Airborne photogrammetric image archives offer interesting possibilities for multi-temporal analyses of environmental evolution. The objective of this investigation was to develop a technique for classifying forest successional stages and performing multi-temporal analyses of the tree canopy based on tree height variances calculated from digital surface models (DSMs) created from photogrammetric imagery. Furthermore, our objective was to evaluate the usability of the technique in assessing the evolution of successional stages in a tropical forest. The local variance calculation in 3D space resulted in an image that was subdivided with a segmentation technique to generate small areas called superpixels. These superpixels, which use the local mean variance as an attribute, are assessed via cluster analysis to evaluate statistical similarity and define successional stage classes. The same superpixel shapes were located in georeferenced historical datasets to enable multi-temporal analysis. The cluster analysis of temporal superpixels enabled the spatiotemporal classification of forest canopy evolution. The technique was used to assess a tropical forest remnant in Brazil. Dense DSMs were generated with stereo-photogrammetric techniques using optical images (both film and digital images) from which height variances were computed. A cluster analysis of superpixels was performed to classify the forest canopy into four successional stages, which were consistent with Brazilian classification rules. The multi-temporal analysis identified six classes of forest cover evolution. Field data were collected in forest plots to validate the generated forest canopy classifications. The results showed that the proposed approach was feasible for forest cover classification and for identifying changes in the vertical forest structure and cover over time using only optical images.

机载摄影测量图像档案库为环境演变的多时间分析提供了有趣的可能性。这项研究的目的是开发一种技术，用于对森林演替阶段进行分类，并根据从摄影测量图像创建的数字表面模型（DSM）计算出的树高变化对树冠进行多时相分析。此外，我们的目标是评估该技术在评估热带森林演替阶段演变中的可用性。在3D空间中进行局部方差计算得出的图像经过细分技术细分后生成了称为超像素的小区域。这些将局部均值方差用作属性的超像素，通过聚类分析来评估统计相似度并定义连续阶段类。相同的超像素形状位于地理参考的历史数据集中，以实现多时间分析。时间超像素的聚类分析使森林冠层演变的时空分类成为可能。该技术用于评估巴西的热带森林残留物。使用立体摄影测量技术，使用光学图像（胶片图像和数字图像）生成密集的DSM，并从中计算出高度变化。进行了超像素的聚类分析，将森林冠层分为四个连续的阶段，这与巴西的分类规则是一致的。多时相分析确定了森林覆盖演化的六类。在林地中收集现场数据，以验证生成的林冠层分类。结果表明，所提出的方法对于仅通过光学图像进行森林覆盖物分类以及识别垂直森林结构和覆盖物随时间的变化是可行的。