The global cryosphere is experiencing accelerated melting due to climate change.Currently,the Karakoram anomaly is under discussion with a debate about the possibility that the anomaly may have recently ended.This stu...The global cryosphere is experiencing accelerated melting due to climate change.Currently,the Karakoram anomaly is under discussion with a debate about the possibility that the anomaly may have recently ended.This study aims to evaluate the up-to-date changes in snow cover in the western Karakoram region.We observed the snow cover changes in Passu and Ghulkin valleys in the Hunza River basin(HRB)of the Karakoram through multitemporal Landsat satellite data between 1995 and 2022.We found a significant reduction in snow cover in these valleys,with an average reduction rate of 0.42 km~2/yr,resulting in a total reduction of~11.46 km~2 between 1995 and 2022.This reduction in snow cover is consistent with the mass loss of glaciers in the Karakoram region in recent years.The decline in snow cover in these valleys is also consistent with the meteorological data.The temperature in summer(June)has significantly increased whereas the precipitation in the accumulation season(March)has decreased.These rapid changes suggest that it is crucially important to monitor the snow cover on a regular basis to support downstream management of snowmelt runoff.In addition,there is a need of planning for mitigation and adaptation strategies for snow-related hazards.展开更多
Scientific and comprehensive monitoring of snow cover changes in the Pamirs is of great significance to the prevention of snow disasters around the Pamirs and the full utilization of water resources. Utilize the 2010-...Scientific and comprehensive monitoring of snow cover changes in the Pamirs is of great significance to the prevention of snow disasters around the Pamirs and the full utilization of water resources. Utilize the 2010-2020 snow cover product MOD10A2, Synthesis by maximum, The temporal and spatial variation characteristics of snow cover area in the Pamirs in the past 11 years have been obtained. Research indicates: In terms of interannual changes, the snow cover area of the Pamir Plateau from 2010 to 2020 generally showed a slight decrease trend. The average snow cover area in 2012 was the largest, reaching 54.167% of the total area. In 2014, the average snow cover area was the smallest, accounting for only 44.863% of the total area. In terms of annual changes, there are obvious changes with the change of seasons. The largest snow area is in March, and the smallest snow area is in August. In the past 11 years, the average snow cover area in spring and summer showed a slow decreasing trend, and there was almost no change in autumn and winter. In terms of space, the snow cover area of the Pamirs is significantly affected by altitude, and the high snow cover areas are mainly distributed in the Karakoram Mountains and other areas with an altitude greater than 5000 meters.展开更多
Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.9...Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and premonsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snowcovered area on Passu glacier decreased by 24.50%,3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre-and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of-0.67,-0.89,-0.75 and-0.77 with the average seasonal snowcovered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.展开更多
Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information...Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant(SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis(PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting(SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.展开更多
The analysis of the 3 stages' (1988,1996,2000) variation of land-cover is performed according to Thematic Mapper (TM) and Enhancement Thematic Mapper(ETM) satellite image by combining ground GIS database with G...The analysis of the 3 stages' (1988,1996,2000) variation of land-cover is performed according to Thematic Mapper (TM) and Enhancement Thematic Mapper(ETM) satellite image by combining ground GIS database with GPS field collected data in the area of Xiaowan-Dachaoshan Reservoirs of Lancangjiang River cascaded Hydropower Area. Consequently, the land-cover is divided into five subclasses, namely water, paddy field and wetland, bare dryland and sparse shrub, secondary forest and density forest. The result showed that the areas of bare land, upland and secondary forest decreased in 1988-1996, whereas from 1996 to 2000, water body and density forest keep invariability while the areas of paddy field and wetland, bare dryland and sparse scrub increasing and the area of secondary forest decrease; Features of reciprocal transformation between density forest and other type of land-cover had two points, i.e. secondary forest, bare dryland and sparse shrub converted to density forest; and density forest converted to secondary forest and paddy field and wetland. It reflects the dynamic variation of density forest; the area which slope less than 8° and greater than 15° shows bigger variation, however, less change in 8°-15°.展开更多
Remote-sensing data for protected areas in northern Togo, obtained in three different years (2007, 2000, and 1987), were used to assess and map changes in land cover and land use for this drought prone zone. The nor...Remote-sensing data for protected areas in northern Togo, obtained in three different years (2007, 2000, and 1987), were used to assess and map changes in land cover and land use for this drought prone zone. The normalized difference vegetation index (NDVI) was applied to the images to map changes in vegetation. An unsupervised classification, followed by classes recoding, filtering, identifications, area computing and post-classification process were applied to the composite of the three years of NDVI images. Maximum likelihood classification was applied to the 2007 image (ETM+2007) using a supervised classification process. Seven vegetation classes were defined from training data sets. The seven classes included the following biomes: riparian forest, dry forest, flooded vegetation, wooded savanna, fallows, parkland, and water. For these classes, the overall accuracy and the overall kappa statistic for the classi- fied map were 72.5% and 0.67, respectively. Data analyses indicated a great change in land resources; especially between 1987 and 2000 proba- bly due to the impact of democratization process social, economic, and political disorder from 1990. Wide-scale loss of vegetation occurred during this period. However, areas of vegetation clearing and regrowth were more visible between 2000 and 2007. The main source of confusion in the contingency matrix was due to heterogeneity within certain classes. It could also be due to spectral homogeneity among the classes. This research provides a baseline for future ecological landscape research and for the next management program in the area.展开更多
This study examines the spatial and temporal forest cover changes in Swat and Shangla districts to understand the deforestation pattern in context of the recent security conflict in these districts. We used multi-reso...This study examines the spatial and temporal forest cover changes in Swat and Shangla districts to understand the deforestation pattern in context of the recent security conflict in these districts. We used multi-resolution satellite images to assess the long term deforestation from 2001 to 2009 and also to identify episodic forest cutting areas appeared during the conflict period of Oct. 2007 - Oct. 2008. There are only 58 ha of deforestation identified during the conflict period while 1268 ha of gross annual deforestation were assessed during last eight years. Most of the deforestation patches persist around the administrative boundaries at sub-district levels (tehsils) which can be attributed to ambiguity in unclear jurisdiction between the forest official. The results highlight that the forest cutting appeared in Swat and Shangla during the conflict period is not as significant when compared with the long term deforestation pattern in the area. On the one side the results of the study are supportive to the picture that emerges from international studies which report high rate of deforestation in the country and on the other side it negates any relation between the security situation and the increasing deforestation in the north western Pakistan. The study concludes that deforestation assessments require verification by independent sources of data, such as satellite imagery to improve our understanding of deforestation processes.展开更多
[Objectives]This study was conducted to investigate insect activity rhythms in airport ground cover areas,and provide guidance for insect control and bird strike prevention.[Methods]The daily rhythm of insects in the ...[Objectives]This study was conducted to investigate insect activity rhythms in airport ground cover areas,and provide guidance for insect control and bird strike prevention.[Methods]The daily rhythm of insects in the northern area of the airport was studied,and their activity rhythms and characteristics under different weather conditions were analyzed.[Results]In rainy days,the insect number in the three sample areas was low.In cloudy days,insect activity was relatively stable,and insect number was consistent in the morning and evening,and maintained in a stable range,while in sunny weather,insect activity was increased,and the number changed greatly with time.For a single tussock plant growth area,the number of insects was at a relatively low level under rainy weather.In fine weather,the peak period of insect activity was between 10:00-11:00 and 14:00-15:00.[Conclusions]This study can provide a reference for the formulation of safe flight departure time.展开更多
The research on the land use/cover change is one of the frontiers and the hot spots in the global change research. Based on the Chinese resource and environment spatial-temporal database, and using the ...The research on the land use/cover change is one of the frontiers and the hot spots in the global change research. Based on the Chinese resource and environment spatial-temporal database, and using the Landsat TM and ETM data of 1990 and 2000 respectively, we analyzed the spatial-temporal characteristics of land use/cover changes in the Dongting Lake area during the last decade. The result shows that during the last ten years there were three land-use types that had changed remarkably. The cultivated land decreased by 0.57% of the total cultivated land. The built-up land and water area expanded, with an increase of 8.97% and 0.43% respectively. The conversion between land use types mostly happened among these three land-use types, especially frequently between cultivated land and water area. The land-use change speed of land-use type is different. Three cities experienced the greatest degree of land-use change among all the administrative districts, which means that the land use in these cities changed much quickly. The following changed area was the west and south of the Dongting Lake area. The slowest changed area is the north and east area.展开更多
According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processin...According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processing, and calculate the sum of square N of time series value of each pixel. The pixels with N>0.25 are classified as vegetation coverage area; the pixels with N<0.25 are classified as non-vegetation coverage area. As to vegetation coverage area, we use the second-order difference method to judge the frequency of peak value of EVI time series data. Within one year, the vegetation coverage area with peak value happening 1 time is woodland and grassland; the vegetation coverage area with peak value happening 2 times is arable land; the vegetation coverage area with peak value happening 3 times or more is vegetable land. Supervised classification method is used to identify cities, towns, water area in non-vegetation coverage area and woodland, grassland in vegetation coverage area. We draw the land cover classification diagram of Four-Lake Area in the period 2001-2007. In comparison with the land cover classification based on multitemporal ETM data in 2001, the difference of area of arable land is within 10%. Using MODIS-EVI data, we can rapidly and efficiently conduct land cover classification with low cost. The dynamic analysis results indicate that the area of arable land is in the process of declining, while the area of other cover types shows an increasing trend.展开更多
[Objective] The reseamh aimed to analyze variation of the vegetation cover in Poyang Lake area from 1991 to 2005. [Method] Based on Landsat TM remote sensing images of 1991 and 2005 in Poyang Lake area, NDVI dimidiate...[Objective] The reseamh aimed to analyze variation of the vegetation cover in Poyang Lake area from 1991 to 2005. [Method] Based on Landsat TM remote sensing images of 1991 and 2005 in Poyang Lake area, NDVI dimidiate pixel model was used to calculate vegetation cover- age. By transfer matrix, temporal-spatial change of the vegetation cover grade in the area was analyzed. [ Result] Vegetation cover in this region overall presented increase trend from 1991 to 2005, and forestry area increased somewhat. But at the same time, farmland area decreased to some extent. Sandlot and bare land also increased slightly. [ Conclusion] Governments and relevant departments should reasonably allocate land re- sources and protect natural ecology environment.展开更多
Urban river riparian spaces and their natural systems are valuable to urban dwellers;but are increasingly affected and ruined by human activities and in particular, urbanization processes. In this research, land sat a...Urban river riparian spaces and their natural systems are valuable to urban dwellers;but are increasingly affected and ruined by human activities and in particular, urbanization processes. In this research, land sat and sentinel satellite imagery apt for change detection in vegetation cover, both landsat and sentinel imagery, covering the period between 1970 and 2021 in epochs of 1973, 1984, 1993, 2003, 2015 and 2021 years were used to establish the correlation between vegetation cover and built-up area along River Riara river reserve. The images were analysed to extract the built-up areas along the river reserve, including the buildings, and the rate of human settlements, which influenced vegetation cover. Normalized Difference Built-Up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were computed using the Short-Wave Infrared (SWIR) and the Near Infra-Red (NIR) bands to show the rate of change over the years. Results indicate NDVI values were high, compared to NDBI values along river Riara in the years 1973 and 1993 implying that there was more vegetation cover then. However, in the year 2021, the NDVI indicated the highest value at 0.88, with the complementary NDBI indicating the highest NDBI value at 0.47. This represents a significant increase in built-up areas since 2015 more than in previous epochs. Either, there was a significant increase in NDBI values, from 0.24 in 1993 to 0.47 in 2021. More so, the R-squared value at 0.80 informed 80% relationship between NDBI and NDVI values indicating a negative correlation.展开更多
基金supported by ICIMODfunded by the governments of Afghanistan,Australia,Austria,Bangladesh,Bhutan,China,India,Myanmar,Nepal,Norway,Pakistan,Sweden,and Switzerland。
文摘The global cryosphere is experiencing accelerated melting due to climate change.Currently,the Karakoram anomaly is under discussion with a debate about the possibility that the anomaly may have recently ended.This study aims to evaluate the up-to-date changes in snow cover in the western Karakoram region.We observed the snow cover changes in Passu and Ghulkin valleys in the Hunza River basin(HRB)of the Karakoram through multitemporal Landsat satellite data between 1995 and 2022.We found a significant reduction in snow cover in these valleys,with an average reduction rate of 0.42 km~2/yr,resulting in a total reduction of~11.46 km~2 between 1995 and 2022.This reduction in snow cover is consistent with the mass loss of glaciers in the Karakoram region in recent years.The decline in snow cover in these valleys is also consistent with the meteorological data.The temperature in summer(June)has significantly increased whereas the precipitation in the accumulation season(March)has decreased.These rapid changes suggest that it is crucially important to monitor the snow cover on a regular basis to support downstream management of snowmelt runoff.In addition,there is a need of planning for mitigation and adaptation strategies for snow-related hazards.
文摘Scientific and comprehensive monitoring of snow cover changes in the Pamirs is of great significance to the prevention of snow disasters around the Pamirs and the full utilization of water resources. Utilize the 2010-2020 snow cover product MOD10A2, Synthesis by maximum, The temporal and spatial variation characteristics of snow cover area in the Pamirs in the past 11 years have been obtained. Research indicates: In terms of interannual changes, the snow cover area of the Pamir Plateau from 2010 to 2020 generally showed a slight decrease trend. The average snow cover area in 2012 was the largest, reaching 54.167% of the total area. In 2014, the average snow cover area was the smallest, accounting for only 44.863% of the total area. In terms of annual changes, there are obvious changes with the change of seasons. The largest snow area is in March, and the smallest snow area is in August. In the past 11 years, the average snow cover area in spring and summer showed a slow decreasing trend, and there was almost no change in autumn and winter. In terms of space, the snow cover area of the Pamirs is significantly affected by altitude, and the high snow cover areas are mainly distributed in the Karakoram Mountains and other areas with an altitude greater than 5000 meters.
基金funded by National Natural Science Foundation of China (41421061, 41630754)Chinese Academy of Sciences (KJZD-EW-G03-04)the State Key Laboratory of Cryospheric Science(SKLCS-ZZ-2017)
文摘Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and premonsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snowcovered area on Passu glacier decreased by 24.50%,3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre-and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of-0.67,-0.89,-0.75 and-0.77 with the average seasonal snowcovered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.
基金Under the auspices of National Science and Technology Major Project of China(No.04-Y20A35-9001-15/17)the Program for JLU Science and Technology Innovative Research Team(No.JLUSTIRT,2017TD-26)the Changbai Mountain Scholars Program,Jilin Province,China
文摘Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant(SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis(PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting(SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.
文摘The analysis of the 3 stages' (1988,1996,2000) variation of land-cover is performed according to Thematic Mapper (TM) and Enhancement Thematic Mapper(ETM) satellite image by combining ground GIS database with GPS field collected data in the area of Xiaowan-Dachaoshan Reservoirs of Lancangjiang River cascaded Hydropower Area. Consequently, the land-cover is divided into five subclasses, namely water, paddy field and wetland, bare dryland and sparse shrub, secondary forest and density forest. The result showed that the areas of bare land, upland and secondary forest decreased in 1988-1996, whereas from 1996 to 2000, water body and density forest keep invariability while the areas of paddy field and wetland, bare dryland and sparse scrub increasing and the area of secondary forest decrease; Features of reciprocal transformation between density forest and other type of land-cover had two points, i.e. secondary forest, bare dryland and sparse shrub converted to density forest; and density forest converted to secondary forest and paddy field and wetland. It reflects the dynamic variation of density forest; the area which slope less than 8° and greater than 15° shows bigger variation, however, less change in 8°-15°.
基金supported by the Chinese Ministry of Sciences and Technology--the host of China-Africa Science and Technology Partnership Program(CASTEP)the National Special Research Program for Forestry Welfare of China(201104009)
文摘Remote-sensing data for protected areas in northern Togo, obtained in three different years (2007, 2000, and 1987), were used to assess and map changes in land cover and land use for this drought prone zone. The normalized difference vegetation index (NDVI) was applied to the images to map changes in vegetation. An unsupervised classification, followed by classes recoding, filtering, identifications, area computing and post-classification process were applied to the composite of the three years of NDVI images. Maximum likelihood classification was applied to the 2007 image (ETM+2007) using a supervised classification process. Seven vegetation classes were defined from training data sets. The seven classes included the following biomes: riparian forest, dry forest, flooded vegetation, wooded savanna, fallows, parkland, and water. For these classes, the overall accuracy and the overall kappa statistic for the classi- fied map were 72.5% and 0.67, respectively. Data analyses indicated a great change in land resources; especially between 1987 and 2000 proba- bly due to the impact of democratization process social, economic, and political disorder from 1990. Wide-scale loss of vegetation occurred during this period. However, areas of vegetation clearing and regrowth were more visible between 2000 and 2007. The main source of confusion in the contingency matrix was due to heterogeneity within certain classes. It could also be due to spectral homogeneity among the classes. This research provides a baseline for future ecological landscape research and for the next management program in the area.
基金the financial assistance by the United Nations Development Programme (UNDP) - Pakistan through the Pakistan Wetlands Programme
文摘This study examines the spatial and temporal forest cover changes in Swat and Shangla districts to understand the deforestation pattern in context of the recent security conflict in these districts. We used multi-resolution satellite images to assess the long term deforestation from 2001 to 2009 and also to identify episodic forest cutting areas appeared during the conflict period of Oct. 2007 - Oct. 2008. There are only 58 ha of deforestation identified during the conflict period while 1268 ha of gross annual deforestation were assessed during last eight years. Most of the deforestation patches persist around the administrative boundaries at sub-district levels (tehsils) which can be attributed to ambiguity in unclear jurisdiction between the forest official. The results highlight that the forest cutting appeared in Swat and Shangla during the conflict period is not as significant when compared with the long term deforestation pattern in the area. On the one side the results of the study are supportive to the picture that emerges from international studies which report high rate of deforestation in the country and on the other side it negates any relation between the security situation and the increasing deforestation in the north western Pakistan. The study concludes that deforestation assessments require verification by independent sources of data, such as satellite imagery to improve our understanding of deforestation processes.
文摘[Objectives]This study was conducted to investigate insect activity rhythms in airport ground cover areas,and provide guidance for insect control and bird strike prevention.[Methods]The daily rhythm of insects in the northern area of the airport was studied,and their activity rhythms and characteristics under different weather conditions were analyzed.[Results]In rainy days,the insect number in the three sample areas was low.In cloudy days,insect activity was relatively stable,and insect number was consistent in the morning and evening,and maintained in a stable range,while in sunny weather,insect activity was increased,and the number changed greatly with time.For a single tussock plant growth area,the number of insects was at a relatively low level under rainy weather.In fine weather,the peak period of insect activity was between 10:00-11:00 and 14:00-15:00.[Conclusions]This study can provide a reference for the formulation of safe flight departure time.
基金Knowledge Innovation Project of CAS No.KZCX2-310-01+1 种基金 No.KZCX2-SW-415 No.KZCX1-Y-02
文摘The research on the land use/cover change is one of the frontiers and the hot spots in the global change research. Based on the Chinese resource and environment spatial-temporal database, and using the Landsat TM and ETM data of 1990 and 2000 respectively, we analyzed the spatial-temporal characteristics of land use/cover changes in the Dongting Lake area during the last decade. The result shows that during the last ten years there were three land-use types that had changed remarkably. The cultivated land decreased by 0.57% of the total cultivated land. The built-up land and water area expanded, with an increase of 8.97% and 0.43% respectively. The conversion between land use types mostly happened among these three land-use types, especially frequently between cultivated land and water area. The land-use change speed of land-use type is different. Three cities experienced the greatest degree of land-use change among all the administrative districts, which means that the land use in these cities changed much quickly. The following changed area was the west and south of the Dongting Lake area. The slowest changed area is the north and east area.
基金Supported by National Natural Science Foundation of China(40971113)Innovative Group Project of Natural Science Foundation of Hubei Province (2006ABC013)
文摘According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processing, and calculate the sum of square N of time series value of each pixel. The pixels with N>0.25 are classified as vegetation coverage area; the pixels with N<0.25 are classified as non-vegetation coverage area. As to vegetation coverage area, we use the second-order difference method to judge the frequency of peak value of EVI time series data. Within one year, the vegetation coverage area with peak value happening 1 time is woodland and grassland; the vegetation coverage area with peak value happening 2 times is arable land; the vegetation coverage area with peak value happening 3 times or more is vegetable land. Supervised classification method is used to identify cities, towns, water area in non-vegetation coverage area and woodland, grassland in vegetation coverage area. We draw the land cover classification diagram of Four-Lake Area in the period 2001-2007. In comparison with the land cover classification based on multitemporal ETM data in 2001, the difference of area of arable land is within 10%. Using MODIS-EVI data, we can rapidly and efficiently conduct land cover classification with low cost. The dynamic analysis results indicate that the area of arable land is in the process of declining, while the area of other cover types shows an increasing trend.
基金Supported by Industry Special Item,Department of Science and Technology,China(2009416029)"Study on Change of the Vegetation Index in Poyang Lake Region"Item,Meteorological Science and Technology Innovation Fund in Jiangxi,ChinaNational Natural Science Fund Item,China(40871240/D011004)
文摘[Objective] The reseamh aimed to analyze variation of the vegetation cover in Poyang Lake area from 1991 to 2005. [Method] Based on Landsat TM remote sensing images of 1991 and 2005 in Poyang Lake area, NDVI dimidiate pixel model was used to calculate vegetation cover- age. By transfer matrix, temporal-spatial change of the vegetation cover grade in the area was analyzed. [ Result] Vegetation cover in this region overall presented increase trend from 1991 to 2005, and forestry area increased somewhat. But at the same time, farmland area decreased to some extent. Sandlot and bare land also increased slightly. [ Conclusion] Governments and relevant departments should reasonably allocate land re- sources and protect natural ecology environment.
文摘Urban river riparian spaces and their natural systems are valuable to urban dwellers;but are increasingly affected and ruined by human activities and in particular, urbanization processes. In this research, land sat and sentinel satellite imagery apt for change detection in vegetation cover, both landsat and sentinel imagery, covering the period between 1970 and 2021 in epochs of 1973, 1984, 1993, 2003, 2015 and 2021 years were used to establish the correlation between vegetation cover and built-up area along River Riara river reserve. The images were analysed to extract the built-up areas along the river reserve, including the buildings, and the rate of human settlements, which influenced vegetation cover. Normalized Difference Built-Up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were computed using the Short-Wave Infrared (SWIR) and the Near Infra-Red (NIR) bands to show the rate of change over the years. Results indicate NDVI values were high, compared to NDBI values along river Riara in the years 1973 and 1993 implying that there was more vegetation cover then. However, in the year 2021, the NDVI indicated the highest value at 0.88, with the complementary NDBI indicating the highest NDBI value at 0.47. This represents a significant increase in built-up areas since 2015 more than in previous epochs. Either, there was a significant increase in NDBI values, from 0.24 in 1993 to 0.47 in 2021. More so, the R-squared value at 0.80 informed 80% relationship between NDBI and NDVI values indicating a negative correlation.