Landsat TM/OLI images of the built-up areas in Chengde City(Shuangqiao District and Shuangluan District) in 2000,2009 and 2014 were adopted,Support Vector Machine(SVM) was applied to classify the images automatically,...Landsat TM/OLI images of the built-up areas in Chengde City(Shuangqiao District and Shuangluan District) in 2000,2009 and 2014 were adopted,Support Vector Machine(SVM) was applied to classify the images automatically,land use/cover map of the study area was obtained after precision test.Through analyzing the land use types in the study area and also applying transfer matrix to monitor the changes,the results showed that human activities and urbanization in the past 14 years have brought the increasing tendency of construction land,and the negative growth of arable land,forest and shrubbery land.It is urgent to adopt corresponding measures to maintain the coverage of forest and shrubbery land in the built-up area of Chengde City,so as to reduce the impact of human activities and improve urban ecological environment.展开更多
Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the streng...Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48<span style="white-space:nowrap;">°</span>C, 1.01<span style="white-space:nowrap;">°</span>C, and 3.76<span style="white-space:nowrap;">°</span>C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.展开更多
Landsat data are the longest available records that consistently document global change.However,the extent and degree of cloud coverage typically determine its usability,especially in the tropics.In this study,scene-b...Landsat data are the longest available records that consistently document global change.However,the extent and degree of cloud coverage typically determine its usability,especially in the tropics.In this study,scene-based metadata from the U.S.Geological Survey Landsat inventories,ten-day,monthly,seasonal,and annual acquisition probabilities(AP)of targeted images at various cloud coverage thresholds(10%to 100%)were statistically analyzed using available Landsat TM,ETM+,and OLI observations over mainland Southeast Asia(MSEA)from 1986 to 2015.Four significant results were found.First,the cumulative average acquisition probability of available Landsat observations over MSEA at the 30%cloud cover(CC)threshold was approximately 41.05%.Second,monthly and ten-day level probability statistics for the 30%CC threshold coincide with the temporal distribution of the dry and rainy seasons.This demonstrates that Landsat images acquired during the dry season satisfy the requirements needed for land cover monitoring.Third,differences in acquisition probabilities at the 30%CC threshold are different between the western and eastern regions of MSEA.Finally,the ability of TM,ETM+,and OLI to acquire high-quality imagery has gradually enhanced over time,especially during the dry season,along with consequently larger probabilities at lower CC thresholds.展开更多
Forests are fundamental to maintaining ecological security and achieving regional sustainable development in China. Forest land change can result in many ecological problems including soil erosion, water shortages dro...Forests are fundamental to maintaining ecological security and achieving regional sustainable development in China. Forest land change can result in many ecological problems including soil erosion, water shortages drought and biodiversity loss. Based on landscape ecology and logistic regression we explored the spatiotemporal patterns and factors affecting forest land changes from 1985 to 2000 in the Beijing-Tianjin-Hebei Region of China. The results show decreased local fragmentation of woodland landscapes and that the shapes of forest patches have become more regular. For forest land cover change, soil organic matter content, slope type I （〈5°）, distance to the nearest village and per capita GDP were the most important independent variables from 1985 to 2000. This study indicates that spatial heterogeneity can affect the predictability of logistic regression models for forest land change.展开更多
利用NCAR最新的公用陆面模式CLM3.0,通过数值模拟初步研究了植被叶面积指数(LAI,leaf area index)异常变化对陆面状况的可能影响,结果表明,植被LAI的异常变化能够引起地表能量平衡、地表水循环等陆面状况的异常。(1)植被LAI的异常变化...利用NCAR最新的公用陆面模式CLM3.0,通过数值模拟初步研究了植被叶面积指数(LAI,leaf area index)异常变化对陆面状况的可能影响,结果表明,植被LAI的异常变化能够引起地表能量平衡、地表水循环等陆面状况的异常。(1)植被LAI的异常变化主要影响太阳辐射在植被与地表之间的分配,以及地表的感热、潜热通量。植被LAI增大,能够引起植被吸收的太阳辐射增加,而到达土壤表面的太阳辐射减小,并导致植被的蒸发、蒸腾潜热通量增加,造成地表的蒸发潜热和感热通量不同程度的减小。(2)植被LAI增大时,植被对降水的拦截和植被叶面的蒸发增大,植被的蒸腾作用也明显增强;植被LAI增加会使得热带地区各个季节的土壤表面蒸发、地表径流减小,而土壤湿度有所增加;LAI增加造成中高纬度地区土壤蒸发的减少主要出现在夏季;LAI增加还能够引起中高纬地区冬、春积雪深度不同程度的增加,造成春末、夏初地表径流的增加。(3)植被LAI增加能够使得叶面和土壤温度有所下降,但植被LAI的变化对叶面、土壤温度的影响相对较小。展开更多
基金Sponsored by Program of Hebei Provincial Department of Education(Z2015080)Scientific and Technological Support Program of Chengde City(2015035)Scientific and Technological Research Program of Hebei Normal University for Nationalities(FY201523)
文摘Landsat TM/OLI images of the built-up areas in Chengde City(Shuangqiao District and Shuangluan District) in 2000,2009 and 2014 were adopted,Support Vector Machine(SVM) was applied to classify the images automatically,land use/cover map of the study area was obtained after precision test.Through analyzing the land use types in the study area and also applying transfer matrix to monitor the changes,the results showed that human activities and urbanization in the past 14 years have brought the increasing tendency of construction land,and the negative growth of arable land,forest and shrubbery land.It is urgent to adopt corresponding measures to maintain the coverage of forest and shrubbery land in the built-up area of Chengde City,so as to reduce the impact of human activities and improve urban ecological environment.
文摘Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48<span style="white-space:nowrap;">°</span>C, 1.01<span style="white-space:nowrap;">°</span>C, and 3.76<span style="white-space:nowrap;">°</span>C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.
基金This work was supported by the National Natural Science Foundation of China(NSFC)under grants(41301090 and 41271117).
文摘Landsat data are the longest available records that consistently document global change.However,the extent and degree of cloud coverage typically determine its usability,especially in the tropics.In this study,scene-based metadata from the U.S.Geological Survey Landsat inventories,ten-day,monthly,seasonal,and annual acquisition probabilities(AP)of targeted images at various cloud coverage thresholds(10%to 100%)were statistically analyzed using available Landsat TM,ETM+,and OLI observations over mainland Southeast Asia(MSEA)from 1986 to 2015.Four significant results were found.First,the cumulative average acquisition probability of available Landsat observations over MSEA at the 30%cloud cover(CC)threshold was approximately 41.05%.Second,monthly and ten-day level probability statistics for the 30%CC threshold coincide with the temporal distribution of the dry and rainy seasons.This demonstrates that Landsat images acquired during the dry season satisfy the requirements needed for land cover monitoring.Third,differences in acquisition probabilities at the 30%CC threshold are different between the western and eastern regions of MSEA.Finally,the ability of TM,ETM+,and OLI to acquire high-quality imagery has gradually enhanced over time,especially during the dry season,along with consequently larger probabilities at lower CC thresholds.
基金National Natural Science Foundation of China(41361111)the Natural Science Foundation of Jiangxi Province(20143ACB21023)+2 种基金the Fok Ying Tung Foundation(141084)the Technology Foundation of Jiangxi,Education Department of China(KJLD14033)the Key project of Social Science Foundation of Jiangxi Province(15ZQZD10)
文摘Forests are fundamental to maintaining ecological security and achieving regional sustainable development in China. Forest land change can result in many ecological problems including soil erosion, water shortages drought and biodiversity loss. Based on landscape ecology and logistic regression we explored the spatiotemporal patterns and factors affecting forest land changes from 1985 to 2000 in the Beijing-Tianjin-Hebei Region of China. The results show decreased local fragmentation of woodland landscapes and that the shapes of forest patches have become more regular. For forest land cover change, soil organic matter content, slope type I （〈5°）, distance to the nearest village and per capita GDP were the most important independent variables from 1985 to 2000. This study indicates that spatial heterogeneity can affect the predictability of logistic regression models for forest land change.
文摘利用NCAR最新的公用陆面模式CLM3.0,通过数值模拟初步研究了植被叶面积指数(LAI,leaf area index)异常变化对陆面状况的可能影响,结果表明,植被LAI的异常变化能够引起地表能量平衡、地表水循环等陆面状况的异常。(1)植被LAI的异常变化主要影响太阳辐射在植被与地表之间的分配,以及地表的感热、潜热通量。植被LAI增大,能够引起植被吸收的太阳辐射增加,而到达土壤表面的太阳辐射减小,并导致植被的蒸发、蒸腾潜热通量增加,造成地表的蒸发潜热和感热通量不同程度的减小。(2)植被LAI增大时,植被对降水的拦截和植被叶面的蒸发增大,植被的蒸腾作用也明显增强;植被LAI增加会使得热带地区各个季节的土壤表面蒸发、地表径流减小,而土壤湿度有所增加;LAI增加造成中高纬度地区土壤蒸发的减少主要出现在夏季;LAI增加还能够引起中高纬地区冬、春积雪深度不同程度的增加,造成春末、夏初地表径流的增加。(3)植被LAI增加能够使得叶面和土壤温度有所下降,但植被LAI的变化对叶面、土壤温度的影响相对较小。
