Update references in Statement of Need section

Revised citations to include an additional relevant source on tropical forest estimation and updated an existing reference to a more recent publication. These changes enhance the credibility and currency of the referenced research.

paper/paper.md

@@ -35,7 +35,7 @@ PyForestScan is an open-source Python library designed for calculating forest st
 
 # Statement of Need
 
-Remote sensing data, particularly Light Detection and Ranging (LiDAR) data from airborne sensors, are becoming increasingly accessible, offering a detailed understanding of forest ecosystems at fine spatial resolutions. This data is critical for calculating forest structural metrics such as canopy height, canopy cover, Plant Area Index (PAI), Plant Area Density (PAD), and Foliage Height Diversity (FHD), which are essential for forest management, biodiversity conservation, and carbon accounting [@mcelhinnyForestWoodlandStand2005], [@dubayahLidarRemoteSensing2000]. However, working with large-scale LiDAR datasets remains a challenge due to the complexity and size of the data. Additionally, despite Python being a powerful language widely used for geospatial and ecological analysis, as well as machine learning, there is a notable lack of dedicated, open-source tools within the Python ecosystem specifically designed for calculating these forest structural metrics from LiDAR data. Calculating these metrics is non-trivial, and several steps are often required to process the point clouds in order to generate these metrics. Existing open source solutions are primarily in the R programming language [@rousselLidRPackageAnalysis2020], [@dealmeidaLeafRCalculatesLeaf2021] or are proprietary, computationally intensive, or not flexible enough for the variety of ecological contexts in which these metrics are needed [@lastools], [@fusion], [@globalmapper]. This gap makes it difficult for researchers, ecologists, and forest managers to integrate LiDAR-based analysis into their workflows efficiently.
+Remote sensing data, particularly Light Detection and Ranging (LiDAR) data from airborne sensors, are becoming increasingly accessible, offering a detailed understanding of forest ecosystems at fine spatial resolutions. This data is critical for calculating forest structural metrics such as canopy height, canopy cover, Plant Area Index (PAI), Plant Area Density (PAD), and Foliage Height Diversity (FHD), which are essential for forest management, biodiversity conservation, and carbon accounting [@mcelhinnyForestWoodlandStand2005], [@drakeEstimationTropicalForest2002]. However, working with large-scale LiDAR datasets remains a challenge due to the complexity and size of the data. Additionally, despite Python being a powerful language widely used for geospatial and ecological analysis, as well as machine learning, there is a notable lack of dedicated, open-source tools within the Python ecosystem specifically designed for calculating these forest structural metrics from LiDAR data. Calculating these metrics is non-trivial, and several steps are often required to process the point clouds in order to generate these metrics. Existing open source solutions are primarily in the R programming language [@rousselLidRPackageAnalysis2020] [@rousselAirborneLiDARData2024], [@dealmeidaLeafRCalculatesLeaf2021] or are proprietary, computationally intensive, or not flexible enough for the variety of ecological contexts in which these metrics are needed [@lastools], [@fusion], [@globalmapper]. This gap makes it difficult for researchers, ecologists, and forest managers to integrate LiDAR-based analysis into their workflows efficiently.
 
 PyForestScan was developed to fill this gap by providing an open-source, Python-based solution that can handle the complexities of LiDAR data while remaining accessible and efficient. Designed for point clouds captured by airborne LiDAR, it supports commonly used formats such as .las, .laz, and .copc, and integrates with well-established geospatial frameworks for point clouds like Point Cloud Data Abstraction Library (PDAL). The more mathematically intensive calculations of PAD, PAI, and FHD are calculated following established methods by [@kamoskeLeafAreaDensity2019] and [@hurlbertNonconceptSpeciesDiversity1971], and are given by equations (1) - (3). PyForestScan provides tiling mechanisms to calculate metrics across large landscapes, IO support across multiple formats, point cloud processing tools to filter points and create ground surfaces, as well as simple visualization functions for core metrics. By focusing on forest structural metrics, PyForestScan provides an essential tool for the growing need to analyze forest structure at scale in the context of environmental monitoring, conservation, and climate-related research.