The feasibility of extracting forest age of young stands (< 50 yr) in a Pacific Northwest Forest using Landsat Thematic Mapper (TM) spectral bands and topographic information was explored using a neural network approach. Understanding the changes of forest fragmentation through time are important for assessing alterations in ecosystem processes (forest productivity, species diversity, nutrient cycling, carbon flux, hydrology, spread of pests, etc.) and wildlife habitat and populations. The study area teas the H.J. Andrews Experimental Forest on the Blue River Ranger District of the Willamette National Forest in western Oregon. Timber harvesting has occurred in this forest over the past 45 years and has a recorded forest management history. The study area was extracted from a georeferenced TM scene acquired on 7 July 1991. A coincident digital terrain model (DTM) derived from digital topographic elevation data was also acquired. Using this DTM and an image processing software package, slope and aspect images were generated over the study area. Sites were chosen to cover the entire range of forest stand age and slope and aspect. The oldest recorded clearcut stands were logged in 1950. A number of sites were chosen as primary forest which had no recorded history of cutting. Various feed-forward neural networks trained with back propagation were tested to predict forest age from TM data and topographic data. The results demonstrated that neural networks can be used as an initial model for inferring forest age. The best network was a 6-->5-->1 structure with inputs of TM Bands 3, 4, and 5, elevation, slope and aspect. The rms values of the predicted forest age were on the order of 5 years. TM Bands 1, 2, 6, and 7 did not significantly add information to the network for learning forest age. Furthermore, the results suggest that topographic information (elevation, slope, and aspect) can be effectively utilized by a neural network approach. The results of the network approach were significantly better than corresponding linear systems.
