Sökning: "temporal resolution"
Visar resultat 21 - 25 av 166 uppsatser innehållade orden temporal resolution.
21. Microsatellite Constellation for Wildfire Monitoring
Master-uppsats, KTH/GeoinformatikSammanfattning : I flera års tid har antalet svåra och okontrollerade skogsbränder ökat i antal. Det finns ett behov av att detektera skogsbränder med hjälp av satelliter som har högre tidsupplösning samt högre geometrisk upplösning än de satelliter som är i bruk idag. LÄS MER
22. Spatial patterns of Dissolved Organic Matter in Swedish Surface Waters
Master-uppsats, SLU/Dept. of Aquatic Sciences and AssessmentSammanfattning : Dissolved organic matter (DOM) in surface water has been widely studied, in part due to its significance for aquatic ecology and drinking water quality. Across Sweden, increases in the total organic carbon (TOC) concentrations and color of surface waters, known as brownification, were noted in the decades before and after year 2000, though recent analysis has found widespread DOM increase to have ceased after 2010. LÄS MER
23. Data Trustworthiness Assessment for Traffic Condition Participatory Sensing Scenario
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Participatory Sensing (PS) is a common mode of data collection where valuable data is gathered from many contributors, each providing data from the user’s or the device’s surroundings via a mobile device, such as a smartphone. This has the advantage of cost-efficiency and wide-scale data collection. LÄS MER
24. Quantification and Detection of Motion Artifacts in Laser Speckle Contrast Imaging
Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)Sammanfattning : Laser speckle contrast imaging (LSCI) is a non-invasive method for assessment of microcirculatory blood flow. The technique is based on analysis of speckle patterns to build 2D maps of perfusion with high spatial and temporal resolution. LÄS MER
25. Estimation of dissolved organic carbon from inland waters using remote sensing data and machine learning
Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapSammanfattning : This thesis presents the first attempt to estimate Dissolved Organic Carbon (DOC) in inland waters over a large-scale area using satellite data and machine learning (ML) methods. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were tested to retrieve DOC using a filtered version of the recently published open source AquaSat dataset with more than 16 thousand samples across the continental US matched with satellite data from Landsat 5, 7 and 8 missions. LÄS MER