Seasonal Variability of Waterlogging in Rangpur City Corporation Using GIS and Remote Sensing Techniques

Waterlogging hazard is a significant environmental issue closely linked to land use for sustainable urbanization. NDWI is widely and effectively used in identifying and visualizing surface water distribution based on satellite imagery. Landsat 7 ETM+ and Landsat 8 OLI TIRS images of pre and post-monsoon (2002, 2019) have been used. The main objective of this study is to detect the seasonal variation of waterlogging in Rangpur City Corporation (RPCC) in 2002 and 2019. In the present study, we used an integrated procedure by using ArcGIS raster analysis. For pre and post-monsoon, almost 93% accuracy was obtained from image analysis. Results show that in 2002 during the pre and post-monsoon period, waterlogged areas were about 159.58 km2 and 32.32 km2, respectively, wherein in 2019, the changes in waterlogged areas are reversed than 2002. In 2019, during pre-monsoon, waterlogged area areas were 122.79 km2, and during post-monsoon, it increased to 127.05 km2. The research also depicts that the trend of the waterlogging situation largely depends on seasonal rainfall and a flawed drainage system. Keywords : Seasonal variation; Waterlogging; Remote sensing; GIS; Rangpur City Corporation Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Raster analysis of Fused Filament Fabrication process

The objective of the present work is to study the raster generation to realize Fused Filament Fabrication parts. The research in this paper focused on the evaluation of the deposition of a simple geometry with a FFF machine, supported by an analytical model to compute the build time, also evaluating the geometrical variations caused by changes in process parameters. The main parameters were the print temperature and speed as a function of the thermal and rheological properties of the PLA filament. The study identified essential correlations between process parameters, raster dimensions, and filament properties. An experimental procedure, supported by an analytical model, was implemented for computing raster time and material dimensions.

Object-based approach for urban land cover mapping using high spatial resolution data

This paper deals with object-oriented image analysis applied for an urban area. Very high-resolution images in conjunction with object-oriented image analysis have been used for land cover detection. Using the eCognition software with object-oriented methods, not only the spectral information but also the shape, compactness and other parameters can be used to extract meaningful objects. The spectral and geometric diversity of urban surfaces is a very complex research issue. It is the main reason why additional information is needed to improve the outcome of classification. The most consistent and relevant characteristic of buildings is their height. Therefore, elevation data (converted from LIDAR data) are used for building extraction, segmentation and classification. The study deals with the problem, how to determine the most appropriate parameters of segmentation, feature extraction and classification methods. The data extraction includes two phases, the first part consists the following steps: data pre-processing, rule set development, multi-scale image segmentation, the definition of features used to map land use, classification based on rule set and accuracy evaluation. The second part of the data process based on classical raster analysis GIS tools like focal and zonal function.

Walkability scoring: Why and how does a three-dimensional pedestrian network matter?

Previous walkability scoring systems are all based on road networks, even though roads are not designed for pedestrians. To calculate an accurate walking score, we need pedestrian network data. This is especially the case in cities such as Hong Kong, where pedestrians are separated from vehicles by footbridges, underpasses or surface sidewalks. In this paper, we investigate why and how a three-dimensional pedestrian network makes a difference in walkability scoring, using Hong Kong as a case city. We developed a walkability scoring system based on networks and amenities, using multiple open-source programming platforms and languages. Separately, we calculated walkability scores (on a scale of 0–100) using the three-dimensional pedestrian network and road network of the city, comparing the differences between the two. A GIS raster analysis was conducted to extract walkability scoring differences from the two walkability surfaces, followed by a univariate linear model to examine how the scores were underestimated if without using the three-dimensional pedestrian network. Results show that streets were considered twice as walkable if rated by pedestrian network rather than road network. Walkability scores were 92% higher on average. The fitted model shows that the mean score underestimations were significantly different for different three-dimensional network elements. Surface sidewalks had an average underestimation of 33.75 (p < 0.001), footbridges and underground paths expanded the underestimations by 3.85 and 2.97 (both p < 0.001), respectively, and the linkages to footbridge and underground path enlarged the surface sidewalk underestimations by 2.68 and 4.92 (both p < 0.001). We suggest that walkability evaluation systems should be developed on pedestrian networks instead of road networks, especially for high-density cities.

Debris-flow deposition zones – an example from the southern slopes of Stara Planina Mountain (village of Anton area)

The analysis of the deposition zones is one of the main steps in the debris flows hazard assessments. For the area located north and northeast of Anton village is known that in the last 100 years there is at least one debris flow event. Field observations, geological characteristics, and raster analysis for prediction of possible sediment accumulations over the fans, related with debris flow activity were performed for preliminary analysis of the debris flows hazard in the area.

Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust assessment comparing the efficiency of raster data analysis on big data platforms. This research begins to address this issue by establishing a raster data benchmark that employs freely accessible datasets to provide a comprehensive performance evaluation and comparison of raster operations on big data platforms. The benchmark is critical for evaluating the performance of spatial operations on big data platforms. The benchmarking datasets and operations are applied to three big data platforms. We report computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three raster different datasets.

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allows researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are also at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust framework to assess the capabilities of geospatial analysis on big data platforms. This research begins to address this issue by establishing a geospatial benchmark that employs freely accessible datasets to provide a comprehensive comparison across big data platforms. The benchmark is a critical for evaluating the performance of spatial operations on big data platforms. It provides a common framework to compare existing platforms as well as evaluate new platforms. The benchmark is applied to three big data platforms and reports computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three different datasets.

Automated Location Detection of Retention and Detention Basins for Water Management

Retention and detention basins are engineering constructions with multiple objectives; e.g., flood protection and irrigation. Their performance is highly location-dependent, and thus, optimization strategies are needed. LOCASIN (Location detection of retention and detention basins) is an open-source MATLAB tool that enables automated and rapid detection, characterization and evaluation of basin locations. The site detection is based on a numerical raster analysis to determine the optimal dam axis orientation, the dam geometry and the basin area and volume. After selecting a reasonable basin combination, the results are summarized and visualized. LOCASIN represents a user-friendly and flexible tool for policy makers, engineers and scientists to determine dam and basin properties of optimized positions for planning and research purposes. It can be applied in an automated way to solve small and large scale engineering problems. The software is available on GitHub.