<p>A machine vision method based on an eigenvalue realization algorithm was proposed to determine the FY-4 satellite structural modes of vibration. An experiment was designed to prove the feasibility of the proposed method. The experiment results show that the first mode 0.32 Hz and the second mode 1.57 Hz of the FY-4 satellite structure were both clearly distinguished from the background by the proposed method. The comparison of the results of the proposed method and that of simulation and gyroscope testing shows that the error rate of the proposed method is below 0.1% for the first mode and below 6% for the second mode, which is feasible for engineering applications.</p>
The Wenchuan earthquake was a deadly earthquake that occurred on May 12, 2008, in Sichuan province of China. With the help of classic statistic methods, including arithmetic mean, standard deviation and linear trend estimation, vegetation restoration was recognized by analyzing spatio-temporal features of normalized difference vegetation index (NDVI) before and after this earthquake. Results indicate: (1) spatial distribution of NDVI mean values remains similar from 1998 to 2011. Higher values are mainly found in north, whereas lower values are mainly distributed over southeast, which is in good correlation with elevation and landform. Vegetation damage is at different levels in different seismic intensity (SI) regions: the higher SI is, the worse vegetation damage is. (2) Over the whole region, standard deviation is bigger after earthquake than before. Both absolute and relative changes in ecosystem stability increase with increasing SI. In different counties, variation of ecosystem stability is more obvious after earthquake, increase of standard deviation is approximately 6.5 times. Relatively, vegetation regionalization is the smallest analysis unit. Consequently, changes resulting from earthquake are unobvious. (3) Linear trend estimation coefficient increases from 0.0079 before the earthquake to 0.0359 after the earthquake in this whole region. This indicates that the plant ecosystem is rapidly restored between 2009 and 2011. The biggest linear trend is for the hill region, indicating good plant restoration and increase after earthquake. Fluctuation of linear trend estimation coefficient in different counties is more obvious after earthquake. Vegetation restoration after earthquake is most obvious in the regions that suffered the greatest SI (SI10 and SI11). In contrast, fluctuation in linear trend estimation coefficient of annual NDVI mean value for different classes of vegetation is more obvious before earthquake.
Wetlands are among the most important ecosystems on Earth and the major feature of the landscape in almost all parts of
the world, it provides a wide range of ecological regulation services and is very important to the atmospheric humidity in
a region. In order to recognize the influence of wetland on the atmospheric humidity, this paper takes Beijing-Tianjin-
Tangshan region as the study object and quantitatively analyzes their relationship with the help of MODIS satellite
images, monitoring data observed with the ground weather stations, wetland data, and other land surface data. This
research finds that (1) Globe temporal feature of atmospheric humidity is closed to the seasonal change and alternation.
(2) Globe spatial feature of atmospheric humidity is influence by the landform, land cover type. Because study region
adjoins to sea, it is influenced by the distance away from coastline. (3) Wetland's spatial distribution influences with the
spatial distribution of atmospheric humidity. With the distance away from wetland is bigger and bigger, atmospheric
humidity is less and less. However, its change trend is mild.
In this paper, the big coal mining area Yanzhou is selected as the typical research area. According to the
special dynamic change characteristic of the environment in the mining area, the environmental dynamic changes are
timely monitored with the remote sensing detection technology. Environmental special factors, such as vegetation, water,
air, land-over, are extracted by the professional remote sensing image processing software, then the spatial information is
managed and analyzed in the geographical information system (GIS) software. As the result, the dynamic monitor and
query for change information is achieved, and the special environmental factor dynamic change maps are protracted. On
the base of the data coming from the remote sensing image, GIS and the traditional environment monitoring, the
environmental quality is appraised with the method of indistinct matrix analysis, the multi-index and the analytical
hierarchy process. At last, those provide the credible science foundation for the local environment appraised and the
sustained development. In addition, this paper apply the hyper spectrum graphs by the FieldSpec Pro spectroradiometer,
together with the analytical data from environmental chemical, to study the growth of vegetation which were seed in the
land-over consisting of gangue, which is a new method to study the impact to vegetation that are growing in the soil.
Take the Dawenhe River for an example, based on the dynamic analysis of the suspended mudsand flow where the river pushes into the Dongpinghu Lake, according to different sensors and multi-temporal remote sensing images, this paper discusses its impacts on the lake deposition near the Dawenhe River estuary, and points out the cause of forming and development of the delta. On the foundation of the data from experiments at lab and the field test outdoors, this paper analyses the relation between the content of mudsand and max wavelength by the high spectrum, and together with the relativity between the consistency of the mudsand and the satellitic remote sensing image, more precisely quantitative formula is achieved.
With the growing development of the computer, Remote Sensing (RS) and Geographical Information System (GIS), multi-data fusion is becoming more and more important in data processing. In this paper, we are trying to fuse RS images of different time, different resolutions with geophysical data such as aero magnetic data and gravitational data and geochemical data of Au, Ag, Cu, As, Pb and Zn elements. By processing RS images, we get the surface lithological and structural information relating to the gold forming. By processing the geophysical data and geochemical data, we get the information about the gold distribution as well as the environment and geological factors controlling the gold under the ground. By the fusion of all these data, we get both the gold information hiding in the surface and under the ground. After all these work, we use GIS to manage, analyze and display all these results, whether they are raster data, vector data or attribute data. On the basis of all these, we finally define the hopeful areas of the gold that serve as valuable bases for gold exploration.
Nansihu lakes are important water conservancy hinge on the east line project of Translating South Water to North. In this paper, associating with the environment investigation and estimation on the south west of Shandong, we have a dynamic detection on their water quality and water areas with remote sensing covering more than 1300 kilometers. Using the data getting at low water time (Mar.28.2001 CBERS-CCD) as well as at abundant water time (Sep.6.1994 TM), combining with sampling test data on the spot and other remote sensing information, we have an iterative classification on images getting at different time. The result shows that the difference of regional water areas above the Second Level Dams of Nansihu lakes is about 169 square kilometers between low water time and abundant water time. From the images, three classes of water can be separated, including great area wet land. The area of the first class water is 13.3 square kilometers, the second class is 102.7 square kilometers, and the third class is 345.5 square kilometers. All these classes have an obvious relativity with the chemical indexes of water pollution, such as Biology Oxygen Demand (BOD) and Dissolved Oxygen (DO) and Suspended Substance (SS), etc.