Wenze Yue

Based on SPOT remote sensing images and GIS, choosing the central area of the external circle highway in Shanghai as a case study area, the paper studied the spatial scaling effect of the urban landscape pattern with different grains and extents. The conclusions are drawn as follows: (1) The spatial autocorrelation of urban landscape pattern depends on different scales within a certain range of scales, and Moran I and Geary c related to the grains, which characterized the spatial autocorrelation of the urban landscape structure, have the same sensitive points to the scaling at the level of 50 m. (2) The patches of all kinds of landscapes have the fractal character. The fractal dimensions of landscapes respond to scaling differently, and the present nonlinear change trends with grains. The fractal dimensions of landscapes are obviously different at a small grain, but the differences become not obvious with the increasing grain. (3) The landscape diversity are closely linked to the location and the pattern of human activities, especially to economic and social activities. Due to the high land cost in the urban center, the dominant landscapes are mainly for business and culture, and their patches have the characters of high congregation and high fragmentation. While agricultural landscapes with low economic benefit can only be located at the fringe or outskirt of the urban area, and they have the characters of simplex, larger patches and less fragmentation. (4) The landscape diversity depends on spatial scale. With the increasing extent, the Shannon diversity index (SHDI) increases and the spatial pattern of landscape varies dramatically. At 0.5 km extent, the maximum of diversity is in the center of the urban area. The landscape diversity is distributed with a ring mode up and down from the center to the outer. With the increasing extent, the maximum of diversity moves to the urban-rural transition zone where landscape types change dramatically. (5) The semivariogram discloses the spatial variance character and internal mechanism of landscape diversity. At a small scale, the spatial variance of diversity is more complicated. The spatial heterogeneity, which is caused by spatial autocorrelation, contributes a lot to the total spatial heterogeneity of terrestrial ecosystem, while the spatial heterogeneity caused by random factors (scale and measure error) contributes less. The increasing scale washes off the detailed variances in a fine scale. The coarse scale may result in more nugget effect and less contribution, which is caused by spatial autocorrelation.

Scale holds the key to understand pattern-process interactions, and indeed, becomes one of the corner-stone concepts in landscape ecology. Geographic Information System and remote sensing techniques provide an effective tool to characterize the spatial pattern and spatial heterogeneity at different scales. As an example, these techniques are applied to analyze the urban landscape diversity index, contagion index and fractal dimension on the SPOT remote sensing images at four scales. This paper modeled the semivariogram of these three landscape indices at different scales, and the results indicated that the spatial variance characters of diversity index, contagion index and fractal dimension were similar at different scales, which was spatial dependence. The spatial dependence was showed at each scale, the smaller the scale, the stronger the spatial dependence. With the scale reduced, more details of spatial variance were discovered. The contribution of spatial autocorrelation of these three indices to total spatial variance increased gradually, but when the scale was quite small, spatial variance analysis would destroy the interior structure of landscape system. The semivariogram models of different landscape indices were very different at the same scale, illuminating that these models were incomparable at different scales. According to above analyses and based on the study of urban land use structure, 1 km extent was the more perfect scale for Studying the spatial variance of urban landscape pattern in Shanghai. The spatial variance of landscape indices had the character of scale-dependence, and was a function of scale. The results differed at different scales we chose, and thus, the influence of scales on pattern could not be neglected in the research of landscape ecology. The changes of these three landscape indices displayed the regularity of urban spatial structure at different scales, i. e., they were complicated and no regularity at small scale, polycentric at moderate scale, and circle-zoning at big scale.

Abundance of vegetation plays an important role in urban ecosystem, urban planning and development. Traditional classification methods on remote sensing data by assigning each pixel membership in one, and only one have the primary shortcomings of their inability to accommodate spectrally mixed pixels in gradational land covers. The traditional classification methods are giving way to spectral mixture analysis (SMA) gradually which is better in acquiring quantitative information for specific land covers. Vegetation fraction, in a general way, is defined as the areal fractions of vegetation within each pixel. This paper, besides introducing the traditional technique of SMA, discusses the improvement of traditional technique from the aspects of data noise removal, least-squares solution with constraining sum of endmembers fractions to unit, pixel purity index and the selection of endmembers. LSMA is tested further with the Shanghai city as an example. Unmixing pixels with root mean square (RMS) error less than 0.02 accounts for the proportion of 98.5%. The spatial distribution of vegetation is corresponding to actual situation. Then we conclude that: the improved LSMA is appropriate for estimating quantitative vegetation fraction and the technique will be widely applied in urban environment.

Purpose–The purpose of this paper is to develop a simple, easy to implement powder delivery strategy for solid freeform fabrication (SFF) processing. Design/methodology/approach–A specially designed “tower nozzle” located at the center of the processing area dispenses the feedstock powders continuously and uniformly onto the processing area, where powders accumulate progressively as a flat powder bed. During the dispensing, powders are selectively consolidated by melting and solidification using a laser beam ...

Because of the multiplicity in spatial pattem and ecological pmcesses, scale plays an important role to understanding the pattem-proce interactions and, indeed, becomes one of the comer-stone in landscape ecology. Based on the research of Shanghai city, this paper studies the pattem characteritics at different grains by using the methods of landscape ecology; and the semi-variogram is used to analyze the pattern characters at different extents．The results of analysis indicate: (1) Different landscape indices responds dissimilarly to the changes of grains. (2) Spatial variation of landscape diversity shows the most complexity at the smallest extent．(3) The response of landscape indices and SHDI semi-variance to scle is respective. So landscape spatial pattem has scale dependency.

The relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) associated with urban land-use type and land-use pattern is discussed in the City of Shanghai, China using data collected by the Enhanced Thematic Mapper Plus (ETM+) and aerial photographic remote sensing system. There is an apparent correlation between LST and NDVI from the visual interpretation of LST and NDVI contrasts. Mean LST and NDVI values associated with different land-use types are significantly different. Multiple comparisons of mean LST and NDVI values associated with pairings of each land-use type are also shown to be significantly different. The result of a regressive analysis shows an inverse correlation relationship between LST and NDVI within all land-use polygons, the same to each land-use type, but correlation coefficients associated with land-use types are different. An analysis on the relationship between LST, NDVI and Shannon Diversity Index (SHDI) shows a positive correlation between LST and SHDI and a negative correlation between NDVI and SHDI. According to the above results, LST, SHDI and NDVI can be considered to be three basic indices to study the urban ecological environment and to contribute to further validation of the applicability of relatively low cost, moderate spatial resolution satellite imagery in evaluating environmental impacts of urban land function zoning, then to examine the impact of urban land-use on the urban environment in Shanghai City. This provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems.

It is very important to integrate remote sensing with urban geography that the spectral mixture analysis technique is applied to urban land cover evolvement and its eco-environmental effect. Urban land cover is mainly composed of complicated artificial materials, which is the key factor to limit the development of the spectral mixture analysis technique. There are two main aspects in which the technique of spectral mixture analysis is applied to urban geography: one is to calculate vegetation fraction; the other is to build quantitative model of the urban impervious surface obtained from the combination between high albedo fraction and low albedo fraction. The technique of spectral mixture analysis is firstly applied to study urban renewal pattern, scale and mode which happened in Shanghai City from 1997 to 2000.

Based on reviewing the origin, development and basic principles of Geostatistics, this article probed into two kinds of interpolation methods concretely: ordinary Kriging and Cokriging. As no single method among so many available ones to spatial interpolation of climate variables is optimal for all regions and all variables, the article probed into interpolation methods based on Geostatistics by using annual average precipitation and evaporation in Gansu province from 1961 to 1990. Based on different semivariogram theory models we adopt ordinary Kriging and Bivariate Cokriging, by comparing of them we could draw conclusions as fellows:  (1) No matter annual average precipitation or evaporation all presented obvious gradient change on space, the change ranges of both were great, the former was larger than the latter.  Annual average precipitation decreased gradually from southeast to northwestward, but evaporation was opposite, increased gradually from southeast to northwest. (2) According to semivariogram cloud plots and experiment variance minimum principle  selected suitable semivariogram theory models based on Geostatistics interpolation method to interpolate, which could simulate space pattern spreading continuously of regionalized variable well, then get better interpolation effect. Compared ordinary Kriging with Cokriging, as the latter input altitude which had an influence on precipitation and evaporation, it was more rational on space distribution and had a higher interpolation precision. (3) With Geostatistics methods the spatial interpolations could reflect the general space pattern of climate variables better in general, but the spatial interpolations precision of two methods were still not high, which still remained further improving.

"To monitor and model the evolution of urban landscapes, we develop a brightness–darkness–greenness (B–D–G) model. It is based on the vegetation–impervious surface–soil (V–I–S) model, proposed by Ridd (1995) to simplify urban environments to three basic ground components. The model integrates the knowledge of urban landscape composition and spectra of remote sensing. The B–D–G model is a fast and effective method to analyze urban landscape composition and its evolution based on remotely sensed images, by employing an explicit endmember evolution
implication via the endmember spectrum dynamics. We verify this new method through in situ measurements of spectrum and high resolution images. Then, B–D–G model is used to detect the pattern and types of urban renewal. Despite some limitations, B–D–G model provides a new perspective of modeling urban dynamics and monitoring urban landscape evolution."