DOI: 10.1016/j.jag.2017.07.019
Scopus记录号: 2-s2.0-85032511097
论文题名: Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data
作者: Hagensieker R ; , Roscher R ; , Rosentreter J ; , Jakimow B ; , Waske B
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 63 起始页码: 244
结束页码: 256
语种: 英语
英文关键词: Amazon
; Deforestation
; Import vector machines (IVM)
; Markov random fields (MRF)
; Multi-temporal LULC mapping
; SAR
Scopus关键词: deforestation
; land cover
; land use
; Landsat
; machine learning
; Markov chain
; RapidEye
; satellite data
; satellite imagery
; shifting cultivation
; support vector machine
; TerraSAR-X
; tropical forest
; vegetation mapping
; Brazil
; Para [Brazil]
英文摘要: Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79959
Appears in Collections: 气候变化事实与影响
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作者单位: Freie Universität Berlin, Institute of Geographical Sciences, Malteserstr. 74-100, Berlin, Germany; Rheinische Friedrich-Wilhelms-Universitt Bonn, Institute of Geodesy and Geoinformation, Nussallee 15, Bonn, Germany; Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6, Berlin, Germany
Recommended Citation:
Hagensieker R,, Roscher R,, Rosentreter J,et al. Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,63