The Minqin oasis is surrounded on three sides by the Tengger Desert and the Badanjilin Desert, and it prevents these two deserts from converging. However, in recent years it has become the worst ecological environment...The Minqin oasis is surrounded on three sides by the Tengger Desert and the Badanjilin Desert, and it prevents these two deserts from converging. However, in recent years it has become the worst ecological environment in the Lake area due to deficient water resources, continual declines in the groundwater level and quality （increasing mineralization and salination）, which are causing in- creasing desertification. In this study, Landsat Thematic Mapper （TM） remote images from 1992, 1998, 2002, and 2006 of the Lake area of the Minqin oasis are interpreted to analyze the desertification evolution. A combination of an ArcObjects module and a cellular automata model is used to build a model simulating the desertification dynamics; the forecasting accuracy of this model is shown to reach up to 90%. The desertification situation in 2012 is forecasted by this model, and the results showed that, from 2006 to 2012, the green land area will be reduced by 999.92 hm2 （l.59 percent of the total oasis area）, the desertification land area will be reduced by 3,000.68 hrn2 （4.78 percent of the total oasis area）, and sand land area will increase by 4,000.6 hm2 （6.37 per- cent of the total oasis area）. The sand land is predicted to become more widespread, and more than 18% sand land will be distrib- uted in the center of green land in the Lake area. In other words, more and more abandoned green land （mined farm land） will be transformed into sand land, and this will intensify the desertification.展开更多
Urban population explosion may increase ecological environment discomfort,thereby affecting negatively humans’mental and physical performance.Therefore,it is important to detect and monitor vegetation and predict its...Urban population explosion may increase ecological environment discomfort,thereby affecting negatively humans’mental and physical performance.Therefore,it is important to detect and monitor vegetation and predict its ecological benefits.The complex composition of urban environment ground objects,such as steel roofs,plastic courts,and building shadows,significantly interferes with vegetation detection and monitoring.The optimized hyperspectral image-based vegetation index(OHSVI)constructed in this study effectively solves this problem.However,it is difficult to accurately predict the ecological benefits of vegetation based on the two-dimensional vegetation information extracted based on remote sensing images;this is related to the three-dimensional(3D)structure of vegetation and the 3D pattern of buildings.Therefore,wefirst proposed the vegetation ecological benefits index(VEBI)based on the 3D structure of vegetation to reveal how vegetation acts on its 3D surroundings.The method was tested in a playground,an academic building,and a parking space.The results showed that the vegetation extraction accuracy of the OHSVI exceeded 93%,which is better than that of the existing indices.Ourfindings suggest that VEBI may be efficient in predicting 3D vegetation ecological benefits combined with remote sensing and lidar datasets.展开更多
基金supported by the National Natural Science Foundation of China (No. 40501073)the Fundamental Research Funds for the Central Universities (Nos. 11CX05015A and 10CX04047A)
文摘The Minqin oasis is surrounded on three sides by the Tengger Desert and the Badanjilin Desert, and it prevents these two deserts from converging. However, in recent years it has become the worst ecological environment in the Lake area due to deficient water resources, continual declines in the groundwater level and quality （increasing mineralization and salination）, which are causing in- creasing desertification. In this study, Landsat Thematic Mapper （TM） remote images from 1992, 1998, 2002, and 2006 of the Lake area of the Minqin oasis are interpreted to analyze the desertification evolution. A combination of an ArcObjects module and a cellular automata model is used to build a model simulating the desertification dynamics; the forecasting accuracy of this model is shown to reach up to 90%. The desertification situation in 2012 is forecasted by this model, and the results showed that, from 2006 to 2012, the green land area will be reduced by 999.92 hm2 （l.59 percent of the total oasis area）, the desertification land area will be reduced by 3,000.68 hrn2 （4.78 percent of the total oasis area）, and sand land area will increase by 4,000.6 hm2 （6.37 per- cent of the total oasis area）. The sand land is predicted to become more widespread, and more than 18% sand land will be distrib- uted in the center of green land in the Lake area. In other words, more and more abandoned green land （mined farm land） will be transformed into sand land, and this will intensify the desertification.
基金supported by Independent innovation project-strategic special:[Grant Number 24720221004A-3]National Natural Science Foundation of China:[Grant Number 42106172].
文摘Urban population explosion may increase ecological environment discomfort,thereby affecting negatively humans’mental and physical performance.Therefore,it is important to detect and monitor vegetation and predict its ecological benefits.The complex composition of urban environment ground objects,such as steel roofs,plastic courts,and building shadows,significantly interferes with vegetation detection and monitoring.The optimized hyperspectral image-based vegetation index(OHSVI)constructed in this study effectively solves this problem.However,it is difficult to accurately predict the ecological benefits of vegetation based on the two-dimensional vegetation information extracted based on remote sensing images;this is related to the three-dimensional(3D)structure of vegetation and the 3D pattern of buildings.Therefore,wefirst proposed the vegetation ecological benefits index(VEBI)based on the 3D structure of vegetation to reveal how vegetation acts on its 3D surroundings.The method was tested in a playground,an academic building,and a parking space.The results showed that the vegetation extraction accuracy of the OHSVI exceeded 93%,which is better than that of the existing indices.Ourfindings suggest that VEBI may be efficient in predicting 3D vegetation ecological benefits combined with remote sensing and lidar datasets.
