About: Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

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rdf:type	Item
description	article (en) wetenschappelijk artikel (nl) наукова стаття, опублікована у вересні 2016 (uk) im September 2016 veröffentlichter wissenschaftlicher Artikel (de)
publication date	wds:Q58101692-33418430-4971-4E6D-9986-4D975F1063C4
publication date	2016-09-01 00:00:00Z (xsd:dateTime)
cites work	wds:Q58101692-2DB0EECC-46FA-425D-8358-CB5686D8D9FA wds:Q58101692-2FCE31E8-ECBB-4752-A5AB-4611035949C1 wds:Q58101692-307E0371-1639-4A87-9E92-6816D4E852BB wds:Q58101692-8160BDFC-23DB-43E8-80D2-093018D22568 wds:Q58101692-993F761B-6C33-4E44-8406-B6BDE904778B wds:Q58101692-D1FF2E0D-77B1-4604-809C-610AA3202F2C wds:Q58101692-E11172BB-E073-4D9E-A109-C44ACD37D119
cites work	Random Forests Geographic Object-Based Image Analysis - Towards a new paradigm Remote sensing of aquatic vegetation: theory and applications. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery Ecology of freshwater shore zones When trends intersect: The challenge of protecting freshwater ecosystems under multiple land use and hydrological intensification scenarios Maximum growing depth of submerged macrophytes in European lakes
author name string	wds:Q58101692-4BFC21D5-01AF-4A17-9AD2-CBDD7EB9DB5B wds:Q58101692-A93BC5E3-6320-45CE-BEA4-1F84EB729356
author name string	Eva Husson Heather Reese
rdfs:label	Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (en) Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (nl)
skos:prefLabel	Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (en) Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (nl)
name	Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (en) Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (nl)
author	wds:Q58101692-D01ACF39-733B-464A-985F-4E87172BF553
author	Frauke Ecke
title	wds:Q58101692-04AE12BA-438F-4958-BA25-C0F9013ACEF0
title	Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images (en)
page(s)	wds:Q58101692-A05A6C8B-B54B-44FC-8FC2-DDCDEA45168C
page(s)	724
instance of	wds:Q58101692-443FB076-9350-4AAE-A0C7-9087E2C60BF6
instance of	scholarly article
main subject	wds:Q58101692-261A1A75-0022-41E9-B9CA-48F527C2D049 wds:Q58101692-2D018FB9-40FC-48EE-BFE4-14A2B37C1B10
main subject	automation image analysis
DBLP publication ID	wds:Q58101692-384E1826-4161-4905-8C48-AA2CD4C842EC
DBLP publication ID	https://dblp.org/rec/journals/remotesensing/HussonER16
DBLP publication ID	journals/remotesensing/HussonER16
published in	wds:Q58101692-91146E60-E67C-41F2-BD19-CBE20FCF9A16
published in	Remote Sensing
issue	wds:Q58101692-7AD98711-822C-49DD-AB7B-71BDB8125674
volume	wds:Q58101692-C54783A6-A772-45E3-BB6B-FEB0F3F25EF6
issue	9
volume	8
DOI	wds:Q58101692-DF8692C6-1303-44B4-A8E3-4C65850D4EF5
DOI	http://dx.doi.org/10.3390/RS8090724
DOI	10.3390/RS8090724
copyright license	wds:Q58101692-5fb6e1ce-30af-4f1d-8279-656180c7b51c
copyright status	wds:Q58101692-562f5b68-93de-4807-b4d3-791977804ad6
copyright license	Creative Commons Attribution 4.0 International
copyright status	copyrighted
ADS bibcode	wds:Q58101692-5FD27170-47BE-4D3C-A28E-317E6610F0A0
ADS bibcode	2016RemS....8..724H
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q58101692
is cites work of	Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes. Remotely sensed rivers in the Anthropocene: state of the art and prospects Location, location, location: considerations when using lightweight drones in challenging environments
is cites work of	wds:Q58101681-54E2449E-0C65-40E9-9673-9A4929E08702 wds:Q55017278-3146C858-31B9-41F1-8D49-48903B2A5134 wds:Q98071996-A16E50A2-AC6B-4B1A-A751-DB110E85C257 wds:Q57917720-8B94CA3A-35BB-42DD-A6D8-0D673519DB38

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