Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Identifying and Viewing Rooftop Solar Potential: A Case Study for the City of Redlands, California

Increasingly scarce resources pose new challenges to human development in the twenty-first century. As a result of the shifting focus to renewable energy in order to meet the different needs of sustainable development, human society will inevitably experience numerous transformations on a variety of scales. Solar radiation plays a key role in achieving the objectives of sustainable development. Due to the potential variation over space and time, efficient utilization of solar energy requires that people understand the diverse spatial and temporal patterns of incoming solar radiation. This project was aimed at developing a Web-based solar map for the City of Redlands using DEMs generated from high resolution LiDAR data. This online solar map will provide the means to inform people of the latest updates regarding solar radiation, and also support their decision-making process in identifying ideal locations of solar panels for maximizing their benefits

Object-based Urban Building Footprint Extraction and 3D Building Reconstruction from Airborne LiDAR Data

Buildings play an essential role in urban intra-construction, urban planning, climate studies and disaster management. The precise knowledge of buildings not only serves as a primary source for interpreting complex urban characteristics, but also provides decision makers with more realistic and multidimensional scenarios for urban management. In this thesis, the 2D extraction and 3D reconstruction methods are proposed to map and visualize urban buildings. Chapter 2 presents an object-based method for extraction of building footprints using LiDAR derived NDTI (Normalized Difference Tree Index) and intensity data. The overall accuracy of 94.0% and commission error of 6.3% in building extraction is achieved with the Kappa of 0.84. Chapter 3 presents a GIS-based 3D building reconstruction method. The results indicate that the method is effective for generating 3D building models. The 91.4% completeness of roof plane identification is achieved, and the overall accuracy of the flat and pitched roof plane classification is 88.81%, with the user’s accuracy of the flat roof plane 97.75% and pitched roof plane 100%

Methodology for the generation of 3D city models and integration of HBIM models in GIS: Case studies

[EN] The Architecture, Engineering and Construction (AEC) industry increasingly demands the availability of semantic and interactive digital models with the environment, capable of simulating decision-making during its life cycle and representing the results achieved. This motivates the need to develop models that integrate spatial information (GIS) and construction information (HBIM), favouring the achievement of the Smart City and Digital Twin concepts. GIS & HBIM platform is a useful tool, with potential applications in the world of built heritage; but it still has certain inefficiencies related to interoperability, the semantics of the formats and the geometry of the models. The objective of this contribution is to suggest a procedure for the generation of 3D visualization models of existing cities by integrating HBIM models in GIS environments. For this, three software and two types of data sources (existing plans and point cloud) are used. The methodology is tested in four locations of different dimensions, managing to identify the advantages/disadvantages of each application.Carrasco, CA.; Lombillo, I.; Sánchez-Espeso, J. (2022). Methodology for the generation of 3D city models and integration of HBIM models in GIS: Case studies. VITRUVIO - International Journal of Architectural Technology and Sustainability. 7(2):74-87. https://doi.org/10.4995/vitruvioijats.2022.1880874877

LiDAR-Assisted Extraction of Old Growth Baldcypress Stands Along The Black River of North Carolina

The remnants of ancient baldcypress forests continue to grow across the Southeastern United States. These long lived trees are invaluable for biodiversity along riverine ecosystems, provide habitat to a myriad of animal species, and augment the proxy climate record for North America. While extensive logging of the areas along the Black River in North Carolina has mostly decimated ancient forests of many species including the baldcypress, conservation efforts from The Nature Conservancy and other partners are under way. In order to more efficiently find and study these enduring stands of baldcypress, some of which are estimated to be more than 1,000 years old, LiDAR remote sensing and geospatial analysis techniques can be employed. Promising results have been discovered correlating LiDAR-derived metrics and known stands of old growth baldcypress. A number of percentile height metrics and other composite metrics like canopy cover and density were extracted from LiDAR data collected across North Carolina. Along with the metrics, locations of known stands of old growth were used as training data for a supervised classification with the C5.0 decision tree algorithm. C5.0 was used to condense the patterns found across the training data into a set of rules that could then be applied to other areas within the study site or anywhere else across the LiDAR data. Both existing stands and new areas were selected by the machine learning rulesets indicating that the use of machine learning is valid to identify stands of ancient trees along the Black River. Overall C5.0 accuracies of approximately 98.5% (based on training data) and 88.6% (based on independent test data) were achieved. More than 8 km2 of predicted old growth forests, outside of available in situ reference areas, were also identified within the Black River site

REMOTE DETECTION OF EPHEMERAL WETLANDS IN MID- ATLANTIC COASTAL PLAIN ECOREGIONS: LIDAR AND HIGH-THROUGHPUT COMPUTING

Ephemeral wetlands are ecologically important freshwater ecosystems that occur frequently throughout the Atlantic coastal plain ecoregions of North America. Despite the growing consensus of their importance and imperilment, these systems historically have not been a national conservation priority. They are often cryptic on the landscape and methods to detect ephemeral wetlands remotely have been ineffective at the landscape scales necessary for conservation planning and resource management. Therefore, this study fills information gaps by employing high-resolution light detection and ranging (LiDAR) data to create local relief models that elucidate small localized changes in concavity. Relief models were then processed with local indicators of spatial association (LISA) in order to automate their detection by measuring autocorrelation among model indices. Following model development and data processing, field validation of 114 predicted wetland locations was conducted using a random stratified design proportional to landcover, to measure model commission (α) and omission (β) error rates. Wetland locations were correctly predicted at 85% of visited sites with α error rate = 15% and β error rate = 5%. These results suggest that devised local relief models captured small geomorphologic changes that successfully predict ephemeral wetland boundaries in low-relief ecosystems. Small wetlands are often centers of biodiversity in forested landscapes and this analysis will facilitate their detection, the first step towards long-term management

City of Redlands Safe Routes to Schools Shadow Mapping

The City of Redlands seeks to improve the tree canopy coverage over key pedestrian zones with the intention of providing more shade to pedestrians in support of the Safe Routes to School Program. An analysis of the current canopy and the shaded sidewalks allowed city planners to distinguish those areas that are both “walkable” and in need of more shade. LIDAR data can generate a detailed and accurate measurement of the city’s canopy index, and was used to determine the total shadow coverage of trees and buildings. Overlaid with priority sidewalks, this map identified pedestrian zones in need of shade. The results allow the City of Redlands to more clearly understand the current canopy near school zones, and determine areas with a deficit of shade coverage

Visualizations of Downtown San Bernardino and a Proposed Development Using CityEngine

Cities are experiencing increasing growth in population and business infrastructure. These changes have profound impacts on urban planners and stakeholders alike, in how they view and conceptualize potential new developments. In the past, the downtown area of the City of San Bernardino would take on new projects only having a rendering of the proposed building(s), making it time consuming and difficult to understand the wider impact on the surrounding areas. Without view analyses these developments could potentially result in termination due to deadlines or loss of interest from stakeholder. This project addressed this issue by creating 3D renderings of the area using CityEngine and preforming various visual analyses for new development(s). Having CityEngine will deduct meeting time and effectively answer visual questions their various stakeholders have in regard to the developments or cityscape of downtown San Bernardino area. These conclusions of these findings were significant to the downtown City of San Bernardino, and the project was able to be created with the data provided. The data also allowed the project to and create the cityscape of the downtown area and to preform various visual analyses to solidify the project’s fruition

ONLINE MAPPING APPLICATION DEVELOPMENT FOR THE SCHOOL OF FOREST RESOURCES AND ENVIRONMENTAL SCIENCE GEOSPATIAL DATA

This thesis emphasizes current Web GIS technologies with emphasis on architectures, particularly web applications. The goal of the study is based on data sharing and improving the efficiency of students, professionals and researchers utilizing data from the School of Forest Resources and Environmental Science (SFRES) geospatial data. This paper is an overview of the MTU geospatial web page and using ArcGIS JavaScript API to develop a web application. The development of the application was based on open source software tools such as Map Server and Feature Server for the GIS functions, HTML, and JavaScript as programming languages and CSS as a markup language

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PD