DOI: 10.1016/j.jag.2014.08.001
Scopus记录号: 2-s2.0-84920663550
论文题名: Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series
作者: Nitze I ; , Barrett B ; , Cawkwell F
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 34, 期: 1 起始页码: 136
结束页码: 146
语种: 英语
英文关键词: Land cover classification
; Machine-learning
; MODIS
; Random forest
Scopus关键词: accuracy assessment
; land classification
; land cover
; land use change
; MODIS
; NDVI
; satellite data
; spatial resolution
; time series
; vegetation dynamics
; vegetation type
英文摘要: The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates out performed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addi-tion of the next best image periods to the data input the classification accuracies converged quickly totheir limit at around 8-10 images. The binary classification schemes, using two classes only, showed astronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to bea good base for the prediction of image acquisition timing for optimal land cover classification results. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79519
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: School of Geography and Archaeology, University College Cork (UCC), Ireland; Alfred Wegener Institute, Telegrafenberg A43, Pots-dam, Germany
Recommended Citation:
Nitze I,, Barrett B,, Cawkwell F. Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,34(1)