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"Aerial"
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German Historians and the Bombing of German Cities
by
von Benda-Beckmann, Bas
in
Allied bombings Historiography Second World War Germany
,
AUP Wetenschappelijk
,
Bombing, Aerial
2015,2025
Today, strategic aerial bombardments of urban areas that harm civilians, at times intentionally, are becoming increasingly common in global conflicts. This book reveals the history of these tactics as employed by nations that initiated aerial bombardments of civilians after World War I and during World War II.As one of the major symbols of German suffering, the Allied bombing left a strong imprint on German society. Bas von Benda-Beckmann explores how German historical accounts reflected debates on post-war identity and looks at whether the history of the air war forms a counter-narrative against the idea of German collective guilt. Provocative and unflinching, this study offers a valuable contribution to German historiography.
Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery
by
Cavanaugh, Kyle C.
,
Doughty, Cheryl L.
in
Aerial photography
,
Aerial surveys
,
Airborne sensing
2019
Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.
Journal Article
Small Unmanned Aerial Vehicles (Micro-Uavs, Drones) in Plant Ecology
by
Grasty, Monica R.
,
Kohrn, Brendan F.
,
Hendrickson, Elizabeth C.
in
aerial drone (micro-UAV, UAS)
,
aerial photography
,
aerial survey
2016
Premise of the study: Low-elevation surveys with small aerial drones (micro–unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Methods: Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. Results: We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. Discussion: The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.
Journal Article
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs
2022
In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure. Particularly, we investigate different versions of the YOLO objection detection method and compare their performances for the specific application of detecting a safe landing location for a UAV that has suffered an in-flight failure. We compare the performance of YOLOv3, YOLOv4, and YOLOv5l while training them by a large aerial image dataset called DOTA in a Personal Computer (PC) and also a Companion Computer (CC). We plan to use the chosen algorithm on a CC that can be attached to a UAV, and the PC is used to verify the trends that we see between the algorithms on the CC. We confirm the feasibility of utilizing these algorithms for effective emergency landing spot detection and report their accuracy and speed for that specific application. Our investigation also shows that the YOLOv5l algorithm outperforms YOLOv4 and YOLOv3 in terms of accuracy of detection while maintaining a slightly slower inference speed.
Journal Article
From above : the story of aerial photography
Since the birth of photography, photographers have been taking images of the earth from the air - with spectacular visual results. Celebrating over 150 years of these incredible images, this book tells the fascinating story of how these pictures were created and the photographers that have propelled image-taking to bold new heights. Taking advantage of the amazing sense of perspective that aerial photography offers, this incredible collection of images also offers a unique overview of the events, challenges, and changes of the past 150 years of human history.
Beneficial Bombing
2011,2010
The Progressive Era, marked by a desire for economic, political,
and social reform, ended for most Americans with the ugly reality
and devastation of World War I. Yet for Army Air Service officers,
the carnage and waste witnessed on the western front only served to
spark a new progressive movement-to reform war by relying on
destructive technology as the instrument of change. In
Beneficial Bombing Mark Clodfelter describes how American
airmen, horrified by World War I's trench warfare, turned to the
progressive ideas of efficiency and economy in an effort to reform
war itself, with the heavy bomber as their solution to limiting the
bloodshed. They were convinced that the airplane, used as a bombing
platform, offered the means to make wars less lethal than conflicts
waged by armies or navies. Clodfelter examines the progressive
idealism that led to the creation of the U.S. Air Force and its
doctrine that the finite destruction of precision bombing would end
wars more quickly and with less suffering for each
belligerent. What is more, his work shows how these progressive
ideas emerged intact after World War II to become the foundation of
modern U.S. Air Force doctrine. Drawing on a wealth of archival
material, including critical documents unavailable to previous
researchers, Clodfelter presents the most complete analysis ever of
the doctrinal development underpinning current U.S. Air Force
notions about strategic bombing.