Liu Suhong

Study on the spectral response of Brassica Campestris L . leaf to the copper pollution

Brassica Campestis L. was cultivated in the soil at the laboratory. The red edge, the visual spectrum and the near-infrared spectrum of Brassica Campestis L. leaf were used to explore the spectral response of Brassica Campestis L. leaf to the copper stress. As the Cu content in the soil gets increased, the copper level in Brassica Campestris L. leaf would be increased, and the chlorophyll level in Brassica Campestris L. leaf would be decreased. As a result, the visual spectral reflectance (A1) of Brassica Campestris L. leaf is increased, and the blue-shift (moving towards the shorter waveband) degree (S) of the red edge (the ascending region of the reflectivity at 680–740 nm) gets increased. However, the near-infrared spectral reflectance (A2) decreases. With the correlation coefficient R2 more than 0.95, these parameters of A1, A2 and S can be perfectly used to simulate and predict the copper level in Brassica Campestris L. leaf.

Moderate fraction snow mapping in Tibetan Plateau

The spatial distribution of snow cover area is a crucial input to models of hydrology and climate in alpine and other seasonally snow covered areas. The objective in our study is to develop a rapidly automatic and high accuracy snow cover mapping algorithm applicable for the Tibetan Plateau which is the most sensitive about climatic change. Monitoring regional snow extent requires higher temporal frequency-moderate spatial resolution imagery. Our algorithm is based AVHRR and MODIS data and will provide long-term fraction snow cover area map. We present here a technique is based on the multiple endmembers approach and by taking advantages of current approaches, we developed a technique for automatic selection of local reference spectral endmembers

A study on the remote sensing information model about the water pollution caused by mine tailings

The mine tailings, which are generated during the mine development, are normally piled along the bank of the river. They will produce the pollution of the river while being exposed in the air and interacting with the precipitation. They will ultimately inpour into the waters around the mine zone and result in the environment degradation. This paper summarizes the study on the remote sensing information model to describe the water pollution caused by the mine tailings considering the following critical parameters: the terrain factor, sand and metal ion, the impact on the spectrum of the typical surface features. A case study against the tailings piles in this De Xing Copper Mine, demonstrated the practicability and the maneuverability of this model.

Study of remote sensing index indicators about the mine environment evaluation

The mine development becomes somewhat a dilemma. On one hand, it provides the resources people need, while on the other hand, it results in the environmental problem and people are suffering from the diseases caused by the degradation of the environment. The situation is getting so bad in the areas with higher severity of the environment pollution that it becomes difficult in providing the drinkable water and food supplies and even worse, the endemics and the genetic diseases come on. Therefore, it becomes more and more important about how to perform the environment surveillance. One of the solutions is to figure out the range of the environmental changes based upon the investigation and the analysis about the series of the index indicators about the environmental changes. With the research conducted at Dexing Mine, Jiangxi Province, the following parameters are defined in this paper as the index indicators: the water color, the Mine acreage and the mineral residues locations, and the variation of the plant coverage within 1 Km around the water system. The methods to calculate these index indicators are also illustrated in this paper, which are based on the primary geographical information and the remote sensing data. In addition, the spatial characteristics of these index indicators are finely analyzed. All these information are helpful to the risk analysis about the mine development.

A method for estimating chlorophyll content of wheat from reflectance spectra

Chlorophyll and carotenoid, relating to the physiological function of leaves, are two pigments which can absorb the light energy during the process of plant photosynthesis. Among the pigments, the chlorophyll plays an important role in the photosynthesis, and its content, as a predictor of the nutritional status of vegetation, is one of the main factors to evaluate the environment and growth conditions for the winter wheat. This paper, based on the reflectance spectra of wheat in Xiao Tangshan County, in China, took PLS regression as the quantitative inversion method to have established the hyperspectral inversion model between chlorophyll content and the reflectance spectra of wheat. Through analysis, it indicated that the chlorophyll content of wheat was highly relative to the reflectance of hyperspectral from 350 nm to 1060 nm. The correlation coefficient between the prediction value and the measured value is as high as 0.9, and the RMSEP is lower than 0.4. The research provided an effective method to estimate the chlorophyll content using the quantitative inversion technology of hyperspectral RS

The quantity analysis method research of oil and gas geo-anomaly information mining

During oil-gas exploration, much information is collected concerning geo-physical exploration, geo-chemical exploration, remote-sensing and geology exploration. Depending on the traditional exploration methods, it is difficult to make further progress with the deep step in oil exploration, so new theories and methods are urgently needed. Geo-anomaly theory was first put forward by Zhao Pengda, a Chinese math geologist, in 1991. It has been widely applied in solid mineral exploration. In this research, it is promoted in oil and gas exploration. How to mine valuable oil and gas geo-anomaly information and knowledge from all collected data is a big problem. From this point of view, taking oil and gas exploration in one basin in east China as an example, research has been done on mining oil and gas geo-anomaly information using data mining technology. During the research, some new mining methods such as block convulsion filtering, BP neural network and grey prediction are applied besides entropy, and complex methods. Research shows that the block convulsion filtering method is beneficial in removing noise from the data, and grey prediction methods are better ways to extract geo-anomaly information from drilling and seismic data, the BP network method is used well in predicting reserve and cover layer parameters with high accuracy, complex and entropy methods make a great effect in predicting the oil and gas preferable area. Through the above research on oil and gas geo-anomaly information mining, good results have been achieved in quantity methods for oil and gas geo-anomaly information extraction. The research contributes a lot to get effective exploration knowledge and predict preferable objects for oil and gas exploration.

Standard format design and input realization of measured spectral data in the spectral knowledge base

The spectral knowledge base (spectrum library) has been designed to contain over 10,000 pieces of measured spectral data, including spectral data of reflectivity and a large amount of metadata which is composed of data explanation and self-contained parameters. The standardization of the spectral data format based on the storage organization of background database, and the quickness of the data population become critical in developing applications, such as data browse, data query and the model simulating, etc., which are all based on this mass database. In an attempt to better represent the logical relationship among metadata and to simplify the edit and modification of data files, a 3-tier file system built up by several text files with frame description is defined in this paper as the standard data after the study on XML-based expression of geo-spatial metadata. Those files vary according to the types of the ground objects and fulfill two scales of matching among spectral data perfectly. Based on the concept of Windows DNA and techniques of look-up table, COM and ASP, a mechanism is proposed here to check the data format and upload the data into the database in batch, which is implemented primarily in B/S mode with the aid by executing the applications at the client side. For the sake of the integrity about the data population solution, it is also illustrated in this paper about the data batch processing

Algorithm and visual realization of auto-retrieving the new and old sheet numbers of topographic maps

On the basis of comparing the difference between those two map numberings, This work presented a particular algorithm of auto-retrieving the new and old sheet numbers of a certain assigned region and of a certain assigned scale, that is, basing on scale of 1:1000000, we enumerated all the four kinds of distributions on its sheet partition in terms of the longitudes and latitudes at the top-left and bottom-right corners of the assigned rectangular region, and then refined the sheet partition mentioned above according to actually scale we used. Thus, a duplicate traversal throughout all map sheets of assigned scale covered by the assigned region had been accomplished; meanwhile, all desired sheet numbers were obtained. Furthermore, with VB (Visual Basic) and MO (MapObjects), a software with spatial query interface had been developed and put in use as a visual realization of this algorithm. With it, users can quickly get all sheet numbers of certain regions just by selecting them with the mouse. Results show that this application brings average users great convenience in topographic map researching.

Study on data models of image quality assessment for the Chinese-Brazil Earth Resources Satellite

Analysis on Image Characteristic is a key step to understand and make good use of remote sensing image. Data models were employed to analyze for image quality of the Chinese-Brazil Earth Resources Satellite (CBERS-2) and LANDSAT/TM in Li-jiang region, Yunnan province. They include normalized model, univariate image statistics model, the ratio of inner and mutual variance model and spatial resolution model. The normalized model can be used to convert data into same physical quantity in order to make data be comparability. The other models can be used to open out the classified capability and information of data in supervised classification. With the study on data model, a method to analyze and assess remote sensing data was tried to bring forward

MODIS fraction snow mapping

The current MODIS snow-mapping algorithm uses a binary classification, i.e. a pixel will be identified either as snow or non-snow. A common problem with a binary classifier is the snow area changes a lot with the selection of the threshold value of pixel to be classified as snow. In this paper, we present a new way for 500 m MODIS fraction snow mapping based on the high resolution ASTER data. The ASTER and MODIS data are obtained simultaneously and have the similar illumination and sensor viewing geometry because the instruments are on the same platform. As to a series of temporal MODIS data, there is ASTER data capable of being the reference for the particular time period. First the MODIS and ASTER data received for the same date are co-registered with high accuracy, then each MODIS sub-pixel composition can be calculated using ASTER supervised classification results. The local snow, vegetation and rock end-members can be recognized from the MODIS data which is identified as the homogenous area with comparison to the classification result of the ASTER data. A group of end-members can be used for fraction snow mapping for the particular temporal MODIS data. In addition, snow library spectra is used for depressing the terrain effect and the bands selected for ASTER classification to avoid the problem of the sensor saturation.