China-Brazil Earth Resources Satellite Research Articles

Monitoring sandy desertification of Otindag Sandy Land based on multi-date remote sensing images

Sandy desertification is now the main ecological problem in the Otindag Sandy Land. In order to reveal the process of land degradation, especially the latest situation of sandy desertification, a method integrating remote sensing, Geographic Information System (GIS) and field survey was employed to build a sandy desertification dataset for analysis. Remote sensing images included the Landsat Thematic Mapper (TM) image in 1987, the Enhanced Thematic Mapper Plus (ETM +) image in 2000, and the image with the Charge-Coupled Device Camera (CCD) on the China-Brazil Earth Resources Satellite (CBERS) in 2006. Five land-cover classes, including active sand dunes, fixed sand dunes, semi-fixed sand dunes, inter-dune grassland and wetlands, were identified. Results showed that the Otindag Sandy Land has been suffering sandy desertification since 1987 with 2 different desertified stages. The first stage from 1987 to 2000 was a severe sandy desertification period, characterized by the fixed sand dunes decreasing at a high speed, and the semi-fixed and active sand dunes increasing remarkably. The second stage spanned from 2000 to 2006 and the sandy desertification was weakened greatly. Although a large area of fixed sand dunes were transformed to other types, fixed sand dunes were still the dominant type in the Ointdag region at 2006. Spatial change detection based on active sand dunes showed that the expansion area was much larger than the reversion area in the past two decades, and that several active sand belts had been formed, suggesting that sandy desertification controlling of the Otindag Sandy Land will be a long-term task.

GLOBAL BURNED-LAND ESTIMATION IN LATIN AMERICA USING MODIS COMPOSITE DATA

This paper presents results of the AQL2004 project, which has been develope within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLatif). The project intended to obtain monthly burned-land maps of the entire region, from Mexico to Patagonia, using MODIS (moderate-resolution imaging spectroradiometer) reflectance data. The project has been organized in three different phases: acquisition and preprocessing of satellite data; discrimination of burned pixels; and validation of results. In the first phase, input data consisting of 32-day composites of MODIS 500-m reflectance data generated by the Global Land Cover Facility (GLCF) of the University of Maryland (College Park, Maryland, U.S.A.) were collected and processed. The discrimination of burned areas was addressed in two steps: searching for "burned core" pixels using postfire spectral indices and multitemporal change detection and mapping of burned scars using contextual techniques. The validation phase was based on visual analysis of Landsat and CBERS (China-Brazil Earth Resources Satellite) images. Validation of the burned-land category showed an agreement ranging from 30% to 60%, depending on the ecosystem and vegetation species present. The total burned area for the entire year was estimated to be 153 215 km2. The most affected countries in relation to their territory were Cuba, Colombia, Bolivia, and Venezuela. Burned areas were found in most land covers; herbaceous vegetation (savannas and grasslands) presented the highest proportions of burned area, while perennial forest had the lowest proportions. The importance of croplands in the total burned area should be taken with reserve, since this cover presented the highest commission errors. The importance of generating systematic products of burned land areas for different ecological processes is emphasized.

Desempenho do satélite sino-brasileiro de recursos terrestres CBERS-2 no mapeamento da cobertura da terra no Distrito Federal, Brasil

O sucesso do lançamento de dois satélites, a garantia de continuidade desse programa por parte dos governos federais envolvidos e a disponibilização dos seus dados sem custos na rede mundial de computadores fizeram com que o CBERS-2 (Satélite Sino-Brasileiro de Recursos Terrestres, China-Brazil Earth Resources Satellite ) se tornasse uma excelente opção para os usuários em diversas aplicações de dados ópticos de sensoriamento remoto com resolução espacial intermediária. Nesse estudo, dados multiespectrais e multitemporais do CBERS-2 do Distrito Federal (DF) foram processados para analisar o potencial para discriminar classes representativas de cobertura da terra. Para isso, foram analisadas estatisticamente 101 amostras sobre áreas representativas de vegetação natural (formações herbáceas, arbustivas e arbóreas) e uso antrópico (áreas urbanas, reflorestamentos e culturas agrícolas). Resultados dessa pesquisa demonstraram o potencial dos dados do CBERS-2 para discriminar classes da cobertura da terra do DF e uma possível limitação para monitorar as suas variações sazonais devido à cobertura de nuvens persistente na maioria das imagens obtidas em 2005.

UM MODELO PARA ORIENTAÇÃO DE IMAGENS ORBITAIS BASEADO EM FEIÇÕES RETAS

The aim of this paper is to present a model for orientation of pushbroom sensors that allows estimating the polynomial coefficients describing the trajectory of the platform, using linear features as ground control. Considering that pushbroom image acquisition is not instantaneous, six EOP (Exterior Orientation Parameters) for each scanned line must be estimated. The sensor position and attitude parameters are modeled with a time dependent polynomial. The relationship between object and image space is established through a mathematical model based on the equivalence between the vector normal to the projection plane in the image space and to the vector normal to the rotated projection plane in the object space. The equivalence property between planes was adapted to consider the pushbroom geometry. Some experiments with simulated data corresponding to CBERS scene (China-Brazil Earth Resource Satellite) were accomplished in order to test the developed model using straight lines. Moreover, experiments with points ground with the model based on collinearity equations adapted to the pushbroom geometry were also accomplished. The obtained results showed that the proposed model can be used to estimate the EOP of pushbroom images with suitable accuracy.

Multispectral image data fusion using POCS and super-resolution

The problem of image data fusion coming from different sensors imaging the same object is to try to obtain a result that integrates the best characteristics of each one of those sensors. In this work, we want to combine the characteristics of multispectral (better spectral definition) and panchromatic (better space definition) images, using the bands from the satellites Landsat-7 (panchromatic) and CBERS-1—China-Brazil Earth Resources Satellite (four multispectral bands). The process proposes solutions using projection onto convex sets (POCS) techniques divided in two steps: (a) interpolated image processing, regularizing the block artifacts and using super-resolution techniques based on POCS and (b) synthesis, obtained by sequential and parallel projections or by the least squares method.

A novel algorithm of multi-sensor image fusion based on wavelet packet transform

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform to obtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I’Observation de la Therre) image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM (Histogram Matched)-based fusion algorithm and WT-based fusion approach.

In‐flight absolute calibration of the CBERS‐2 IRMSS sensor data

Since the first images from the sensors onboard CBERS‐2 (China–Brazil Earth Resources Satellite) were made available by the National Institute for Space Research (INPE), users have requested information about the conversion of image digital numbers into physical data (radiance or reflectance). This Letter describes the main steps that were carried out to calculate the in‐flight absolute calibration coefficients for CBERS‐2 Infrared Multispectral Scanner (IRMSS) level 2 (radiometric and geometric correction) images considering the reflectance‐based method. Remarks about the preliminary evaluation of the application of these coefficients are also presented.

Extract enclosure culture in lakes based on remote sensing image texture information

Most lakes in eastern China are exploited in the form of enclosure culture. The high density enclosure culture area is easy to cause lake eutrophication and deteriorate the water quality. So the lake enclosure culture area is focused by people and is usually extracted by using the multi-temporal remote sensing data and the multi-spectral classification or the manual digitization means. The multi-spectral classification is easy to confuse enclosure culture area with the natural water area and the manual digitization demands much time. This paper suggests a new method using the image texture information to extract lake enclosure area. The result is more accurate and the method demands less expense. Lake Baima is the study area and the image of the area is multi-spectral data of China Brazil Earth Resources Satellite 02. First the water area、the man-made area and the bare soil which has the similar spectral characteristics with the water are extracted by using the normalized difference water index. Then the principal component analysis was used to compress the multi-spectral data of the study area and enhance the texture information of the image. The third step Grey Level Co-occurrence Matrix (GLCM) was used to analyze the image, select a proper threshold of mean value which quantifies the GLCM. Finally, the lake enclosure culture area was extracted by using the decision tree.

Study of thin cloud removal method for CBERS-02 image

The China Brazil Earth Resources Satellite (CBERS) ended the dependence on foreign satellites. Along with the series of CBERS and the enhancement of satellite applications, it is necessary to search correlative data processing methods. Because cloud appears in the atmosphere, an image with clear sky conditions is often hard to obtain. How to remove cloud is an important sector of increasing image quality. An improved homomorphism filtering method is adopted to remove thin cloud qualitatively at first, and all bands are selected to classify pixels. Based on the fact that visible band images contain more aerosol effects than infrared bands, visible bands are used to determine clear or hazy regions and the reflectances of different surface classes in clear regions are calculated. At last the mean reflectance of the same classes in clear and hazy regions is matched to remove cloud. The image visual effect is improved; meanwhile, the ground reflectance is retrieved to meet the needs of remote sensing quantification.

Study on the relative radiometric correction of CBERS satellite CCD image

The main payload on CBERS-01/02 of China-Brazil Earth Resources Satellite (CBERS) is a push-broom CCD camera with moderate spatial and radiant resolution. Because at lab the data for calibration at satellite assembly stage were unable to be collected, and also because the onboard calibrator after launch was in a different state from imaging, the calibration of CCD image got a series of difficulties involved. In practice, two methods are used in the processing on the ground station: One is extracting calibration data by statistics from the image itself, and the other is the method of histogram match. It was proved that the latter can calibrate the image much better, because it can remove the effect of unstable response of the camera largely and also can overcome the nonlinearity of the camera basically by using Look-Up Table (LUT) calculated from histogram statistics of different temporal images. Considering the problems of CBERS-01, a lot of calibration tests were done before the launch of CBERS-02, in which a set of lab coefficients for relative calibration was formulated after the data collection by using integration-hemisphere in the stage of satellite assembly test. During the on-orbit test, it was found that the calibration result from such coefficients was not satisfying, especially there being response difference between 3 detector arrays, which was attributed to the unstable dark currents of the CCD camera. This paper comes up with a statistic method to remove such response difference. In this method the middle detector array was used as reference to find the response differences of adjacent similar features between these arrays and it was proved to have a broad adaptability.

Software Frame Synchronizer and Related Technology Research of Moving Window Display for CCD Data of CBERS-2

处理速度一直是软件帧同步的瓶颈所在,通过引入KMP和Rabin-Karp算法,寻找到一种高效的帧同步处理算法,提高了处理速度,最终设计完成了通用的卫星软件帧同步器,并进行了相关验证测试和分析,给出了分析比较结果.在分析中巴资源卫星1号02星数据格式的基础上,利用所设计的软件帧同步器和DirectX显示技术,实现了一套针对资源卫星双通道CCD传感器的全软件移动窗显示系统,并对该卫星数据的图像交叠等现象作了相应处理.这套软件帧同步器和移动窗显示系统可以作为开发卫星接收记录系统的重要组成部分.

SPACE SIMULATION OF THE CHINA-BRAZIL EARTH RESOURCES SATELLITE – CBERS

Space Simulation Tests are performed in spacecraft in order to verify equipment proper operation under thermal vacuum conditioning and to verify the correct workmanship in the assembling of the flight spacecraft as a whole. This paper presents the space simulation (thermal vacuum test) developed in the China Brazil Earth Resources Satellite, Flight Model no. 2, that took place at the Integration and Tests Laboratory – LIT, INPE. Measuring approximately 1.8 x 2.0 x 2.2m, weighting 1,500 kg and carrying three cameras as the main payload, this spacecraft is scheduled to be launched in China. The spacecraft was installed in the 3x3m space simulation chamber and the tests run 24 hours a day completing a total of 350 hours. Using the technique of skin-heaters complemented by the thermal vacuum chamber thermally conditioned shrouds and cold plates, dedicated heat inputs and heat sink where applied at the spacecraft surfaces in order to obtain the required high and low acceptance values of temperature and, subsequently, simulating the operational conditions for the necessary electronic subsystems functioning tests of the spacecraft. This test campaign included teams from both China and Brazil, summing a total of 67 people directly involved.

SPACE SIMULATION OF THE CHINA-BRAZIL EARTH RESOURCES SATELLITE – CBERS

Space Simulation Tests are performed in spacecraft in order to verify equipment proper
operation under thermal vacuum conditioning and to verify the correct workmanship in
the assembling of the flight spacecraft as a whole. This paper presents the space simulation
(thermal vacuum test) developed in the China Brazil Earth Resources Satellite, Flight
Model no. 2, that took place at the Integration and Tests Laboratory – LIT, INPE. Measuring
approximately 1.8 x 2.0 x 2.2m, weighting 1,500 kg and carrying three cameras as the
main payload, this spacecraft is scheduled to be launched in China. The spacecraft was
installed in the 3x3m space simulation chamber and the tests run 24 hours a day completing
a total of 350 hours. Using the technique of skin-heaters complemented by the thermal
vacuum chamber thermally conditioned shrouds and cold plates, dedicated heat inputs
and heat sink where applied at the spacecraft surfaces in order to obtain the required high
and low acceptance values of temperature and, subsequently, simulating the operational
conditions for the necessary electronic subsystems functioning tests of the spacecraft.
This test campaign included teams from both China and Brazil, summing a total of 67
people directly involved.

Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification

This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network classifier and its application to the land cover classification of remotely sensed multispectral imagery. First, the key issues of the algorithm and the general procedures are described in detail. Our methodology adopts a real coded GA strategy and hybrid with a back propagation (BP) algorithm. The genetic operators are carefully designed to optimize the neural network, avoiding premature convergence and permutation problems. Second, a SPOT-4 XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also involved for a comparison purpose. Based on an evaluation of the user’s accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for simple land cover classification using multispectral imagery data is established. Thirdly, a more complicate experiment on CBERS (China–Brazil Earth Resources Satellite) data and discussion also demonstrates that carefully designed genetic algorithm-based neural network outperforms than gradient descent-based neural network. This has been supported by the analysis of the changes of connection weights and biases of the neural network. Finally, some concluding remarks and suggestions are also presented.

Assessing the economic impacts of the China-Brazil resources satellite program

This article evaluates and analyzes economic impacts of Brazilian space activities in the local equipment suppliers, taking the case of the international cooperation program between Brazil and China for the development of two medium-sized remote sensing satellite CBERS (China-Brazil Earth Resources Satellite). This program was a landmark for the satellite's industrial capability-building policy conducted by the INPE (National Space Research Institute). The article uses a microeconomic approach, which aims to evaluate ex-post indirect impacts resulting from different kinds of learning obtained during the program execution. The results allow an international comparison and offer a systematic picture of the main difficulties and challenges that face local suppliers' networks of high-tech products. Copyright , Beech Tree Publishing.

An evaluation of Jacchia and MSIS 90 atmospheric models with CBERS data

“CBERS” (China Brazil Earth Resources Satellite) was put into orbit on 14 th of October 1999. The atmospheric drag is the major non-gravitational perturbation affecting the control of ground track. As accuracy requirements increase, greater reliance is placed on the empirical techniques. During the initial operational phase of CBERS, the solar activity is almost at its peak. This phenomenon has provided an opportunity to carry out an evaluation of the atmospheric density models. The study puts emphasis on two commonly used atmospheric density models viz. Jacchia and Mass Spectrometer Incoherent Scatter (MSIS). The analysis is based on the decay histories of CBERS to choose the accurate density model. Density models used for orbit propagation are usually derived empirically from actual flight data. Brief synopsis of some of the models is presented along with some of the density tables and orbit solutions of CBERS. Typical plots of density are presented. The study indicated that the drag estimation is relatively precise using MSIS based models. Among them MSIS-90 density model is observed to be a better compromise in terms of accuracy, flexibility and computational aspects. The analysis would be useful in mitigating the impact of solar activity on orbit prediction and maintenance.

Extraction of Lake Information from Nanjing Image through China-Brazil Earth Resources Satellite No.1 (CBERS-1)

中巴地球资源一号卫星(CBERS-1)是我国的第一颗地球资源卫星,它的发射成功及运行,解决了长期依赖国外卫星数据的缺陷.本文简要介绍了CBERS-1的传感器类型,并利用CBERS-11999年11月29日过境的南京景数据,根据图幅特点,将水系信息尤其是该区内的湖泊信息加以提取,进行分析,较为详实的分析了区内湖泊所面临的环境问题.;CBERS-1(China-Brazil Earth Resources Satellite No.1)is the first earth resources satellite of China.Its successful emission and running overcome the long-term shortage of depending on foreign satellite data.This paper briefly introduces the types of sensor of the CBERS-1.Then, based on the image of Nanjing area on 1999.11.29, the water system information, especially lake information is extracted and analyzed according to the characteristics of this area. Furthermore, the environment problems of this area facing with are fully and accurately analyzed.

CBERS — An international space cooperation program

China Brazil Earth Resources Satellite (CBERS) is a joint program under development by Brazil and China since 1988. The aim of the program is to develop a family of 1.5 ton class multipurpose remote sensing satellites and related ground facilities to supply both countries with multispectral earth remote sensing images. To comply with this objective a binational organization was established involving Brazil and China space national bodies and related industrial organizations. CBERS is an innovative joint program between two developing countries involving high technology. The development of the program was postponed by its complexity, new methodologies and logistics problems. The purpose of this paper is to present a programmatic content with an overview of the program, the development activities and its management specially related to assembling, integration and tests (AIT) issues. The methodology is to present the results achieved in the shared AIT activities, the status of the program, the engagement to comply with the program requirements and a conclusion about spin off and the return of experience.

The Brazilian scientific microsatellite SACI-1

The National Space Research Institute (INPE) is developing the first Brazilian Scientific Microsatellite (SACI-1) based on the vanguard technology and on the experience acquired through projects developed by Brazilian Space Program. The SACI-1 is a 750km polar orbit satellite. The spacecraft will combine spin stabilization with geomagnetic control and has a total mass of 60 kg. The overall dimensions are 640×470×470 mm. The SACI-1 satellite shall be launched together with CBERS (China-Brazil Earth Resource Satellite). Its platform is being designed for multiple mission applications. The Brazilian Academy of Sciences has selected four scientific payloads that characterize the mission. The scientific experiments are: ORCAS (Solar and Anomalous Cosmic Rays Observation in the Magnetosphere), PLASMEX (Study of Plasma Bubbles), FOTSAT (Airglow Photometer), and MAGNEX (Geomagnetic Experiment).

Development of China-Brazil Earth Resources Satellite (CBERS) and Establishment of China Center for Resources Satellite Data and Application (CRESDA)

(1992). Development of China-Brazil Earth Resources Satellite (CBERS) and Establishment of China Center for Resources Satellite Data and Application (CRESDA) Chinese Journal of Population Resources and Environment: Vol. 1, No. 1, pp. 61-62.