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1. Patterns in Foliar Isotopic Nitrogen, Percent Nitrogen, and Site Index for Managed Forest Systems in the United States

   https://doi.org/10.3390/f13101694  

   Buntrock, Laura; Thomas, Valerie A.; Strahm, Brian D.; Fox, Tom; Harrison, Robert; Himes, Austin; Littke, Kim (October 2022, Forests)

   Patterns in foliar nitrogen (N) stable isotope ratios (δ15N) have been shown to reveal trends in terrestrial N cycles, including the identification of ecosystems where N deficiencies limit forest ecosystem productivity. However, there is a gap in our understanding of within-species variation and species-level response to environmental gradients or forest management. Our objective is to examine the relationship between site index, foliar %N, foliar δ15N and spectral reflectance for managed Douglas-fir (Pseudotsuga menziesii) and loblolly pine (Pinus taeda) plantations across their geographic ranges in the Pacific Northwest and the southeastern United States, respectively. Foliage was measured at 28 sites for reflectance using a handheld spectroradiometer, and further analyzed for δ15N and N concentration. Unlike the prior work for grasslands and shrubland species, our results show that foliar δ15N and foliar %N are not well correlated for these tree species. However, multiple linear regression models suggest a strong predictive ability of spectroscopy data to quantify foliar δ15N, with some models explaining more than 65% of the variance in the δ15N. Additionally, moderate to strong explanations of variance were found between site index and foliar δ15N (R2 = 0.49) and reflectance and site index (R2 = 0.84) in the Douglas-fir data set. The development of relationships between foliar spectral reflectance, δ15N and measures of site productivity provides the first step toward mapping canopy δ15N for these managed forests with remote sensing. 

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2. Interpreting floodplain heterogeneity: Using field data to understand unsupervised floodplain classifications.

   https://doi.org/10.1016/j.jhydrol.2023.130508  

   Iskin, Emily; Wohl, Ellen (November 2023, Journal of hydrology)

   The dynamic environment of natural river 昀氀oodplains creates spatial heterogeneity that in昀氀uences 昀氀oodplain functions. Diverse human activities have homogenized natural 昀氀oodplains and reduced their functions across many river networks in the temperate latitudes. Consequently, quanti昀椀cation of 昀氀oodplain heterogeneity is needed to understand patterns of spatial heterogeneity on diverse 昀氀oodplains and to inform 昀氀oodplain restoration. We use a novel approach of spatially connecting 昀椀eld and remotely sensed data in order to interpret the output of, and build upon, a previous unsupervised classi昀椀cation work昀氀ow. We apply the method to three rivers in the US Paci昀椀c Northwest and the Altamaha River in the southeastern US and compare our results to a previous study. We 昀椀nd that 昀椀eld classi昀椀cations, relative topography, and NDVI are useful for interpreting results from the unsupervised classi昀椀cation work昀氀ow. The interpretations are visually interesting, but we propose that it is the heterogeneity within the groups that is vital to 昀氀oodplain functioning. Natural 昀氀oodplains in the Paci昀椀c Northwest and coastal Southeast have moderate to high evenness, moderate to high intermixing, and moderate aggregation; and aggregation and evenness similar to rivers in Colorado and Oklahoma, USA, but lower intermixing. We attribute lower intermixing at the Altamaha River to slower rates of lateral channel migration, and lower intermixing at the Hoh River to the different hydrologic and sediment regimes and less stable braided planform. The results show that the larger rivers in this study (Altamaha, Hoh, and Sol Duc Rivers) have spatial heterogeneity similar to beaver-modi昀椀ed and shortgrass prairie rivers in Colorado, whereas the more inland and smaller river (Lookout Creek) has spatial heterogeneity similar to the tallgrass prairie site (Sand Creek). From the results of an ad hoc sensitivity analysis, we suggest using the highest spatial resolution topographic data available, using aerial imagery/mosaics from the same sensor, and removing largest patch index from the suite of comparable indices. The metrics reveal similarities and differences between rivers in the United States, and indicate that discernable trends may arise from a meta study comparing heterogeneity from more rivers across the country. 

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3. Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

   https://doi.org/10.3390/rs12091406  

   Cohrs, Chris W.; Cook, Rachel L.; Gray, Josh M.; Albaugh, Timothy J. (May 2020, Remote Sensing)

   null (Ed.)

   Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models. 

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4. A New Approach for Modeling Volume Response from Mid-Rotation Fertilization of Pinus taeda L. Plantations

   https://doi.org/10.3390/f11060646  

   Scolforo, Henrique F.; Montes, Cristian; Cook, Rachel L.; Lee Allen, Howard; Albaugh, Timothy J.; Rubilar, Rafael; Campoe, Otavio (June 2020, Forests)

   null (Ed.)

   Mid-rotation fertilization presents an opportunity to increase the economic return of plantation forests in the southeastern United States (SEUS). For this reason, the Forest Productivity Cooperative established a series of mid-rotation fertilization trials in Pinus taeda L. plantations across the SEUS between 1984 and 1987. These trials identified site-specific responses to nitrogen (N) and phosphorus (P) fertilizers, resulting in increased stand production for 6–10 years after fertilization. There are successful volume response models that allow users to quantify the gain in stand productivity resulting from fertilization. However, all the current models depend on empirical relationships that are not bounded by biological response, meaning that greater fertilizer additions continue to create more volume gains, regardless of physiological limits. To address this shortcoming, we developed a bounded response model that evaluates relative volume response gain to fertilizer addition. Site index and relative spacing are included as model parameters to help provide realistic estimates. The model is useful for evaluating productivity gain in Pinus taeda stands that are fertilized with N and P in mid-rotation. 

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5. Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

   https://doi.org/10.3390/rs12122056  

   Rahimzadeh-Bajgiran, Parinaz; Hennigar, Chris; Weiskittel, Aaron; Lamb, Sean (June 2020, Remote Sensing)

   null (Ed.)

   A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2 when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included. 

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