Geography

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Changing Lifestyles Towards a Low Carbon Economy: An IPAT Analysis for China
(MDPI, 2011-12-27) Hubacek, Klaus; Feng, Kuishuang; Chen, Bin
China has achieved notable success in developing its economy with approximate 10 percent average annual GDP growth over the last two decades. At the same time, energy consumption and CO2 emissions almost doubled every five years, which led China to be the world top emitter in 2007. In response, China’s government has put forward a carbon mitigation target of 40%–45% reduction of CO2 emission intensity by 2020. To better understand the potential for success or failure of such a policy, it is essential to assess different driving forces such as population, lifestyle and technology and their associated CO2 emissions. This study confirms that increase of affluence has been the main driving force for China’s CO2 emissions since the late 1970s, which outweighs reductions achieved through technical progress. Meanwhile, the contribution of population growth to CO2 emissions was relatively small. We also found a huge disparity between urban and rural households in terms of changes of lifestyle and consumption patterns. Lifestyles in urban China are beginning to resemble Western lifestyles, and approaching their level of CO2 emissions. Therefore, in addition to the apparent inefficiencies in terms of production technologies there is also a lot of room for improvement on the consumption side especially in interaction of current infrastructure investments and future consumption.
Integrated use of Landsat and Corona data for long-term monitoring of forest cover change and improved representation of its patch size distribution
(2016) Song, Danxia; Townshend, John R; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Forest cover change has profound impact on global carbon cycle, hydrological processes, energy balance, and biodiversity. The primary goal of this dissertation is to improve forest cover change characterization by filling a number of knowledge gaps in forest change studies. These include use of Corona data to extend satellite based forest cover change mapping back to pre-Landsat years in the 1960s, quantification of forest cover change over four decades (1960s – 2005) for a major forested province in China using Corona and Landsat data, and development of more accurate patch size-frequency modeling methods for improved representation of forest disturbances in ecosystem and other spatially explicit models. With comprehensive data coverages in the 1960s, Corona data can be used to extend Landsat-based forest change analysis by up to a decade. The usefulness of such data, however, is hindered by poor geolocation accuracy and lack of multi-spectral bands. In this study, it was demonstrated that combined use of texture features and the advanced support vector machines allowed forest mapping with accuracies of up to 95% using Corona data. Further, a semi-automated method was developed for rapid registration of Corona images with residual errors as low as 100 m. These methods were used to assess the forest cover in the 1960s in Sichuan, a major forest province in China. Together with global forest cover change products derived using Landsat data, these results revealed that the forest cover in Sichuan Province was reduced from 45.19% in the 1960s to 38.98% by 1975 and further down to 28.91% by 1990. It then stayed relatively stable between 1990 and 2005, which contradicted trends reported by inventory data. The turning point between sharp decreases before 1990 and the stable period after 1990 likely reflected transitions in forest policies from focuses on timber production to forest conservation. Representation of forest disturbances in spatially explicit ecosystem models typically relies on patch size-frequency models to allocate an appropriate amount of disturbances to each patch size level. Existing patch size-frequency models, however, do not provide accurate representation of the total disturbance area nor the patch sizes at each frequency level. In this study, a hierarchical method was developed for modeling patch size-frequency distribution. Evaluation of this method over China revealed that it greatly improved the accuracy in representing the patch size at different frequency levels and reduced error in total disturbance area estimation over existing methods from around 40% to less than 10%. The significance of this dissertation is the contribution to improve the characterization of forest cover change by extending the satellite-based forest cover change monitoring back to the 1960s and developing a more accurate patch size distribution model to represent the forest disturbance in ecosystem models. The work in the dissertation has a broader impact beyond developing methods and models, as they provide essential basis to understand the relationship between the long-term change of forest and the socioeconomic transitions. They also improve the capacities of ecosystem and other spatially explicit models to simulate the vegetation dynamics and the resultant biodiversity and carbon dynamics.
Continuous tree distribution in China: A comparison of two estimates from MODIS and Landsat data
(American Geophysical Union, 2006-04-18) Liang, Shunlin; Liu, Ronggao; Liu, Jiyuan; Zhuang, Dafang
Forest change is a major contributor to changes in carbon stocks and trace gas fluxes between terrestrial and atmospheric layers. This study compares two satellite estimates of percent tree distribution data sets over China. One estimate is from the Chinese National Land Cover Data Set (NLCD) generated by a multiyear national land cover project in China through visual interpretation of Landsat thematic mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) images primarily acquired in the year 2000. The other estimate is the Moderate-Resolution Imaging Spectroradiometer (MODIS) standard product (MOD44B) from the same year. The two products reveal some common features, but significant discrepancies exist. Detailed analyses are carried out with different land cover types and over different regions. Comparison results show that the difference of the total tree canopy area for the whole country is 159,000 km2. The pixel counts in the NLCD data set for dense forest are ~4 times those in the MODIS data set with the reverse holding for sparse forest. Generally, the percent tree canopy area of the NLCD data set is larger in eastern China and lower in the Tibetan plateau margin region. For different land cover types the percentage of tree canopy areas shows a good agreement for evergreen forests but a large discrepancy for deciduous forests. The largest variations are associated with grassland and nonvegetation classes. Regarding the spatial distributions of their differences, Inner Mongolia is the place where both data sets show a diverse result, but Guizhou and Fujian present the least divergence among those provinces with the tree canopy area being more than 20,000 km2.

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