Spatial-temporal characteristics of siping urban expansion based on remote sensing and GIS

Based on the RS and GIS methods, Siping city is selected as a study case with four remote sensing images in 25 years. Indices of urban morphology such as fractal dimension and compactness are employed to research the characteristics of urban expansion. Through digital processing and interpreting of the images, the process and characteristics of urban expansion are analysed using urban area change, fractal dimension and compactness. The results showed that there are three terms in this period. It expended fastest in the period of 1979~1991, and in the period of 1992~2001, the emphases on urban redevelopment made it expended slower. And this is in agreement with the Siping Statistical Yearbook. This indicates that the united of metrics of urban morphology and statistical data can be used to satisfactorily describe the process and characteristics of urban expansion. © 2008 IEEE.

Study of remote sensing based parameter uncertainty in Production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.

SATELLITE REMOTE-SENSING PREDICTION OF JAPANESE PILCHARD FISHING GROUND IN THE HUANGHAI SEA AND THE EAST-CHINA-SEA

The thermophily, fishing season and central fishing ground of Japanese pilchard (Sardinops melanosticta) were studied by using satellite remote sensing (SRS) and other methods in Haizhou Bay and Tsushima waters during 1986-1990. A rapid prediction method of fishing ground is presented. Moreover, the results indicated that the thermophilic values of the fish stock are 11-20 degrees C and both fishing grounds are in increasing temperature process from the beginning to the end of the fishing period. The Japanese pilchards gather vigorously at the sea surface temperature of 15-17 degrees C. The water temperature is a key factor affecting the fishing season and the catch of the fishing ground. The increasing temperature process restricts the fishing season development and central fishing ground formation. The accuracy of 15 predictions made in the Haizhou Bay fishing ground is up to 91.3%, and 37 predictions made in the Tsushima, fishing ground shorten the fish detection time by 13.4% - 22% on the average.

Application of effective medium approximation theory to ocean remote sensing under wave breaking

Based on the effective medium approximation theory of composites, the empirical model proposed by Pandey and Kakar is remedied to investigate the microwave emissivity of sea surface under wave breaking driven by strong wind. In the improved model, the effects of seawater bubbles, droplets and difference in temperature of air and sea interface (DTAS) on the emissivity of sea surface covered by whitecaps are discussed. The model results indicate that the effective emissivity of sea surface increases with DTAS increasing, and the impacts of bubble structures and thickness of whitecaps layer on the emissivity are included in the model by introducing the effective dielectric constant of whitecaps layer. Moreover, a good agreement is obtained by comparing the model results with the Rose's experimental data.

A new wind vector algorithm for C-band SAR

Ocean wind speed and wind direction are estimated simultaneously using the normalized radar cross sections or' corresponding to two neighboring (25-km) blocks, within a given synthetic aperture radar (SAR) image, having slightly different incidence angles. This method is motivated by the methodology used for scatterometer data. The wind direction ambiguity is removed by using the direction closest to that given by a buoy or some other source of information. We demonstrate this method with 11 EN-VISAT Advanced SAR sensor images of the Gulf of Mexico and coastal waters of the North Atlantic. Estimated wind vectors are compared with wind measurements from buoys and scatterometer data. We show that this method can surpass other methods in some cases, even those with insufficient visible wind-induced streaks in the SAR images, to extract wind vectors.

A new empirical model for retrieving sea surface salinity in L band

Sea surface salinity is a key physical parameter in ocean science. It is important in the ocean remote sensing to retrieve sea surface salinity by the microwave probe technology. Based on the in situ measurement data and remote sensing data of the Yellow Sea, we have built a new empirical model in this paper, which can be used to retrieve sea surface salinity of the Yellow Sea by means of the brightness temperature of the sea water at L-band. In this model, the influence of the roughness of the sea surface is considered, and the retrieved result is in good agreement with the in situ measurement data, where the mean absolute error of the retrieved sea surface salinity is about 0.288 psu. This result shows that our model has greater retrieval precision compared with similar models.