Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification
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{"title"=>"Unveiling undercover cropland inside forests using landscape variables: A supplement to remote sensing image classification", "type"=>"journal", "authors"=>[{"first_name"=>"Yohannes", "last_name"=>"Ayanu", "scopus_author_id"=>"55185260300"}, {"first_name"=>"Christopher", "last_name"=>"Conrad", "scopus_author_id"=>"7102811204"}, {"first_name"=>"Anke", "last_name"=>"Jentsch", "scopus_author_id"=>"6602883636"}, {"first_name"=>"Thomas", "last_name"=>"Koellner", "scopus_author_id"=>"6507337126"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84939175965", "sgr"=>"84939175965", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0130079", "pmid"=>"26098107", "isbn"=>"1932-6203", "pui"=>"605586320"}, "id"=>"772b4ab5-2df2-3600-bbbd-39bdd3ee406a", "abstract"=>"The worldwide demand for food has been increasing due to the rapidly growing global popu- lation, and agricultural lands have increased in extent to produce more food crops. The pat- tern 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 BaleMountains of Ethiopiawas 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, con- strained 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. Introduction", "link"=>"http://www.mendeley.com/research/unveiling-undercover-cropland-inside-forests-using-landscape-variables-supplement-remote-sensing-ima", "reader_count"=>23, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Researcher"=>4, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>6, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Researcher"=>4, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>6, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>1, "Environmental Science"=>6, "Agricultural and Biological Sciences"=>7, "Social Sciences"=>1, "Computer Science"=>1, "Earth and Planetary Sciences"=>3, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>6}}, "reader_count_by_country"=>{"United States"=>2}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2129725"], "description"=>"<p>Photos labeled <i>a</i> to <i>d</i> show close range view of the undercover cropland taken at the location on the map labeled with the same letters.</p>", "links"=>[], "tags"=>["Boosted regression trees", "food crops", "cropland area"], "article_id"=>1457312, "categories"=>["Uncategorised"], "users"=>["Yohannes Ayanu", "Christopher Conrad", "Anke Jentsch", "Thomas Koellner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130079.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Undercover_cropland_area_calculated_from_field_estimated_percent_cover_for_selected_sample_plots_with_an_area_of_36_hectares_/1457312", "title"=>"Undercover cropland area calculated from field estimated percent cover for selected sample plots with an area of 36 hectares.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:53:41"}
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  • {"files"=>["https://ndownloader.figshare.com/files/2129777", "https://ndownloader.figshare.com/files/2129778", "https://ndownloader.figshare.com/files/2129779", "https://ndownloader.figshare.com/files/2129780", "https://ndownloader.figshare.com/files/2129781", "https://ndownloader.figshare.com/files/2129782", "https://ndownloader.figshare.com/files/2129783", "https://ndownloader.figshare.com/files/2129784"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["Boosted regression trees", "food crops", "cropland area"], "article_id"=>1457361, "categories"=>["Uncategorised"], "users"=>["Yohannes Ayanu", "Christopher Conrad", "Anke Jentsch", "Thomas Koellner"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0130079.s001", "https://dx.doi.org/10.1371/journal.pone.0130079.s002", "https://dx.doi.org/10.1371/journal.pone.0130079.s003", "https://dx.doi.org/10.1371/journal.pone.0130079.s004", "https://dx.doi.org/10.1371/journal.pone.0130079.s005", "https://dx.doi.org/10.1371/journal.pone.0130079.s006", "https://dx.doi.org/10.1371/journal.pone.0130079.s007", "https://dx.doi.org/10.1371/journal.pone.0130079.s008"], "stats"=>{"downloads"=>33, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unveiling_Undercover_Cropland_Inside_Forests_Using_Landscape_Variables_A_Supplement_to_Remote_Sensing_Image_Classification_/1457361", "title"=>"Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-22 03:53:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129716"], "description"=>"<p>Field estimated percent cropland per plot is overlaid on the land use/land cover map.</p>", "links"=>[], "tags"=>["Boosted regression trees", "food crops", "cropland area"], "article_id"=>1457303, "categories"=>["Uncategorised"], "users"=>["Yohannes Ayanu", "Christopher Conrad", "Anke Jentsch", "Thomas Koellner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130079.g001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Study_site_and_land_use_land_cover_classes_a_Location_of_the_study_site_and_distribution_of_sample_plots_b_Major_land_use_land_cover_types_derived_using_Random_Forest_classification_of_RapidEye_images_/1457303", "title"=>"Study site and land use/land cover classes a) Location of the study site and distribution of sample plots b) Major land use/land cover types derived using Random Forest classification of RapidEye images.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:53:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129717"], "description"=>"<p>General workflow of a) Field data sampling b) Image classification c) Validation of classification results.</p>", "links"=>[], "tags"=>["Boosted regression trees", "food crops", "cropland area"], "article_id"=>1457304, "categories"=>["Uncategorised"], "users"=>["Yohannes Ayanu", "Christopher Conrad", "Anke Jentsch", "Thomas Koellner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130079.g002", "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_General_workflow_of_a_Field_data_sampling_b_Image_classification_c_Validation_of_classification_results_/1457304", "title"=>"General workflow of a) Field data sampling b) Image classification c) Validation of classification results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:53:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129719"], "description"=>"<p>Boosted Regression Trees fitted model showing the relative importance of influential factors of undercover cropland area calculated from field estimated percent cover.</p>", "links"=>[], "tags"=>["Boosted regression trees", "food crops", "cropland area"], "article_id"=>1457306, "categories"=>["Uncategorised"], "users"=>["Yohannes Ayanu", "Christopher Conrad", "Anke Jentsch", "Thomas Koellner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130079.g003", "stats"=>{"downloads"=>2, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boosted_Regression_Trees_fitted_model_showing_the_relative_importance_of_influential_factors_of_undercover_cropland_area_calculated_from_field_estimated_percent_cover_/1457306", "title"=>"Boosted Regression Trees fitted model showing the relative importance of influential factors of undercover cropland area calculated from field estimated percent cover.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:53:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129721"], "description"=>"<p>Undercover cropland area predicted from most influential topographic factors identified using Boosted Regression Trees.</p>", "links"=>[], "tags"=>["Boosted regression trees", "food crops", "cropland area"], "article_id"=>1457308, "categories"=>["Uncategorised"], "users"=>["Yohannes Ayanu", "Christopher Conrad", "Anke Jentsch", "Thomas Koellner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0130079.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Undercover_cropland_area_predicted_from_most_influential_topographic_factors_identified_using_Boosted_Regression_Trees_/1457308", "title"=>"Undercover cropland area predicted from most influential topographic factors identified using Boosted Regression Trees.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:53:41"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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