Purpose of Review

The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure.

Recent Findings

Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories— – approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies.

Summary

To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.

中文翻译：

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估计森林属性的变化并增强增长预测：使用机载3D点云数据的现有方法和未来方向的回顾

审查目的

三维点云的可用性不断提高，包括机载激光扫描和数字航空摄影测量，这使得能够以较高的属性精度和空间细节导出森林资源清单信息。当在两个时间点可用时，点云数据集可提供丰富的信息源，以详细分析森林结构的变化。

最近的发现

现有针对多种森林类型的研究表明，可以使用不同的方法，详细程度或源数据来执行这些分析。通过回顾相关发现，我们强调了双时相和多时相点云在增强森林生长分析方面的潜力。我们将现有方法分为两大类：–侧重于基于对两个或多个森林资源属性随时间的预测的变化估计方法，以及预测森林资源属性的方法。在讨论所使用的方法和结果的准确性之前，我们将通过比较输入的机载数据集来描述在两个或多个时间点获取的点云如何用于两种类型的分析。

概括

总而言之，我们概述了需要进一步研究的突出研究空白，包括需要更好地了解可以使用某些方法应用哪些三维数据集。我们还将讨论这些数据集对预期结果的可能影响，树对树匹配和分析的改进，与增长模拟器的集成以及最终由完全由点云数据驱动的增长模型的开发。