Karst regions are typical ecologically fragile zones constrained by a geological setting. In karst regions

vegetation

soil

and rock are cross-distributed or discontinuous

thereby resulting in high spatial and temporal heterogeneities of land cover. Landcover classification based on a single optical image is generally not satisfactory. During the plant senescence period

for instance

the fractional cover of non-photosynthetic vegetation significantly influences the classification accuracy of bare land and sparse land.Meanwhile

the change in vegetation in karst regions is characterized by a combination of seasonal

gradual

and abrupt changes.Concerning the aforementioned issue

this paper presents a novel phenology-driven land-cover classification strategy

which consists of performing Breaks For Additive Season and Trend（ BFAST） for time-series decomposition

extracting phenological markers [e. g.

start of season

end of season

length of season

base level of season

timing of mid-season

peak value of season

amplitude of season

rate of grow-up and senescence

gross spring greenness

and net spring greenness]from the phenological trajectory. Support Vector Machine（ SVM） methods are also conducted to classify karst land cover. Specifically

a three-step strategy is proposed for phenology-driven land-cover classification. First

Normalized Difference Vegetation Index（ NDVI） time series is derived from remotely sensed images acquired by Moderate Resolution Imaging Spectrometer（ MODIS）. BFAST approach is used to decompose the time series into seasonal

trend

and remainder components by integrating iterative change detection method. Second

with the decomposed seasonal and trend components of NDVI time series

seasonal parameters are extracted for the growing seasons. Finally

combined with the composited surface reflectance bands and vegetation index

phenological parameters are used to construct the decision space for SVM classifier.An experiment is employed to validate the proposed method

in which the 4-year 16-day composite MODIS NDVI time series（ 2010—2013） located in Qiubei and Yanshan counties in Yunnan province

Eastern China is used for karst land-cover classification. Land-cover classification in different feature spaces of satellite image time series in 2013 is compared. Experimental results show that phenological information and the combination of both achieve better SVM classification performance than pure spectral information. The overall accuracy and kappa coefficient reach 88. 94% and 0. 8693 respectively. For the classification of shrub

sparse land

and upland

the improvement of their user accuracy and product accuracy is approximately 20%.BFAST method can effectively decompose the NDVI time series of different land covers in karst regions into trend

seasonal

and remainder components. Differences among the phenological characteristics of land cover may improve their separability. Based on the extracted phenological markers from the phenological trajectory

land-cover classification in karst regions can be performed efficiently. However

according to the within-building classification decision space

the compositions of all phenological properties will bring in data redundancy

cost considerable time

and consume substantial computing resources. Efficient feature selection and optimization methods may be used to improve the classification performance of the proposed method. The spatial resolution of MODIS image is also a limiting factor.