[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation covera...[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation coverage changes of the study area in different time period under the GIS platform, with the aim to reveal the spatial distribution rules of the vegetation cover in Eastern Jilin Province during the recent 10 years. The Markov Model and Grey System G （1, 1） theory model were used to predict the vegetation cover change trend in Eastern Jilin Province. [Result] The vegetation cover increased a little, but staying stable in general. The regions with great changes were mainly around the lake and river. The prediction results of Markov Model and Grey System G （1, 1） theory model were consistent with the observed measurement. [Conclusion] This study provided referential basis for the effective protection of the vegetation coverage in mountainous forest, and important reference value for the scientific decision-making on the forest construction planning in Jilin Province as well as in China and sustainable development of social economy.展开更多
The wind pressure characteristics on a saddle roof at wind direction along the connection of the low points are systematically studied by the wind tunnel test. First, the distributions of the mean and the fluctuating ...The wind pressure characteristics on a saddle roof at wind direction along the connection of the low points are systematically studied by the wind tunnel test. First, the distributions of the mean and the fluctuating pressures on the saddle roof are provided. Through the wind pressure spectra, the process of generation, growth and break down of the vortex on the leading edge is presented from a microscopic aspect and then the distribution mechanism of the mean and fluctuating pressures along the vulnerable leading edge is explained. By analysis of the wind pressure spectra near the high points, it can be inferred that the body induced turbulence reflects itself as a high-frequency pressure fluctuation. Secondly, the third-and fourth-order statistical moments of the wind pressure are employed to identify the non-Gaussian nature of the pressure time history and to construct an easy tool to localize regions with a non-Gaussian feature. The cause of the non-Gaussian feature is discussed by virtue of the wind pressure spectra. It is concluded that the non-Gaussian feature of the wind pressure originates from the effects of flow separation and body-induced turbulence, and the former effect plays an obvious role.展开更多
The role of forests is being actively considered under the agenda of REDD＋ （Reducing Emissions from Deforestation and Forest Degradation plus） aimed at reducing emissions related to changes in forest cover and fore...The role of forests is being actively considered under the agenda of REDD＋ （Reducing Emissions from Deforestation and Forest Degradation plus） aimed at reducing emissions related to changes in forest cover and forest quality. Forests in general have undergone negative changes in the past in the form of deforestation and degradation, while in some countries positive changes are reported in the form of conservation, sustainable management of forests and enhancement of carbon stock. The present study in the Kashmir Himalayan forests is an effort to assess historical forest cover changes that took place from 1980 to 2009 and to predict the same for 2030 on the basis of past trend using geospatial modeling approach. Landsat data （Multispectral Scanner （MSS）, Thematic Mapper （TM） and Enhanced Thematic Mapper （ETM＋）） was used for the years 1980, 199o and （2001, 2009） respectively and change detection analysis between the dates was performed. The maps generated were validated through ground truthing. The study area （3375.62 km^2） from 1980-2009 has uffered deforestation and forest degradation of about 126 km^2 and 239.02 km^2 respectively which can be claimed under negative options of REDD＋, while as the area that experienced no change （1514 km^2） can be claimed under conservation. A small area （23.31 km^2） observed as positive change can be claimed under positive options. The projected estimates of forest cover for 2030 showed increased deforestation and forest degradation on the basis of trend analysis using Cellular Automata （CA） Markov modeling. Despite the fact that country as a whole has registered a net positive change in the past few decades, but there are regions like Kashmir region of western Himalaya which have constantly undergoing deforestation as well as degradation in the past few decades.展开更多
Features of atmospheric circulation and thermal structures are discussed using the NCAR/NCEP data to reveal the reasons for the late onset and anomalous southward persistence of the South China Sea Summer Monsoon(SCSS...Features of atmospheric circulation and thermal structures are discussed using the NCAR/NCEP data to reveal the reasons for the late onset and anomalous southward persistence of the South China Sea Summer Monsoon(SCSSM) in 2005.The results show that three factors are crucial.First,a strong Arabian High overlaps with a high-latitude blocking high and channels strong cold air to southern Asia.Second,the Tibetan Plateau has a bigger snow cover than usual in spring and the melting of snow cools down the surface.Third,the Somali Jet breaks out at a much later date,being not conducive to convection over Indochina.The former two factors restrict atmospheric sensible heating over the Tibetan Plateau and nearby regions while the third one limits latent heating over Indochina.All of the factors slow down atmospheric warming and postpone the onset of SCSSM.Long after the onset of SCSSM,strong cold air over India advances the Southwest Monsoon northward slowly,resulting in weaker convection and latent heating over the Tibetan Plateau and nearby areas.The negative feedback conversely inhibits further northward movement of Southwest Monsoon.展开更多
Every year during summer, natural and human-induced forest fires threaten the environment in the largely forested areas of the Himalayan region and the local population living near these forests. Nepal, with its multi...Every year during summer, natural and human-induced forest fires threaten the environment in the largely forested areas of the Himalayan region and the local population living near these forests. Nepal, with its multitude of forests, is one of the most forest fire-prone areas in the region. This study examines the possibility of averting forest fires, minimizing their frequency and the damage they cause, through advanced mapping of forest fire prone areas using a VHSR （very-high spatial resolution） satellite image of GeoEye-1, DEM （digital elevation data） created from topographic maps and additional data layers （e.g., precipitation, settlements）. The study was conducted in Kayer Khola, Chitwan district, Nepal. The classification of the satellite image has been performed using OBIA （object-based image analysis） techniques taking into account spectral, spatial and context information as well as hierarchical properties. The land cover classification result was thereafter combined with additional data in ArcGIS, where the input layers were reclassified and all classes of the input layers ranked according to their proneness to forest fires. Fire prone areas were delineated in five classes ranging from very high to very low. The study revealed that 82% of fires occur in forest areas. This case study in Kayer Khola shows that OBIA and GIS modeling techniques can be used to successfully identify forest fire-prone areas. The mapping of forest fire-prone areas will enable forest departments in countries of the Himalayan region to delineate forest fire prone areas, which can guide the forest departments set up appropriate fire-fighting infrastructure in these areas and thus help, minimize or avert forest fires.展开更多
Crop damages by wildlife is a frequent form of human-wildlife conflict. Identifying areas where the risk of crop damages is highest is pivotal to set up preventive measures and reduce conflict. Species distribution mo...Crop damages by wildlife is a frequent form of human-wildlife conflict. Identifying areas where the risk of crop damages is highest is pivotal to set up preventive measures and reduce conflict. Species distribution models are routinely used to predict species distribution in response of environmental changes. The aim of this paper was assessing whether species distribution models can allow to identify the areas most at risk of crop damages, helping to set up management strategies aimed at the mitigation of human-wildlife conflicts. We obtained data on wild boar Sus scrofa damages to crops in the Alta Murgia National Park, Southern Italy, and related them to landscape features, to identify areas where the risk of wild boar damages is highest. We used MaxEnt to build species distribution models. We identified the spatial scale at which landscape mostly affects the distribution damages, and optimized the regularization parameter of models, through an information-theoretic approach based on AIC. Wild boar damages quickly increased in the period 2007-2011; cereals and legtmaes were the crops more affected. Large areas of the park have a high risk of wild boar damages. The risk of damages was related to low cover of urban areas or olive grows, intermediate values of forest cover, and high values of shrubland cover within a 2-km radius. Temporally independent validation data demonstrated that models can successfully predict damages in the future. Species distribution models can accurately identify the areas most at risk of wildlife damages, as models calibrated on data collected during only a subset of years correctly predicted damages in the subsequent year [Current Zoology 60 （2）： 170-179, 2014].展开更多
基金Supported by the Project of China Geological Survey(1212010911084)~~
文摘[Objective] This study aimed to investigate the dynamic changes of vegetation cover and its prediction method. [Method] The NDVl was used as data source to perform the spatial overlay analysis on the vegetation coverage changes of the study area in different time period under the GIS platform, with the aim to reveal the spatial distribution rules of the vegetation cover in Eastern Jilin Province during the recent 10 years. The Markov Model and Grey System G （1, 1） theory model were used to predict the vegetation cover change trend in Eastern Jilin Province. [Result] The vegetation cover increased a little, but staying stable in general. The regions with great changes were mainly around the lake and river. The prediction results of Markov Model and Grey System G （1, 1） theory model were consistent with the observed measurement. [Conclusion] This study provided referential basis for the effective protection of the vegetation coverage in mountainous forest, and important reference value for the scientific decision-making on the forest construction planning in Jilin Province as well as in China and sustainable development of social economy.
基金The National Natural Science Foundation of China (No.50678036)Jiangsu Civil Engineering Graduate Center for Innovation and Academic Communication Foundation
文摘The wind pressure characteristics on a saddle roof at wind direction along the connection of the low points are systematically studied by the wind tunnel test. First, the distributions of the mean and the fluctuating pressures on the saddle roof are provided. Through the wind pressure spectra, the process of generation, growth and break down of the vortex on the leading edge is presented from a microscopic aspect and then the distribution mechanism of the mean and fluctuating pressures along the vulnerable leading edge is explained. By analysis of the wind pressure spectra near the high points, it can be inferred that the body induced turbulence reflects itself as a high-frequency pressure fluctuation. Secondly, the third-and fourth-order statistical moments of the wind pressure are employed to identify the non-Gaussian nature of the pressure time history and to construct an easy tool to localize regions with a non-Gaussian feature. The cause of the non-Gaussian feature is discussed by virtue of the wind pressure spectra. It is concluded that the non-Gaussian feature of the wind pressure originates from the effects of flow separation and body-induced turbulence, and the former effect plays an obvious role.
文摘The role of forests is being actively considered under the agenda of REDD＋ （Reducing Emissions from Deforestation and Forest Degradation plus） aimed at reducing emissions related to changes in forest cover and forest quality. Forests in general have undergone negative changes in the past in the form of deforestation and degradation, while in some countries positive changes are reported in the form of conservation, sustainable management of forests and enhancement of carbon stock. The present study in the Kashmir Himalayan forests is an effort to assess historical forest cover changes that took place from 1980 to 2009 and to predict the same for 2030 on the basis of past trend using geospatial modeling approach. Landsat data （Multispectral Scanner （MSS）, Thematic Mapper （TM） and Enhanced Thematic Mapper （ETM＋）） was used for the years 1980, 199o and （2001, 2009） respectively and change detection analysis between the dates was performed. The maps generated were validated through ground truthing. The study area （3375.62 km^2） from 1980-2009 has uffered deforestation and forest degradation of about 126 km^2 and 239.02 km^2 respectively which can be claimed under negative options of REDD＋, while as the area that experienced no change （1514 km^2） can be claimed under conservation. A small area （23.31 km^2） observed as positive change can be claimed under positive options. The projected estimates of forest cover for 2030 showed increased deforestation and forest degradation on the basis of trend analysis using Cellular Automata （CA） Markov modeling. Despite the fact that country as a whole has registered a net positive change in the past few decades, but there are regions like Kashmir region of western Himalaya which have constantly undergoing deforestation as well as degradation in the past few decades.
基金National Key Fundamental Research Development Project (2004CB418302)
文摘Features of atmospheric circulation and thermal structures are discussed using the NCAR/NCEP data to reveal the reasons for the late onset and anomalous southward persistence of the South China Sea Summer Monsoon(SCSSM) in 2005.The results show that three factors are crucial.First,a strong Arabian High overlaps with a high-latitude blocking high and channels strong cold air to southern Asia.Second,the Tibetan Plateau has a bigger snow cover than usual in spring and the melting of snow cools down the surface.Third,the Somali Jet breaks out at a much later date,being not conducive to convection over Indochina.The former two factors restrict atmospheric sensible heating over the Tibetan Plateau and nearby regions while the third one limits latent heating over Indochina.All of the factors slow down atmospheric warming and postpone the onset of SCSSM.Long after the onset of SCSSM,strong cold air over India advances the Southwest Monsoon northward slowly,resulting in weaker convection and latent heating over the Tibetan Plateau and nearby areas.The negative feedback conversely inhibits further northward movement of Southwest Monsoon.
文摘Every year during summer, natural and human-induced forest fires threaten the environment in the largely forested areas of the Himalayan region and the local population living near these forests. Nepal, with its multitude of forests, is one of the most forest fire-prone areas in the region. This study examines the possibility of averting forest fires, minimizing their frequency and the damage they cause, through advanced mapping of forest fire prone areas using a VHSR （very-high spatial resolution） satellite image of GeoEye-1, DEM （digital elevation data） created from topographic maps and additional data layers （e.g., precipitation, settlements）. The study was conducted in Kayer Khola, Chitwan district, Nepal. The classification of the satellite image has been performed using OBIA （object-based image analysis） techniques taking into account spectral, spatial and context information as well as hierarchical properties. The land cover classification result was thereafter combined with additional data in ArcGIS, where the input layers were reclassified and all classes of the input layers ranked according to their proneness to forest fires. Fire prone areas were delineated in five classes ranging from very high to very low. The study revealed that 82% of fires occur in forest areas. This case study in Kayer Khola shows that OBIA and GIS modeling techniques can be used to successfully identify forest fire-prone areas. The mapping of forest fire-prone areas will enable forest departments in countries of the Himalayan region to delineate forest fire prone areas, which can guide the forest departments set up appropriate fire-fighting infrastructure in these areas and thus help, minimize or avert forest fires.
文摘Crop damages by wildlife is a frequent form of human-wildlife conflict. Identifying areas where the risk of crop damages is highest is pivotal to set up preventive measures and reduce conflict. Species distribution models are routinely used to predict species distribution in response of environmental changes. The aim of this paper was assessing whether species distribution models can allow to identify the areas most at risk of crop damages, helping to set up management strategies aimed at the mitigation of human-wildlife conflicts. We obtained data on wild boar Sus scrofa damages to crops in the Alta Murgia National Park, Southern Italy, and related them to landscape features, to identify areas where the risk of wild boar damages is highest. We used MaxEnt to build species distribution models. We identified the spatial scale at which landscape mostly affects the distribution damages, and optimized the regularization parameter of models, through an information-theoretic approach based on AIC. Wild boar damages quickly increased in the period 2007-2011; cereals and legtmaes were the crops more affected. Large areas of the park have a high risk of wild boar damages. The risk of damages was related to low cover of urban areas or olive grows, intermediate values of forest cover, and high values of shrubland cover within a 2-km radius. Temporally independent validation data demonstrated that models can successfully predict damages in the future. Species distribution models can accurately identify the areas most at risk of wildlife damages, as models calibrated on data collected during only a subset of years correctly predicted damages in the subsequent year [Current Zoology 60 （2）： 170-179, 2014].
