基于QuickBird影像的郑州市城区景观格局评价

以2005年8月QuickBird卫星遥感数据解译的郑州市城区土地利用类型和城市绿地覆盖景观类型图为基础,利用ArcView和Fragstats软件,对全城区及不同功能区土地利用景观、城市绿地覆盖景观指数进行分析。结果表明,郑州市城区景观格局主要表现为城市绿地景观,面积占总面积的35.51%,未利用地景观面积最小,只占到0.33%。城市绿地覆盖在空间形态上接近圆形,平均面积小、景观破碎化程度高。工业用地和居住区用地空间形态接近长方形。城区东北部和西南区域土地利用表现为以道路为骨架的格网状空间结构。景观格局水平分布表现出明显的区域差异,城区西北区域景观异质最高,东部城市绿地覆盖景面积较西部多,存在少数斑块控制整个景观的特点。     

Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK

This paper presents an application of object-oriented techniques for habitat classification based on remotely sensed images and ancillary data. The study reports the results of habitat mapping at multiple scales using Earth Observation (EO) data at various spatial resolutions and multi temporal acquisition dates. We investigate the role of object texture and context in classification as well as the value of integrating knowledge from ancillary data sources. Habitat maps were produced at regional and local scales in two case studies; Schleswig-Holstein, Germany and Wye Downs, United Kingdom. At the regional scale, the main task was the development of a consistent object-oriented classification scheme that is transferable to satellite images for other years. This is demonstrated for a time series of Landsat TM/ETM+ scenes. At the local scale, investigations focus on the development of appropriate object-oriented rule networks for the detailed mapping of habitats, e.g. dry grasslands and wetlands using very high resolution satellite and airborne scanner images. The results are evaluated using statistical accuracy assessment and visual comparison with traditional field-based habitat maps. Whereas the application of traditional pixel-based classification result in a pixelised (salt and pepper) representation of land cover, the object-based classification technique result in solid habitat objects allowing easy integration into a vector-GIS for further analysis. The level of detail obtained at the local scale is comparable to that achieved by visual interpretation of aerial photographs or field-based mapping and also retains spatially explicit, fine scale information such as scrub encroachment or ecotone patterns within habitats.     

Using satellite imagery to assess plant species richness: The role of multispectral systems

Question: The use of variations in the spectral responses of remotely sensed images was recently proposed as an indicator of plant species richness (Spectral Variation Hypothesis, SVH). In this paper we addressed the issue of the potential use of multispectral sensors by testing the hypothesis that only some of the bands recorded in a remotely sensed image contain information related to the variation in species richness. Location: Montepulciano Lake, central Italy. Methods: We assessed how data compression techniques, such as Principal Component Analysis (PCA), influence the relationship between spectral heterogeneity and species richness and evaluated which spectral interval is the most adequate for predicting species richness by means of linear regression analysis. Results: The original multispectral data set and the first two non-standardized principal components can both be used as predictors of plant species richness (R²∼ 0.48; p < 0.001), confirming that PCA is an effective tool for compressing multi-spectral data without loss of information. Using single spectral bands, the near infrared band explained 41% of variance in species richness (p < 0.01), while the visible wavelengths had much lower prediction powers. Conclusions: The potential of satellite data for estimating species richness is likely to be due to the near infrared bands, rather than to the visible bands, which share highly redundant information. Since optimal band selection for image processing is a crucial task and it will assume increasing importance with the growing availability of hyperspectral data, in this paper we suggest a ‘near infrared way’for assessing species richness directly from remotely sensed data.