Application of Spectral and Environmental Variables to Map the Kissimmee Prairie Ecosystem Using Classification Trees

GIScience & Remote SensingGIScience & Remote Sensing, Volume 48, Number 3 / July-September 2011

Sean Griffin, John Rogan, and Daniel Miller Runfola

“This paper compares a variety of classification tree-based approaches to map 10 vegetation cover classes and a single built-up class in the Kissimmee Prairie Ecosystem, an endangered grass-shrubland landscape in south-central Florida (USA). This comparison is provided to identify an effective and replicable mapping methodology and facilitate the ongoing regional-scale management and monitoring of grass-shrubland ecosystems. Results showed that the best-performing models included environmental variables, due to the ability of these variables to help distinguish spectrally similar classes. The highest overall proportional accuracy of 81% was the result of incorporating linear spectral mixture analysis and geo-environmental variables into the classification tree.”

GIS-based Identification of Spatial Variables Enhancing Heat and Poor Air Quality in Urban Areas

Applied Geography

Applied Geography, Volume 33, April 2012

H. Merbitz, M. Buttstädt, S. Michael, W. Dott, and C. Schneider

“Highlights

  • GIS-based modeling of urban particulate matter (PM) and temperature hot spots.
  • Identification of spatial variables influencing PM concentrations and temperature.
  • Delimitation of zones with high risk of coinciding air pollution and heat stress.
  • Health related chemical characterization of particulate air pollution (PM10, PM2.5).

“Due to anthropogenic climate change heat waves are expected to occur more frequently in the future, which might cause adverse health effects for urban population. Especially the combination of high temperatures and poor air quality impinges on the well-being of man. This accentuates the need for assessing the health risks of residents regarding air pollutants and anomalously high summer air temperatures. However, comprehensive information on the spatial and temporal distribution of temperature and particulate matter (PM) concentration in cities are presently difficult to obtain since only few measurement sites exist.

“In order to identify hot spots with high health risks for distinct groups of urban population, measurement campaigns were carried out, capturing the spatial distribution of temperature and PM concentrations in the City of Aachen, Germany (pop. 245,000).

“Several locations were selected to examine spatial influences such as topography, building density, vegetation and traffic on temperature and PM. The findings permit the detection of urban environmental variables that contribute to both temperature enhancement and poor air quality. Those variables were used as spatial predictors for the identification of possible hot spots inside and outside the area of field measurements. The zones of enhanced risks of high air temperature and PM levels were detected by means of GIS based geo-statistic modeling. These areas were mainly identified in the inner city, which is characterized by a dense building structure and heavy traffic.

“A chemical characterization of different PM fractions complements the GIS based investigations. The analysis of toxicologically relevant components provides information on air quality at urban, suburban and rural sites. The results of the chemical analyses support the results obtained from geo-statistical modeling. It reveals high concentrations of health relevant airborne species like metals and polycyclic aromatic hydrocarbons within the zone of enhanced risk for the coincidence of temperature stress and PM pollution.”