Wu P.-F.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
Wu P.-F.,Key Laboratory of 3D Information Acquisition and Application |
Wu P.-F.,Capital Normal University |
Gong H.-L.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 5 more authors.
Chinese Journal of Ecology | Year: 2011
In this paper, the landscape maps of Kwanting Reservoir watershed were interpreted based on the six Landsat images in 1978-2009, and, by employing the Lorenz curve and Gini coefficient in economics, the distribution uniformity and the dynamic changes of the landscape types were analyzed. Buffer analysis was also used to improve the current popular method to enable the Lorenz curve to be smoother and the Gini coefficient to be more accurate. In the study area, there existed obvious spatiotemporal differences in the distribution uniformity of various landscape types. Farmland had the most uniform distribution, artificial forest land had the most nonuniform distribution in 1978-1998, and natural grassland had the most non-uniform distribution after 2004. The levels of various landscape types distribution uniformity were negatively correlated the areas of the landscape types, and the changes of the distribution uniformity were correlated to the changed manners of the areas of the landscape types, i. e., the changes in part area or in whole area could alter the evolution direction of certain landscape type. In recent 30 years, the natural landscapes in the study area had an overall degeneration, manifesting in their decreasing area, more and more uneven distribution, and increasing replacement by artificial landscapes. Source
Wu P.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
Wu P.,Key Laboratory of 3D Information Acquisition and Application |
Wu P.,Capital Normal University |
Gong H.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 5 more authors.
Proceedings - 2012 20th International Conference on Geoinformatics, Geoinformatics 2012 | Year: 2012
In this paper, the transfer matrix of land use and cover change (LUCC) is studied as a network, in which land use types are nodes and area conversions between different land use types are links and the node betweenness method of complex network is applied to identify the key changed land use types. Compared with traditional method, complex network takes into account not only quantitative relation, but also transfer direction and system concept, namely the status and role of each land type in the transfer matrix network. Also, this method has been validated based on six Landsat images from 1978 to 2009 of Guishui river basin located in northwest Beijing. The results showed that grassland and farmland are all the key changed land use types from 1978 to 1993, and farmland and woodland were the key changed land use types during the periods 1993 to 2004 and 2004 to 2009, respectively. © 2012 IEEE. Source
Liu H.,Capital Normal University |
Liu H.,Key Laboratory of 3D Information Acquisition and Application |
Liu H.,Beijing Key Laboratory of Resources Environment and GIS |
Liu H.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 8 more authors.
Chinese Journal of Applied Ecology | Year: 2014
Hyperspectral reflectance information is a crucial method to detect total nitrogen content in plant leaves, meanwhile, vegetation nitrogen content has a strong relationship with nitrogen in water. Taking Mencheng Lake Wetland Park supplied with reclaimed water as study area, the vegetation hyperspectral data (Phragmites australis and Typha angustifolia), and the content of total nitrogen in water were detected to investigate the feasibility of estimating total nitrogen content in reclaimed water based on hyperspectral reflectance information from emergent plants. We established simple linear regression model, stepwise multiple linear regression model and partial least square regression model based on four hyperspectral indices (spectral indices, normalized difference indices, trilateral parameters, absorption feature parameters), respectively. The accuracy of these models was coefficient of determination (R2) and root mean square error (RMSE). The results showed that stepwise multiple linear regression model and partial least square regression model predicted more accurately than simple linear regression model, and the accuracy of prediction models based on P. australis reflectance spectra was higher than those on T. angustifolia. Partial least square regression model was the most useful explorative tool for unraveling the relationship between spectral reflectance of P. australis and total nitrogen content in water with R2 of 0.854 and RMSE of 0.647.500-700 nm was the best band range for detecting water total nitrogen content. The reflectance ratio of green peak and red valley could be effectively predicted by the absorption feature parameters. ©, 2014, Editorial Board of Chinese Journal of Applied Ecology. All right reserved. Source
Li H.,Capital Normal University |
Li H.,Key Laboratory of 3D Information Acquisition and Application of Ministry |
Li H.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
Gong Z.,Capital Normal University |
And 8 more authors.
Acta Geographica Sinica | Year: 2012
The reservoir wetland of Beijing, constitutes one of the important eco-systems in Beijing. The driving factors index system of Beijing reservoir wetland landscape evolution in the study area was built in the two aspects of the natural environment and socio-economy Natural driving factors include precipitation, temperature, entry water and groundwater depth; social economic driving factors include the resident population, urbanization rate and per capita GDP. Using TM images from 1984 to 2010 to extract reservoir wetland's spatial distribution information of Beijing, we analyzed the area of reservoir wetland change laws in nearly 30 years. The driving mechanism of reservoir wetland evolution in the study area was explored by the Logistic regression model in different periods. The results indicated that in different phases, the driving factors and their influence on reservoir wetland evolution had certain differences. During 1984-1998, the leading driving factors were annual average precipitation and entry water index with the contribution rate of Logistic regression being 5.78 and 3.50, respectively, which was mainly affected by natural environmental factors; from 1998 to 2004, the impact of human activities intensified and man-made reservoir wetland reduced, and the main driving factors were the number of residents, groundwater depth and urbanization rate with the contribution rate of Logistic regression 9.41, 9.18, and 7.77, respectively. During 2004-2010, reservoir wetland evolution was impacted by both natural and socio-economic factors, and the dominant driving factors were urbanization rate and precipitation with the contribution rate of 6.62 and 4.22, respectively. Source
Gong Z.,Capital Normal University |
Gong Z.,Key Laboratory of 3D Information Acquisition |
Gong Z.,Key Laboratory of Resources Environment |
Gong Z.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 14 more authors.
International Journal of Applied Earth Observation and Geoinformation | Year: 2015
Vegetation abundance is a significant indicator for measuring the coverage of plant community. It is alsoa fundamental data for the evaluation of a reservoir riparian zone eco-environment. In this study, a sub-pixel Markov model was introduced and applied to simulate dynamics of vegetation abundance in theGuanting Reservoir Riparian zone based on seven Landsat Thematic Mapper/Enhanced Thematic MapperPlus/Operational Land Imager data acquired between 2001 and 2013. Our study extended Markov model'sapplication from a traditional regional scale to a sub-pixel scale. Firstly, Linear Spectral Mixture Analysis(LSMA) was used to obtain fractional images with a five-endmember model consisting of terrestrialplants, aquatic plants, high albedo, low albedo, and bare soil. Then, a sub-pixel transitive probabilitymatrix was calculated. Based on the matrix, we simulated statuses of vegetation abundance in 2010and 2013, which were compared with the results created by LSMA. Validations showed that there wereonly slight differences between the LSMA derived results and the simulated terrestrial plants fractionalimages for both 2010 and 2013, while obvious differences existed for aquatic plants fractional images, which might be attributed to a dramatically diversity of water level and water discharge between 2001and 2013. Moreover, the sub-pixel Markov model could lead to an RMSE (Root Mean Square Error) of0.105 and an R2 of 0.808 for terrestrial plants, and an RMSE of 0.044 and an R2 of 0.784 for aquatic plantsin 2010. For the simulated results with the 2013 image, an RMSE of 0.126 and an R2 of 0.768 could beachieved for terrestrial plants, and an RMSE of 0.086 and an R2 of 0.779 could be yielded for aquatic plants. These results suggested that the sub-pixel Markov model could yield a reasonable result in a short period. Additionally, an analysis of dynamics of vegetation abundance from 2001 to 2020 indicated that thereexisted an increasing trend for the average fractional value of terrestrial plants and a decreasing trendfor aquatic plants. © 2014 Elsevier B.V. Source