scholarly journals Applying remotely sensed habitat descriptors to assist reintroduction programs: A case study in the hazel dormouse

2021 ◽  
Author(s):  
Emma L. Cartledge ◽  
Melanie Baker ◽  
Ian White ◽  
Andrea Powell ◽  
Ben Gregory ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Hazel Dormouse

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

2021 ◽  
Vol 24 ◽  
pp. 100618
Author(s):  
Philipe Riskalla Leal ◽  
Ricardo José de Paula Souza e Guimarães ◽  
Fábio Dall Cortivo ◽  
Rayana Santos Araújo Palharini ◽  
Milton Kampel
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Precipitation Time Series ◽  
New Approach

Evolutionary history and species delimitations: a case study of the hazel dormouse, Muscardinus avellanarius

2016 ◽  
Vol 18 (1) ◽  
pp. 181-196 ◽  
Author(s):  
A. Mouton ◽  
A. Mortelliti ◽  
A. Grill ◽  
M. Sara ◽  
B. Kryštufek ◽  
...  
Keyword(s):  
Evolutionary History ◽  
Hazel Dormouse ◽  
Muscardinus Avellanarius

scholarly journals Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States

2019 ◽  
Author(s):  
Ran Goldblatt ◽  
Nicholas Jones ◽  
Jenny Mannix
Keyword(s):  
Natural Disasters ◽  
Critical Infrastructure ◽  
Health And Safety ◽  
Remotely Sensed ◽  
Current Data ◽  
Small Island ◽  
Island States ◽  
Management Actions ◽  
Small Island States

Over the last few decades, many countries, especially Caribbean island ones, have been challenged by the devastating consequences of natural disasters, which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure are key for an effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. However, large portions of the world, including countries exposed to natural disasters, remain unmapped. In this study, we propose a methodology that relies on remotely sensed measurements (e.g. VIIRS, Sentinel-2 and Sentinel-1) and derived classification schemes (e.g. forest and built-up land cover) to predict the completeness of OSM building footprints in three small island states (Haiti, Dominica and St. Lucia). We find that the combinatorial effects of these predictors explain up to 94% of the variation of the completeness of OSM building footprints. Our study extends the existing literature by demonstrating how remotely sensed measurements could be leveraged to evaluate the completeness of OSM database, especially in countries at high risk of natural disasters. Identifying areas that lack coverage of OSM features could help prioritize mapping efforts, especially in areas vulnerable to natural hazards and where current data gaps pose an obstacle to timely and evidence-based disaster risk management actions.

Case Study of the Use of Remotely Sensed Data for Modeling Flood Inundation on the River Severn, U.K.

2008 ◽  
Vol 134 (5) ◽  
pp. 533-540 ◽  
Author(s):  
N. G. Wright ◽  
I. Villanueva ◽  
P. D. Bates ◽  
D. C. Mason ◽  
M. D. Wilson ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Flood Inundation ◽  
Remotely Sensed Data
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