Urban-Rural Environmental Gradient in a Developing City: Testing ENVI GIS Functionality

International audienceThe research performs urban ecosystem analysis supported by ENVI GIS by integrated studies on land cover types and geospatial modeling of Taipei city. The paper deals with the role of anthropogenic pressure on the structure of the landscape and change of land cover types. Methods included assessment of the impact from anthropogenic activities on the natural ecosystems, evaluation of the rate and scale of landscape dynamics using remote sensing data and GIS. The research aims to assist environmentalists and city planners to evaluate strategies for specific objectives of urban development in Taiwan, China. The study aims at the assessment of the landscape development and environmental analysis of the Taipei city based on GIS and remote sensing methods. Methodology includes analysis of the environmental settings of the Taiwan region (land cover characteristics, climatic and topographic settings) and anthropogenic factors of the social landscapes. Research objectives included GIS based geo-visualization, spatial analysis of the raster data, i.e. satellite images and. Different factors involved in the landscape formation were studied and analyzed, both individually and in combination. The paper studies urban processes in Taipei in general and how GIS and remote sensing data can be used for the spatial analysis. The research deals with classification and analysis of the selected landscapes in Taiwanese ecosystems, and suggest development of the effective methods for wetlands assessment using a multi-metric approach. The research included analysis of the degree of landscapes disturbance according to the calculated metrics index values for specific land-use categories within the landscapes. The interplay between both parts of the ecosystem, i.e. the anthropogenic impacts of the global city on the one side, and ecological patterns and processes on the other, is aimed to achieve balanced mutual coexistence within the global ecosystems. Modified land use types affect hydrological components in the surrounding watersheds and results in changes of the land use patterns, namely, landscape composition, configuration, connectivity, variety and abundance of patch types within a landscape, which are the primary descriptors of the landscape pattern. The complex functioning, structure and interrelationships of the landscape components in urban ecosystem are largely influenced by the anthropogenic activities, inasmuch as the population concentrate in the metropolises and large cities. The location of Taipei in the southern subtropical humid climatic zone largely influences its environmental settings. Various disasters and hazards associated with specific climate settings such as torrential rains, occasional floods, rainstorms, seasonal typhoons have serious impacts and environmental consequences on the city functioning. Frequent and intense typhoons together with stormy rains and floods are natural disasters specific for Taiwan. Consequences of hazards include significant property damage, destroyed farmlands, crashed roads, partially or completely destroyed houses, human injures, diseases and lives losses. Urban landscapes of Taipei are being formed during long historical process, as a result of the ongoing entangled interactions between natural forces and anthropogenic social factors.Methodologically, current study is focused on technical approach of raster data processing. There are numerous classification algorithms and techniques that determine natural spectral groups from the initial pixels sets: Parallelepiped classification, Neural Nets, Decision Trees, Mahalanobis Distance, Minimum Distance, and Maximum Likelihood classifiers, ISOCLUST, K-means. Usually, it is not easy to decide, which classifier method is a priori the best cartographic solution for actual research problem, due to different factors that vary significantly: characteristics of raster images, mapping scales, specific situation of the study area, reflectance properties of the local land cover types, landscape structure and heterogeneity.Results: A report on the statistics was generated and recorded as an 'Error Matrix', 'Kappa Statistics' and 'Accuracy'. Afterwards, to visually inspect classification process, the selected areas with the most diverse landscape structure and high heterogeneity of the land cover types were verified and validated by the overlapping of the Google Earth using activated function “connect to Google Earth”. The “Link Google Earth to View” and “Sync Google Earth to View” options enabled to synchronize the view areas between the Google Earth and current image view. This enabled to check up highly heterogeneous areas where questions about land cover type arose. The land cover types were visually assessed and identified. The resulting clusters enabled to analyze spectral and textural characteristics of the land cover types in the selected area. Based on cluster segmentation, homogenous land cover classes were differentiated