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The Institute of Geographic Sciences and Natural Resources Research (IGSNRR) was established in 1999 through the merger of the former Institute of Geography (IOG), founded in 1940, and the former Commission for the Integrated Survey of Natural Resources (CISNAR), founded in 1956.
In the past half century, IGSNRR and its predecessors have led the way in geographical research in China, making major research contributions in the rational use of natural resources; ecological and environmental protection; comprehensive land consolidation; sustainable regional development; and resource and environmental information systems.
Much of the work conducted by these institutes has had a very great national impact and has received national awards. Examples include research on the spatial differentiation of China’s natural environment; research on the comprehensive management and exploitation of land in medium- and low-yield fields in the Huang-Huai-Hai Plain; study of the uplift of the Qinghai-Tibet Plateau and its effects on the natural environment and human activity; compilation of the National Physical Atlas of China; theoretical and applied research on regional development patterns; and establishment of the Chinese Ecosystem Research Network (CERN), etc. Since 1978, IGSNRR and its predecessors have won 248 national and provincial-level science and technology awards, of which 43 were national awards.
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Resources
Displaying 6 - 10 of 12Farmland Marginalization and Policy Implications in Mountainous Areas: A Case Study of Renhuai City, Guizhou
Farmland resources in mountainous areas are important for regional food security and ecological security. Studies concerning changes in farmland use in mountainous areas are of considerable significance in China. Here, we analyzed marginalization characteristics of farmland in Renhuai city from 2005 to 2011 and driving factors using land information systems, surveys of farmer households and statistical data. Our results indicate that from 2005 to 2011, 3095.76 hm² of farmland was converted to forest land and natural reserve, accounting for 5.45% of the total farmland area.
Spatial Distribution Patterns and the Evolution Process of Carbon Storage in a Typical Karst Canyon Area
Qinglong County in Guizhou, China is a typical karst canyon area. Using quadrat methods and a land use transfer matrix we studied the carbon storage spatial distribution pattern and evolution process over three independent periods (1988, 1999 and 2009) in this area. Based on the results we estimated the carbon pool capacity of the entire karst canyon area in Guizhou and contribution ratios. Carbon storage and average carbon density of the karst area in Qinglong decreased at first, and then increased over the sampling period.
Quantitative Measurement of Urban Expansion and its Driving Factors in Qingdao: an Empirical Analysis Based on County Unit Data
Qingdao is one of the essential growth poles in the process of new-type urbanization in Shandong Province. The study on the relationship between urban expansion and driving factors in this area is representative. This paper examined urban expansion from the perspective of non-urban to urban conversion, detailing an empirical investigation into the spatiotemporal variations and impact factors of urban expansion in Qingdao.
Response of Germination Physiology of Cajanus cajan Seeds to Drought Stress: Comparison Between Karst Water and Allogenic Water Treatments
In this paper, responses of germination physiology of pigeon pea (Cajanus cajan) seeds to drought stress in karst water environment and non-karst (allogenic) water environment were studied to explore the adaptability of pigeon pea to karst environment.
Land use Dynamics and Landscape Patterns in Shanghai, Jiangsu and Zhejiang
Land use change and landscape patterns have a large effect on land productivity and ecosystem biodiversity. Based on geographical information system technology and remote sensing data related to land use and land cover of Jiangsu and Zhejiang provinces and Shanghai (Jiang-Zhe-Hu area), we analyzed patterns of landscape change and predicted land use dynamics using the CA-MARKOV model. We also analyzed the conversion rate and area among landscape classes using the CA-Markov model.