@techreport{bechtoldEnhancedForestInventory2015,
  title = {The {{Enhanced Forest Inventory}} and {{Analysis Program}} {\dbend} {{National Sampling Design}} and {{Estimation Procedures}}},
  author = {Bechtold, William A. and Patterson, Paul L.},
  year = {2015},
  number = {SRS-GTR-80},
  pages = {SRS-GTR-80},
  address = {Asheville, NC},
  institution = {U.S. Department of Agriculture, Forest Service, Southern Research Station},
  doi = {10.2737/SRS-GTR-80},
  urldate = {2025-03-25},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\5R8M6EJQ\Bechtold and Patterson - 2015 - The Enhanced Forest Inventory and Analysis Program � National Sampling Design and Estimation Procedu.pdf}
}

@book{burkhartForestMeasurements2019,
  title = {Forest Measurements},
  author = {Burkhart, Harold E. and Avery, Thomas Eugene and Bullock, Bronson P.},
  year = {2019},
  edition = {Sixth edition},
  publisher = {Waveland Press, Inc.},
  address = {Long Grove, Illinois},
  isbn = {978-1-4786-3618-2},
  langid = {english},
  annotation = {OCLC: 1046068322},
  file = {C:\Users\tara\Zotero\storage\E8PYZ5QF\Burkhart et al. - 2019 - Forest measurements.pdf}
}

@article{chiBigDataRemote2016,
  title = {Big {{Data}} for {{Remote Sensing}}: {{Challenges}} and {{Opportunities}}},
  shorttitle = {Big {{Data}} for {{Remote Sensing}}},
  author = {Chi, Mingmin and Plaza, Antonio and Benediktsson, Jon Atli and Sun, Zhongyi and Shen, Jinsheng and Zhu, Yangyong},
  year = {2016},
  month = nov,
  journal = {Proceedings of the IEEE},
  volume = {104},
  number = {11},
  pages = {2207--2219},
  issn = {0018-9219, 1558-2256},
  doi = {10.1109/JPROC.2016.2598228},
  urldate = {2025-03-26},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\PCSBLVV7\Chi et al. - 2016 - Big Data for Remote Sensing Challenges and Opportunities.pdf}
}

@article{estebanEstimatingForestVolume2019,
  title = {Estimating {{Forest Volume}} and {{Biomass}} and {{Their Changes Using Random Forests}} and {{Remotely Sensed Data}}},
  author = {Esteban, Jessica and McRoberts, Ronald and {Fern{\'a}ndez-Landa}, Alfredo and Tom{\'e}, Jos{\'e} and N{\cyrchar\cyrae}sset, Erik},
  year = {2019},
  month = aug,
  journal = {Remote Sensing},
  volume = {11},
  number = {16},
  pages = {1944},
  issn = {2072-4292},
  doi = {10.3390/rs11161944},
  urldate = {2025-03-25},
  abstract = {Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\UDSTJGX8\Esteban et al. - 2019 - Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data.pdf}
}

@article{fassnachtRemoteSensingForestry2024a,
  title = {Remote Sensing in Forestry: Current Challenges, Considerations and Directions},
  shorttitle = {Remote Sensing in Forestry},
  author = {Fassnacht, Fabian Ewald and White, Joanne C and Wulder, Michael A and N{\ae}sset, Erik},
  editor = {Achim, Alexis},
  year = {2024},
  month = jan,
  journal = {Forestry: An International Journal of Forest Research},
  volume = {97},
  number = {1},
  pages = {11--37},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpad024},
  urldate = {2025-03-25},
  abstract = {Remote sensing has developed into an omnipresent technology in the scientific field of forestry and is also increasingly used in an operational fashion. However, the pace and level of uptake of remote sensing technologies into operational forest inventory and monitoring programs varies notably by geographic region. Herein, we highlight some key challenges that remote sensing research can address in the near future to further increase the acceptance, suitability and integration of remotely sensed data into operational forest inventory and monitoring programs. We particularly emphasize three recurrent themes: (1) user uptake, (2) technical challenges of remote sensing related to forest inventories and (3) challenges related to map validation. Our key recommendations concerning these three thematic areas include (1) a need to communicate and learn from success stories in those geographic regions where user uptake was successful due to multi-disciplinary collaborations supported by administrative incentives, (2) a shift from regional case studies towards studies addressing `real world' problems focusing on forest attributes that match the spatial scales and thematic information needs of end users and (3) an increased effort to develop, communicate, and apply best-practices for map and model validation including an effort to inform current and future remote sensing scientists regarding the need for and the functionalities of these best practices. Finally, we present information regarding the use of remote sensing for forest inventory and monitoring, combined with recommendations where possible, and highlighting areas of opportunity for additional investigation.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\3J738ZWQ\Fassnacht et al. - 2024 - Remote sensing in forestry current challenges, considerations and directions.pdf}
}

@article{grayForestInventoryAnalysis2012,
  title = {Forest {{Inventory}} and {{Analysis Database}} of the {{United States}} of {{America}} ({{FIA}})},
  author = {Gray, Andrew and Brandeis, Thomas and Shaw, John and McWilliams, William and Miles, Patrick},
  year = {2012},
  month = sep,
  journal = {Biodiversity \& Ecology},
  volume = {4},
  pages = {225--231},
  issn = {16139801},
  doi = {10.7809/b-e.00079},
  urldate = {2025-03-25},
  abstract = {Extensive vegetation inventories established with a probabilistic design are an indispensable tool in describing distributions of species and community types and detecting changes in composition in response to climate or other drivers. The Forest Inventory and Analysis Program measures vegetation in permanent plots on forested lands across the United States of America (GIVD ID NAUS-001). Plot sizes and protocols for measuring tree species are standardized across the country. Additional standardized protocols have been implemented to measure the abundance of non-tree vascular plant and epiphytic lichen species. Research using this and related regional datasets have provided new insights into the key biophysical drivers of community composition and their importance at different spatial scales. Studies have also explored regional differences in species diversity patterns, documented the importance of non-native species, and described the importance of environment and management on the distribution of selected species. Although representation of locally rare community types may be low, the probabilistic sample ensures that ecological drivers are regionally significant and that results are representative of a region as a whole. Remeasurement of permanent plots provides direct evidence of vegetation change and enables detection of impacts due to climate, natural disturbance, and forest management.},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\PFFP37QJ\Gray et al. - 2012 - Forest Inventory and Analysis Database of the United States of America (FIA).pdf}
}

@book{IssuesGeneralizedAdditive,
  title = {Issues {\textbar} {{Generalized Additive Models}}},
  urldate = {2025-04-03},
  abstract = {An introduction to generalized additive models (GAMs) is provided, with an emphasis on generalization from familiar linear models. It makes extensive use of the mgcv package in R. Discussion includes common approaches, standard extensions, and relations to other techniques. More technical modeling details are described and demonstrated as well.},
  file = {C:\Users\tara\Zotero\storage\WP4QNREV\issues.html}
}

@article{jeronimoApplyingLiDARIndividual2018a,
  title = {Applying {{LiDAR Individual Tree Detection}} to {{Management}} of {{Structurally Diverse Forest Landscapes}}},
  author = {Jeronimo, Sean M A and Kane, Van R and Churchill, Derek J and McGaughey, Robert J and Franklin, Jerry F},
  year = {2018},
  month = jun,
  journal = {Journal of Forestry},
  volume = {116},
  number = {4},
  pages = {336--346},
  issn = {0022-1201, 1938-3746},
  doi = {10.1093/jofore/fvy023},
  urldate = {2025-03-25},
  abstract = {LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscapes. We investigated how ITD results varied across diverse structural conditions in California's Sierra Nevada mixed-conifer forests and what this taught us about when and how to apply ITD. Our results suggest that it is advantageous to use ITD when it improves analysis interpretability, when measuring horizontal patterns, or when field data are unavailable. In the latter case, it is best to focus on measures dominated by large trees, like basal area and biomass. Thinking of ITD results as ``tree-approximate objects'' including one dominant tree and up to a few subordinate tree respects LiDAR's strengths and limitations; we illustrate how this concept keeps analyses consistent across varying structural conditions.},
  copyright = {https://academic.oup.com/journals/pages/about\_us/legal/notices},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\BGZVWBL7\Jeronimo et al. - 2018 - Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes.pdf}
}
@article{jhaEvaluationRegressionMethods2023,
  title = {Evaluation of Regression Methods and Competition Indices in Characterizing Height-Diameter Relationships for Temperate and Pantropical Tree Species},
  author = {Jha, Sakar and Yang, Sheng-I and Brandeis, Thomas J. and Kuegler, Olaf and {Marcano-Vega}, Humfredo},
  year = {2023},
  month = nov,
  journal = {Frontiers in Forests and Global Change},
  volume = {6},
  pages = {1282297},
  issn = {2624-893X},
  doi = {10.3389/ffgc.2023.1282297},
  urldate = {2025-05-04},
  abstract = {Height-diameter relationship models, denoted as H-D models, have important applications in sustainable forest management which include studying the vertical structure of a forest stand, understanding the habitat heterogeneity for wildlife niches, analyzing the growth rate pattern for making decisions regarding silvicultural treatments. Compared to monocultures, characterizing allometric relationships for uneven-aged, mixed-species forests, especially tropical forests, is more challenging and has historically received less attention. Modeling how the competitive interactions between trees of varying sizes and multiple species affects these relationships adds a high degree of complexity. In this study, five regression methods and five distance-independent competition indices were evaluated for temperate and pantropical tree species in different physiographic regions. A total of 163,922 individual tree measurements from the US Department of Agriculture, Forest Inventory and Analysis (FIA) database were used in analyses, which cover Appalachian plateau (AP) and Ridge and Valley (VR) in the southeastern US, as well as Caribbean (CAR) and Pacific (PAC) islands. Results indicated that the generalized additive model (GAM) and the Pearl and Reed model provided more accurate predictions than other regression methods examined. Models with competition indices had a varying level of predictability, while diameter ratio, cumulative distribution function and partitioned stand density index (PSDI) were found to improve the prediction accuracy for AP, VR and CAR. The results of this work provide additional insights on modeling H-D relationships for a variety of species in temperate and pantropical forests.},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\9QA88D8R\Jha et al. - 2023 - Evaluation of regression methods and competition indices in characterizing height-diameter relations.pdf}
}


@article{patenaudeQuantifyingForestGround2004,
  title = {Quantifying Forest above Ground Carbon Content Using {{LiDAR}} Remote Sensing},
  author = {Patenaude, G. and Hill, R.A and Milne, R. and Gaveau, D.L.A. and Briggs, B.B.J. and Dawson, T.P.},
  year = {2004},
  month = nov,
  journal = {Remote Sensing of Environment},
  volume = {93},
  number = {3},
  pages = {368--380},
  issn = {00344257},
  doi = {10.1016/j.rse.2004.07.016},
  urldate = {2025-03-25},
  abstract = {The UNFCCC and interest in the source of the missing terrestrial carbon sink are prompting research and development into methods for carbon accounting in forest ecosystems. Here we present a canopy height quantile-based approach for quantifying above ground carbon content (AGCC) in a temperate deciduous woodland, by means of a discrete-return, small-footprint airborne LiDAR. Fieldwork was conducted in Monks Wood National Nature Reserve UK to estimate the AGCC of five stands from forest mensuration and allometric relations. In parallel, a digital canopy height model (DCHM) and a digital terrain model (DTM) were derived from elevation measurements obtained by means of an Optech Airborne Laser Terrain Mapper 1210. A quantile-based approach was adopted to select a representative statistic of height distributions per plot. A forestry yield model was selected as a basis to estimate stemwood volume per plot from these heights metrics. Agreement of r=0.74 at the plot level was achieved between ground-based AGCC estimates and those derived from the DCHM. Using a 20 20 m grids superposed to the DCHM, the AGCC was estimated at the stand level and at the woodland level. At the stand level, the agreement between the plot data upscaled in proportion to area and the LiDAR estimates was r=0.85. At the woodland level, LiDAR estimates were nearly 24\% lower than those from the upscaled plot data. This suggests that field-based approaches alone may not be adequate for carbon accounting in heterogeneous forests. Conversely, the LiDAR 20 20 m grid approach has an enhanced capability of monitoring the natural variability of AGCC across the woodland.},
  copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\9W7234PY\Patenaude et al. - 2004 - Quantifying forest above ground carbon content using LiDAR remote sensing.pdf}
}

@article{wuGeemapPythonPackage2020,
  title = {Geemap: {{A Python}} Package for Interactive Mapping with {{Google Earth Engine}}},
  shorttitle = {Geemap},
  author = {Wu, Qiusheng},
  year = {2020},
  month = jul,
  journal = {Journal of Open Source Software},
  volume = {5},
  number = {51},
  pages = {2305},
  issn = {2475-9066},
  doi = {10.21105/joss.02305},
  urldate = {2025-04-07},
  abstract = {Wu, Q., (2020). geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software, 5(51), 2305, https://doi.org/10.21105/joss.02305},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\G75MUVAJ\Wu - 2020 - geemap A Python package for interactive mapping with Google Earth Engine.pdf}
}

@book{wulderRemoteSensingForest2012,
  title = {Remote {{Sensing}} of {{Forest Environments}}: {{Concepts}} and {{Case Studies}}},
  shorttitle = {Remote {{Sensing}} of {{Forest Environments}}},
  author = {Wulder, Michael A. and Franklin, Steven E.},
  year = {2012},
  month = dec,
  publisher = {Springer Science \& Business Media},
  abstract = {Successful remote sensing methods and applications are rooted in the science, art, and technology of earth observation, part of the larger emerging world of geographical information science. Sensors are increasingly sensitive to the phenomena we wish to observe and image analysis systems are increasingly able to transform the data and deliver the required information. At the same time, advances in forest science and management continue to develop momentum. A renewed commitment to understanding forests at different scales and with a process-based perspective has helped generate demand for information about forests that is feasibly acquired only by remote sensing. Remote Sensing of Forest Environments: Concepts and Case Studies is an edited volume intended to provide readers with a state-of-the-art synopsis of the current methods and applied applications employed in remote sensing the world's forests. The contributing authors have sought to illustrate and deepen our understanding of remote sensing of forests, providing new insights and indicating opportunities that are created when forests and forest practices are considered in concert with the evolving paradigm of remote sensing science. Following background and methods sections, this book introduces a series of case studies that exemplify the ways in which remotely sensed data are operationally used, as an element of the decision-making process, and in the scientific study of forests. Remote Sensing of Forest Environments: Concepts and Case Studies is designed to meet the needs of a professional audience composed of both practitioners and researchers. This book is also suitable as a secondary text for graduate-level students in Forestry, Environmental Science, Geography, Engineering, and Computer Science.},
  googlebooks = {zIvuBwAAQBAJ},
  isbn = {978-1-4615-0306-4},
  langid = {english},
  keywords = {Computers / Optical Data Processing,Computers / Software Development & Engineering / Computer Graphics,Computers / Software Development & Engineering / General,Science / Earth Sciences / Geography,Technology & Engineering / Agriculture / Forestry,Technology & Engineering / Electronics / General,Technology & Engineering / Imaging Systems,Technology & Engineering / Remote Sensing & Geographic Information Systems}
}

@article{yangEstimatingMaximumStand2022,
  title = {Estimating Maximum Stand Density for Mixed-Hardwood Forests among Various Physiographic Zones in the Eastern {{US}}},
  author = {Yang, Sheng-I and Brandeis, Thomas J.},
  year = {2022},
  month = oct,
  journal = {Forest Ecology and Management},
  volume = {521},
  pages = {120420},
  issn = {03781127},
  doi = {10.1016/j.foreco.2022.120420},
  urldate = {2025-03-25},
  abstract = {Quantifying maximum stand density is important to evaluate the potential stand density of the target population in forest management. However, most of the past research in the eastern US was mainly focused on planted monocultures, coniferous forests, a few commercially important species at the stand level, or in a particular geographic region. This study aimed to estimate the maximum stand density for mixed-hardwood forests across physiographic zones in the eastern US between two decades (1996--2009 and 2010--2021). Data used in analyses were collected from the US national forest inventory established and maintained by the USDA Forest Service's Forest Inventory and Analysis (FIA) program.},
  langid = {english},
  file = {C:\Users\tara\Zotero\storage\N6EUWH64\Yang and Brandeis - 2022 - Estimating maximum stand density for mixed-hardwood forests among various physiographic zones in the.pdf}
}

@misc{ipyleaflet,
    url = {https://ipyleaflet.readthedocs.io/en/stable/},
    Author = {{ipyleaflet}},
    Title = {{ipyleaflet: A Jupyter Widget for Leaflet Maps}},
    Year = {2023},
    Publisher = {GitHub},
    Journal = {GitHub repository}
}

@misc{ipywidget,
    url = {https://ipywidgets.readthedocs.io/en/stable/},
    Author = {{ipywidgets}},
    Title = {{ipywidgets: Interactive Widgets for the Jupyter Notebook}},
    Year = {2017},
    Publisher = {GitHub},
    Journal = {GitHub repository}
}

@misc{pandas,
    url = {https://pandas.pydata.org/},
    Author = {{pandas}},
    Title = {{pandas: A Python Data Analysis Library}},
    Year = {2011},
    Publisher = {GitHub},
    Journal = {GitHub repository}
}

@misc{geopandas,
    url = {https://geopandas.org/},
    Author = {{geopandas}},
    Title = {{geopandas: Python Geospatial Analysis Library}},
    Year = {2013},
    Publisher = {GitHub},
    Journal = {GitHub repository}
}

@misc{geemap,
    url = {https://doi.org/10.21105/joss.02305},
    Author = {{geemap}},
    Title = {{geemap: A Python Package for Interactive Mapping and Geospatial Analysis with Google Earth Engine}},
    Year = {2020},
    Publisher = {GitHub},
    Journal = {GitHub repository}
}

@misc{ee,
    url = {https://doi.org/10.1016/j.rse.2017.06.031},
    Author = {{Google Earth Engine}},
    Title = {{Google Earth Engine: Planetary-scale geospatial analysis for everyone}},
    Year = {2016},
    Publisher = {GitHub},
    Journal = {GitHub repository}
}