December 16, 2013WSI, A Quantum Spatial Company, is excited to announce our recent article – “Rivers Gone Digital ” – featured in the fall issue of The Freshwater Trust’s ‘freshwater’ magazine. This piece highlights three geospatial technologies that have revolutionized the science behind restoring river systems. WSI has been at the forefront of these technologies to equip restoration clients and researchers with a broad spectrum of applications and analyses never before possible.
September 04, 2013
Portland, Oregon, September 4, 2013 – Watershed Sciences, Inc. (WSI) is pleased to announce that it has entered into a definitive agreement to merge with two of the leading providers of geospatial services and solutions – Aero-Metric, Inc. (AeroMetric) and Photo Science, Inc. (Photo Science) to form Quantum Spatial.
Quantum Spatial delivers professional services and solutions for clients across all industry verticals with specialized expertise in energy, transportation, utilities, environmental, mining, national security, federal agencies, state and local government and commercial applications. Quantum Spatial plans to invest significantly to enhance its solutions, as well as expand its newly launched family of geospatial applications, which is a suite of cloud-based, enterprise-wide, operational solutions. As a result of this merger, Quantum Spatial’s domestic and international clients will benefit from enhanced capabilities and solutions. In addition, clients will benefit from accelerated research and development through significant investments to create new solutions that will address the evolving challenges faced by each end market. Clients will also have complete access to the creativity and knowledge of the firms’ expanded core capabilities and resources.
Additionally, Quantum Spatial is pleased to announce that Patrick Olson, the CEO of AeroMetric, will be the new Chief Executive Officer of Quantum Spatial. G. Michael Ritchie, the President and CEO of Photo Science will serve as the COO of Quantum Spatial. Matt Boyd and Russ Faux, former Co-CEO’s of WSI will serve on the Executive Committee of Quantum Spatial and will lead the Solutions Applications effort across all industries.
“The increased societal use of spatial data is spawning disruptive new technologies, continual innovation, and altered competitive pressure,” said Matt Boyd, Co-CEO of WSI. “These factors combine to create radical change and new opportunities. Clients want advanced analytics and answers now. Quantum Spatial will lead and accelerate this transformation."
Russ Faux added, ”This merger represents a strategic move that capitalizes on the collective resources, expertise, and experience of all three firms to advance the global geospatial market place. As Quantum Spatial, we are excited to expand upon our service offerings while continuing to maintain the personalized service and spirit of collaboration that our clients have come to expect and appreciate.”
“I am excited to be part of the creation of Quantum Spatial and the ability to provide unmatched solutions, knowledge and scale to our clients,” said Patrick Olson. “We look forward to continuing to expand our mission of providing on-target geospatial solutions, utilizing the in-depth knowledge and expertise of the industry’s largest professional and technical staff. The added capabilities and geographic reach created by this merger will better position us to address the critical needs and priorities of our clients’ ever changing requirements across all of our markets.”
Michael Ritchie noted, “Together, we are uniquely positioned to provide the market with the most comprehensive set of geospatial solutions available in the Americas and to greatly increase our unique tools and applications through collaboration and increased research and development. ”
The company’s consolidation will be seamless. Quantum Spatial will work closely with its clients, partners, and employees to make the integration process a success. Our clients will continue to work with the same people within the respective firms that they have in the past while gaining full access to the expanded capabilities and solutions of the combined company.
About Quantum Spatial, Inc.
Quantum Spatial is the largest provider of location-based tools, analytics and data in North America, utilizing proprietary software tools and applications to create solutions that can be delivered in a tailored, fully-hosted environment to provide clients with a unique portal through which they can better understand and interact with geospatial data. The firm delivers professional services and solutions for clients across all industry verticals with specialized expertise in energy, transportation, utilities, environmental, mining, national security, federal agencies, state and local government and commercial applications. For more information about Quantum Spatial please call 1-855-255-5212 or visit www.quantumspatial.com.
About Watershed Sciences, Inc.
Watershed Sciences, Inc. (WSI) specializes in utilizing applied airborne remote sensing data capture and analysis to develop a variety of assessment, visualization, and software solutions. The firm has earned a reputation for providing innovative professional solutions for clients in utility, commercial forestry, natural resource management, utilities, energy, and green technology. These proprietary methodologies and solutions have served to empower project stakeholders to maximize the use of geospatial information in their internal business practices. For more information please visit www.wsidata.com.
About Aero-Metric, Inc.
Aero-Metric, Inc. (AeroMetric) is a full-service geospatial solutions company built on over eight decades of consistent, quality service and enduring client relationships. The firm offers a comprehensive range of on target geospatial solutions, from the latest in enterprise GIS on-premise and in the cloud, to professional mapping services including photogrammetric, LiDAR, satellite, and airborne imaging technology. For more information please visit www.aerometric.com.
About Photo Science, Inc.
Photo Science, Inc. (Photo Science) is a geospatial solutions provider specializing in aerial acquisition (imagery and LiDAR), data collection/processing, photogrammetric mapping, mobile mapping, remote sensing, Geographic Information Systems (GIS), systems integration, and GPS surveying services. For more information please visit www.photoscience.com.
August 02, 2013Comprehension of geologic processes is of great concern to many modern industries such as infrastructure planning, energy distribution, forestry management, and emergency response. Utilizing an automated approach, WSI has developed an accurate and efficient methodology, which detects areas of failed terrain using high-resolution LiDAR data (15 pts/m2). The result is a robust, efficient, cost-effective dataset that indicates the presence of landslides, fault lines and other forms of failed terrain.
LiDAR modeling reveals subtle surface features that are undetectable via aerial photographs or field observation, which leads to unparalleled richness in topographic models. WSI LiDAR accuracies (RMSEz 3-5 cm) are second to none, providing the necessary foundation for assessing terrain attributes associated with landslides.
WSI has developed a unique methodology that incorporates existing techniques from published literature and adheres to the rigors of the scientific method. Statistical analysis indicates terrain classification accuracy of 95%, a vast improvement compared to existing detection methods.
WSI’s landslide detection relies on a detailed bare-earth DEM derived from a LiDAR ground model. Ground-classified LiDAR points are first processed to create a variety of raster images (≤ 1-meter cell size), which reveal different landform features unique to landslides and faults. These images are then used as inputs for a series of semi-automated classification steps. The various algorithms include a combination of iso-clustering, principal component analysis, and support vector machines, which result in an initial landslide classification.
A step-wise procedure systematically pares the model, excluding non-landslide and non-fault areas from the analysis (e.g., cultivated fields, roads, lakes) that may appear in the initial processing steps.
Iso-Clustering is used to separate the survey region into several distinct classes. This classification technique is an iterative optimization procedure that repeatedly assigns cluster centers, based on the multi-dimensional, topographic characteristics of each input raster image. After each classification step, the algorithm evaluates the results by measuring the minimum distances within each cluster. Each iteration of the procedure causes unique morphological clusters to migrate together. Due to the distinctive morphological signature of these hazardous areas, high-risk landslide features are gradually separated from non-landslide areas. The process is finalized when the most stable arrangement of classified clusters is found and hazard regions are built. Employing zonal statistics, these regions are simplified and condensed down until a dataset outlining potential landslide areas is created.
Once the automated detection is completed, manual editing is performed to eliminate false-positives and to refine the geometry of landslide delineations and fault lines. Upon completing landslide delineation, the landslide areas are automatically assigned respective slope angle, aspect, mean annual precipitation, vegetation cover, and soil attributes.
Continued refinement and corroboration promises enhancement in the automation process, further increasing productivity and reducing cost.
June 21, 2013
WSI was recently featured in "LiDAR News" for its deployment of Riegl’s first-of-its-kind small footprint hydrographic sensor to demonstrate potential for shallow water mapping of riverine environments in the Pacific Northwest. This front-page article details the exploratory LiDAR survey conducted on the Sandy River in Western Oregon.
June 12, 2013
Within the forestry management industry and carbon exchange markets there is a need to develop new processes to improve the precision and accuracy of forest composition and biometric computation. Airborne LiDAR surveys produce highly detailed representations of surface features such as trees, and can be employed for essential forestry applications, including stand delineation, forest composition, and carbon quantification. LiDAR data can be used to analyze large areas while simultaneously reducing the amount of time and resources typically dedicated to a traditional field-based inventory. Used in tandem with onsite measurements, LiDAR data can provide accurate forest models of entire study areas.
WSI is unique in its modeling analysis because LiDAR point cloud data is used as the direct input for model calculations. In comparison to raster-derived computations, point cloud calculations result in greater precision and more robust outputs. The modeling process first identifies and delineates individual trees within the LiDAR data, then implements a moving window analysis based on every treetop point to quantify the local metrics unique to every tree in the survey area.
Point Cloud vs Raster Analysis
The graphic below illustrates the difference in precision between raster-based height model (top left) and point cloud derived height model (top right). The contrast is especially evident when the height models are viewed in three dimensions (raster- bottom left, point cloud- bottom right).
The LiDAR point cloud moving window analysis produces a variety of forest composition metrics such as tree height, canopy cover, stem density, and ground slope.
Forest composition metrics are combined to generate stand classifications. The stratified data provide detailed information for stand delineation and an improved knowledge base for placement of field inventory plots.
Modeling ResultsIn conjunction with field data and regression analysis, LiDAR point cloud data are utilized to compute forest structure calculations such as biomass and basal area as well as canopy volume, mean tree height, Lorey’s mean height, and quadratic mean diameter.
Accuracy and Statistics
An ordinary least squares linear regression was used to generate equations for forest metric calculations. The models compared LiDAR point cloud data to 76 field plots (n=76); all metrics indicate strong relationships between the response variable and the predictors.