RiverScope: High-Resolution River Masking Dataset

Industrial and urban activity has inevitably changed the water environment and caused significant impacts on water resources’ quality and quantity. The identification of related impacts is particularly important in the context of increasing water shortages due to climate change. Overlapping industrial impacts and drought occurrence have resulted in the long-lasting deterioration of surface water status. Therefore, the mitigation of negative impacts is crucial for relevant and sustainable water management in river basins. One of the most impactful branches of industry is underground coal mining, which requires dewatering deposits and excavations. Mine waters discharged into rivers have induced significant increases of salinity, while urban wastewaters have increased biogenic contamination in surface waters. Sustainable development goals require water protection, energy transition, and circularity; therefore, coal will be repurposed in favor of alternative sources of energy. The phasing out of coal and cessation of dewatering of mines would rapidly reduce mine waters’ impact on the environment. However, in heavily industrialized urban basins, the share of natural waters in river flows is exceptionally low—due to significant and long-lasting transformations, industrial and urban wastewaters are the main constitutive components in certain river hydrological regimes. The case study of Bytomka in the Upper Silesian Coal Basin, Southern Poland is a vivid example of a river basin significantly impacted by urban and industrial activity over a long-term period. The Bytomka River’s water status and the development of its watershed area is an example of complex and overlapping impacts, wherein sustainable water management requires proper recognition of prevailing factors such as mine water discharges, climate change and drought periods, wastewater impacts, and urbanization of the water basin area. The presented study reveals key findings showing that future coal mine closures would result in significant water resource shortages due to a reduction of mine water discharges, significant biogenic (N and P) pollution increases, and hazards of harmful algal blooms. Therefore, there is an urgent need to increase the retention potential of the watershed, use nature-based solutions, and mitigate negative impacts of the coal mining transition. The increase in treatment capability of industrial wastewater and sewage discharge would help to cope with the natural water vulnerability induced by the impacts of climate change.

Rapid urban growth in Banjarbaru, Indonesia has led to environmental challenges, particularly the Urban Heat Island (UHI) phenomenon and reduced carbon sequestration due to declining vegetation cover. This study aims to: (1) analyze spatial patterns of UHI and Thermal Humidity Index (THI) to identify thermally comfortable and uncomfortable areas; (2) assess the relationship between vegetation density (NDVI) and carbon stock potential; and (3) propose spatial strategies to enhance thermal comfort and carbon absorption capacity in support of Low Carbon Green City (LCGC) development. Using a spatial analysis approach, the study utilized Sentinel-2 and Landsat 9 satellite imagery, field measurements via wireless sensor networks (WSN), and GIS-based modeling techniques. Land Surface Temperature (LST), THI, and NDVI were analyzed and correlated with carbon stock data derived from vegetation plot surveys. The results indicate a strong inverse correlation between NDVI and both LST and THI, confirming that vegetation plays a crucial role in regulating microclimate and thermal comfort. Areas with low NDVI exhibited higher surface temperatures and THI values, indicating intense thermal stress. Carbon stock analysis showed that plots with higher NDVI values held significantly greater carbon reserves, with a regression model (R² = 0.850) validating NDVI as a strong predictor for aboveground biomass. Furthermore, high UHI and THI zones overlapped with areas of low carbon stock, highlighting the need for targeted ecological interventions. To address these challenges, the study proposes three spatial strategies: urban farming, green building, and green corridor development. These approaches aim to improve biomass density, reduce surface temperatures, and enhance carbon sequestration potential. Collectively, they support climate adaptation, urban resilience, and participation in carbon trading initiatives. This research contributes a practical framework for integrating ecological and spatial planning toward sustainable urban transformation. Keywords: Urban Heat Island; NDVI; Carbon Stock; Thermal Comfort; Green City Design

Thompson Rivers University (TRU), Kamloops, Canada, and Universidad Norbert Wiener (UNW), Lima, Peru, work together in a bilingual project about data mining, managing activities and resources with the help of structures in intellectual capital. Otherwise, without the use of these structures, it would have been almost impossible to have a continuous contact among the persons in the project. The change of authorities at UNW, was not an obstacle to the continuity of the project. Results of this collaboration are achieved in the way of knowledge sharing. In year 1, a team of professionals in health sciences traveled from Canada to Peru, mainly to give courses. In year 2, an expert in computer science came, together with the professionals in health; this new expert gave conferences in data mining in English language. In year 3, the expert in computer science gave a main conference and conducted workshops. Year 4 is for joint research and publishing of findings. Three results are: a) conferences and workshops in health sciences and information technology are given in Peru by Canadian experts; b) research papers are produced and published in international congresses and indexed journals; and c) a bilingual experience is given to students in Peru, since conferences and workshops are given in English to Spanish speaking students. A progressive increase in intellectual capital is measured every year during the four-year project. The research question for this paper will be: what is the contribution of intellectual capital to the management of international bilingual projects? The four-year experience, will give the answer to this research question. Outcomes already obtained show that good practices in intellectual capital are contributing to the success of this four-year project.

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

Recognizing the ever-increasing vulnerability of coastal urban cities in Asia due to climate change impacts and variability and also due to fast-growing urban development, this study focusing on climate disaster resilience is conducted in order to measure the existing level of climate disaster resilience of the targeted areas using a Climate Disaster Resilience Index. The index is developed based on five resilience-based dimensions: natural, physical, social, economic and institutional. The scope of this study is limited to climate-induced disasters, such as cyclone, flood, heat wave, drought and heavy rainfall induced landslide. For each individual city case, resilience information is presented as overall resilience, and separate physical, social, economic and institutional resilience. Higher values of resilience are equivalent to higher preparedness to cope with climate and disasters and inversely. Based on the results, policy points and recommendations are suggested by the authors and provide encouragement of city governments’ engagements in specific cist services, institution and capacity building. Not only are outputs from this study useful for city governments, but they also provide valuable knowledge and information to other local and national stakeholders having a similar target: the enhancement of community resilience.

Constructivist approaches to computer science education emphasize that as well as knowledge, thinking skills and processes are involved in active knowledge construction. K-12 computer science curricula must not be based on fashions and trends, but on contents and processes that are observable in various domains of computer science, that can be taught at every intellectual level, that will stay relevant in the longer term, and that are related to everyday language and/or thinking. Only recently, two empirically determined lists, one of central content concepts (algorithm, computer, data, system, etc.) and another of central process concepts (problem solving and problem posing, analyzing, classifying, generalizing, etc.), have become available for computer science education. This paper tackles the problem of finding content and process concepts to be taught in combination. Computer science experts are surveyed in order to identify combinations of content and process concepts-so-called blocks-that are relevant to computer science education. By using cluster analyses, 15 central blocks for teaching computer science in schools are determined. The results of this study may serve as a reference system for the systematic design of instruction in K-12 computer science education.

The physical and chemical quality within traditional water resources of Khamis Mushait City, Saudi Arabia, that are being used for various urban purposes were assessed to explore consistency. The network sampling and cross-sectional techniques were implemented. Google earth digital satellite map of the study area was used as a master map for sampling. A total of 66 water samples were collected randomly from surface and well water located at and around the city. All samples were subjected to standard analysis. The results have revealed the overall means of turbidity, conductivity, total dissolved solids, pH, chlorides, hardness as CaCO3, sulphate, ammonia, nitrate, copper, iron, manganese, and zinc in water samples taken from deep wells, shallow wells and surface water. These results showed that shallow wells had the highest values for conductivity, total dissolved solids, chloride, hardness, sulphate, nitrate, copper, manganese, and zinc. However, surface water had the highest levels for turbidity, pH, and ammonia. Iron contents of deep wells and surface water samples were nearly the same. Thus, samples taken from deep wells were superior to those from shallow wells and surface water sources. There were significant correlations with each other between physicochemical parameters except for manganese, which showed no significant correlation with any parameter. Iron showed only a significant correlation with zinc. Recommended hygienic measures are also discussed. Keywords: Physicochemical, traditional water resources quality, hygienic measures, Khamis Mushait, satellite map, Saudi Arabia.

Remote sensing is one of the very important parts in the advance image processing which is used to acquiring satellite image to know about geo-spatial information. Remote sensing is widely used to monitor different resources available on earth surface, out of which the land used area monitoring is one of very important part. There are many techniques and methods are proposed for satellite image classification based on different theme. Here, in this paper theme of classification is land used and land area cover mapping using LISS-III satellite image. LISS-III satellite image have been classified in different area covered by water, forest, mangroves and developments by selecting the pixel based classification. Here multiple linear regressions is used on initial training data set, after evaluation the new features data set is applied to artificial neural network to check the impact of best training data set on the classification accuracy. The accuracy of classification algorithm is calculated using confusion matrix and Kappa coefficient. It is observed with help of confusion matrix and Kappa coefficient that the accuracy of ANN classifier without evaluated training data set is 90.19% and Kappa Coefficient is 0.8533 and when training data set evaluated with the help of multiple Linear regression then ANN accuracy is 98.74% and the Kappa Coefficient is 0.9766 which is improved and very good. So with the proposed method classification accuracy of ANN classifier is improvement for classification of LISS-III satellite image.

The study of Lake Hamrin is of enormous importance in understanding changes in water levels and their impact on the local environment. It helps to clarify the role of climatic and human factors in the decline of its water resources from 2013 to 2023 in order to ensure sustainable solutions. Satellite imagery from Landsat 8 is used, and processing is performed using ArcGIS Pro and ENVI. The normalized vegetation index (NDVI) is applied to show the changes in the vegetation cover and reveal the amount of change in the surface water area of the lake. It is noted that the water area changes in one year as it increases in the rainy season and decreases in the summer, and it also varies from one year to another with the difference in the amount of rainfall. In March 2019, the largest area of the water surface of Hemrin Lake appeared, as the water surface area of the lake reached 326.87190 km² due to the increase in rainfall this year. It is noted that the lake's water area decreased during the rainy season due to the lack of rain, as the lake's water area decreased to 121.52340 km² in January 2021, while the smallest area of Hemrin Lake's water surface is reached in October 2021, as the lake's water surface area reached 28.79910 km², and the surface water area became 59.23530 km² in February 2022 due to the lack of rain during this year. In 2023, a relative increase in the lake's water area began to appear due to the return of seasonal rains this year, reaching 80.88570 km² in April, which proves to us that the change in rainfall in the region is one of the main reasons for the change in the area and water level in Hemrin Lake. The values of the earth's surface temperature are also revealed, and these values varied in the study area according to the different elements of the earth's surface and differed from one season to another and from one year to another. It is natural for the surface temperature of Hemrin Lake to rise in the summer and decrease in the winter. As the smallest area of the lake's water surface is 28.79910 km², the value of the lake's water surface temperature reached 24.9°C in October 2021. It is assumed that the area and water level of the lake decrease with the rise in temperature, but in August 2023, when the lake's water surface temperature is 31.9742°C, the lake's water surface area reached 69.75360 km². The main reasons for the decline in the water levels of Hemrin Lake are the lack of rainfall in recent years due to climate change and the lack of water releases from Lake Darbandikhan due to the lack of water coming to it from the Iranian side, as well as the significant decline in the waters of the Alwend River due to its interruption by the Iranian side due to the construction of dams and the change in its course and the cessation of its flow in the summer.

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.

Questo progetto ha preso spunto da uno studio condotto da Giusy Zevolini e pubblicato nel 2003 sulla Rivista dell’Istituto Nazionale di Archeologia e Storia dell’Arte. Nell’articolo la studiosa proponeva un’ipotesi di ricomposizione di due opere scultoree in marmo, entrambe eseguite durante il XV secolo, una delle quali sicuramente dall’artista toscano Isaia da Pisa. Le due parti, in origine probabilmente inserite in un unico complesso scultoreo monumentale, all’interno della chiesa agostiniana della SS. Trinita di Viterbo, oggi sono conservate in due siti distinti del capoluogo del territorio della Tuscia. Virtual Reconstruction of the Tabernacolo of Isaia da Pisa for the Church of SS. Trinita in Viterbo This study is based upon a characteristic, Art History paper, published in 2003. Specifically it suggests a reconstruction hypotesis concerning two marble objects, created in the XV Century, which were supposed to belong to each other, in a monumental complex, inside the Augustinian Church of the SS. Trinita in Viterbo (Italy). In the article the iconography was rendered by simply stitching pictures together, losing the objects proportions, without any care given to the likely missing parts, and of course showing the image in 2D. Despite scholars mostly agreeing that the two marble artefacts were made by the same artist (Isaiada Pisa) and that both belong to the same church, they are still notsure if the pieces are a unique structure. The virtual re-composition helps to enhance the visual perception of the whole construction, which is now available just in 2D flat images, and to understand how it originally would have looked. The two objects, today placed in different locations in the city of Viterbo, studied in this paper are: a marble tabernacle with Angels and Christ; a marble tympanum with a blessing by Christ. Both sculptures were acquired witha triangulation laser scanner in two different surveys in Viterbo: the first one, at the Church of the SS. Trinita, was carried out in August 2010 and the second one, at the Civic Museum, in February 2011. The well-known 3D post processing pipeline for laser scanner data was applied. The software used for the post processing was MeshLab 130a, 64bit version. In the final step all three dimensional models have been recomposed in a unique context. At a glance, the first thing that appears clear is how the monument made by Isaiada Pisa was massive and impressive in dimensions. Assuming an average height of the tabernacle of one meter and half from theground, its vertical pattern was around 4 meters. The importance of the work, as well as its size, is also supported by archival documentation which records the presence of an important artist like Isaia da Pisa in Viterbo. This case study shows in a practical way the importance of interaction and brainstorming between Art History/Archaeology specialists and experts in computer science (especially in three-dimensional modelling) working in a multidisciplinary context where the different expertises cannot be separated from each other. The reconstruction of the Tabernacle of the Virgin is part of a still on-going project.

• Hydrologic models were used to assess remotely sensed surface water estimates. • The accuracy of satellite surface water estimates diminished for waterbodies < 2 ha. • Vegetation reduces accuracy of surface water detection, especially during wet years. Millions of small waterbodies are dispersed throughout the middle of the North American continent, and billions of dollars have been invested to conserve, restore, and manage these waterbodies in the 20th and 21st centuries. Small waterbody conservation has been supported by different stakeholders aiming at improving water quality, enhancing floodwater storage, and supporting migratory bird breeding habitat. Conservation agencies are using hydrological and biological monitoring, modeling, and mapping to adaptively manage small waterbodies in the face of stressors such as invasive species and climate change. As remote sensing estimates of small waterbody surface water extent have become easier to access, understanding the capabilities and limitations of using remote sensing, especially in areas lacking surface water monitoring, is important for conservation decision making. Here, we used in situ monitoring and process-based hydrological modeling to explore remote sensing accuracy, especially related to waterbody size, emergent aquatic vegetation cover, and climatic conditions. Overall, we found that the accuracy of satellite and aerial imagery surface water mapping approaches vastly decreased for waterbodies smaller than 2 ha. We also found emergent vegetation could be masking surface water in waterbodies larger than 2 ha and that accuracy of some remote sensing estimates may decrease during wetter climatic periods. These results indicate that sensors commonly used for surface water applications alone may not be able to accurately detect small waterbody surface water, which supports the need for combining monitoring and modeling to understand how small waterbodies may respond to future changes in climate and land use.

India’s infrastructure requirements are fuelled by increasing urbanization, a more significant contribution of urban areas to employment generation, a shift to the services-based economy, and the rising importance of climate change and disaster resilience. The National Infrastructure Pipeline (NIP) is seen as a foundation for achieving India’s economic goal of becoming a 5 trillion economy by 2024–2025. The National Monetization Plan (NMP) is a vital component to financing the NIP. This research uses content analysis to understand how responsive these two documents are to key factors that drive the growth of India’s infrastructure: urbanization, land availability, incorporating sustainability principles, and involvement of the private sector. It is found that the importance given to these factors in the NIP is not sustained in the NMP, with the consequence that the insights obtained while developing the NIP will not inform a significant effort in its financing. It is useful to set out integrated, risk-informed thinking about the life-cycle of monetization. Strengthening the PPP models of NMP with the appropriate valuation of location in cities, land values, and the risks and constraints represented by sustainability would leverage the urban multiplier.

The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=−0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.

This research analyzes water availability in the Tulancingo Valley (Hidalgo State, Mexico), a representative region with notable industrial and agricultural activities, over the period from 2013 to 2050. A conceptual model was developed and calculated with the Water Evaluation and Planning System (WEAP) simulation platform, calibrated with 2014 data, to estimate future water demand under mitigation scenarios that incorporate inertial population and industrial growth, as well as projected climate change trends. The simulation identifies the key actions that support sustainable water-resource management. Results show that agricultural groundwater demand is the dominant pressure on the aquifer, which is projected to become overexploited by 2050 (−185.65 hm3). The most effective mitigation strategies involve increasing the use of available surface water in both industrial and agricultural sectors; under these measures, the aquifer could recover and reach an annual availability of 231.7 hm3, ensuring long-term water sustainability of the valley. The modeling approach applied here offers a useful framework for similar assessments in other complex areas.