Abstract
There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O) to produce three novel outcomes: (1) we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2) we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3) we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future.
Highlights
Land managers, foresters, wildlife biologists, and other environmental scientists all need inexpensive and accessible information characterizing current and past conditions in order to monitor changes in the age/size of forests under their purview [1,2,3,4,5]
We independently evaluated our final forest structure classification for 2013 using field data collected as part of the US Forest Service Forest Inventory and Analysis (FIA) program during
The best 6-class prediction for the 2013 master image had an overall accuracy of 68% and a standard error of 3%, while the 2-class prediction had an overall accuracy of 98% with a standard error of
Summary
Foresters, wildlife biologists, and other environmental scientists all need inexpensive and accessible information characterizing current and past conditions in order to monitor changes in the age/size of forests under their purview [1,2,3,4,5]. Managers can observe how forests have increased or decreased across structure classes within a landscape context. This can be especially important in multiple-use areas where different uses are often opposed to one another, for instance, on US Forest Service land where tree harvest and Forests 2018, 9, 157; doi:10.3390/f9040157 www.mdpi.com/journal/forests. A time series analysis of forest structure can provide empirical evidence of loss or gain of different ages of forest, which sets the backdrop for discussion concerning current and future management policies. Most successful forest structure mapping has been performed utilizing Light Detection and Ranging (lidar) data [12,13,14,15,16,17]. Lidar data are acquired by using pulsed laser measurements to provide a
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