Ordinary mod p representations of the metaplectic cover of p-adic SL_2

We classify the genuine ordinary mod p representations of the metaplectic group SL(2,F)-tilde, where F is a p-adic field, and compute its genuine mod p spherical and Iwahori Hecke algebras. The motivation is an interest in a possible correspondence between genuine mod p representations of SL(2,F)-tilde and mod p representations of the dual group PGL(2,F), so we also compare the two Hecke algebras to the mod p spherical and Iwahori Hecke algebras of PGL(2,F). We show that the genuine mod p spherical Hecke algebra of SL(2,F)-tilde is isomorphic to the mod p spherical Hecke algebra of PGL(2,F), and that one can choose an isomorphism which is compatible with a natural, though partial, correspondence of unramified ordinary representations via the Hecke action on their spherical vectors. We then show that the genuine mod p Iwahori Hecke algebra of SL(2,F)-tilde is a subquotient of the mod p Iwahori Hecke algebra of PGL(2,F), but that the two algebras are not isomorphic. This is in contrast to the situation in characteristic 0, where by work of Savin one can recover the local Shimura correspondence for representations generated by their Iwahori fixed vectors from an isomorphism of Iwahori Hecke algebras.

Land-cover changes in an urban lake watershed in a mega-city, Central China

Urbanization can exert a profound influence on land covers and landscape characteristics. In this study, we characterize the impact of urbanization on land cover and lacustrine landscape and their consequences in a large urban lake watershed, Donghu Lake watershed (the largest urban lake in China), Central China, by using Landsat TM satellite images of three periods of 1987, 1993 and 1999 and ground-based information. We grouped the land covers into six categories: water body, vegetable land, forested land, shrub-grass land, open area and urban land, and calculated patch-related landscape indices to analyze the effects of urbanization on landscape features. We overlaid the land cover maps of the three periods to track the land cover change processes. The results indicated that urban land continuously expanded from 9.1% of the total watershed area in 1987, to 19.4% in 1993, and to 29.6% in 1999. The vegetable land increased from 7.0% in 1987, 11.9% in 1993, to 13.9% in 1999 to sustain the demands of vegetable for increased urban population. Concurrently, continuous reduction of other land cover types occurred between 1987 and 1999: water body decreased from 30.4% to 23.8%, and forested land from 33.6% to 24.3%. We found that the expansion of urban land has at least in part caused a decrease in relatively wild habitats, such as urban forest and lake water area. These alterations had resulted in significant negative environmental consequences, including decline of lakes, deterioration of water and air quality, and loss of biodiversity.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

Industrial development and land use/cover change and their effects on local environment: a case study of Changshu in eastern coastal China

The interactions among industrial development, land use/cover change (LUCC), and environmental effects in Changshu in the eastern coastal China were analyzed using high-resolution Landsat TM data in 1990, 1995, 2000, and 2006, socio-economic data and water environmental quality monitoring data from research institutes and governmental departments. Three phases of industrial development in Changshu were examined (i.e., the three periods of 1990 to 1995, 1995 to 2000, and 2000 to 2006). Besides industrial development and rapid urbanization, land use/cover in Changshu had changed drastically from 1990 to 2006. This change was characterized by major replacements of farmland by urban and rural settlements, artificial ponds, forested and constructed land. Industrialization, urbanization, agricultural structure adjustment, and rural housing construction were the major socio-economic driving forces of LUCC in Changshu. In addition, the annual value of ecosystem services in Changshu decreased slightly during 1990-2000, but increased significantly during 2000-2006. Nevertheless, the local environmental quality in Changshu, especially in rural areas, has not yet been improved significantly. Thus, this paper suggests an increased attention to fully realize the role of land supply in adjustment of environment-friendly industrial structure and urban-rural spatial restructuring, and translating the land management and environmental protection policies into an optimized industrial distribution and land-use pattern.