System monitoring and diagnosing can be achieved through measuring sensing data.
The effect of buildings on storage and conveyance is modeled with a binary density function i x,y) that equals unity when (x,y) corresponds to a void, and nil otherwise, and can be measured using remote sensing data such as classified aerial imagery; the effect of buildings on flow resistance is modeled with a drag formulation.
The data used are from a 719-person survey of two shopping centers in Santa Barbara, California, specifically designed to measure sense of place.
In many cases, using remote sensing data to measure and monitor urban environmental conditions will be more straightforward than using them for urban planning purposes, where traditional sources of governmental and private sector data are more easily obtained and understood.
Wireless sensor networks (WSNs) emphasize data-centric approach for data transmission and the observer concerned merely indicator values measured within a monitored area rather than sensing data of some certain node.
Daily precipitation obtained from the National Aeronautics and Space Administration NASAandand Tropical Rainfall Measuring Mission (TRMM), provided remote sensing data in Port-au-Prince at a resolution of 0.25 × 0.25 degrees from October 1, 2010 to August 31, 2011 (see http://trmm.gsfc.nasa.gov/ for details).
GIS facilitates the interpretation of remote sensing data by linking biophysical information measured by remote sensing to maps of natural and cultural landscape attributes, to field measurements, and to socioeconomic information derived through household surveys.
For example, night-time light and remote sensing data are widely used to measure economic output [21 25] and poverty [41, 42].
Meaningful relationships between forest structure attributes measured in representative field plots on the ground and remotely sensed data measured comprehensively across the same forested landscape facilitate the production of maps of forest attributes such as basal area (BA) and tree density (TD).
High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species.
