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Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification

Ayanu, Yohannes Zergaw and Conrad, Christopher and Jentsch, Anke and Koellner, Thomas (2015) Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification. PLoS One, 10 (8), pp. 1-21. Public Library of Science (PLoS). DOI: 10.1371/journal.pone.0130079 ISSN 1932-6203

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Official URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130079

Abstract

The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.

Item URL in elib:https://elib.dlr.de/101080/
Document Type:Article
Title:Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ayanu, Yohannes Zergawyohanneszer (at) yahoo.comUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) uni-wuerzburg.deUNSPECIFIED
Jentsch, Ankeanke.jentsch (at) uni-wuerzburg.deUNSPECIFIED
Koellner, ThomasUniversity BayreuthUNSPECIFIED
Date:22 June 2015
Journal or Publication Title:PLoS One
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.1371/journal.pone.0130079
Page Range:pp. 1-21
Publisher:Public Library of Science (PLoS)
ISSN:1932-6203
Status:Published
Keywords:Forest, Trees, Ecosystem, Agroforest, Forest ecology, Farms, Land use, Ethiopia
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:18 Jan 2016 10:08
Last Modified:08 Mar 2018 18:49

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