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| Titel |
Assessment of seasonal features based on Landsat time series for tree crown cover mapping in Burkina Faso |
| VerfasserIn |
Jinxiu Liu, Janne Heiskanen, Ermias Aynekuly, Petri Pellikka |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250131945
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| Publikation (Nr.) |
 EGU/EGU2016-12401.pdf |
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| Zusammenfassung |
| Tree crown cover (CC) is an important vegetation attribute for land cover characterization,
and for mapping and monitoring forest cover. Free data from Landsat and Sentinel-2 allow
construction of fine resolution satellite image time series and extraction of seasonal features
for predicting vegetation attributes. In the savannas, surface reflectance vary distinctively
according to the rainy and dry seasons, and seasonal features are useful information for CC
mapping. However, it is unclear if it is better to use spectral bands or vegetation indices (VI)
for computation of seasonal features, and how feasible different VI are for CC
prediction in the savanna woodlands and agroforestry parklands of West Africa. In this
study, the objective was to compare seasonal features based on spectral bands and
VI for CC mapping in southern Burkina Faso. A total of 35 Landsat images from
November 2013 to October 2014 were processed. Seasonal features were computed
using a harmonic model with three parameters (mean, amplitude and phase), and
spectral bands, normalized difference vegetation index (NDVI), green normalized
difference vegetation index (GNDVI), normalized difference water index (NDWI),
tasseled cap (TC) indices (brightness, greenness, wetness) as input data. The seasonal
features were employed to predict field estimated CC (n = 160) using Random
Forest algorithm. The most accurate results were achieved when using seasonal
features based on TC indices (R2: 0.65; RMSE: 10.7%) and spectral bands (R2:
0.64; RMSE: 10.8%). GNDVI performed better than NDVI or NDWI, and NDWI
resulted in the poorest results (R2: 0.56; RMSE: 11.9%). The results indicate that
spectral features should be carefully selected for CC prediction as shown by relatively
poor performance of commonly used NDVI. The seasonal features based on three
TC indices and all the spectral bands provided superior accuracy in comparison
to single VI. The method presented in this study provides a feasible method to
map CC based on seasonal features with possibility to integrate medium resolution
satellite observation from several sensors (e.g. Landsat and Sentinel-2) in the future. |
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