The mid-latitude arid and semi-arid area is one of the major surface types in China, and is facing the serious desertification hazard. Landsat Thematic Mapper data were used in this paper to monitor the vegetation cover change in east part of the Hunshandake sandy land, a semi-arid region in Inner Mongolia Autonomous Region. Combined with the monthly rainfall and air temperature data, the possible affecting factors are discussed. The NDVI in middle September are 0.47, 0.67, 0.65 and 0.33 in 1987, 1996, 1998and 2001, respectively, which highly correlated both with the annual mean rainfall and precipitation of July and August, indicating moisture is one of the most important factors affecting vegetation condition in arid and semi-arid region. The air temperature shows little effect on NDVI, but the region with higher NDVI often associatted lower radiative temperature derived from TM band 6. There are some area showed vegetation degradation which could not be recovered in a short time of period, most of them located in poor vegetated areas. And the degraded area is increasing from 1996 (3.8%) and to 1998 (6.0%). The vegetation condition shows improvement in 2001, and this may be associated with the effort made by Chinese government to improve the ecological and environment situation of this region. This indicates that anthropogenic activities is another improtant factor affecting the NDVI in arid and semi-arid regions, especially in the ecologically more sensitive areas.
Snow parameters, such as snow extent, snow depth and snow water equivalent are essential not only in understanding of land surface processes which is the basis of climate model but also in snow disaster assessment. Passive microwave remote sensing has advantages in retrieving these parameters,especially snow depth. However, this kind of technique has not been applied to monitoring snow in Tibetan Plateau so far. So we tried to monitor snow operationally in this area by means of SSM/I data since last winter, providing the local governmental sector with daily snow depth map. In the meantime,the in-situ snow depth data in Tibetan Plateau were collected to validate the retrieval algorithm employed in this study. In this paper, SSM/I images before and after a heavy fall of snow are analyzed and compared with MODIS images .The results show thatthe snow extent from SSM/I data is consistent with that from MODIS data, and that snow depths from SSM/I are very helpful for local snow assessment though SSM/I derived snow depth is significantly overestimated compared to in-situ data. With its retrieval algorithm being improved, passive microwave remote sensing has less effect of atmosphere and cloud and hence will be the most important tool in monitoring snow in Tibetan Plateau, especially when the new data of AMSR-E on board Aqua satellite are available.
In order to observe fine structures of rainfall, the rainfall radar reflectivity data with a high range resolution of 125 m were obtained by using a ground-based X-band meteorological radar. Using these high resolution radar data, several parameters within simulated radar spatial smoothing windows whose sizes correspond to the resolutions of ground-based and spaceborne radars, such as rain nonuniformity strength,the spatial smoothing error of Z-R relationship based radar rainfall measurement, etc., were computed.The possible correlations between these parameters were also analyzed. The results show that, the small-scale rain nonuniformity is significant, and even in a spatial smoothing window of 1 km size, the reflectivity excursion (defined as the difference between maximum and minimum) above 10 dB is common.The significant rain nonuniformity results in that, the rainrate smoothing error cannot be neglected, and even for a smoothing window of 0.5 km size which is similar to resolutions of ground-based radars, can reach up to above 30%. Comparisons of the smoothing errors computed from radar data with between the 125 m high resolution and the 1 km low resolution shows that the smoothing errors from the 1 km resolution data would be underestimated. The underestimation would be about 25% for smoothing windows of 4 and 3 km, and about 40% for that of 2 km.
The HY-1 satellite is the first ocean satellite of China for detecting ocean color and sea surface temperature. The main sensors on board include 10-band Chinese Ocean Color and Temperature Scanner(COCTS) and 4-band CCD imager. According to different data characters of two remote sensors, this paper introduces their typical applications in the Oceans respectively. The article briefly introduces application algorithms and its productions also, and some results are given. The scope of typical applications introduced included ocean pollution and ocean disaster detecting, sea ice and sea surface temperature forecasting, study on change of coast zone, information on ocean fishery environment and Ocean Primary Productivity study. There achieved better effects in daily operation using some application products of them, it represents HY - 1 carried its point as an experimental satellite. The typical applications of HY-1 satellite data provide valuable scientific references and experience for developments of successive ocean satellites in China.
A new method of hyperspectral anomaly detection is presented in this paper. As a kind of multivariate data, the points of hyperspectral data always stay in a hyper-plane in high-dimensional space.More researches show that the points of anomaly spectral and noise stay out of the configuration. This geometrical feature is useful for small targets detection. We can calculate the normal vector of hyper-plane and project all anomaly pixel vectors to the normal direction, thus we can segment the anomaly target from the hyper-plane. The method adapts to detect small targets in complicated scene. We design a simulation experiment to verify the algorithm and apply it to real data, and get satisfying results.
In this paper, a new data subspace partition method is proposed for reduction of hyperspectral image dimensionality. This method involves three steps: subspace partition of whole data space, feature extraction based on principal component analysis (PCA) in subspace and feature selection based on class separability criterion. The main merits of the proposed method are that it much more makes full use of neighboring correlation of hyperspectral data bands than those existing methods, and it realizes automatic subspace partition. In order to testify the effectiveness of the proposed method, classification experiments of hyperspectral images are conducted on AVIRIS data. The experiment investigation shows that the
classification result in our new method is improved compared with both segmented principal component transformation ( SPCT ) and adaptive subspace decomposition method ( ASD ). When the data dimensionality is reduced 90% by using the proposed method, the overall classification accuracy of nine classes of ground cover reaches 80.2%.
As a comprehensive technology, remote sensing is more and more widely used in the constructing fields of national economy and defence. Especially in the applications of martial information scouting, the enemy objects information of posture, orientation, geometrical sizes and physical characteristics can be got by processing, recognizing and computing of the color sensing images from them. The information supplies the important basis for fight command, enemy object recognition and strike result evaluation. It is a complicated course to analyze and process the color remote sensing images and object extraction is an important part of it. Aiming at the characters of color remote sensing images, traditional methods of
boundary detection, for example the arithmetic of Laplace, Sobel, Hough and so on, are applied for the typical example of remote sensing images to process at first in this paper and their deficiencies in use are illustrated by the examples. Then, Author adopts the fuzzy cluster method (FCM) and selects the appropriate color characters to segment the object areas from the images effectively and quickly. The investigation provides the reliable foundation for remote sensing images recognition and computation.Finally, the effectiveness of the method is demonstrated by the examples. The investigation of the paper was partially used in the strike result evaluation model of some type of weapon, and the result was satisfied with the requirement.
Stripe noise is one of the most important factors, which influence the qualities of MODIS 1B images. In this paper, the main causes and the characteristics of the stripe noises in MODIS 1B images are deeply analyzed. To remove the stripe noises, three kinds of algorithms, the improved Fourier Transform,Wavelet Transform and the Interpolation Algorithm, are developed based on the above analysis. On academic level, the abilities to remove the stripe noises and their limitations are compared. Analyzed from the processed results, the three kinds of algorithms can more or less remove the stripe noises in MODIS 1B images, but have great differences among them: the improved Fourier Transform has more influences on
non-noises region than the other two and the Interpolation Algorithm has the less negative influences on MODIS 1B images in removing the stripe noises. Based on the comparation of the mean value, standard deviation and the edge information of the images processed by above three kinds of algorithms, the conclusion that in removing the MODIS stripe noises, the improved Interpolation Algorithm is the best,and the Wavelet Transform is better than the Fourier Transform can be made.
Classification is always an important research orientation of remote sensing. In This paper, we extract endmeber pixed information at first, then use artificial neural network (SOFM) algorithm to classify the rest pixels, and estimate the percent of the different land use from Landsat ETM image in the Heihe River Basin. ETM were orthorectified using a digital photogrammetric software package with ground control points collected through differential GPS. We make a topographical and atmosphere correction and got fractions of land use (water (f1), vegetation (f2), and soil (f3), building (f4)) from land use map. We extract the endmember information from the image and classify the rest pixels.Experimental results indicate that classification by SOFM is better than the supervised classification by comparing with the land use statistics.
Multi-sensor synchronization is a question that must be solved for development of airborne remote sensing. Two methods were put forward by the authors to synchronize multi-sensor with GPS receiver. The first is to use the UTC time of the GPS receiver to synchronize multi-sensor, and the second is to divide the outputting frequency synchronized with the 1PPS pulse on the GPS receiver to synchronize multi-sensor.
The NASA' s Earth Science Enterprise(ESE) is introduced and analyzed in this paper. ESE aims at scientific understanding of the Earth system and increasing the duration and accuracy of the forecasts in weather, climate and natural hazards, and answers a series of science questions by observing(especially satellite observation), modeling the processes controlling the Earth system. The ESE defines the aims of science, application and technology in the next ten years separately, and arranges different satellite missions according to different science questions. The characteristics of the ESE are summarized at the end of the paper.
GIS is a very complicated and costly information system which involves multi specialty subjects.To construct a well-performed, robust, scalable and easily-maintained GIS application, it' s necessary to use methodologies of the Software Engineering to organize and manage the constructing process. There are two main software engineering methodologies, the structured methodology and the Object-oriented methodology, in the development of the GIS applications. They differed from each other very much on the basic concept. As a significant methodology of the Software Engineering after the presence of the structured software process, the Unified Software Process, which is derived from the Object Oriented Software Methodology, has been used widely and increasingly in the modern software engineering practices, including the GIS development, by means of absorbing and developing the distilments of the OOmethodology. Meanwhile, the UML-Unified Modeling Language, as a successful standard system
modeling language, has also been used in all phases of the whole developing process of software. As a result, it will cause huge impacts on the developing methodologies of GIS application. Tries to expound and approach a most important and current software methodology, namely the Unified Software Process,by one real developing instance. It will be somewhat valuable for the practical developing of GIS.
Geographical Information System (GIS) is a computer system for obtaining, storing,manipulating, analyzing, displaying data that are spatially referenced the Earth, and is increasingly widely used in many fields. With the development of GIS technology, database technique will become one of the key techniques for promoting the suitability of any geographical information system. As one of the core database techniques, data dictionary technique will play an important role in the development operation of the system. In this paper, the concept of data dictionary set and dictionary program, as well as the application of data dictionary technique to ArcView GIS were discussed according to the status quo of data dictionary researches. The paper defined basic data dictionary set as the collection of dictionaries of field attributes, codes, relations and files, which were handled by dictionary programs. Taking the dictionary programming as the interface for database operation in the system application program, we present a few cases to show how the data dictionary technique be used in improving the flexibility and suitability of database management in the system. As a case study on employing data dictionary technique, we found that it was more easier to maintain and update the Forestry Statistics extension in ArcView GIS, which in turn became more adaptive to new circumstances in the future.
A review is presented for development of red tide detection methods with satellite remotely sensed data. The algorithms for remote sensing of red tide are described, which is based on bio-optical principles including use of variation velocity of chlorophll a concentration and sea surface temperature change as well as the multi-spectral water leaving radiance provided by spaceborne sensors.
Image texture analysis has received a considerable amount of attention over the last few years as it has played an important role in the classification of the remote sensing images. Here introduced the texture analysis methods used for remote sensing images and made them categorised and sorted. The categories include: statistical method, structural method, model based method and mathematical transform method. Then it presented the texture analysis methods of every category, enumerated the texture feature respectively and introduced the advantage, disadvantage and adaptability of these methods.These methods include: grey level co-occurrence matrices method, the grey level run length method, autocorrelation method, Markov random field method, fractal model, power frequency method, wave-let transform method, first order histogram method, third order histogram method, grey level difference vectors, filters of space field, texture spectrum method, prospectrum, autoregressive model, binary stack method and logic operators. After that, it made comparison of classification performance via these methods. Finally, it analysed the trend of remote sensing texture analysis in the recent years and bring forward some prospects.
