In order to reveal the dynamic characteristics of the forest in Bashang area of Hebei Province, MODIS reflectivity and NDVI data with a spatial resolution of 250 m were used for forest classification, and a Thematic Mapper (TM) image in 2005 was resorted to aid training sample selection. With Random Forest Algorithm and time series of MODIS images, the forest in Bashang area was monitored from 2000 to 2015 in every two years. Compared with widely used classifiers such as maximum likelihood classifier and BP artificial neural network algorithm, Random Forest classifier showed the best performance with its overall accuracy and Kappa coefficient being 91.89% and 0.88 respectively. Binary coding was applied to the eight phases of forest distribution images, which can easily and rapidly reflect the changing trajectory from phase to phase. It showed that the severe forest changes mainly occurred in counties of Fengning, Weichang, Zhangbei and Guyuan during the years of 2000, 2010, and 2013.

The article analyzes carbon emissions from industrial and living departments from 1995 to 2012, and urbanization rate is measured by three indicators, namely ratio of build-up area, ratio of non-agriculture population, and ratio of urban population. The results show that 1) the total amount of carbon emissions as well as the per capita carbon emissions is rising in 1995–2012, accordingly carbon emissions per unit of output present a decreasing trend. 2) In process of urbanization, Beijing, Shanghai and Tianjin have relatively lower carbon emissions and higher urbanization rates in urbanization stages. 3) The carbon emission of unit urbanization rate measured by three indicators show that most provinces present reduction trend till 2000 and then increase. Inner magnolia ranks the top in the carbon emission per urbanization rate 1. Hebei, Henan and Shandong rank the top in carbon emission per urbanization rate 2 and 3. 4) In the view of provinces, energy structure has limited impacts on carbon emissions, energy intensity has negative effect, and industrial structure has both positive and negative effect while economic level and population size have positive effect for all provinces. The results can provide scientific reference for the regional carbon emission reduction strategy.