Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing seaso...Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing season, and thus the principle of the interaction between NDVI profile and the growing status of crops was discussed. As a case in point, the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed. By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination, scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield. These relation could be described with linear, cubic polynomial, and exponential regression, and the cubic polynomial regression was the best way. In general, NDVI reflects growing status of green vegetation, so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.展开更多
The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, res...The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.展开更多
The influences of coal mining in an arid environment on vegetation coverage, land-use change, desertification, soil and water loss were discussed. A series of available TM/ETM＋ images with no cloud cover from July/Au...The influences of coal mining in an arid environment on vegetation coverage, land-use change, desertification, soil and water loss were discussed. A series of available TM/ETM＋ images with no cloud cover from July/August in different years （1990, 1995, 2000 and 2005） were used to analyze the change in various land environmental factors over time. The results show that while mining activity initially had a marked adverse impact on the environment, mine rehabilitation measures have also subsequently played a great role in improving vegetation cover and controlling land desertification and loss of water and soil. The effect of coal mining on vegetation cover is dependent upon the soil type and natural indigenous flora. Results of this investigation imply that mining activity has a greater effect on the vegetation of loess areas than at sandy sites. Although local vegetation coverage was improved by planting in the mining area, the total area of land affected by desertification still in- creased from 26.81% in 1990 when large-scale mine construction was introduced, to 46.79% in 1995. With continuous efforts at rehabilitation, the vegetation cover in the Shendong coal mining area was increasing, and loss of water and soil were effec- tively controlled since 1995. Subsequently, the total area of extreme desertification decreased to 23.24% in 2000 and further to 18.68% in 2005. The total area affected by severe loss of water and soil also decreased since the early 1990＇s （70.61% in 1990, 71.43% in 1995）, to 43.64% in 2000 and 34.93% in 2005, respectively.展开更多
Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index （NDVI） products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has ...Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index （NDVI） products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity of urban and natural landscapes from QuickBird, Satellite pour l＇observation de la Terre （SPOT）, Ad- vanced Spacebome Thermal Emission and Reflection Radiometer （ASTER） and Landsat Thematic Mapper （TM） images with variogram analysis. Instead of a logarithmic relationship with pixel size observed in the corresponding aggregated images, the spatial variability decayed and the spatial structures decomposed more slowly and complexly with spatial resolution for real multisensor im- ages. As the spatial resolution increased, the proportion of spatial variability of the smaller spatial structure decreased quickly and only a larger spatial structure was observed at very coarse scales. Compared with visible band, greater spatial variability was observed in near infrared band for both densely and less densely vegetated landscapes. The influence of image size on spatial heterogeneity was highly dependent on whether the empirical sernivariogram reached its sill within the original image size. When the empirical semivariogram did not reach its sill at the original observation scale, spatial variability and mean characteristic length scale would increase with image size; otherwise they might decrease. This study could provide new insights into the knowledge of spatial heterogeneity in real multisen- sor images with consideration of their nominal spatial resolution, image size and spectral bands.展开更多
In recent years, the pressure of increasing coastal industries and tourism activities has, in some areas, led to the clearing of many coastal habitats along the Qatar＇s shorelines for the construction of tourist reso...In recent years, the pressure of increasing coastal industries and tourism activities has, in some areas, led to the clearing of many coastal habitats along the Qatar＇s shorelines for the construction of tourist resorts, tourism-related development and industrial facilities. Such threats are leading to the increasing demand for detailed mangrove maps for the purpose of measuring the extent of decline in mangrove ecosystems. Detailed mangrove maps at the community or species level are, however, not easy to produce, mainly because mangrove forests are very difficult to access. Without doubt, remote sensing is a serious alternative to traditional field-based methods for mangrove mapping, as it allows information to be gathered from the forbidding environment of mangrove forests, which otherwise, logistically and practically speaking, would be extremely difficult to survey. Remote sensing applications for mangrove mapping at the fundamental level are already well established but, surprisingly, a number of advanced remote sensing applications have remained unexplored for the purpose of mangrove mapping at a finer level. Consequently, the aim of this paper is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies. Temporal Landsat TM image of 1986, Landsat ETM image of 2000 and Resourcesat-1 LISS 3 image of 2008 are used to calculate percentage change in mangrove cover at AI Dhakira site using geometrically registered and radiometrically corrected historical Landsat and Resourcesat-1 images. Region masks are employed to isolate the unwanted area from the images. NDVI （normalized difference vegetation index） is used to detect mangroves using near-infrared and red bands which are computed from the satellite images. The ground-truthing visit to AI Dhakira site is conducted to confirm the results of the analysis. Change detection is applied and mangrove in the study area is found to have decreased by about 8.79% from 2000 to 2008.展开更多
基金Under the auspices of Beijing Precision Agriculture Project of the State Development Planning Commission(A00300100584-RS02).
文摘Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing season, and thus the principle of the interaction between NDVI profile and the growing status of crops was discussed. As a case in point, the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed. By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination, scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield. These relation could be described with linear, cubic polynomial, and exponential regression, and the cubic polynomial regression was the best way. In general, NDVI reflects growing status of green vegetation, so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.
基金Projects NCET-04-0484 supported by the New-Century Outstanding Young Scientist Program from the Ministry of Education and D0605046040191-101Beijing Science and Technology Program
文摘The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.
文摘The influences of coal mining in an arid environment on vegetation coverage, land-use change, desertification, soil and water loss were discussed. A series of available TM/ETM＋ images with no cloud cover from July/August in different years （1990, 1995, 2000 and 2005） were used to analyze the change in various land environmental factors over time. The results show that while mining activity initially had a marked adverse impact on the environment, mine rehabilitation measures have also subsequently played a great role in improving vegetation cover and controlling land desertification and loss of water and soil. The effect of coal mining on vegetation cover is dependent upon the soil type and natural indigenous flora. Results of this investigation imply that mining activity has a greater effect on the vegetation of loess areas than at sandy sites. Although local vegetation coverage was improved by planting in the mining area, the total area of land affected by desertification still in- creased from 26.81% in 1990 when large-scale mine construction was introduced, to 46.79% in 1995. With continuous efforts at rehabilitation, the vegetation cover in the Shendong coal mining area was increasing, and loss of water and soil were effec- tively controlled since 1995. Subsequently, the total area of extreme desertification decreased to 23.24% in 2000 and further to 18.68% in 2005. The total area affected by severe loss of water and soil also decreased since the early 1990＇s （70.61% in 1990, 71.43% in 1995）, to 43.64% in 2000 and 34.93% in 2005, respectively.
基金Under the auspices of National Natural Science Foundation of China(No.41071267,41001254)Natural Science Foundation of Fujian Province(No.2012I0005,2012J01167)
文摘Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index （NDVI） products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity of urban and natural landscapes from QuickBird, Satellite pour l＇observation de la Terre （SPOT）, Ad- vanced Spacebome Thermal Emission and Reflection Radiometer （ASTER） and Landsat Thematic Mapper （TM） images with variogram analysis. Instead of a logarithmic relationship with pixel size observed in the corresponding aggregated images, the spatial variability decayed and the spatial structures decomposed more slowly and complexly with spatial resolution for real multisensor im- ages. As the spatial resolution increased, the proportion of spatial variability of the smaller spatial structure decreased quickly and only a larger spatial structure was observed at very coarse scales. Compared with visible band, greater spatial variability was observed in near infrared band for both densely and less densely vegetated landscapes. The influence of image size on spatial heterogeneity was highly dependent on whether the empirical sernivariogram reached its sill within the original image size. When the empirical semivariogram did not reach its sill at the original observation scale, spatial variability and mean characteristic length scale would increase with image size; otherwise they might decrease. This study could provide new insights into the knowledge of spatial heterogeneity in real multisen- sor images with consideration of their nominal spatial resolution, image size and spectral bands.
文摘In recent years, the pressure of increasing coastal industries and tourism activities has, in some areas, led to the clearing of many coastal habitats along the Qatar＇s shorelines for the construction of tourist resorts, tourism-related development and industrial facilities. Such threats are leading to the increasing demand for detailed mangrove maps for the purpose of measuring the extent of decline in mangrove ecosystems. Detailed mangrove maps at the community or species level are, however, not easy to produce, mainly because mangrove forests are very difficult to access. Without doubt, remote sensing is a serious alternative to traditional field-based methods for mangrove mapping, as it allows information to be gathered from the forbidding environment of mangrove forests, which otherwise, logistically and practically speaking, would be extremely difficult to survey. Remote sensing applications for mangrove mapping at the fundamental level are already well established but, surprisingly, a number of advanced remote sensing applications have remained unexplored for the purpose of mangrove mapping at a finer level. Consequently, the aim of this paper is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies. Temporal Landsat TM image of 1986, Landsat ETM image of 2000 and Resourcesat-1 LISS 3 image of 2008 are used to calculate percentage change in mangrove cover at AI Dhakira site using geometrically registered and radiometrically corrected historical Landsat and Resourcesat-1 images. Region masks are employed to isolate the unwanted area from the images. NDVI （normalized difference vegetation index） is used to detect mangroves using near-infrared and red bands which are computed from the satellite images. The ground-truthing visit to AI Dhakira site is conducted to confirm the results of the analysis. Change detection is applied and mangrove in the study area is found to have decreased by about 8.79% from 2000 to 2008.
