Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data
| Authors: | Daniel J. Hayes, Warren B. Cohen, Steven A. Sader, Daniel E. Irwin |
| Year: | 2008 |
| Type: | Scientific Journal |
| Station: | Pacific Northwest Research Station |
| DOI: | https://doi.org/10.1016/j.rse.2007.06.003 |
| Source: | Remote Sensing of Environment DOI: 10.1016/j.rse.2007.06.003 |
Abstract
This article describes a series of fundamental analyses designed to test and compare the utility of various MODIS data and products for detecting land cover change over a large area of the tropics. The approach for estimating proportional forest cover change as a continuous variable was based on a reduced major axis regression model. The model relates multispectral and multi-temporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for several study sites across the Central American region. Three MODIS data sets with diverse attributes were evaluated on model consistency, prediction accuracy, and practical utility in estimating change in forest cover over multiple time intervals and spatial extents. Models based on anniversary date acquisitions of the 1-kilometer resolution NEAR product proved to be the most consistent and practical to implement. Linear regression models based on spectral indices that correlate with change in the brightness, greenness, and wetness spectral domains of these data estimated proportional change in forest cover with less than 10% prediction error over the full spatial and temporal extent of this study.