Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R² higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

Landsat data are an ideal data source for deriving fine-resolution land cover maps, and integrating temporal features extracted from time-series normalized difference vegetation index (NDVI) data achieves better performance. This paper compares the different roles of NDVI statistic features and phenology features in land cover classification at a finer scale. Time-series NDVI with fine resolution is first obtained by fusing Landsat-8 Operational Land Imager and moderate resolution imaging spectrometer (MODIS) NDVI via spatiotemporal fusion algorithm. Statistic and phenology features are then extracted from the fused data and added into random forest (RF) classifier. Performance under different classifiers and importance of phenology features are further discussed. Results show that both NDVI statistic features and phenology features have great effects on improving the classification accuracy after adding them to Landsat spectral bands. The overall accuracy is improved approximately 3% and 5%. Phenology features contain majority information of statistic features, and better reflect the seasonal variations of time-series NDVI, especially for vegetation types. Additionally, neural network classifier achieved similar trends of results with RF but lower accuracy, while support vector machine classifier seems to be poor in dealing with high-dimension temporal features, especially in regions with abundant vegetation. Among phenology features, maximum value, large integrated value, and base value have the highest importance scores, while start, end, and middle times of season provide extra information for identifying grass and nongrass.