Urban growth and sprawl have drastically altered the ecosystems and ecosystem services. The objectives of this study are
to using grid square method to investigate the spatial and temporal dynamics of urban growth in 50 global cities using
Landsat ETM/TM imagery from 1985 – 2011. First, MLC classification method were used to produce land cover maps
by using Landsat images from 1985’s, 1993’s, and 2007’s (completed); then intersect the land cover maps with 1-km2 grid cell maps to represents the proportion of each land cover category within each 1-km2 grid cell (ongoing); finally, combining the proportional land cover maps to investigated the relationship between land cover changes based on grid square cells for three intervals (i.e. around 1985, around 1993, and around 2007). Change analysis unveiled large changes in land cover and land use have occurred from 1985’s to 2007’s. The case in Tokyo, Japan shows the
Settlements area has rapidly expanded to the surrounding sub urban area which was mainly located flat areas or along the
transportation lines. The area of Settlements doubled over the past two decades, increasing from 12.5% of the study area
in 1987 to 23.5% in 2011. The correlation analysis in Tokyo shows strong, negatively linear relationship between the
Settlements change and cropland change (r = - 0.78), suggesting that the vast area of cropland area have been converted
to Settlements during the last two decades. In the next step, we will analyze the other 49 cities using 1-km2 grid cell approach and calculate the correlation coefficient matrix between the changes of land cover categories from 1985’s -
2007’s for each cities. Furthermore, we expect to compare and contrast the rates and patterns of expansion, and drivers of
land cover change in 50 cities.
The Moderate Resolution Imaging Spectroradiometer (MODIS) data offers a unique combination of spectral, temporal, and spatial resolution in comparison to other global sensors. The MODIS Enhanced Vegetation Index (EVI) product has several advantages, which make it suitable for regional land cover mapping. This paper investigates the application of MODIS EVI time-series data for mapping temperate arid and semi-arid land cover at a moderate resolution (500 m), for regional land-cover/land-use monitoring purposes. A 16-day composite EVI time-series data for 2003 (22 March 2003 - 30 September 2003) was adopted for the study. A land cover map was generated for the Inner Mongolia Autonomous Region using 7 tiles of MODIS EVI time-series data and Self-Organizing Map (SOM) neural network classification. Land-use GIS data, Landsat TM/ETM, and ASTER data were employed as reference data. The results show that the overall accuracy of land cover classification is about 84% with a Kappa coefficient of 0.8170. These results demonstrate that the SOM neural network model could work well for the multi-temporal MODIS EVI data, and suggest a potential of using MODIS EVI time-series remote sensing data to monitor desertification in Inner Mongolia with limited ancillary data and little labor-input in comparison with using high-spatial resolution remote sensing data.
With urban and township development and E-Government program promotion in China city remote sensing as base data has developed rapidly. The technique demands in accuracy and effective edge detection and extraction from higher resolution image become important focal area. In the current popular image processing software packages there are some existing edge detection convolution kernels suchc as Sobel, Robert, Prewitt, Kirsch, Gauss-Laplace kernels. In general the kernels all work based on algorithm of convolution kernel in spatial territory of the image. However, satellite sensors capture spatial and spectral signatures of surfaces at same time. Use of both spatial and spectral features to establish a edge detection process is a new notion for achieving more accuracy results. In the paper we introduce a spatial and spectral integrated method which is designed in four stages. The result suggests that four stages process can achieve more cleanly and accuracy edges of city construction than that results of using other algorithms. The procedure is summarized in figure 1.
IHS transform was one of typical method for remote sensing data fusion. In recent years, newly developed method that combines advantages of IHS and Wavelet algorithms makes image fusion. In this case after the Wavelet substitution based on pixels or features, and then transforms inversely with IHS in Munsell color space. In this paper we introduce a high frequency substitution method to improve spatial resolutions of imagery. The procedure of the method introduced as flowchart, in which the dot line area is our newly added method. The resolution was greatly improved comparing original image. In cooperating with the demand of on going Minjiang river, Si Chuan, China. A 15m resolution PAN band and 30m resolution 7 bands of ETM data were selected for the method testing, the steps of method test showing in flow chart of this paper. In the future the dots area was our newly developed wavelet high frequency substitute. Improved NDVI imagery raised the quality for monitoring land cover change factor in the project of Return Farmland Back to Forest or Grassland.
For the traditional method of hyper-plane segmentation, the location of hyper-plane in data space was given by statistical method. In the case of the statistical value of regions is smaller than in the region, the statistical method was not effective. The character of genetic algorithm is global searching optimally. Taken this mathematical advantage the location of Hyper-plane could be located easily. In this paper, EOS/MODIS imagery data is used to test this method. The result is proved that Genetic Algorithms-Hyper-plane is better than MLC method by using same training data.
In recent years, the Asian dust storm project was carried out. One of tasks was to study dust rising mechanism in dust source area. Surface temperature condition was regarded as one of the important factors for dust rise. In the study we retrieved surface temperature by using NOAA/AVHRR data. Basedon the published articles, traditionally, split window algorithm was use to deriving surface temperatures in the case of our study area mostly desert area, there was only three field observation data available in Talimu basin, at Dunhuang and Changwu. It was very difficult to validate the results. However, there were 52 county wearther observation stations in the area. The data might be used as import data in artificial neural network calculation. Most success examples of remote sensing data classification by using neural networks were in the condition of network training and classifying in the same types of data such as spatial data. For the use different data type collected by different techniques system such as satellite system and ground weather observation data to training, to find rule and to direct classification could be more impersonal which was one of the nature of artifical neural network method. In our case 52 weather temperature data were used from 52 observation stations where they were also the same positions for collecting AVHRR 1b data CH2, CH4, CH5 thermal data. Both groups of data were applied as fundamental import data in for artificial neural network calculation. Finally resultant rule was applied for classifying 15000 x 3 pixels in the whole area. The result was more reliable than that of split window not only because uncertainty caused by variations of topography but also it was very difficult to validate in field.
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