Analysis Of Satellite Imagery Research Articles

Analyzing the association between the hydrodynamics and bank erosion along the Padma River: 2020 monsoon floods

The 2020 Monsoon floods submerged two-thirds of Bangladesh, profoundly affecting the stable char areas along the Padma River. This study addresses the under-explored link between Monsoonal flood hydrology and river morphological changes, introducing a novel approach that integrates one-dimensional steady flow hydraulic modeling, dual-sensor satellite imagery analysis (Sentinel-1 SAR and Sentinel-2 optical), and multiple machine learning techniques to assess flood impacts on bank erosion in the most affected reaches. Multiple linear regression (MLR), random forest (RF), artificial neural networks (ANN), and support vector machines (SVM) along with other statistical analyses, were employed to elucidate relationships between hydraulic variables and bank erosion. Results indicated a gradual decrease in river discharge-related variables from July to October, with peak erosion in July, primarily along the right banks and concave areas. In analyzing the relationships between hydraulic variables and bank erosion, the ANN model outperformed others, identifying flow area, stream power, and shear stress as the most influential predictors through importance analysis. This integrated approach offers a transferable framework for analyzing flood-induced bank erosion, enhancing riverbank erosion understanding crucial for flood risk management strategies in Bangladesh and similar river systems worldwide.

Identifying Sulphurous Water Discharge from Legacy Oil and Gas Wells using Spectral Band Analysis of Aerial and Satellite Imagery

Legacy oil and gas wells are a significant source of hydrogen sulphide and methane gas release to the atmosphere. Unanticipated occurrences of gas release in urbanized areas can pose human health risks. Several high hydrogen sulphide-emitting wells have been identified in Norfolk County, Ontario, Canada, based on the remote detection of sulphurous water discharging to the surface along the wellbore. Although oil and gas well records report well status and location, community reports of noxious hydrogen sulphide odours suggest potentially compromised well seals and inaccuracies in their documented location. Given the broad-scale nature of this issue and limitations in site access, a remote-sensing based methodology utilizing satellite and high-resolution aerial photography could support identification of undocumented sulphurous water leaks associated with compromised oil and gas wells and subsequent characterization of hydrological and ecohydrological impacts over time. This study presents a multifaceted approach to identifying sulphurous leaking wells utilizing complimentary remote sensing imagery and image analysis tools within ArcGIS. Southwestern Ontario Orthophotography Project air photos and Sentinel-2 satellite imagery were determined to be the most applicable sources of imagery based on their respective advantages in resolution and revisit time. The application of a normalized difference vegetation index showed that major well leaks could exhibit signs of vegetative scarcity, while minor well leaks may have only a limited impact on vegetation. A band combination utilizing the green and near infrared bands was created to enhance the detection of sulphurous water leaks. Utilizing the identified band combination, a pair of potentially undiscovered leaks were located west of a fluvial river channel. Continued research should attempt to employ an automated approach for discerning additional sulphurous water leaks in the region or at different points in time. Possible techniques may involve the deep learning object and change detection models incorporated in ArcGIS.

Improving semantic segmentation accuracy in thin cloud interference scenarios by mixing simulated cloud-covered samples

Thin cloud interference presents a significant challenge for the semantic segmentation of optical satellite imagery, which directly degrades the model accuracy and causes difficulties in sample selection. This paper generated a dataset named Populus euphratica and Tamarix chinensis discrimination (PTD), containing both cloudless and thin cloud scenarios. Based on this PTD dataset, an enhanced Atmospheric Scattering Model with Nonlinear Optimization (ASM_NL) was proposed to simulate high-fidelity thin clouds by incorporating two vital nonlinear terms: the point spread function and the Perlin noise. Additionally, we adopt a strategy of mixing simulated thin cloud-covered images (STCI) into the training set at a certain proportion to improve the semantic segmentation accuracy in thin cloud-covered scenarios. The conclusions are as follows: 1) ASM_NL can simulate high-fidelity clouds at an average Jensen-Shannon distance of 0.0699. 2) When dealing with medium- and high-cloud density datasets, mixing STCI proved to be more effective than cloud removal in mitigating thin cloud interference, resulting in average macro F1 score improvements of 0.164 and 0.094, respectively. 3) The semantic segmentation accuracy improved significantly by mixing STCI with a minimal proportion of 1/60, demonstrating the activation of model transfer capabilities. This study provides a concise and efficient methodology for effectively mitigating thin cloud interference in deep learning-based optical satellite imagery analysis.

The Kerch Peninsula in Transition: A Comprehensive Analysis and Prediction of Land Use and Land Cover Changes over Thirty Years

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.

High-dimensional detection of Landscape Dynamics: a Landsat time series-based algorithm for forest disturbance mapping and beyond

ABSTRACT Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions.

Three decades of land use and land cover changes in an East African lake and their implications for the conservation of bird communities

AbstractLand use and land cover (LULC) dynamics using GIS and remote sensing techniques measure extent and spatial structure, which are critical for monitoring biomes. A digital LULC detection technique with multi‐temporal satellite imagery analysis was employed at four timepoints from 1987 to 2017 and performed using supervised classifications method in ERDAS imagine 2015. For image classification, the five major LULC types included forests, built‐up land, agricultural land, water bodies and grasslands. LULC of grasslands, forests and water bodies reduced, while LULC of agricultural and built‐up lands increased. Avifaunal surveys were conducted at 11 sites across the LULC classes to compare species richness and abundance. The decrement of forests and water bodies would result in habitat loss, affecting endemic and threatened avifauna, as forests showed highest avifaunal species diversity (H′ = 3.80). Despite the decrement, grasslands demonstrated higher avifaunal species richness (107) and abundance (2767). Species richness decreased significantly from grasslands to water bodies and agricultural land (p < 0.05), and from forests to agricultural and built‐up land (p < 0.05), and water bodies (p < 0.01). Avifaunal abundance decreased significantly from grasslands to all other LULC classes. Regular LULC change assessment is integral to avifaunal habitat and biodiversity conservation efforts, particularly at Lake Tana.

The overlooked carbon cache: Unveiling organic carbon storage in small floodplain lake sediments under humid continental climate changes

Small floodplain lakes (SFLs), characterized by their diminutive size (<10,000 m2), have been consistently overlooked in assessments of organic carbon (C) stock and the impacts of climate changes. This study aims to ascertain both the quantity and quality of carbon stored within the sediments of SFLs. Furthermore, we endeavour to evaluate the influence of ongoing climate change on the floodplain lake structure within the continental humid climate zone on the example of the Vistula River valley and estimate the CO2 emission from dry floodplain lakes. The sedimentary organic carbon content (TOC) ranged from 2.3–64 g kg−1, averaging at 3.4 g kg−1. Consequently, the sediments in the analysed SFLs of the river section potentially harbour up to 5,785 Mg TOC. Notably, the abundance of easily soluble carbon fractions (humic and fulvic acids) was relatively low (∼15 %), implying a stable immobilization of organic C compounds and minimal impact of sediments on water quality. Satellite imagery analysis for the years 2017–2020 revealed a 31 % reduction in the total lake surface area within a 100-km-long segment of the Vistula River. The decreasing surface area of floodplain lakes and rising mean air temperatures increase CO2 emissions and have an additional impact on climate warming. Our research underscores that investigating lake C storage concerning climate change demands a nuanced approach. While higher temperatures stimulate primary production, the reduction in lake surface area and the escalating role of small lakes, such as SFLs, in C storage should be paramount considerations.

AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization.

AI-Driven Strategies for Reducing Deforestation

Recent advancements in data science, coupled with the revolution in digital and satellite technology, have catalyzed the potential for artificial intelligence (AI) applications in forestry and wildlife sectors. Recognizing the critical importance of addressing land degradation and promoting regeneration for climate regulation, ecosystem services, and population well-being, there is a pressing need for effective land use planning and interventions. Traditional regression approaches often fail to capture underlying drivers' complexity and nonlinearity. In response, this research investigates the efficacy of AI in monitoring, predicting, and managing deforestation and forest degradation compared to conventional methods, with a goal to bolster global forest conservation endeavors. Employing a fusion of satellite imagery analysis and machine learning algorithms, such as convolutional neural networks and predictive modelling, the study focuses on key forest regions, including the Amazon Basin, Central Africa, and Southeast Asia. Through the utilization of these AI-driven strategies, critical deforestation hotspots have been successfully identified with an accuracy surpassing 85%, markedly higher than traditional methods. This breakthrough underscores the transformative potential of AI in enhancing the precision and efficiency of forest conservation measures, offering a formidable tool for combating deforestation and degradation on a global scale.

Geostatistical Analysis of Lineament Domains: The Study Case of the Apennine Seismic Province of Italy

Regional-scale swarms of subparallel linear topographic features, known as lineament domains, are a common feature of planetary surfaces. Lineament domains are superficial manifestations of the crustal stress field trajectory. Notably, one of the effects of active tectonics is seismicity. Italy is one of the most seismically active regions in the Mediterranean, with many destructive earthquakes that have occurred in past centuries. Here, we assess the seismic meaning of the main lineament domain in the tectonically active region of Central Italy. We describe the use of an automated analysis of satellite imagery coupled with spatial grid analysis to identify three lineament domains of the Central Apennines. Spatial and azimuthal comparisons of the main lineament domain (i.e., the Apennine Domain), with the known locations of earthquakes (moment magnitude of Mw &gt; 5.5) that occurred during the past century, revealed the most seismically active tectonic areas and their spatial distributions. Further, we present a conceptual seismo-geodynamic model for the Central Apennines, which is characterized by regional arching and explains the presence of an extensional tectonic regime in the upper crustal layer of the active Apennines fold-and-thrust belt.

Integrated approach to soil salinity assessment using SEM in Sirdarya province, Uzbekistan

The majority of irrigated land in the Sirdarya area in Uzbekistan is susceptible to salinization. Yet, due to the benefits of its geographical and temporal data, satellite imaging has the ability to efficiently monitor territorial resources. The objectives of this paper are (1) to find how strong surface water mineralization used for irrigation correlated with soil salinity, and (2) to introduce a simplified approach for the soil salinity assessment using SEM based on satellite imagery analysis. The main focus of this research is to explore the potential of using SEM methodology for soil salinity assessment in the study area by streamlining spatial image analysis processes. Throughout data collection of 261 surface water samples and 206 soil samples in Sirdaria irrigation district, while satellite images and other official datasets were our secondary material. We used the Inverse Distance Weighting interpolation method to map soil salinity in ArcGIS, achieving an interpolation accuracy of 72% and a Kappa value of 81%. Statistical packages in R software served as analyst tools to validate our findings. Our findings imply that while MODIS satellite imagery has a lesser connection with EC values, however, the EC values in terms of soil salinization in Sirdarya Province can be predicted using several Landsat bands without using remote sensing indices with correlation coefficients of R ≥ 0.8. We proved that the wavelength ranges of 0.64–0.67 μm and 2.11–2.29 μm are acceptable for mapping soil salinity. Nevertheless, continuous research is needed to understand the mechanisms behind this relationship and the potential applications of these findings in other drylands, land management, and environmental monitoring.

Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Kurgan Phenomenon in the Southern Caucasus: Results of an Interdisciplinary Multi‐Method Remote Sensing Survey Along the Kurekçay Valley (Goranboy Province, Western Azerbaijan)

ABSTRACTThis paper presents the results of a pilot study that combines findings from satellite remote sensing analyses and an intensive interdisciplinary survey conducted in November 2021 on kurgans along the Kurekçay Valley, in western Azerbaijan. Through the integration of various methods, including historical and contemporary satellite and aerial imagery analysis, topographic mapping and geophysical prospections, this research remotely maps and quantifies the density, size, morphology and distribution of the burial mounds in the area. Unlike previous studies focusing solely on individual burial mounds, this investigation adopts a broader approach by contextualizing the kurgans within their respective landscapes. The results contribute to a deeper understanding of the physical and symbolic aspects and relationships of these structures, providing valuable insights into the enduring burial practices that shaped the funerary traditions of the Southern Caucasus during the 4th and 1st millennium BCE. Furthermore, this approach supports an assessment of the condition of the individual kurgans, thereby opening new perspectives to develop innovative plans to protect these burial mounds and their settings.

Damage to medical complexes in the Gaza Strip during the Israel–Hamas war: a geospatial analysis

IntroductionMedical facilities are civilian objects specially protected during armed conflict by international humanitarian law (IHL). These protections are customarily applied regardless of the conflict, parties or contexts involved. Attacks on...

Probability of Erosion Utilizing Google Earth Engine and the RUSLE Method in the Tuntang Watershed

The Tuntang Watershed is an important watershed in Central Java. Management of watersheds in the Tuntang stream is a priority for various parties to carry out. One of the things that threatens the sustainability of the Tuntang watershed is erosion. The erosion rate can lead to sediment accumulation and siltation in the Tuntang River reservoir, which can cause catastrophic flooding. Flood disaster mitigation caused by erosion needs to be done, one of which is by calculating the erosion rate per year that occurs in the Tuntang watershed. This study calcultated the predicted erosion rate (per year in the Tuntang watershed) using the Revised Universal Soil Loss Equation (RUSLE) method, processed using the Google Earth Engine (GEE). Google offers a cloud-storage technology called GEE. Programming in JavaScript is required to operate GEE. GEE is a petabyte-scale data-based tool that can be used to analyze and archive geospatial data that is open source. The computing environment is designed for the processing of geospatial data, including the depiction of spatial analysis of satellite imagery. Data for RUSLE is obtained from the database in GEE, and the results can be imaged on a map. According to the study’s findings, the degree of soil erosion throughout the Tuntang Watershed was essentially constant, with Moderate erosion predominating in the majority of locations. Senjoyo Sub Watershed, Rowopening Sub Watershed, and Tuntang Hilir Sub Watershed are the primary locations with severe erosion. Rowopening Sub Watershed is the region that is the worst.

Understanding the transition of community land use from shifting cultivation to cash cropping in southern Tanintharyi, Myanmar

Abstract Many tropical landscapes have experienced the loss of traditional cultivation practices as they have transitioned to other land use systems. The Tanintharyi Region of southern Myanmar is a landscape experiencing a rapid land use regime shift from traditional subsistence farming to permanent cash crop agriculture. Despite previous research in this region on the expansion of large‐scale agribusiness, such as oil palm and rubber plantations, little is known about how the small‐scale shifting cultivation system practiced by the local Karen ethnic people in Tanintharyi has changed over recent decades or the underlying reasons for this transition. Our study explores this transition process and its drivers from a social and land system change perspective in four villages of Bokpyin Township in Tanintharyi. We investigated the drivers of land cover and land use change by collecting information through focus groups and individual interviews with community members who previously practised shifting cultivation. We also quantified the overall change in land cover and land use through remote sensing analysis of Landsat 8 (2020) and declassified KH‐9 satellite imagery (1976). Prior to a period of civil war in the 1980s, cultivators used a traditional ‘slash‐and‐burn’ agricultural system for subsistence use. The present land use system is dominated by permanent betel nut cultivation, which represents the primary income source for farming households. The reported drivers of the transition to cash crop farming are greater income potential and livelihood security, risk of losing fallow land due to the needs for new cultivation areas for in‐migration and new centralized land use policies and administration after the national ceasefire agreement in 2012. The documented change in land use systems has been paired with a concurrent net loss of forest that, given the conflicted governance within the landscape, seems likely to continue without additional land use planning efforts. Our study contributes to an increased understanding of changes in shifting cultivation systems that are being documented globally. Furthermore, our example of combining qualitative interviews with analysis of historical satellite imagery to detect and explain land use regime shifts can serve as a model for future work in different landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Megafires and koala occurrence: a comparative analysis of field data and satellite imagery

Megafires can have a devastating effect on koala populations. With climate change increasing habitat vulnerability to wildfires, understanding how to efficiently measure the impact of these events on koalas is essential. We analysed the relationship between the 2019-2020 megafires and the probability of koala occurrence in Mid North Coast NSW. We found that two on-field and one satellite-derived variables measuring fire severity equally explained koala occurrence. The probability of koala occurrence decreased with increasing fire severity. This supports the use of remote sensing imagery to efficiently monitor the impact of future fire events on koala populations in the region.

The pace of global river meandering influenced by fluvial sediment supply

Meandering rivers move gradually across the floodplains, and this river movement presents socioeconomic risks along river corridors and regulates terrestrial biogeochemical cycles. Experimental and field studies suggest that fluvial sediment supply can exert a primary control on lateral migration rates of rivers. However, we lack an understanding of the relative importance of environmental boundary conditions, such as floodplain vegetation and sediment supply, in setting the pace of river meandering across different environmental settings. Here, we combine the analysis of satellite imagery and global-in-scale sediment and water discharge models to evaluate the controls on lateral migration rates of 139 meandering rivers that span a wide range in size, climate, and bank vegetation. We show that migration rates normalized by the channel width monotonically increase with the volumetric sediment flux normalized by the characteristic size of the river. This relation is consistent across rivers in vegetated and unvegetated catchments, indicating that enhanced lateral migration rates in unvegetated basins is likely not only facilitated by lower bank mechanical strength, but also by higher normalized sediment supply in ephemeral rivers. Using three case examples, we also demonstrate that width-normalized meander migration rates respond to spatial gradients in sediment supply caused by river impoundments, highlighting the prominent role of sediment supply in setting the pace of meander migration. Our results suggest that sediment-supply variations caused by climate, land-cover and land-use changes can lead to predictable changes in meandering river evolution and ultimately drive architectural changes in sedimentary stratigraphy.

Temporal analysis of settlement areas and city footprints on construction and demolition waste quantification using Landsat satellite imagery

Municipal solid waste management has seen a surge in the use of satellite imagery in decision-making processes, yet its application to analyze quantitative variations in construction and demolition (C&D) waste remains under-investigated. This study employs satellite imagery and multivariate analysis to comprehensively assess and predict C&D waste generation in four diverse urban jurisdictions of Canada (Regina) and the USA (Seattle, Buffalo, and Philadelphia). Factors such as settlement area expansion, economic activities, and population growth significantly influence C&D waste rates. Stepwise multivariate regression models tailored to different city types, such as moderately populated (Group 1) and highly populated (Group 2), showcase acceptable predictive capabilities. For moderately populated cities, settlement area, average humidity, and GDP are identified as key predictors, while for highly populated cities, settlement area, unemployment rate, and building permit value prove effective indicators. These models, characterized by R² values from 0.70 to 0.94, provide tailored insights for distinct demographic conditions, aiding waste management planning. This research underscores the importance of satellite imagery and multivariate analysis in understanding C&D waste dynamics and empowers policymakers and waste management agencies with evidence-based strategies for effective waste management in urban centers.