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1. Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison

   https://doi.org/10.3390/rs15184407  

   Bester, Michelle S.; Maxwell, Aaron E.; Nealey, Isaac; Gallagher, Michael R.; Skowronski, Nicholas S.; McNeil, Brenden E. (September 2023, Remote Sensing)

   The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies. 

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2. Generalizing systematic adaptive cluster sampling for forest ecosystem inventory

   Xu, Q. (February 2021, Forest ecology and management)

   null (Ed.)

   Reliable statistical inference is central to forest ecology and management, much of which seeks to estimate population parameters for forest attributes and ecological indicators for biodiversity, functions and services in forest ecosystems. Many populations in nature such as plants or animals are characterized by aggregation of tendencies, introducing a big challenge to sampling. Regardless, a biased or imprecise inference would mislead analysis, hence the conclusion and policymaking. Systematic adaptive cluster sampling (SACS) is designunbiased and particularly efficient for inventorying spatially clustered populations. However, (1) oversampling is common for nonrare variables, making SACS a difficult choice for inventorying common forest attributes or ecological indicators; (2) a SACS sample is not completely specified until the field campaign is completed, making advance budgeting and logistics difficult; (3) even for rare variables, uncertainty regarding the final sample still persists; and (4) a SACS sample may be variable-specific as its formation can be adapted to a particular attribute or indicator, thus risking imbalance or non-representativeness for other jointly observed variables. Consequently, to solve these challenges, we aim to develop a generalized SACS (GSACS) with respect to the design and estimators, and to illustrate its connections with systematic sampling (SS) as has been widely employed by national forest inventories and ecological observation networks around the world. In addition to theoretical derivations, empirical sampling distributions were validated and compared for GSACS and SS using sampling simulations that incorporated a comprehensive set of forest populations exhibiting different spatial patterns. Five conclusions are relevant: (1) in contrast to SACS, GSACS explicitly supports inventorying forest attributes and ecological indicators that are nonrare, and solved SACS problems of oversampling, uncertain sample form, and sample imbalance for alternative attributes or indicators; (2) we demonstrated that SS is a special case of GSACS; (3) even with fewer sample plots, GSACS gives estimates identical to SS; (4) GSACS outperforms SS with respect to inventorying clustered populations and for making domain-specific estimates; and (5) the precision in design-based inference is negatively correlated with the prevalence of a spatial pattern, the range of spatial autocorrelation, and the sample plot size, in a descending order. 

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3. Forest Inventory of a Northern Hardwood Forest: Bird Area, 1991 - present, Hubbard Brook Experimental Forest

   https://doi.org/10.6073/pasta/58dfdebfd1b6440510def2394ab92c53  

   Battles, John J; Cleavitt, Natalie (January 2022, Environmental Data Initiative)

   The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991 by Tom Siccama who created and designed this tree survey. The inventory is representative of approximately 2.5 km2 of mid elevation northern hardwood forest. The data set is particularly geared toward producing accurate mortality and recruitment estimates. It consists of a total inventory of all trees greater than or equal to 10 cm dbh within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and 290° bearing in an east-west direction for 2200 to 2900 m. In 1991, each live stem greater than or equal to 10 cm dbh was tagged with a unique number. Tree vigor is assessed every two years and diameter is remeasured every ten years. Every two years, new tags are placed on stems that have grown into the 10 cm diameter class. A survey of smaller trees (greater than or equal to 2 to less than 10 cm dbh) was first taken in 1991 and is resurveyed every ten years. This dataset includes 1991 and subsequent samplings. Data from an earlier sampling in 1981 can be found in: Sherry, T., D. Holmes, and T. Siccama. 2019. Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/206b98f6553f1ff95cf584dd2185554e (Accessed 2021-09-16). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. These data have been used in the following publication: Siccama, T.G., Fahey, T.J., Johnson, C.E., Sherry, T.W., Denny, E.G., Girdler, E.B., Likens, G.E., and Schwarz, P.A. 2007. Population and biomass dynamics of trees in a northern hardwood forest at Hubbard Brook. Can. J. For. Res. 37(4): 737–749. doi:10.1139/X06-261. 

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4. Wide-angle effects for peculiar velocities

   https://doi.org/10.1093/mnras/staa2129  

   Castorina, Emanuele; White, Martin (October 2020, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT The line-of-sight peculiar velocities of galaxies contribute to their observed redshifts, breaking the translational invariance of galaxy clustering down to a rotational invariance around the observer. This becomes important when the line-of-sight direction varies significantly across a survey, leading to what are known as ‘wide-angle’ effects in redshift-space distortions. Wide-angle effects will also be present in measurements of the momentum field, i.e. the galaxy density-weighted velocity field, in upcoming peculiar velocity surveys. In this work, we study how wide-angle effects modify the predicted correlation function and power spectrum for momentum statistics, both in autocorrelation and in cross-correlation with the density field. Using both linear theory and the Zel'dovich approximation, we find that deviations from the plane-parallel limit are large and could become important in data analysis for low-redshift surveys. We point out that even multipoles in the cross-correlation between density and momentum are non-zero regardless of the choice of line of sight, and therefore contain new cosmological information that could be exploited. We discuss configuration space, Fourier space, and spherical analyses; providing exact expressions in each case rather than relying on an expansion in small angles. We hope these expressions will be of use in the analysis of upcoming surveys for redshift-space distortions and peculiar velocities. 

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5. Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981

   https://doi.org/10.6073/pasta/18a5a1790d985b38a47a4c79c2f673ef  

   Sherry, Thomas W; Holmes, Richard T; Siccama, Thomas G (January 2024, Environmental Data Initiative)

   The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991. The inventory is representative of approximately 2.5 km-squared of mid elevation northern hardwood forest. It consists of a total inventory of all trees >=10 cm dbh, within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and run roughly in an east-west direction for 2200 to 2900 m. In 1991, each live stem >=10 cm dbh was tagged with a unique number. Tree vigor is assessed every two years and diameter is remeasured every ten years. Every two years, new tags are placed on stems that have grown into the 10 cm diameter class. A survey of smaller trees (>=2 to <10 cm dbh) was first taken in 1991 and is resurveyed every ten years. This dataset includes the initial inventory values measured in 1981. The full timeline of tree inventory data for this site is available at https://doi.org/10.6073/pasta/58dfdebfd1b6440510def2394ab92c53 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

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