scholarly journals An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data

2013 ◽  
Vol 24 (3) ◽  
pp. 328-335 ◽  
Author(s):  
Jiayun Yao ◽  
Sarah B Henderson
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Forest Fire ◽  
Empirical Model ◽  
Remote Sensing Data ◽  
Geographic Area ◽  
Smoke Exposure ◽  
Sensing Data ◽  
Fire Smoke ◽  
Large Geographic Area

Forest Fire Smoke Exposure Assessment: Use of Remote Sensing Data in a Pollution Dispersion Model

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S203
Author(s):  
S B. Henderson ◽  
B Burkholder ◽  
M Brauer ◽  
P L. Jackson ◽  
B Klinkenberg ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Exposure Assessment ◽  
Forest Fire ◽  
Dispersion Model ◽  
Remote Sensing Data ◽  
Smoke Exposure ◽  
Pollution Dispersion ◽  
Sensing Data ◽  
Fire Smoke

scholarly journals Summaries of certain spatial patterns retrieved from multidate remote-sensing data

2004 ◽  
Vol 2004 (2) ◽  
pp. 287-300
Author(s):  
Hema Nair
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Spatial Patterns ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Geographic Area ◽  
Image Mining ◽  
Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as “Y isF” can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

Forest fire analysis with remote sensing data

2001 ◽  
Vol 22 (12) ◽  
pp. 2265-2277 ◽  
Author(s):  
F. Sunar ◽  
C. Özkan
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Remote Sensing Data ◽  
Sensing Data
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