Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo valley,Chile

Satellite image time series (SITS) provide spectral–temporal features that describe phenological changes in vegetation over the growing season,which is expected to facilitate the classification of crop types. While most SITS-based crop type classifications were focused on NDVI (normalized difference vegetation index) temporal profiles,less attention has been paid to using the complete image spectral resolution of the time series. In this work we assessed different approaches to SITS-based classification of four major fruit-tree crops in the Maipo Valley,central Chile,during the 2013–14 growing season. Wecompared four feature sets from a time series comprised of eight cloud-free Landsat-8 images: the full-band SITS,theNDVI andNDWI (normalized differencewater index) temporal profiles,and an image stack with all the feature sets combined. State-of-the-art classifiers (linear discriminant analysis,LDA; random forest; and support vector machine) were applied on each feature set at different training sample sizes (N=100,200,400,800 and 2291 fields),and classification results were assessed by cross-validation of the misclassification error rate (MER). For all the feature sets overall results were good (MERs ≤ 0.21) although substantially improved classification accuracies were achieved when the full-band SITS was employed (MER 0.14–0.05). Classifications applied on the NDVI temporal profile consistently had the worst performance. For a sample size of 200 fields,LDA using the full-band SITS of image dates 1,3,6 and 8 produced the best tradeoff between the number of images and classification accuracy (MER = 0.06),being the green,red,blue and SWIR (short-wave infrared) bands of image date 1 (acquired at the early greenup stage) the most relevant for crop type discrimination. Our results show the importance of considering the complete image spectral resolution for SITS-based crop type classifications as the commonly usedNDVI temporal profile and their red and near infrared bands were not found the most significant to discriminate the crop types of interest. Furthermore,in light of the good results obtained,the methodology used here might be transferred to similar agricultural lands cultivated with the same crop types,thus providing a reliable and relatively efficient methodology for creating and updating crop inventories.

Información de Publicación

Autores:

Marco Peña

Institución: Universidad Alberto Hurtado
Facultad: Ciencias Sociales
Unidad: Geografía

Alexander Brenning

Institución: Friedrich Schiller University Jena
Facultad: Faculty for Chemistry and Earth Sciences
Unidad: Department of Geography

Revista:

Remote Sensing of Environment

Año:

2015

Mes:

Diciembre

Volumen:

CLXXI

Páginas:

234-244

Url de Publicación:

http://www.sciencedirect.com/science/article/pii/S0034425715301814.