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Sadhana Jain Divyani Kohli Ram Mohan Rao Wietske Bijker 《Journal of the Indian Society of Remote Sensing》2011,39(2):203-212
In recent years, there has been lot of emphasis on the study of urban land use/ land cover changes to discover the growth
pattern due to rapid urbanisation. This study presents spatial metrics and gradient analysis approach for quantifying and
capturing changes in urban landscape using LISS III imagery of 1999, 2001 and 2004 of Gurgaon, India. A combination of spatial
metrics i.e. percentage of landscape, mean patch size, number of patches, landscape shape index and largest patch index, available
in Fragstats ver. 3.3, have been used to quantify the patterns of urban growth in different directions in terms of size, shape
and complexity of development. The local built-up areas were quantified by the “moving window” technique. A gradient analysis
has been carried out through sampling from a reference point to 8 km in 16 directions with a window size of 500 mts. Results
of this study demonstrate the potential of spatial metrics and gradient modelling to quantify the impact of regional factors
on the growth pattern of Gurgaon city. 相似文献
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E.W. Bijker 《Coastal Engineering》1992,17(3-4)
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Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images 总被引：1，自引：0，他引：1
Juan P. Ardila Valentyn A. Tolpekin Wietske Bijker Alfred Stein 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):762-775
Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches. 相似文献
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The year 2010 through 2011 witnessed a number of disasters such as floods in Pakistan and Eastern Europe and earthquakes in Chile, China and Haiti. In response, earth observation (EO) data, geographic information science (GIS) technologies and services were used to provide information before, during and after the disaster occurred. However, use of EO for disaster management still faces a number of challenges due to the lack of common established standards for producing disaster products, the lack of coordination between a large number of suppliers leading to a large collection of datasets on websites of coordinating agencies and the lack of an established framework for monitoring and authenticating the level of quality and reliability of the products delivered to the targeted users. Assessing the quality of such products is a challenge to any potential user of such datasets. The methodology presented here integrates the role of EO expert and targeted end-user into one model where the first phase involves the expert and the second phase the end-user. The expert handles the technical and expertise aspect of EO data by rating the level of conformance of a product to the parameters of a ??quality information template?? (QIT), and the end-user explores various rated datasets and sets preferences for decision-making based on this QIT. The end-user has the possibility of accessing the product through an interactive web platform. The preferences set are used for weighing and ranking for the combination of the potential datasets and the task to be performed. 相似文献
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Martin Yemefack Wietske Bijker Steven M. De Jong 《International Journal of Applied Earth Observation and Geoinformation》2006
This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM+ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM+ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM+ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape. 相似文献
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