Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea

DOI zum Zitieren dieses Dokuments: https://doi.org/10.1371/journal.pone.0190476
URN to cite this document: urn:nbn:de:bvb:703-epub-4231-5

Title data

Bogner, Christina ; Seo, Bumsuk ; Rohner, Dorian ; Reineking, Björn:
Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea.
In: PLOS ONE. Vol. 13 (2018) Issue 1 . - No. e0190476.
ISSN 1932-6203
DOI: https://doi.org/10.1371/journal.pone.0190476

[img] PDF
journal.pone.0190476.pdf - Published Version
Available under License Creative Commons BY 4.0: Attribution .

Download (4MB)

Project information

Project title:
Project's official titleProject's id
Open Access PublizierenNo information

Abstract

Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampling technique (smote) with Random Forest to classify land cover classes in a small agricultural catchment in South Korea using modis time series. This area faces a major soil erosion problem and policy measures encourage farmers to replace annual by perennial crops to mitigate this issue. Our major goal was therefore to improve the classification performance on annual and perennial crops. We compared four different classification scenarios on original imbalanced and synthetically oversampled balanced data to quantify the effect of smote on classification performance. smote substantially increased the true positive rate of all oversampled minority classes. However, the performance on minor classes remained lower than on the majority class. We attribute this result to a class overlap already present in the original data set that is not resolved by smote. Our results show that resampling algorithms could help to derive more accurate land use and land cover maps from freely available data. These maps can be used to provide information on the distribution of land use classes in heterogeneous agricultural areas and could potentially benefit decision making.

Further data

Item Type: Article in a journal
Keywords: Classification; rare land cover types; South Korea
DDC Subjects: 500 Science > 550 Earth sciences, geology
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science III
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Biogeography
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Ecological Modelling
Faculties
Language: German
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-4231-5
Date Deposited: 26 Feb 2019 11:27
Last Modified: 27 Feb 2019 07:45
URI: https://epub.uni-bayreuth.de/id/eprint/4231

Downloads

Downloads per month over past year