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1As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy 2 cover layer, a pilot project was launched to test the use of high resolution photography coupled 3 with extensive ancillary data to map the distribution of tree canopy cover over four study regions 4 in the conterminous US. Two stochastic modeling techniques, Random Forests (RF) and 5Stochastic Gradient Boosting (SGB), are compared. The objectives of this study were first to 6 explore the sensitivity of RF and SGB to choices in tuning parameters. Second, to compare the 7 performance of the two final models by assessing the importance of, and interaction between, 8 predictor variables, the global accuracy metrics derived from an independent test set, as well as 9 the visual quality of the resultant maps of tree canopy cover. The predictive accuracy of RF and 10 SGB was remarkably similar on all four of our pilot regions. In all four study regions, the 11 independent test set MSE was identical to 3 decimal places, with the largest difference in Kansas 12where RF gave an MSE of 0.0113 and SGB gave an MSE of 0.0117. With correlated predictor 13 variables, stochastic gradient boosting had a tendency to concentrate variable importance in 14 fewer variables, while Random Forest tended to spread importance out amongst more variables. 15 RF is simpler to implement than SGB, as RF both has fewer parameters needing tuning, and also 16 was less sensitive to these parameters. As stochastic techniques, both RF and SGB introduce a 17 new component of uncertainty: repeated model runs will potentially result in different final 18 predictions. We demonstrate how RF allows the production of a spatially explicit map of this 19 stochastic uncertainty of the final model. 20

Tree canopy cover is a fundamental component of the landscape, and the amount of cover influences fire behavior, air pollution mitigation, and carbon storage. As such, efforts to empirically model percent tree canopy cover across the United States are a critical area of research. The 2001 national-scale canopy cover modeling and mapping effort was completed in 2006, and here we present results from a pilot study for a 2011 product. We examined the influence of two different modeling techniques (random forests and beta regression), two different Landsat imagery normalization processes, and eight different sampling intensities across five different pilot areas. We found that random forest out-performed beta regression techniques and that there was little difference between models developed based on the two different normalization techniques. Based on these results we present a prototype study design which will test canopy cover modeling approaches across a broader spatial scale.

The Multi-Resolution

One challenge to implementing spectral change detection algorithms using multitemporal Landsat data is that key dates and periods are often missing from the record due to weather disturbances and lapses in continuous coverage. This paper presents a method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover. These charts are able to detect changes from both deforestation and subtler forest degradation and thinning. First, harmonic regression residuals are computed after fitting models to interannual training data. These residual time series are then subjected to Shewhart X-bar control charts and exponentially weighted moving average charts. The Shewhart X-bar charts are also utilized in the algorithm to generate a data-driven cloud filter, effectively removing clouds and cloud shadows on a location-specific basis. Disturbed pixels are indicated when the charts signal a deviation from data-driven control limits. The methods are applied to a collection of loblolly pine (Pinus taeda) stands in Alabama, USA. The results are compared with stands for which known thinning has occurred at known times. The method yielded an overall accuracy of 85%, with the particular result that it provided afforestation/deforestation maps on a per-image basis, producing new maps with each successive incorporated image. These maps matched very well with observed changes in aerial photography over the test period. Accordingly, the method is highly recommended for on-the-fly change detection, for changes in both land use and land management within a given land use.

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The sequestration of atmospheric carbon (C) in forests has partially offset C emissions in the United States (US) and might reduce overall costs of achieving emission targets, especially while transportation and energy sectors are transitioning to lower-carbon technologies. Using detailed forest inventory data for the conterminous US, we estimate forests’ current net sequestration of atmospheric C to be 173 Tg yr−1, offsetting 9.7% of C emissions from transportation and energy sources. Accounting for multiple driving variables, we project a gradual decline in the forest C emission sink over the next 25 years (to 112 Tg yr−1) with regional differences. Sequestration in eastern regions declines gradually while sequestration in the Rocky Mountain region declines rapidly and could become a source of atmospheric C due to disturbances such as fire and insect epidemics. C sequestration in the Pacific Coast region stabilizes as forests harvested in previous decades regrow. Scenarios simulating climate-induced productivity enhancement and afforestation policies increase sequestration rates, but would not fully offset declines from aging and forest disturbances. Separating C transfers associated with land use changes from sequestration clarifies forests’ role in reducing net emissions and demonstrates that retention of forest land is crucial for protecting or enhancing sink strength.

No abstract