Adaptive Smoothing for Noisy DEMs

Geomorphometry 2011Geomorphometry 2011, 07-09 September 2011, Esri, Redlands, California

John Gallant

“DEMs derived from dense remotely sensed measurements, including lidar- and radar-based DEMs, provide much greater surface detail than traditional interpolated DEMs but suffer from random noise that perturbs measures of surface shape such as slope and flow direction. Smoothing is an effective method of reducing noise but also tends to impact on important surface features, lowering hilltops, raising valleys and obliterating important fine detail.

Shaded relief of a sub-section of the area in Figures 1 and 3 from SRTM data before (left) and after (right) adaptive smoothing.

Shaded relief of a sub-section of the area in Figures 1 and 3 from SRTM data before (left) and after (right) adaptive smoothing.

“This paper describes a multiscale adaptives moothing approach that responds to both the relief and noise level in a DEM by smoothing aggressively where the noise is larger than the local relief and smoothing little or not at all where noise is less than relief. The method is simple and efficient and can be readily implemented in a raster GIS environment. The method is demonstrated on noisy SRTM data”

All presentation materials and reviewed papers from Geomorphometry 2011 are available at

Coupling Community Mapping and Supervised Classification to Discriminate Shade Coffee from Natural Vegetation

Applied Geography

Applied Geography, Volume 34, May 2012

Guillermo C. Martínez-Verduzco, J. Mauricio Galeana-Pizaña, and Gustavo M. Cruz-Bello


  • We coupled supervised classification with Community Mapping to separate Shade coffee and forest.
  • We elicited local knowledge from study area inhabitants through participatory workshops.
  • This coupled method produced similar accuracy levels to supervised classification.
  • This combined method demonstrated being less time and resources consuming.

“Discriminating between Shade coffee plantations and Natural vegetation using Remote Sensing is particularly difficult in zones where both coverages have almost the same mix of species, as is the case in several areas of the Chiapas highlands. This investigation couples supervised classification with Community Mapping to separate these vegetation classes. Local knowledge of the study area was elicited from local inhabitants through workshops. The participants were asked to delimit both coverages inside the areas they knew the best (confidence map) with the help of printed orthophotos to build a land use map. The accuracy of this coupled method was similar to supervised classification alone and with less time and resources invested. This method can be applied in the rural zones of developing countries, as it is easy to understand and is cheaper than similar alternatives.”