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Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series
Author(s) -
Lopes Mailys,
Frison PierreLouis,
Crowson Merry,
WarrenThomas Eleanor,
Hariyadi Bambang,
Kartika Winda D.,
Agus Fahmuddin,
Hamer Keith C.,
Stringer Lindsay,
Hill Jane K.,
Pettorelli Nathalie
Publication year - 2020
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13359
Subject(s) - remote sensing , land cover , radar , satellite , time series , cloud cover , satellite imagery , temporal resolution , series (stratigraphy) , environmental science , computer science , geography , cloud computing , land use , geology , machine learning , telecommunications , paleontology , civil engineering , physics , quantum mechanics , aerospace engineering , engineering , operating system
The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel‐1 and Sentinel‐2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel‐1, +2.5% for Sentinel‐2 and +2% combining Sentinel‐1 and Sentinel‐2). Overall, combining Sentinel‐2 and Sentinel‐1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open‐source tools satellite imagery.