Forest disturbance imagery can be used to develop forest stand age maps

Time series collections of satellite imagery can be used to identify disturbances when the forest canopy is either partially or completely lost through land conversion, weather or insect damage, fire, or harvesting. With Landsat satellite imagery going back over three decades, it is possible to create a digital map of annual forest disturbances.
Forest disturbances can be classified as stand-clearing or partial disturbances, and as natural disturbances (due to fire, weather, or insects), or human-caused disturbances such as timber harvesting.

A recent study in Virginia by Dr. Joby Kauffman and Dr. Steve Prisley used several machine learning algorithms to classify forest disturbances identified by the Vegetation Change Tracker (VCT) product into stand-clearing or partial disturbances. By identifying the year in which stand-clearing disturbances occur, it is possible to then develop maps for forest stand age when these disturbances are followed by forest regeneration.

The Virginia Department of Forestry (VDOF) has maintained harvest records since 2009, making it possible to compare spatial records of timber harvest with disturbances identified by VCT. In an accuracy assessment using the harvest records, Kauffman and Prisley found that the VCT product correctly detected forest disturbances 87 percent of the time. Eighty-six percent of stand-clearing disturbances were correctly classified by the best of the machine learning algorithms.

The map of stand-clearing disturbances, combined with year of disturbance, represents an approximate stand age map (except when stand-clearing disturbances are not followed immediately by forest regeneration). 

The stand age map for Virginia was used to estimate a forest age-class distribution which closely matched the age-class distribution reported by forest inventory plots from the FIA program. Furthermore, maps of this type can be used to study harvest area (and stand size) trends over time, and to map locations and types of land use conversions following forest clearing.

Subsequent research as part of the Dr. Kauffman’s dissertation has distinguished partial harvests from non-harvest disturbances using parcel boundary data from public land ownership records. These efforts are improving the utility of time-series data from the Landsat program to generate information useful for forest planning and analysis.

  

Reference 

Kauffman, J.S. and S.P. Prisley. 2016. Automated estimation of forest stand age using vegetation change tracker and machine learning. Mathematical and Computational Forestry & Natural Resources Sciences 8(1):4-13.