Silva, B.; Roos, K.; Fries, A.; Rollenbeck, R.; Beck, E. & Bendix, J. (2014): <b>Mapping Two Competing Grassland Species from a Low-Altitude Helium Balloon</b>. <i>IEEE Journal of selected topics in applied earth observations and remote sensing</i> <b>7</b>(7), 3038 - 3049.
Resource Description
Title:
Mapping Two Competing Grassland Species from a Low-Altitude Helium Balloon
FOR816dw ID:
1280
Publication Date:
2014-03-08
License and Usage Rights:
PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
email:
biene.tomate <at> gmx.net
Faculty of Biology, Chemistry and Geoscience
Department of Plant Physiology
Universitätsstr. 30
95440 Bayreuth
Germany
Individual:
Andreas Fries
Contact:
email:
andy_fries <at> gmx.de
Loja
Ecuador
Individual:
Ruetger Rollenbeck
Contact:
email:
rollenbe <at> staff.uni-marburg.de
Laboratory for Climatology and Remote Sensing
Faculty of Geography
Philipps University of Marburg
Deutschhausstr. 10
35032 Marburg
Germany
Individual:
Erwin Beck
Contact:
email:
erwin.beck <at> uni-bayreuth.de
Universitätsstr. 30
Faculty of Biology, Chemistry and Geoscience
University of Bayreuth
95440 Bayreuth
Germany
Individual:
Jörg Bendix
Contact:
email:
bendix <at> staff.uni-marburg.de
Faculty of Geography
Deutschhausstraße 10
Philipps University of Marburg
Laboratory for Climatology and Remote Sensing
35032 Marburg
Germany
Abstract:
This paper describes a method of low-altitude remote<br/>
sensing in combination with in situ measurements (leaf area, spectroscopy, and position) to monitor the postfire canopy recovery of two competing grassland species. The method was developed in the Andes of Ecuador, where a tethered balloon with a digital camera was deployed to record a time series of very high spatial resolution<br/>
imagery ( nominal resolution = 2cm ) of an experimental plot covered by two competing species: 1) the pasture grass, Setaria sphacelata; and 2) the invasive southern bracken, Pteridium arachnoideum. Image processing techniques were combined to solve geometric issues and construct high-quality mosaics for image classification. The semiautomatic and object-oriented classification method was based on geometrical and textural attributes of image segments and showed promising results for detecting the invasive bracken fern in Setaria pastures (performance by area under the curve, AUC = 0.88). Valuable insights are given into vegetation monitoring applications using unmanned aerial vehicles, which produces a time series of species-specific maps, including foliage projective cover (FPC) and leaf area index (LAI). This new method constitutes an important and accessible tool for ecological investigations of competing species in pastures and validation of remote sensing information on mountain environments.
Additional Infos:
This article has been accepted on 8-3-2014 for inclusion in a future issue of this journal.
