Browsing by Author "Schroeder, Todd A."

Now showing 1 - 4 of 4
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    Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United States
    Ritz, Alison L.; Thomas, Valerie A.; Wynne, Randolph H.; Green, P. Corey; Schroeder, Todd A.; Albaugh, Timothy J.; Burkhart, Harold E.; Carter, David R.; Cook, Rachel L.; Campoe, Otavio C.; Rubilar, Rafael A.; Rakestraw, Jim (Elsevier, 2022-09)
    Remote sensing offers many advantages to supplement traditional, ground-based forest measurements, such as limiting time in the field and fast spatial coverage. Data from airborne laser scanning (lidar) have provided accurate estimates of forest height, where, and when available. However, lidar is expensive to collect, and wall-to-wall coverage in the United States is lacking. Recent studies have investigated whether point clouds derived from digital aerial photogrammetry (DAP) can supplement lidar data for estimating forest height due to DAP's lower costs and more frequent acquisitions. We estimated forest heights using point clouds derived from the National Agricultural Imagery Program (NAIP) DAP program in the United States to create a predicted height map for managed loblolly pine stands. For 534 plots in Virginia and North Carolina, with stand age ranging from 1 year to 42 years old, field-collected canopy heights were regressed against the 90th percentile of heights derived from NAIP point clouds. Model performance was good, with an R2 of 0.93 and an RMSE of 1.44 m. However, heights in recent heavily thinned stands were consistently underestimated, likely due to between-row shadowing leading to a poor photogrammetric solution. The model was applied to non-thinned evergreen areas in Virginia, North Carolina, and Tennessee to produce a multi-state 5 m x 5 m canopy height map. NAIP-derived point clouds are a viable means of predicting canopy height in southern pine stands that have not been thinned recently.
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    Effects of establishment fertilization on Landsat-assessed leaf area development of loblolly pine stands
    House, Matthew N.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Rachel L.; Carter, David R.; Van Mullekom, Jennifer H.; Rakestraw, Jim; Schroeder, Todd A. (Elsevier, 2024-03-15)
    Loblolly pine (Pinus taeda L.) plantations in the southeastern United States are among the world's most intensively managed forest plantations. Under intensive management, a common practice is fertilizing at establishment. The objective of this study was to investigate the effect of establishment fertilization on leaf area development of loblolly pine plantation stands (n = 3997) over 16 years compared to stands that did not receive nutrient additions at planting. Leaf area index (LAI) is a meaningful biophysical indicator of vigor and an important functional and structural element of a planted stand. The study area was stratified by plant hardiness zone to account for climatic differences and soil type (texture and drainage class), using the Cooperative Research in Forest Fertilization (CRIFF) groupings. LAI was estimated from Landsat imagery to create trajectories of mean stand LAI over 16 years. Establishment fertilization, on average, (1) increased stand LAI beginning at year two, with a peak at years six and seven, and (2) decreased the time required for a stand to reach a winter LAI of 1.5 by almost two years. Fertilization responses varied by climate zone and soil drainage class, where the warmest zones benefited the most, particularly in poorly drained soils. Past year 10, the differences in LAI between fertilized and unfertilized stands were not practically important. Using Landsat data in a cloud-computing environment, we demonstrated the benefits of establishment fertilization to stand LAI development using a large sample over the native range of loblolly pine.
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    How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?
    Cohen, Warren B.; Healey, Sean P.; Yang, Zhiqiang; Stehman, Stephen V.; Brewer, C. Kenneth; Brooks, Evan B.; Gorelick, Noel; Huang, Chengqaun; Hughes, M. Joseph; Kennedy, Robert E.; Loveland, Thomas R.; Moisen, Gretchen G.; Schroeder, Todd A.; Vogelmann, James E.; Woodcock, Curtis E.; Yang, Limin; Zhu, Zhe (MDPI, 2017-03-26)
    Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest.
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    Trends in Forest Recovery After Stand-Replacing Disturbance: A Spatiotemporal Evaluation of Productivity in Southeastern Pine Forests
    Putnam, Daniel Jacob (Virginia Tech, 2023-05-22)
    The southeastern United States is one of the most productive forestry regions in the world, encompassing approximately 100 million ha of forest land, about 87% of which is privately owned. Any alteration in this region's duration or rate of forest recovery has consequential economic and ecological ramifications. Despite the need for forest recovery monitoring in this region, a spatially comprehensive evaluation of forest spectral recovery through time has not yet been conducted. Remote sensing analysis via cloud-computing platforms allows for evaluating southeastern forest recovery at spatiotemporal scales not attainable with traditional methods. Forest productivity is assessed in this study using spectral metrics of southern yellow pine recovery following stand-replacing disturbance. An annual cloudfree (1984-2021) Landsat time series intersecting ten southeastern states was constructed using the Google Earth Engine API. Southern yellow pine stands were detected using the National Land Cover Database (NLCD) evergreen class, and pixels with a rapidly changing spectrotemporal profile, suggesting stand-replacing disturbance, were found using the Landscape Change Monitoring System (LCMS) Fast Loss product. Spectral recovery metrics for 3,654 randomly selected stands in 14 Level 3 EPA Ecoregions were derived from their 38-year time series of Normalized Burn Ratio (NBR) values using the Detecting Breakpoints and Estimating Segments in Trend (DBEST) change detection algorithm. Recovery metrics characterizing the rate (NBRregrowth), duration (Y2R), and magnitude (K-shift) of recovery from stand-replacing disturbances occurring between 1989 and 2011 were evaluated to identify long-term and wide-scale changes in forest recovery using linear regression and spatial statistics respectively. Sampled stands typically recover 35% higher in NBR than pre-disturbance and, on average, spectrally recover within seven years of disturbance. Recovery rate is shown to be increasing over time; temporal slope estimates for NBRregrowth suggest a 33% increase in early recovery rate between 1984 and 2011. Similarly, recovery duration measured with Y2R decreased by 43% during the study period with significant spatial variation. Results suggest that the magnitude of change in stand condition between rotations has decreased by 21% during the study period, has substantial regional divisions in high and low magnitude recovery between coastal and inland stands, and low NBR value sites have the most potential to increase their NBR value. Observed spatiotemporal patterns of spectral recovery suggest that changes in management interventions, atmospheric CO2, and climate over time have changed regional productivity. Results from this study will aid the understanding of changing productivity in southern yellow pine and will inform the management, monitoring, and modeling of this ecologically and economically important forest ecosystem.