| 1. |
- Duncanson, Laura, et al.
(författare)
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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
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Tidskriftsartikel (refereegranskat)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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| 2. |
- Malmborg, Jens, et al.
(författare)
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Prediction models of free-field vibrations from railway traffic
- 2017
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Ingår i: COMPDYN 2017 : 6 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering - 6 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering. - Athens : Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece. ; , s. 4810-4820
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Konferensbidrag (refereegranskat)abstract
- Many cities experience an increasing population leading to a need for urban densification. In these cities, unused land close to railways will have to be developed with new residential and office buildings. The infrastructural demand will also increase, resulting in heavily trafficked roads and railways close to w here people work and live. Annoyance from traffic-induced vibrations and noise is expected to be a growing issue. To predict the level of vibration and noise in buildings caused by railway and road traffic, calculation models are needed.In the present pa per, a simplified prediction model is briefly described. This prediction model is based on the assumption that the ground and railway embankment can be described in an axisymmetric model , to provide the transfer functions between the track and the free-field . In the paper, the error that arise by assuming axisymmetric response is studied by comparing the response in a three-dimensional finite-element model. Transfer functions at several positions in the free-field are compared
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| 3. |
- Abel, Olubunmi, et al.
(författare)
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Development of a Smartphone App for a Genetics Website : The Amyotrophic Lateral Sclerosis Online Genetics Database (ALSoD)
- 2013
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Ingår i: JMIR mhealth and uhealth. - : JMIR Publications, Inc.. - 2291-5222. ; 1:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: The ALS Online Genetics Database (ALSoD) website holds mutation, geographical, and phenotype data on genes implicated in amyotrophic lateral sclerosis (ALS) and links to bioinformatics resources, publications, and tools for analysis. On average, there are 300 unique visits per day, suggesting a high demand from the research community. To enable wider access, we developed a mobile-friendly version of the website and a smartphone app. Objective: We sought to compare data traffic before and after implementation of a mobile version of the website to assess utility. Methods: We identified the most frequently viewed pages using Google Analytics and our in-house analytic monitoring. For these, we optimized the content layout of the screen, reduced image sizes, and summarized available information. We used the Microsoft. NET framework mobile detection property (HttpRequest. IsMobileDevice in the Request. Browser object in conjunction with HttpRequest. UserAgent), which returns a true value if the browser is a recognized mobile device. For app development, we used the Eclipse integrated development environment with Android plug-ins. We wrapped the mobile website version with the WebView object in Android. Simulators were downloaded to test and debug the applications. Results: The website automatically detects access from a mobile phone and redirects pages to fit the smaller screen. Because the amount of data stored on ALSoD is very large, the available information for display using smartphone access is deliberately restricted to improve usability. Visits to the website increased from 2231 to 2820, yielding a 26% increase from the pre-mobile to post-mobile period and an increase from 103 to 340 visits (230%) using mobile devices (including tablets). The smartphone app is currently available on BlackBerry and Android devices and will be available shortly on iOS as well. Conclusions: Further development of the ALSoD website has allowed access through smartphones and tablets, either through the website or directly through a mobile app, making genetic data stored on the database readily accessible to researchers and patients across multiple devices.
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