ABSTRACT
Based on the remote sensing data in 1997, 2001, 2005, 2009 and 2013, this article classified the landscape types of Shunyi, and the ecological risk index was built based on landscape disturbance index and landscape fragility. The spatial auto-correlation and geostatistical analysis by GS+ and ArcGIS was used to study temporal and spatial changes of ecological risk. The results showed that eco-risk degree in the study region had positive spatial correlation which decreased with the increasing grain size. Within a certain grain range (<12 km), the spatial auto-correlation had an obvious dependence on scale. The random variation of spatial heterogeneity was less than spatial auto-correlation variation from 1997 to 2013, which meant the auto-correlation had a dominant role in spatial heterogeneity. The ecological risk of Shunyi was mainly at moderate level during the study period. The area of the district with higher and lower ecological risk increased, while that of mode-rate ecological risk decreased. The area with low ecological risk was mainly located in the airport region and forest of southeast Shunyi, while that with high ecological risk was mainly concentrated in the water landscape, such as the banks of Chaobai River.
Subject(s)
Conservation of Natural Resources , Ecology , Ecosystem , Beijing , Forests , Risk Assessment , Rivers , Spatio-Temporal AnalysisABSTRACT
The objective of the research is to apply hyperspectral technique into eco-restoring monitoring. Through the ASD Fields HH portable field spectrometer, the hyperspectral data of dominant plant species in vegetation at different eco-restoring stages in semi-arid grassland in Helin County, Inner Mongolia were collected. The original spectrum reflected data were pretreated by wavelet threshold denoising through ViewSpecPro software before analysis. Using the first derivative spectra between 660 and 800 nm, and the methods of detrended canonical correspondence analysis (DCCA) by Canoco 4. 5 software, the canopy hyperspectral datum of 6 dominant plant species was calculated. The results indicated that the dominant plant species at early succession stage were Setaria viridis and Caragana microphylia, at 5 years eco-restoring stage they were Salsola collina and Caragana microphylia and at late succession stage they were Pinus sylvestnis var. mongolica and Salsola collina, same as field survey. The graph of DCCA indicated that the influential bands of dominant species canopy at early eco-succession stage were short bands, with a large variation among species, the influential bands at 5 years eco-restoring stage were near infrared bands between 1 000 and 1 050 nm, and that at late stage were near infrared bands of 1 040-1 075 nm. The DCCA also showed obviously differences in canopy spectrum among 6 dominant species, and obviously differences among 3 eco-restoring stages.
