[Monitor of Cyanobacteria Bloom in Lake Taihu from 2001 to 2013 Based on MODIS Temporal Spectral Data].

Algal bloom highly impacts the ecological balance of inland lakes. Remote sensing provides real-time and large-scale observations, which plays an increasingly significant role in the monitoring of algal bloom. Various Vegetation Indices (VIs) derived from satellite images have been used to monitor algae. With threshold segmentation of VI, the area of algal bloom can be extracted from images. However, the result of threshold segmentation only reflects the condition of algae when images were generated. Compared to separated VI data obtained at a particular moment of time, temporal spectral VI data contains phonological information of algae, which may be used to evaluate algal bloom more accurately and comprehensively. This study chose MODIS NDVI data of the Lake Taihu from 2001 to 2013, and constructed temporal spectral data for each year. Then, we determined the feature temporal spectra of severe cyanobacteria bloom, moderate cyanobacteria bloom, slight cyanobacteria bloom and aquatic plants, and separated these four kinds of objects using SVM (Support Vector Machine) algorithm, getting the spatial distribution and area of them. In order to compare the results of our method with traditional threshold segmentation method, we chose 8 separated NDVI images from the temporal spectral data of 2007. With the threshold 0.2 and 0.4, cyanobacteria bloom was classified into three degrees: severe cyanobacteria bloom, moderate cyanobacteria bloom, and slight cyanobacteria bloom. By comparison, it showed that our method reflected cyanobacteria bloom more comprehensively, and could distinguish cyanobacteria and aquatic plants using the phonological information provided by NDVI temporal spectra. This study provides important information for monitoring the algal bloom trends and degrees of inland lakes, and temporal spectral method may be used in the forecast of algal bloom in the future.

[Study on hyperspectral response to surface alteration by oil and gas microseepage under loess area].

The present takes the Qingyang, Gansu as an example, based on the mechanism of oil-gas microseepage, the spectra of the loess samples were measured, and the contents of carbonate minerals, clay minerals, Fe2+ and Fe3+, were analyzed. From their contents, it was shown that the carbonate mineralization and the red fading phenomenon are obvious for the known oil-gas field. Meanwhile, the parameters of absorption spectra of the loess samples were identified after the continuum was removed. The multiple regression analysis between the spectrum parameters (independent variables) and the mineral contents (the dependent variables) was implemented. The results indicate that the absorption depth is more sensitive. Thus, by their spectrum absorption parameters, 14 spectra of loess samples were clustered into two groups: samples in the known oil-gas field and unknown oil-gas field.

[Vegetation stress spectra and their relations with the contents of metal elements within the plant leaves in metal mines in Heilongjiang].

In this paper, Duobao Mt. and Tong Mt. were taken as examples. The spectra of the crown or leaf of the vegetation were collected. Fourteen metal elements in the rock, soil (A, B, C) and vegetation (root, trunk, leaf), and biological chemical parameters were measured. It was indicated that different metal elements were selected and enriched in different vegetation. The red edge position (REP) and the absorbing depth are related to biological chemical parameters. A multivariable regression equation was built between the absorption depths and the contents of metal elements. The relative coefficients between the absorbing depths and chemical elements, including Co, Cu, N, Mo, Ag, Sb, W, Pb and As, are greater than 0.75. Thus, it is important to analyze and measure the contents of metal elements by hyper-spectral remote sensing of vegetation stress spectrum.

[Spatiotemporal differentiation of construction land expansion in a typical town of south Jiangsu Province].

Choosing Xinzhuang Town in south Jiangsu Province as study area, and by using 1980, 1991, 2001, and 2009 high-resolution remote sensing images and GIS spatial analysis technology, an integrated expansion degree index model was established based on the existing indicators of construction land expansion, and the general and spatiotemporal differentiation characteristics of construction land expansion in the Town in three time periods of 1980-2009 were quantitatively analyzed. In 1980-2009, with the acceleration of rural urbanization and industrialization, the area of construction land in the Town increased significantly by 19.24 km2, and especially in 2001-2009, the expanded area, expanded contribution rate, and expansion intensity reached the maximum. The construction land expansion had an obvious spatial differentiation characteristic. In 1980-1991, the newly increased construction land mainly concentrated in town area. After 1991, the focus of construction land gradually spread to the villages with developed industries. Most of the increased construction lands were converted from paddy field and dry land, accounting for 88.1% of the total increased area, while the contribution from other land types was relatively small.

[Spatiotemporal pattern of urban growth and its driving forces in urban agglomeration of central Liaoning Province, China].

Based on the five temporal Landsat TM remote sensing data of 1988, 1992, 1997, 2000, and 2004, and by using GIS spatial analysis and landscape pattern analysis, this paper analyzed the spatiotemporal pattern of urban growth and its driving forces in the urban agglomeration of central Liaoning Province (UACLP). From 1988 to 2004, the urban area in the UACLP had being increased from 812.55 km2 to 1345.86 km2, with an average growth rate of 32.96 km2 per year. The urban growth rate increased rapidly after 1997, and the urban growth intensity was up to the peak in 1997-2000. The urban growth was mainly concentrated in the central dense belt of the UACLP. From 1988 to 1997, the urban growth was relatively slow, its spatial pattern was compact, and edge growth and filling were the main urban growth types. From 1997 to 2004, the urban growth became faster with diffused spatial pattern and complex patch shape, and "frog leap" and diffusion were the main urban growth types. Non-agricultural population growth, economic growth, urban spatial mutual attraction, industrial development, and development zones construction policies were the main driving forces of urban growth in the UACLP.

[Spatiotemporal expansion of urban and rural built-up areas in Shenyang City: an analysis based on remote sensing and GIS technology].

By using 1985, 1995, 1997, 2000 and 2004 satellite images and GIS technology, three indices including built-up area density, expansion intensity index, and fractal dimension were chosen to analyze the spatiotemporal characteristics, spatial differentiation, and morphological changes of urban and rural built-up areas in Shenyang City in 1985-2004, with the main driving factors discussed. In the study period, the high-density area of urban built-up area in the City increased year by year, and that of rural built-up area changed slightly before 1997 but increased gradually thereafter. The increased area, expansion speed, and expansion intensity of built-up area were evidently greater in urban than in rural area. An obvious spatial differentiation was observed in the expansion of built-up area between urban and rural areas, with the high-speed expansion mainly concentrated in urban area. The fractal dimension in urban area increased gradually, which meant that the integrated configuration of urban area became more and more complex, while that in rural area changed irregularly, because of the lack of reasonable planning and construction. Economic development, population growth, transportation, natural environment, policy-guiding, and urban planning were the main driving forces of the expansion of built-up areas in Shenyang City.

[Simulation and prediction of urban and rural settlement growth and land use change in Yingkou City].

Based on the 1988, 1992, 1997, 2000, and 2004 Landsat TM remote sensing data of Yingkou City, Liaoning Province, the urban and rural settlement growth and land use change in the city from 2005 to 2030 were simulated and predicted by using the SLEUTH urban growth and land use change model with six scenarios (current trend scenario, no protection scenario, moderate protection scenario, managed growth scenario, ecologically sustainable growth scenario, and regional and urban comprehensive planning scenario). The results showed that in the city, the increased area of urban and rural settlement growth from 1988 to 2004 was 14.93 km2, and the areas of water area, orchard, mine, and agricultural land changed greatly from 1997 to 2004. From 2005 to 2030, based on ecologically sustainable growth scenario, the urban and rural settlement growth would have a slow increase, and agricultural land and forestland would be better protected; under no protection scenario, the urban and rural settlement growth would have a rapid increase, and large area of agricultural land would be lost; under current trend scenario, the agricultural land loss would be similar to that under no protective scenario, but the loss pattern could be different; under moderate protection scenario and managed growth scenario, the agricultural land would have a smaller loss; while under regional and urban comprehensive planning scenario, the urban and rural settlement growth would be mainly distributed in urban development area and urban fringe. The SLEUTH model with different scenarios could simulate how the different land management policies affect urban and rural settlement growth and land use change, which would be instructive to the coordination of Chinese urban and rural settlement development and the socialist new rural reconstruction.