Image Processing Reference
In-Depth Information
500 m/pixel, and 1 km/pixel MODIS NDVI datasets, and obtained similar weak
correlations with land cover type even at smallest spatial scale of analysis. In
choosing to focus on relatively high spatial resolution ASTER data for landscape
metrics, and high temporal resolution MODIS data for biophysical parameters,
we highlight the particular strengths of each dataset for urban/peri-urban analysis
and monitoring. We conclude however that standardized, remotely sensed datasets
with high spatial, spectral, and temporal resolution will be required to meet the
challenge of understanding, and perhaps more importantly predicting, local to
global ecosystem change due to urban expansion and development.
Chapter Summary
New high spatial, temporal, and spectral resolution remotely sensed data
have sparked a renewed interest in the investigation of physical, climatic,
and social processes associated with human-dominated systems., i.e. cities.
The interdisciplinary nature of such research likewise encourages the creative
use of tools and data from different disciplines and sources. Integration of
remotely sensed and ancillary geospatial data for highly-accurate land cover
classification can be easily performed using an expert systems approach. An
expert land cover classification system was built using ASTER data and land
use information for the Phoenix, Arizona, USA metropolitan area. Landscape
spatial structure for the Phoenix area was obtained using several landscape
metric algorithms. Spatial metrics used include Class Area, Mean Patch
Size, Edge Density, and the Interspersion and Juxtaposition Index. Linkages
between urban spatial structure and biophysical parameters (albedo, fraction
of photosynthetically active radiation, leaf area index, day/night surface
temperature, and the normalized difference vegetation index) obtained from
MODIS were investigated using linear regression of gridded landscape metric
data. Our results indicate some control of these biophysical parameters by
urban/peri-urban landscape structure. The correlations are not strong however,
and may reflect both the spatial heterogeneity of the Phoenix metropolitan
region and the relatively low variance of the MODIS data over the urban/peri-
urban region at the 1 km/pixel scale.
The approach and results we present are of use to urban ecologists and land
planners, as landscape structural analysis and measures of ecosystem function
provide useful monitoring tools for regional habitat and climatic alteration
associated with urbanization. Use of the uniform spatial reference systems
provided by remotely sensed data in comparative studies permits quantitative
evaluation of the configuration of existing developed and open space. This
could improve the scope of usually small-scale and project-related analyses
of local environmental change, and thus represents an important tracking
system for regional planning and investigation of the ecological effects of
increasing global urbanization.
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