About: Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data

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article by Gaia Vaglio Laurin et al published March 2014 in ISPRS Journal of Photogrammetry and Remote Sensing

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) наукова стаття, опублікована в березні 2014 (uk) article scientifique publié en 2014 (fr) im März 2014 veröffentlichter wissenschaftlicher Artikel (de) article by Gaia Vaglio Laurin et al published March 2014 in ISPRS Journal of Photogrammetry and Remote Sensing (en)
publication date	wds:Q59282212-EED62C6F-49FC-4FE1-9E91-1B7ED08105B2
publication date	2014-03-01 00:00:00Z (xsd:dateTime)
author name string	wds:Q59282212-04747C92-4FA5-43BC-BFB6-D12C08970C61 wds:Q59282212-24B62DBF-A4AD-470F-A632-01B710B8A197 wds:Q59282212-35928CCF-3DF9-433B-8571-4B65AC120A9E wds:Q59282212-C20F5575-CE01-4643-B75E-60128C8C2DA1
author name string	Qi Chen Fabio Del Frate Jeremy A. Lindsell Leila Guerriero
rdfs:label	Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (en) Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (nl)
skos:prefLabel	Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (en) Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (nl)
name	Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (en) Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (nl)
author	wds:Q59282212-4418AD84-5889-4C5E-89B2-C4B87EAAA01E wds:Q59282212-80AC24EB-60A2-4DC8-9F5B-61543609B266 wds:Q59282212-9AB0D2A9-D5DC-4A1B-99A6-B9751640F136 wds:Q59282212-CBBBFAA0-6D52-479F-B689-3C05C64D5D95
author	Riccardo Valentini David A. Coomes Gaia Vaglio Laurin Francesco Pirotti
title	wds:Q59282212-339A9A65-C023-4D06-93A8-FB02AB70C978
title	Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data (en)
page(s)	wds:Q59282212-9EF79F03-6BB9-4363-A6E2-7B905EDAD17F
page(s)	49-58
instance of	wds:Q59282212-BE3AA25E-E607-4F1A-B418-4625DD522DDC
instance of	scholarly article
main subject	wds:Q59282212-2F05CC03-6259-4F68-835E-C2D8C644E54C wds:Q59282212-A1CAB421-2FC4-403A-A6BF-889F544C1183 wds:Q59282212-BD5843AD-146F-4BAC-85D1-DD6D93DDE3F1
main subject	remote sensing tropical forest lidar
published in	wds:Q59282212-9B83F004-AF43-4532-B55B-7EDED541803F
published in	ISPRS Journal of Photogrammetry and Remote Sensing
volume	wds:Q59282212-53E4C8F4-D7C7-4B07-BD45-4C5423F8863A
volume	89
DOI	wds:Q59282212-C0CB0577-1767-48C9-A478-11FB29CC89A2
DOI	http://dx.doi.org/10.1016/J.ISPRSJPRS.2014.01.001
DOI	10.1016/J.ISPRSJPRS.2014.01.001
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q59282212
is cites work of	A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya Remote sensing-based landscape indicators for the evaluation of threatened-bird habitats in a tropical forest. Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data Delineation of Individual Tree Crowns from ALS and Hyperspectral data: a comparison among four methods How to map forest structure from aircraft, one tree at a time. Monitoring tropical forest carbon stocks and emissions using Planet satellite data Using lidar data to analyse sinkhole characteristics relevant for understory vegetation under forest cover-case study of a high karst area in the dinaric mountains Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests
is cites work of	wds:Q58307974-B4E16D98-3E9D-4E09-8A05-D5FA53DF3843 wds:Q58654615-9BF5C402-B94D-437F-AA9D-448948EF242A wds:Q58471343-E2927E25-3755-4662-9222-4EA38676EA48 wds:Q31110299-B2A59C35-FDD1-49DC-862D-A7BB590E7365 wds:Q35875122-F54C1146-B34C-423C-B7A2-0BBC268F24C6 wds:Q55208799-6F720F6A-56F3-4BDF-AB18-DDA0AA88BA1E wds:Q91526282-F4D8D550-B54A-4F6E-94F0-660AB0E650CA wds:Q30000629-DEB0616A-6D36-4CBC-8209-EC17B3771DE0 wds:Q57190308-D0C73B9C-31B7-4785-97A2-24D3E0D1F317 wds:Q57248677-EF8C0357-8DEE-4668-B410-67FEBD1DC43F wds:Q58395722-2810569D-6C5D-473E-8CA2-C4E22B243FF7 wds:Q33773826-AF631520-5DFF-4D46-BFE6-104162CF3BAA wds:Q59282203-0046D688-DCCB-4421-9B36-ADFF509DEDE9 wds:Q38687318-E92351F1-20CA-43A7-9E17-8F2D47B31029

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