The originality of the REDD proposal is its incentives-based mechanism designed to reward the governments of developing countries for their performance in reducing deforestation as measured against a baseline. This mechanism is founded on the hypothesis that developing countries ‘pay’ an opportunity cost to conserve their forests and would prefer other choices and convert their wooden lands to other uses. The basic idea is, therefore, to pay rents to these countries to compensate for the anticipated foregone revenues.

Updating land cover maps from remotely sensed data in a timely manner is important for many areas of scientific research. Unfortunately, traditional classification procedures are very labor intensive and subjective because of the required human interaction. Based on the strategy of updating land cover data only for the changed area, we proposed an integrated, automated approach to update land cover maps without human interaction.

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R² values, fluctuated according to the scale, season, and regression method used.