Using Spatial Autocorrelation Techniques and Multi-temporal Satellite Data for Analyzing Urban Sprawl

Satellite time series offer great potential for a quantitative assessment of urban expansion, urban sprawl and for monitoring of land use changes and soil consumption. This study deals with the spatial characterization of expansion of urban areas by using spatial autocorrelation techniques applied to multi-date Thematic Mapper (TM) satellite images. The investigation focused on several very small towns close to Bari. Urban areas were extracted from NASA Landsat images acquired in 1976, 1999 and 2009, respectively. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and spatial autocorrelation techniques to reveal spatial patterns. Urban areas were analyzed using both global and local autocorrelation indexes. This approach enables the characterization of pattern features of urban area expansion and it improves land use change estimation. The obtained results showed a significant urban expansion coupled with an increase of irregularity degree of border modifications from 1976 to 2009

Remote sensing and the future of landscape ecology

Landscape ecology focuses on the analysis of spatial pattern and its relationship to ecological processes. As a scientifi c discipline, landscape ecology has grown rapidly in recent years, supported by developments in GIS and spatial analysis techniques. Although remote sensing data are widely employed in landscape ecology research, their current and potential roles have not been evaluated critically. To provide an overview of current practice, 438 research papers published in the journal Landscape Ecology for the years 2004–2008 were examined for information about use of remote sensing. Results indicated that only 36% of studies explicitly mentioned remote sensing. Of those that did so, aerial photographs and Landsat satellite sensor images were most commonly used, accounting for 46% and 42% of studies, respectively. The predominant application of remote sensing data across these studies was for thematic mapping purposes. This suggests that landscape ecologists have been relatively slow to recognize the potential value of recent developments in remote sensing technologies and methods. The review also provided evidence of a frequent lack of key detail in studies recently published in Landscape Ecology, with 75% failing to provide any assessment of uncertainty or error relating to image classifi cation and mapping. It is suggested that the role of remote sensing in landscape ecology might be strengthened by closer collaboration between researchers in the two disciplines, by greater integration of diverse remote sensing data with ecological data, and by increased recognition of the value of remote sensing beyond land-cover mapping and pattern description. This is illustrated by case studies drawn from Latin America (focusing on forest loss and fragmentation) and the UK (focusing on habitat quality for woodland birds). Such approaches might improve the analytical and theoretical rigour of landscape ecology, and be applied usefully to issues of outstanding societal interest, such as the impacts of environmental change on biodiversity and ecosystem services

Mapping Selective Logging in Mixed Deciduous Forest

Abstract This study assesses the performance of five Machine Learning Algorithms (MLAs) Sustainable Development of Forests). Monitoring programs cover large spatial extents, and require sizable quantities of remotely sensed data, thus presenting a unique set of data processing and image interpretation challenges. Aside from the large volume of data to be processed, most complications are related to the paucity of ground reference data caused by cost and time constraint