ABSTRACT FROM AUTHOR]

Global climate change primarily affects the spatiotemporal variation in physical quantities, such as relative humidity, atmospheric pressure, ambient temperature, and, notably, precipitation levels. Accurate precipitation predictions remain elusive, necessitating tools for detailed spatiotemporal analysis to better understand climate impacts on the environment, agriculture, and society. This study compared three learning models, the autoregressive integrated moving average (ARIMA), random forest regression (RF-R), and the long short-term memory neural network (LSTM-NN), using monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The inputs for these models were based on satellite images obtained from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The LSTM-NN model outperformed others, precisely replicating precipitation observations in both training and testing datasets, significantly reducing the root mean square error (RMSE), with average monthly deviations of approximately 19 mm per location. Evaluation metrics (RMSE, MAE, R2, MSE) underscored the LSTM model's robustness and accuracy in capturing precipitation patterns. Consequently, the LSTM model was chosen to predict precipitation over a 16-month period starting from August 2023, offering a reliable tool for future meteorological forecasting and planning in the region. [ABSTRACT FROM AUTHOR]

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Satellite imagery allows us to view landscapes from a bird’s eye view, providing a new dimension in appreciating the environments we inhabit. This alternative perspective has the potential to shape individual perceptions of landscapes and play a pivotal role in land management decision-making and communication. However, the interpretation and appreciation of landscapes seen in satellite imagery may vary among observers. This study investigates the relationship between individuals’ ability to interpret images from eye-level and satellite perspectives, their familiarity with the landscape, and their appreciation of land cover from this viewpoint. To achieve this, a survey was conducted presenting respondents with images of land cover classes captured at eye level and from satellite imagery of the Yungay municipality in Chile. Participants were asked to interpret the primary land use land cover (LULC) depicted in the imagery and indicate their appreciation of that landscape. Variation in the interpretation of LULC was observed between the image source and land cover type. For instance, forest classes seen in eye-level imagery were more accurately interpreted compared to satellite imagery, while the reverse was true for agriculture. These differences in interpretation also impacted the appreciation scores assigned to the landscapes in the images. Specifically, if respondents perceived an image to be dominated by a traditionally appreciated land cover (e.g., Native Vegetation), they provided a higher score, even if the image depicted another class (e.g., Plantation Forestry). These findings highlight that considering the influence of satellite imagery in shaping perception is crucial in supporting land management activities. [ABSTRACT FROM AUTHOR]

Abstract Satellite imagery is a potent tool for estimating human wealth and poverty, especially in regions lacking reliable data. This study compares a range of poverty estimation approaches from satellite images, spanning from expert-based to fully machine learning-based methodologies. Human experts ranked clusters from the Tanzania DHS survey using high-resolution satellite images. Then expert-defined features were utilized in a machine learning algorithm to estimate poverty. An explainability method was applied to assess the importance and interaction of these features in poverty prediction. Additionally, a convolutional neural network (CNN) was employed to estimate poverty from medium-resolution satellite images of the same locations. Our analysis indicates that increased human involvement in poverty estimation diminishes accuracy compared to machine learning involvement, exemplified with the case of Tanzania. Expert defined features exhibited significant overlap and poor interaction when used together in a classifier. Conversely, the CNN-based approach outperformed human experts, demonstrating superior predictive capability with medium-resolution images. These findings highlight the importance of leveraging machine learning explainability methods to identify predictive elements that may be overlooked by human experts. This study advocates for the integration of emerging technologies with traditional methodologies to optimize data collection and analysis of poverty and welfare.

Background Informing local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance. Methods We leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees. Results Using OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making. Conclusions Gathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

There is an urgent need for the development of cutting-edge port infrastructure monitoring solutions that exploit different multimodal data towards timely optimal port management strategies and decision-making. Advanced monitoring applications allow for optimising maintenance, rehabilitation, and upgrade actions by assessing the structural integrity of port structures that is affected by a vast variety of stressors such as aging, imposed loads, inadequate maintenance treatments, human-induced factors, natural hazards, and the ever-changing climate. The purpose of this research is to enhance Remote Sensing (RS) port monitoring practices by investigating the potential of combining different types of RS methods to record and assess infrastructure condition. Two RS types of data, a) satellite imagery and b) aerial imagery from Unmanned Aerial Vehicle (UAV), were considered for structural monitoring at Lavrio port, located in northeastern Attica, Greece. In particular, the applied monitoring program was focused on its windward rubble mound structure where temporal changes in the armour layer were detected. Significant parameters regarding spatial resolution and UAV flight characteristics were further investigated aiming at ensuring high-quality data. The overall research indicated that RS synergies proved to be a promising practice for acquiring advanced spatial and temporal information on port infrastructure condition.

On August 14, 2021, a Mw 7.2 earthquake struck the Tiburon Peninsula of western Haiti triggering thousands of landslides. Three days after the earthquake on August 17, 2021, Tropical Storm Grace crossed shallow waters offshore of southern Haiti triggering more landslides worsening the situation. In the aftermath of these events, several organizations with disaster response capabilities or programs activated to provide information on the location of landslides to first responders on the ground. Utilizing remote sensing to support rapid response, one organization manually mapped initiation point of landslides and three automatically detected landslides. The 2021 Haiti event also provided a unique opportunity to test different automated landslide detection methods that utilized both SAR and optical data in a rapid response scenario where rapid situational awareness was critical. As the methods used are highly replicable, the main goal of this study is to summarize the landslide rapid response products released by the organizations, detection methods, quantify accuracy and provide guidelines on how some of the shortcomings encountered in this effort might be addressed in the future. To support this validation, a manually mapped polygon-based landslide inventory covering the entire affected area was created and is also released through this effort.

The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. Several variants of each algorithm were tested, adopting different parameters typical for each of them. Each variant was classified multiple (20) times, using training samples of different sizes: from 100 pixels to 200,000 pixels. The tests were conducted independently on 3 Sentinel-2 satellite images, identifying 5 basic land cover classes: built-up areas, soil, forest, water, and low vegetation. Typical metrics were used for the accuracy assessment: Cohen’s kappa coefficient, overall accuracy (for whole images), as well as F-1 score, precision, and recall (for individual classes). The results obtained for different images were consistent and clearly indicated an increase in classification accuracy with the increase in the size of the training sample. They also showed that among the tested algorithms, the XGB algorithm is the most sensitive to the size of the training sample, while the least sensitive is SVM, which achieved relatively good results even when using training samples of the smallest sizes. At the same time, it was pointed out that while in the case of RF and XGB algorithms the differences between the tested variants were slight, the effectiveness of SVM was very much dependent on the gamma parameter – with too high values of this parameter, the model showed a tendency to overfit, which did not allow for satisfactory results.

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This comprehensive review explores the ecological significance of the Argane stands (Argania spinosa) in southwestern Morocco and the pivotal role of remote sensing technology in monitoring forest ecosystems. Argane stands, known for their resilience in semi-arid and arid conditions, serve as a keystone species, preventing soil erosion, maintaining ecological balance, and providing habitat and sustenance to diverse wildlife species. Additionally, they produce an extremely valuable Argane oil, offering economic opportunities and cultural significance to local communities. Remote sensing tools, including satellite imagery, LiDAR, drones, radar, and GPS precision, have revolutionized our capacity to remotely gather data on forest health, cover, and responses to environmental changes. These technologies provide precise insights into canopy structure, density, and individual tree health, enabling assessments of Argane stand populations and detection of abiotic stresses, biodiversity, and conservation evaluations. Furthermore, remote sensing plays a crucial role in monitoring vegetation health, productivity, and drought stress, contributing to sustainable land management practices. This review underscores the transformative impact of remote sensing in safeguarding forest ecosystems, particularly the Argane forest stands, and highlights its potential for continued advancements in ecological research and conservation efforts.

The Russian invasion of Ukraine has underscored the strategic importance of advanced space technologies in modern warfare. This paper examines the pivotal role of the private remote sensing market in supporting military operations, particularly through satellite imagery and synthetic aperture radar (SAR) technology. By leveraging high-resolution commercial satellite data, Ukraine has significantly enhanced its situational awareness and operational effectiveness despite lacking sovereign space capabilities. The study explores the integration of private remote sensing services into military contracts, their benefits, and the implications for future conflicts and space policy. It also addresses the challenges of data sovereignty, transparency, accountability, and the ethical use of satellite imagery. This research employs a multi-faceted approach, including a comprehensive literature review, case study analysis, and qualitative assessments of current remote sensing technologies and their applications in military and humanitarian contexts. Key sources include peer-reviewed articles, government reports, and accounts from industry experts. The study also examines the regulatory frameworks governing remote sensing data and the evolving relationship between private companies and governmental agencies.

Transportation is a crucial element in the development of an area, especially in the context of developing transportation routes in Lampon Beach, Banyuwangi Regency. With inadequate road access, the development of road routes on this beach is a priority to increase the region's economic productivity by utilizing the potential of natural resources. This research applied the spatial data-based Technology-Based Integration Method with the support of ArcGIS and Civil 3D software. Two road traces were generated, with the total length of trace 1 is 4.76 km and trace 2 is 4.98 km; the volume data of trade 1 with excavation amounted to 52,064.09 m3, and embankment amounted to 15,002.59 m3. Meanwhile, the volume of trade 2 with excavation amounted to 89,464.92 m3, and the barrier amounted to 12,537.02 m3. These results demonstrate the efficiency and accuracy of infrastructure planning on the geographical and spatial characteristics of the region. The assessment of excavation and embankment costs provides an overview of the resources required by applicable standards and regulations. This approach contributes significantly to selecting efficient road routes and a better understanding the resources needed for infrastructure development.

Timely acquisition of ash cloud heights is crucial for aviation safety and forecasting volcanic ash dispersion and fallout. Since visual observations are not always available, we assess the suitability of retrieving ash cloud heights from brightness temperature and plume direction observed in GOES-16 satellite imagery, VOLCAT solutions, and Washington-VAAC advisories during the 2022–2023 eruption of Cotopaxi volcano, Ecuador. We find that these satellite-derived height estimates consistently yield lower values than visual cameras. While the plume direction method and Washington-VAAC advisories produce the closest approximations, they also exhibit significant deviations. Remarkably, the brightness temperature method, despite producing the lowest height values, shows the best linear regression with visual observations. Near-real-time retrieval of ash cloud height from GOES-16 imagery is a promising alternative to direct visual observation, particularly at night, in adverse weather, or for remote volcanoes, especially if improvements, such as incorporating high-resolution local meteorological models, are introduced.

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset. [ABSTRACT FROM AUTHOR]

AbstractThis study assessed sandbar-topography changes in Nakdong River Estuary. The island shorelines in the study area, including Jinwoo Island, Shinja Island, and Doyo Sandbar, were analyzed using SPOT satellite images from 2006 to 2022. Shoreline analysis via ArcGIS revealed that Jinwoo Island is expanding at 6.46 m/year towards Nulcha Island. Shinja Island’s western side is expanding at 4.76 m/year, while the eastern side is retreating at 24.41 m/year. Doyo Sandbar’s southern shoreline is retreating at 14.69 m/year, with a gradual decrease in speed. Significant topographical changes indicate that the interlinked barrier islands are undergoing deposition and erosion, altering the shoreline. [ABSTRACT FROM AUTHOR]

Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively. [ABSTRACT FROM AUTHOR]

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

Sentinel-2 satellites are one of the major instruments in remote sensing (RS) technology that has revolutionized Earth observation research, as its main goal is to offer high-resolution satellite data for dynamic monitoring of Earth's surface and climate change detection amongst others. However, visual observation of Sentinel-2 satellite data has revealed that most images obtained during the winter season contain snow noise, posing a major challenge and impediment to satellite RS analysis of land surface. This singular effect hampers satellite signals from capturing important surface features within the geographical area of interest. Consequently, it leads to information loss, image processing problems due to contamination, and masking effects, all of which can reduce the accuracy of image analysis. In this study, we developed a snow-cover removal (SCR) model based on the Cycle-Consistent Adversarial Networks (CycleGANs) architecture. Data augmentation procedures were carried out to salvage the effect of the limited availability of Sentinel-2 image data. Sentinel-2 satellite images were used for model training and the development of a novel SCR model. The SCR model captures snow and other prominent features in the Sentinel-2 satellite image and then generates a new snow-free synthetic optical image that shares the same characteristics as the source satellite image. The snow-free synthetic images generated are evaluated to quantify their visual and semantic similarity with original snow-free Sentinel-2 satellite images by using different image qualitative metrics (IQMs) such as Structural Similarity Index Measure (SSIM), Universal image quality index (Q), and peak signal-to-noise ratio (PSNR). The estimated metric data shows that Q delivers more metric values, nearly 95%, than SSIM and PRSN. The methodology presented in this study could be beneficial for RS research in DL model development for environmental mapping and time series modeling. The results also confirm the DL technique's applicability in RS studies. [ABSTRACT FROM AUTHOR]

Cloud cover and shadows often hinder the accurate analysis of satellite images, impacting various applications, such as digital farming, land monitoring, environmental assessment, and urban planning. This paper presents a new approach to enhancing cloud-contaminated satellite images using a novel variational model for approximating the combination of the temporal and spatial components of satellite imagery. Leveraging this model, we derive two spatial-temporal methods containing an algorithm that computes the missing or contaminated data in cloudy images using the seamless Poisson blending method. In the first method, we extend the Poisson blending method to compute the spatial-temporal approximation. The pixel-wise temporal approximation is used as a guiding vector field for Poisson blending. In the second method, we use the rate of change in the temporal domain to divide the missing region into low-variation and high-variation sub-regions to better guide Poisson blending. In our second method, we provide a more general case by introducing a variation-based method that considers the temporal variation in specific regions to further refine the spatial–temporal approximation. The proposed methods have the same complexity as conventional methods, which is linear in the number of pixels in the region of interest. Our comprehensive evaluation demonstrates the effectiveness of the proposed methods through quantitative metrics, including the Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Metric (SSIM), revealing significant improvements over existing approaches. Additionally, the evaluations offer insights into how to choose between our first and second methods for specific scenarios. This consideration takes into account the temporal and spatial resolutions, as well as the scale and extent of the missing data. [ABSTRACT FROM AUTHOR]

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the vegetation production changes in Khuzestan province, Iran using MODIS data production, meteorological data, vegetation maps as well as topographic and field monitoring data in CASA model. The study area was divided into different climatic classes based on multivariate statistical method, so the vegetation of each climatic region was examined separately for changes in NPP values. Production changes due to degradation were calculated using the Miami model and subsequently, the rain use efficiency (RUE) and the light use efficiency (LUE) and correlation indices between the CASA model and ground data were determined. The results of this study (R2) showed that the accuracy of this model varies depending on the type of climatic regions (R2 = 0.80 to R2 = 0.15). In different climatic regions, the rate of NPP changes (very humid 68 gC/m2 to ultra-dry 15 gC/m2) varies in rangeland types. The highest rate of vegetation production is observed seasonally in May. Degradation conditions also reduced RUE and LUE. However, in hyper-arid regions, adaptations of plants in some different species (Hammada Spp.) increase their efficiency compared to other vegetation types. The results showed the importance of vegetation and climate classification in vegetation production studies. [ABSTRACT FROM AUTHOR]

Halting and reversing the ongoing insect decline requires in-depth knowledge on key drivers. Due to their sensitivity to habitat quality, butterflies are valuable indicators for grassland management intensity, including mowing. However, most studies examining mowing regime impacts on butterflies are limited to small spatial extents. Here, we tested the potential of citizen science butterfly monitoring data for assessing landscape-level effects of mowing regimes (number of mowing events and timing of the first event) and edge density (density of boundaries between different land-cover types) on butterfly richness, abundance, and community composition. We used generalised linear mixed-effects models to relate nationwide data from the German Butterfly Monitoring Scheme (DEBMS) to high-resolution satellite imagery on mowing events in permanent grasslands (grasslands excluded from crop rotation). As butterfly transects may not consistently be located within grasslands, we ran our models for different thresholds from 0 to 50%, representing increasing shares of the transect route situated within permanent grasslands (10% intervals). We did not find significant associations between mowing regimes and butterflies when focussing on species richness and abundance of all species inhabiting open land. However, we found strong positive associations of delayed mowing with the abundance of grassland specialists with increasing grassland shares per transect. Further, we found negative associations of delayed mowing with the annual number of generations and of more frequent mowing with the abundance of specialists, depending on the share of grassland per transect. Edge density had a positive association with species richness and abundance of species inhabiting open land, as well as abundance of grassland indicator species and grassland specialists in landscapes with a low grassland share per transect. Our findings underscore the importance of low-intensity managed permanent grasslands at the landscape scale for specialised butterflies. Additionally, we highlight the importance of a high density of boundaries for open-land and specialised butterflies, particularly in landscapes with highly fragmented permanent grasslands. To improve future analyses of grassland management impacts, we recommend expanding DEBMS monitoring sites to cover a larger grassland management intensity gradient and to place more transects within grasslands. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Tanah dan Sumberdaya Lahan is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents an in-depth analysis of land use and land cover change on the Kerch Peninsula over a period spanning three decades. Convolutional neural networks were employed in conjunction with satellite imagery analysis to map and quantify the changes in land use and cover. This revealed significant trends and transformations within the peninsula's landscape. The analysis revealed a notable increase in urban expansion, particularly at the expense of natural ecosystems. Furthermore, there was a notable reversion of agricultural lands to grasslands, driven by economic downturns and reduced agricultural activity. These land cover changes underscore the urgency of implementing sustainable land management policies. The study recommends the establishment of conservation easements to protect remaining natural ecosystems, the initiation of reforestation programs to restore degraded lands, and the development of comprehensive water management strategies to address the peninsula's hydrological challenges. Furthermore, the study underscores the pivotal importance of integrating change analysis and predictive modeling to anticipate future land cover scenarios and inform effective land management strategies. The model developed through this research, which employs advanced remote sensing and GIS technologies, provides a robust framework for understanding and managing land use and land cover change. This model can serve as a reference for similar regions globally, offering insights that can inform sustainable land use practices and policy decisions. The findings of this study have implications that extend beyond the Kerch Peninsula. They provide insights that can inform the management of land use changes and the conservation of natural landscapes in regions facing comparable socio-economic and environmental challenges. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study employs advanced geospatial analytical techniques to evaluate the vulnerability of Nigeria's coastal states and their constituent local government areas to flood hazards, which represent a critical and escalating risk within the coastal hazard paradigm intensified by climate change phenomena. The study's objective is to utilize geospatial data to delineate and quantify the intensity and distribution of flood susceptibility, thus establishing a foundational framework for developing comprehensive disaster management strategies in response to the challenges posed by climate variability. The research uses satellite imagery and geographic information system (GIS)-based hydrological modeling to delineate regions susceptible to flooding, synthesizing topographical and hydrological data to stratify areas into discrete flood susceptibility categories. The findings indicate that the Delta coastal State of Nigeria contains extensive medium to high-risk flood zones spanning 8304.57 km2. While the Bayelsa coastal State of Nigeria presents critical areas at high to very high flood risk, encompassing 5506.61 km2 at high risk and 1826.88 km2 at very high risk, this highlights the urgent necessity for immediate and strategic mitigation measures. This research highlights the critical importance of geospatial technology in shaping disaster management and enhancing community resilience against increasing flood frequencies. As Nigeria's coastal regions face escalating flood susceptibility, advanced geospatial methods are vital for assessing and mitigating these climate-induced threats, contributing to climate-resilient planning and aligning with Sustainable Development Goal 13: Climate Action. The study's geospatial approach delivers precise flood risk evaluations and guides targeted mitigation efforts, marking significant progress in managing coastal hazards in a changing climate. [ABSTRACT FROM AUTHOR]