Spatiotemporal variations in leaf-out phenology of typical European tree species and their responses to climate change.

Over the past decades, global warming significantly affected the spring phenology of plants. Many studies have reported the temporal and spatial patterns of spring phenological changes in China, but relatively less is known for that in Europe, which is also located in the temperate area of the Northern Hemisphere. To facilitate the regional comparison of phenological change and understand its response to climate change, we used the data of first leaf date (FLD) in Europe (1980-2014) and the corresponding meteorological data to examine the spatiotemporal variations in leaf-out phenology of four typical tree species ( and ), and to identify the major climatic factors driving such variations.

[Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices.].

Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear.

[Flower species identification and coverage estimation based on hyperspectral remote sensing data in Hulunbeier grassland].

Monitoring grassland species and area real-timely and accurately is of great significance in species diversity research, as well as in sustainable development of ecosystem. Flowers have their own unique spectral characteristics. Compared with the nutrient stage, species are more easily identified by florescence.

[Hyperspectral remote sensing monitoring of grassland degradation].

The distributing of China's grassland is abroad and the status of grassland degradation is in serious condition. So achieving real-time and exactly grassland ecological monitoring is significant for the carbon cycle, as well as for climate and on regional economies. With the field measured spectra data as data source, hyperspectral remote sensing monitoring of grassland degradation was researched in the present article.