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1. An Accuracy Assessment of Field and Airborne Laser Scanning–Derived Individual Tree Inventories using Felled Tree Measurements and Log Scaling Data in a Mixed Conifer Forest

   https://doi.org/10.1093/forsci/fxae015  

   Sparks, Aaron M; Corrao, Mark V; Keefe, Robert F; Armstrong, Ryan; Smith, Alistair_M S (March 2024, Forest Science)

   Roberts, Scott (Ed.)

   Abstract On-the-ground sample-based forest inventory methods have been the standard practice for more than a century, however, remote sensing technologies such as airborne laser scanning (ALS) are providing wall-to-wall inventories based on individual tree measurements. In this study, we assess the accuracy of individual tree height, diameter, and volume derived from field-cruising measurements and three ALS data-derived methods in a 1.1 ha stand using direct measurements acquired on felled trees and log-scale volume measurements. Results show that although height derived from indirect conventional field measurements and ALS were statistically equivalent to felled tree height measurements, ALS measured heights had lower root mean square error (RMSE) and bias. Individual tree diameters modeled using a height-to-diameter-at-breast-height model derived from local forest inventory data and the software ForestView had moderate RMSE (8.3–8.5 cm) and bias (-3.0 – -0.3 cm). The ALS-based methods underdetected trees but accounted for 78%–91% of the field reference harvested merchantable volume and 71%–99% of the merchantable volume scaled at the mill. The results also illustrate challenges of using mill-scaled volume estimates as validation data and highlight the need for more research in this area. Overall, the results provide key insights to forest managers on accuracies associated with conventional field-derived and ALS-derived individual tree inventories. Study Implications: Forest inventory data provide critical information for operational decisions and forest product supply chain planning. Traditionally, forest inventories have used field sampling of stand conditions, which is time-intensive and cost-prohibitive to conduct at large spatial scales. Remote sensing technologies such as airborne laser scanning (ALS) provide wall-to-wall inventories based on individual tree measurements. This study advances our understanding of the accuracy of conventional field-derived and ALS-derived individual tree inventories by evaluating these inventories with felled tree and log scaling data. The results provide key insights to forest managers on errors associated with conventional field and ALS-derived individual tree measurements. 

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2. Spatiotemporal trends of black walnut forest stocking under climate change

   https://doi.org/10.3389/ffgc.2022.970379  

   Ebrahimi, Aziz; Abbasi, Akane O.; Liang, Jingjing; Jacobs, Douglass F. (September 2022, Frontiers in Forests and Global Change)

   Basal area is a key measure of forest stocking and an important proxy of forest productivity in the face of climate change. Black walnut ( Juglans nigra ) is one of the most valuable timber species in North America. However, little is known about how the stocking of black walnut would change with differed bioclimatic conditions under climate change. In this study, we projected the current and future basal area of black walnut. We trained different machine learning models using more than 1.4 million tree records from 10,162 Forest Inventory and Analysis (FIA) sample plots and 42 spatially explicit bioclimate and other environmental attributes. We selected random forests (RF) as the final model to estimate the basal area of black walnut under climate change because RF had a higher coefficient of determination ( R 2 ), lower root mean square error (RMSE), and lower mean absolute error (MAE) than the other two models (XGBoost and linear regression). The most important variables to predict basal area were the mean annual temperature and precipitation, potential evapotranspiration, topology, and human footprint. Under two emission scenarios (Representative Concentration Pathway 4.5 and 8.5), the RF model projected that black walnut stocking would increase in the northern part of the current range in the USA by 2080, with a potential shift of species distribution range although uncertainty still exists due to unpredictable events, including extreme abiotic (heat, drought) and biotic (pests, disease) occurrences. Our models can be adapted to other hardwood tree species to predict tree changes in basal area based on future climate scenarios. 

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3. Relative density of United States forests has shifted to higher levels over last two decades with important implications for future dynamics

   https://doi.org/10.1038/s41598-021-98244-w  

   Woodall, C. W.; Weiskittel, A. R. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract Tree size-density dynamics can inform key trends in forest productivity along with opportunities to increase ecosystem resiliency. Here, we employ a novel approach to estimate the relative density (RD, range 0–1) of any given forest based on its current size-density relationship compared to a hypothetical maximum using the coterminous US national forest inventory between 1999 and 2020. The analysis suggests a static forest land area in the US with less tree abundance but greatly increased timber volume and tree biomass. Coupled with these resource trends, an increase in RD was identified with 90% of US forest land now reaching a biologically-relevant threshold of canopy closure and/or self-thinning induced mortality (RD > 0.3), particularly in areas prone to future drought conditions (e.g., West Coast). Notably, the area of high RD stands (RD > 0.6) has quintupled over the past 20 years while the least stocked stands (RD < 0.3) have decreased 3%. The evidence from the coterminous US forest RD distribution suggest opportunities to increase live tree stocking in understocked stands, while using density management to address tree mortality and resilience to disturbances in increasingly dense forests. 

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4. Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty

   https://doi.org/10.1111/gcb.16038  

   Heilman, Kelly A.; Dietze, Michael C.; Arizpe, Alexis A.; Aragon, Jacob; Gray, Andrew; Shaw, John D.; Finley, Andrew O.; Klesse, Stefan; DeRose, R. Justin; Evans, Margaret E. K. (January 2022, Global Change Biology)

   Abstract Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree‐ring and forest inventory data within a Bayesian state‐space model at a multi‐site, regional scale, focusing onPinus ponderosavar.brachypterain the southwestern US. Leveraging the complementarity of these two data sources, we parsed the ecological complexity of tree growth into the effects of climate, tree size, stand density, site quality, and their interactions, and quantified uncertainties associated with these effects. New measurements of trees, an ongoing process in forest inventories, were used to confront forecasts of tree diameter with observations, and evaluate alternative tree growth models. We forecasted tree diameter and increment in response to an ensemble of climate change projections, and separated forecast uncertainty into four different causes: initial conditions, parameters, climate drivers, and process error. We found a strong negative effect of fall–spring maximum temperature, and a positive effect of water‐year precipitation on tree growth. Furthermore, tree vulnerability to climate stress increases with greater competition, with tree size, and at poor sites. Under future climate scenarios, we forecast increment declines of 22%–117%, while the combined effect of climate and size‐related trends results in a 56%–91% decline. Partitioning of forecast uncertainty showed that diameter forecast uncertainty is primarily caused by parameter and initial conditions uncertainty, but increment forecast uncertainty is mostly caused by process error and climate driver uncertainty. This fusion of tree‐ring and forest inventory data lays the foundation for robust ecological forecasting of aboveground biomass and carbon accounting at tree, plot, and regional scales, including iterative improvement of model skill. 

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5. Not-so-random forests: Comparing voting and decision tree ensembles for characterizing partial harvest events

   https://doi.org/10.1016/j.jag.2023.103561  

   Pasquarella, Valerie J.; Morreale, Luca L.; Brown, Christopher F.; Kilbride, John B.; Thompson, Jonathan R. (December 2023, International Journal of Applied Earth Observation and Geoinformation)

   Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances. 

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