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Keyword: tree canopy cover

Shape selection in Landsat time series

Media Gallery Posted on: September 15, 2016
The tree canopy patterns in these time series Landsat map images, taken over a section of the central Rocky Mountains near Steamboat Springs, Colorado, provide information about canopy lost to disturbance events.

Shape selection in Landsat time series

Science Spotlights Posted on: September 01, 2016
There is new methodology for fitting ecologically feasible “shapes” to time series of Landsat imagery for modeling, mapping, and monitoring annual forest disturbance dynamics. Through a case study of fire, harvest and bark beetle outbreak, scientists illustrate how resultant fitted values and parameters can be fed into empirical models to map disturbance causal agent and tree canopy cover changes coincident with disturbance events through time.

Shape selection in Landsat time series: A tool for monitoring forest dynamics

Publications Posted on: August 10, 2016
We present a new methodology for fitting nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades.

Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA

Publications Posted on: May 02, 2016
Tree canopy cover significantly affects human and wildlife habitats, local hydrology, carbon cycles, fire behavior, and ecosystem services of all types. In addition, changes in tree canopy cover are both indicators and consequences of a wide variety of disturbances from urban development to climate change. There is growing demand for this information nationwide and across all land uses.

Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance

Publications Posted on: October 06, 2015
As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF) and stochastic gradient boosting (SGB), are compared.

Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance

Publications Posted on: August 18, 2015
Random forests (RF) and stochastic gradient boosting (SGB), both involving an ensemble of classification and regression trees, are compared for modeling tree canopy cover for the 2011 National Land Cover Database (NLCD). The objectives of this study were twofold. First, sensitivity of RF and SGB to choices in tuning parameters was explored.