Add new references and links, update acknowledgments

This commit adds new references in the bibliography and updates citations in the paper's markdown file. It includes precise URLs for Jupyter notebooks and additional acknowledgment for Ryan Perroy's contributions. The .gitignore file is also updated to exclude .pdf and .jats files.

.gitignore

@@ -175,4 +175,7 @@ test_data/output.tif
 # tifs created from tests
 *.tif
 
-docs/example_data/ept
+docs/example_data/ept
+
+*.pdf
+*.jats

paper/paper.bib

@@ -366,3 +366,21 @@ @software{copc_format
   url          = {https://copc.io/},
   note         = {Latest version accessed on 2024-11-22}
 }
+
+@article{tangAlgorithmTheoreticalBasis2019,
+  title = {Algorithm {{Theoretical Basis Document}} ({{ATBD}}) for {{GEDI L2B Footprint Canopy Cover}} and {{Vertical Profile Metrics}}},
+  author = {Tang, Hao and Armston, John},
+  year = {2019},
+  month = dec,
+  langid = {english},
+  file = {/Users/iosefa/Zotero/storage/IBWZ8BJW/Tang and Armston - GEDI L2B Footprint Canopy Cover and Vertical Profi.pdf}
+}
+
+@misc{NOAA_HI_Lidar_2019,
+  author       = {Office for Coastal Management},
+  year         = {2024},
+  title        = {2018 - 2020 NOAA USGS Lidar: Hawaii, HI from 2019},
+  publisher    = {NOAA National Centers for Environmental Information},
+  url          = {https://www.fisheries.noaa.gov/inport/item/68082},
+  note         = {Accessed: 2024}
+}

paper/paper.md

@@ -29,13 +29,13 @@ bibliography: paper.bib
 
 # Summary
 
-PyForestScan is an open-source Python library designed for calculating forest structural metrics from Light Detection and Ranging (lidar) point cloud data at scale. The software calculates key ecological metrics such as foliage height diversity (FHD), plant area density (PAD), canopy height, plant area index (PAI), and digital terrain models (DTMs), efficiently handles large-scale lidar datasets, and supports input formats including the Entwine Point Tile (EPT) format, .las, .laz, and .copc files. In addition to metrics computation, the library supports the generation of GeoTIFF outputs and integrates with geospatial libraries like the Point Cloud Data Abstraction Library (PDAL) [@howard_butler_2024_13993879; @BUTLER2021104680], making it a valuable tool for forest monitoring, carbon accounting, and ecological research.
+PyForestScan is an open-source Python library designed for calculating forest structural metrics from Light Detection and Ranging (lidar) point cloud data at scale. The software calculates key ecological metrics such as foliage height diversity (FHD), plant area density (PAD), canopy height, plant area index (PAI), and digital terrain models (DTMs), efficiently handles large-scale lidar datasets, and supports input formats including the Entwine Point Tile (EPT) format [@manning_entwine], .las, .laz, and .copc files. In addition to metrics computation, the library supports the generation of GeoTIFF outputs and integrates with geospatial libraries like the Point Cloud Data Abstraction Library (PDAL) [@howard_butler_2024_13993879; @BUTLER2021104680], making it a valuable tool for forest monitoring, carbon accounting, and ecological research.
 
 # Statement of Need
 
 Remote sensing data, particularly point cloud data from airborne lidar sensors, are becoming increasingly accessible, offering a detailed understanding of forest ecosystems at fine spatial resolutions over large areas. This data is useful for calculating forest structural metrics such as canopy height, canopy cover, PAI, PAD, FHD, as well as DTMs, which are essential for forest management, biodiversity conservation, and carbon accounting [@mcelhinnyForestWoodlandStand2005; @drakeEstimationTropicalForest2002; @pascualRoleImprovedGround2020; @guerra-hernandezUsingBitemporalALS2024; @pascualIntegratedAssessmentCarbon2023; @pascualNewRemoteSensingbased2021a]. 
 
-Despite Python's prominence as a powerful language for geospatial and ecological analysis, there is a notable lack of dedicated, open-source tools within the Python ecosystem specifically designed for calculating comprehensive forest structural metrics from airborne lidar point-cloud data. This gap is significant given Python's extensive libraries for data science and its increasingly important role in ecology and deep learning [@doi:10.1111/2041-210X.13901]. Existing open-source solutions that offer some of these metrics are primarily available in the R programming language. For instance, lidR [@rousselLidRPackageAnalysis2020a; @rousselAirborneLiDARData2024] provides functions for point cloud manipulation, metric computation, and visualization but lacks native calculations for foliage height diversity (FHD) and plant area index (PAI). Another tool, leafR [@dealmeidaLeafRCalculatesLeaf2021], calculates FHD, leaf area index (LAI), and leaf area density (LAD) - both of which are very similar to PAI and PAD - but is limited in processing large datasets due to the absence of tiling functionality. Moreover, the importance of scale in lidar-based analyses of forest structure is well-documented [@doi:10.1111/2041-210X.14040], and leafR does not allow users to modify voxel depth, which can be important for accurate estimation of structural metrics across different forest types and scales. Similarly, canopyLazR [@kamoskeLeafAreaDensity2019] provides tools to calculate LAD and LAI from point cloud lidar data but only allows the calculation of these metrics and also lacks support for tiling mechanisms, limiting its applicability to large datasets. Proprietary solutions like LAStools [@lastools], FUSION [@fusion], and Global Mapper [@globalmapper] offer tools to calculate some of these metrics -mostly canopy height- but may not provide the flexibility required for diverse ecological contexts and are often inaccessible due to licensing costs. This lack of a comprehensive, scalable Python-based solution makes it challenging for researchers, ecologists, and forest managers to integrate point-cloud-based analysis into their Python workflows efficiently. This is particularly problematic when working with large datasets or when integrating analyses with other Python-based tools, such as those used for processing space-based waveform lidar data from the Global Ecosystem Dynamics Investigation (GEDI) mission [@DUBAYAH2020100002] [also cite ATBD], which also provides data on PAI, plant area volume density (PAVD), and FHD.  
+Despite Python's prominence as a powerful language for geospatial and ecological analysis, there is a notable lack of dedicated, open-source tools within the Python ecosystem specifically designed for calculating comprehensive forest structural metrics from airborne lidar point-cloud data. This gap is significant given Python's extensive libraries for data science and its increasingly important role in ecology and deep learning [@doi:10.1111/2041-210X.13901]. Existing open-source solutions that offer some of these metrics are primarily available in the R programming language. For instance, lidR [@rousselLidRPackageAnalysis2020a; @rousselAirborneLiDARData2024] provides functions for point cloud manipulation, metric computation, and visualization but lacks native calculations for foliage height diversity (FHD) and plant area index (PAI). Another tool, leafR [@dealmeidaLeafRCalculatesLeaf2021], calculates FHD, leaf area index (LAI), and leaf area density (LAD) - both of which are very similar to PAI and PAD - but is limited in processing large datasets due to the absence of tiling functionality. Moreover, the importance of scale in lidar-based analyses of forest structure is well-documented [@doi:10.1111/2041-210X.14040], and leafR does not allow users to modify voxel depth, which can be important for accurate estimation of structural metrics across different forest types and scales. Similarly, canopyLazR [@kamoskeLeafAreaDensity2019] provides tools to calculate LAD and LAI from point cloud lidar data but only allows the calculation of these metrics and also lacks support for tiling mechanisms, limiting its applicability to large datasets. Proprietary solutions like LAStools [@lastools], FUSION [@fusion], and Global Mapper [@globalmapper] offer tools to calculate some of these metrics -mostly canopy height- but may not provide the flexibility required for diverse ecological contexts and are often inaccessible due to licensing costs. This lack of a comprehensive, scalable Python-based solution makes it challenging for researchers, ecologists, and forest managers to integrate point-cloud-based analysis into their Python workflows efficiently. This is particularly problematic when working with large datasets or when integrating analyses with other Python-based tools, such as those used for processing space-based waveform lidar data from the Global Ecosystem Dynamics Investigation (GEDI) mission [@tangAlgorithmTheoreticalBasis2019; @DUBAYAH2020100002], which also provides data on PAI, plant area volume density (PAVD), and FHD.  
 
 In addition to the lack of Python-based software for calculating forest structural metrics like PAI, PAD, and FHD, working with large-scale point clouds remains a challenge due to complexities inherent in the size of the data. Lidar datasets can vary in point densities—from about 2-3 points per square meter in airborne surveys covering vast landscapes to upwards of 2,000 points per square meter in high-resolution drone-based surveys, potentially resulting in terabytes of data. To manage these large volumes, datasets are typically divided into fixed-size tiles, which must be individually loaded into memory for analysis. This approach can introduce inflexibility because analyses may need to conform strictly to tile boundaries, potentially causing boundary effects when calculating metrics that span across tiles. While tools like lidR can handle tiling and mitigate boundary effects natively, they do not fully leverage the advanced spatial indexing provided by formats like EPT [@manning_entwine] and Cloud Optimized Point Cloud (COPC) [@copc_format]. Additionally, fixed tile sizes may not align with varying memory constraints or specific workflow needs, limiting the ability to adjust tile sizes dynamically based on data density and processing requirements. For example, extracting point clouds over specific polygons within tiles, or performing exploratory data analysis over a large region consisting of several tiles can be overly time-consuming as it often requires reading all data into memory. 
 
@@ -64,14 +64,14 @@ In equation (3), $FHD$ is calculated as the Shannon entropy of the vertical dist
 
 # Usage
 
-To facilitate usage of the software, we have included Jupyter notebooks in the Github repository detailing how to get started using PyForestScan as well as how to calculate forest metrics. The example dataset is a one-square-kilometer tile derived from a 2019 aerial lidar survey of the Big Island of Hawaii [CITE]. The Jupyter notebooks include an example data set of a point cloud with a nominal pulse spacing of 0.35 meters and was captured over a dry forest environment. The data has been preprocessed to classify ground and vegetation points [CITE]. More details are available in the documentation. 
+To facilitate usage of the software, we have included [Jupyter notebooks](https://github.com/iosefa/PyForestScan/tree/main/docs/examples) in the [GitHub repository](https://github.com/iosefa/PyForestScan) detailing how to get started using PyForestScan as well as how to calculate forest metrics. The Jupyter notebooks include an example data set of a point cloud with a nominal pulse spacing of 0.35 meters and was captured over a dry forest environment. This example dataset is a one-square-kilometer tile derived from a 2018-2020 aerial lidar survey of the Big Island of Hawaii [@NOAA_HI_Lidar_2019]. The data has been preprocessed to classify ground and vegetation points [@guerra-hernandezHighresolutionCanopyHeight2024]. More details are available in the documentation. 
 
 # Contributions
 JEHP developed the concept with input from BPL; JEHP wrote the initial versions of the software and automatic tests with contributions from BPL; BPL and JEHP wrote the software documentation and created Jupyter notebooks for example usage; and both authors wrote the manuscript.
 
 # Acknowledgements
 
-We would like to express our gratitude to Juan Guerra-Hernandez and Adrian Pascual for providing a noise-free classified point cloud dataset [@guerra-hernandezHighresolutionCanopyHeight2024], which was instrumental in testing and validating the PyForestScan library. This work was enabled in part by funding from the National Science Foundation award: 2149133. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
+We would like to express our gratitude to Juan Guerra-Hernandez and Adrian Pascual for providing a noise-free classified point cloud dataset [@guerra-hernandezHighresolutionCanopyHeight2024], which was instrumental in testing and validating the PyForestScan library. We would also like to thank Ryan Perroy for his feedback and help in revising this manuscript. This work was enabled in part by funding from the National Science Foundation award: 2149133. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
 
 # References