Evaluating environmental sensitivity at the basin scale through the use of geographic information systems and remotely sensed data: an example covering the Agri basin (Southern Italy)

CATENA ◽  
2000 ◽  
Vol 40 (1) ◽  
pp. 19-35 ◽  
Author(s):  
F Basso ◽  
E Bove ◽  
S Dumontet ◽  
A Ferrara ◽  
M Pisante ◽  
...  
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Southern Italy ◽  
Remotely Sensed ◽  
Geographic Information ◽  
Environmental Sensitivity ◽  
Remotely Sensed Data ◽  
Basin Scale

A Spatial Analysis of Selected Art: a GIScience-Humanities Interface

2020 ◽  
Vol 14 (1-2) ◽  
pp. 154-175
Author(s):  
Daniel A. Griffith
Keyword(s):  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Autocorrelation ◽  
Matrix Algebra ◽  
Remotely Sensed ◽  
Geographic Information ◽  
Statistical Analyses ◽  
Remotely Sensed Images

This exposition presents little-known connections between geography, through geographic information systems (GISs), mathematics, through matrix algebra, and art, through paintings and images, adding to the geo-humanities, spatial humanities, and humanistic mathematics literature. To this end, findings summarized for spatial statistical analyses of selected Susie Rosmarin paintings (which are reminiscent of visualizations of certain mathematical quantities known as eigenvectors), remotely sensed images that have appeared in art exhibits, and selected famous paintings by historically renowned artists reveal that spatial autocorrelation constitutes a fundamental element of art. These analyses extend the tradition of visualizing fractals as art, and interfacing cartography with art. This paper promotes analytical art, and establishes additional commonalities for GIScience, mathematics, and art.

Mapping fuels in the Chihuahuan Desert borderlands using remote sensing, geographic information systems, and biophysical modeling

2009 ◽  
Vol 39 (10) ◽  
pp. 1917-1927 ◽  
Author(s):  
H. M. Poulos
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Spectral Characteristics ◽  
Geographic Information ◽  
Error Rates ◽  
Spatially Explicit ◽  
Biophysical Modeling ◽  
Remotely Sensed Data ◽  
Fuel Loads ◽  
Topographic Positions

This study integrated field, geographic information systems, and remotely sensed data to generate spatially explicit fuel maps for Big Bend National Park in Texas and the Maderas del Carmen Protected Area in Coahuila, Mexico. We used hierarchical cluster analysis, and classification and regression trees to (i) identify the dominant fuel types in each of the study areas and (ii) build spatially explicit predictive fuels maps. Four fuel types were identified that differed significantly in their live and dead fuel characteristics. Spectral characteristics, topographic position, soil moisture, and solar radiation were the major influences on fuel distribution patterns. Fine-fuel loads were highest in open woodlands on lower topographic positions that had high grass cover. The highest shrub loadings were found on exposed, upper topographic positions. Timber-type fuel loads with high 1, 10, 100, and 1000 hour fuels loads dominated high-elevation valley bottoms. The error rates of the maps were approximately 16%, which falls within the range of typical fuel mapping misclassification rates. The map products from this study are currently being used as inputs for landscape-scale fire modeling and for guiding fuel-reduction treatments using fire and fire surrogates, such as thinning.

Sign in / Sign up

Export Citation Format

Share Document