Visible and infrared satellite images, in combination with detailed landscape information, suggest an appreciable effect of spatial variations in landscape on cumulus cloud formation over relatively flat terrain. These effects are noticeable when forcing from the atmosphere is weak, e.g., when fronts or other disturbances are absent. A case is presented in which clouds are observed to form first over a mesoscale-size area (100 x 300 km) of harvested wheat in Oklahoma, where the ground temperature is warmer than adjoining areas dominated by growing vegetation. In addition, clouds are suppressed over relatively long bands downwind of small manmade lakes and areas characterized by heavy tree cover. The observed variability of cloud relative to landscape type is compared with that simulated with a one-dimensional boundary-layer model.Clouds form earliest over regions characterized by high, sensible heat flux, and are suppressed over regions characterized by high, latent heat flux during relatively dry atmospheric conditions. This observation has significance in gaining understanding of the feedback mechanisms of land modification on climate, as well as understanding relatively short-range weather forecasting.

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A survey of shallow (fair weather) cumulus clouds over part of Amazonia yields evidence of enhanced frequency where the forest had been cleared. The survey covers one dry-season month from 1988. It employs a threshold algorithm to construct an image of cumulus cloud cover from sets of geostationary satellite visible-infrared image pairs. Cumulus images were constructed for two times. The morning image shows no association of the cumulus index with cultural features. However, in the afternoon image a patch of high index values coincides with deforestation along highway BR-364 in the state of Rondonia.

Existing techniques for identifying, associating, and tracking storms rely on heuristics and are not transferrable between different types of geospatial images. Yet, with the multitude of remote sensing instruments and the number of channels and data types increasing, it is necessary to develop a principled and generally applicable technique. In this paper, an efficient, sequential, morphological technique called the watershed transform is adapted and extended so that it can be used for identifying storms. The parameters available in the technique and the effects of these parameters are also explained.The method is demonstrated on different types of geospatial radar and satellite images. Pointers are provided on the effective choice of parameters to handle the resolutions, data quality constraints, and dynamic ranges found in observational datasets.