Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. For this purpose, detection and delineation of individual tree crowns (ITCs) is a prerequisite. Here, we present a new method of automatic tree crown delineation based only on very high resolution images from WorldView-2 satellite and apply it to a region of the Atlantic rain forest with highly heterogeneous tropical canopy cover – the Santa Genebra forest reserve in Brazil. The method works in successive steps that involve pre-processing, selection of forested pixels, enhancement of borders, detection of pixels in the crown borders, correction of shade in large trees and, finally, segmentation of the tree crowns. Principally, the method uses four techniques: rolling ball algorithm and mathematical morphological operations to enhance the crown borders and ease the extraction of tree crowns; bimodal distribution parameters estimations to identify the shaded pixels in the gaps, borders, and crowns; and focal statistics for the analysis of neighbouring pixels. Crown detection is validated by comparing the delineated ITCs with a sample of ITCs delineated manually by visual interpretation. In addition, to test if the spectra of individual species are conserved in the automatic delineated crowns, we compare the accuracy of species prediction with automatic and manual delineated crowns with known species. We find that our method permits detection of up to 80% of ITCs. The seven species with over 10 crowns identified in the field were mapped with reasonable accuracy (30.5–96%) given that only WorldView-2 bands and texture features were used. Similar classification accuracies were obtained using both automatic and manual delineation, thereby confirming that species’ spectral responses are preserved in the automatic method and thus permitting the recognition of species at the landscape scale. Our method might support tropical forest applications, such as mapping species and canopy characteristics at the landscape scale.

在景观尺度上绘制热带树木物种的地图，为生态学家和森林经营者提供信息，这是遥感界面临的新挑战。为此，必须对单个树冠（ITC）进行检测和描绘。在这里，我们提出了一种仅基于WorldView-2卫星的高​​分辨率图像进行树冠自动定界的新方法，并将其应用于具有高度非均质热带冠层覆盖的大西洋雨林地区-巴西的Santa Genebra森林保护区。该方法以连续的步骤工作，包括预处理，森林像素的选择，边界的增强，树冠边界中像素的检测，大树的阴影校正以及最后树冠的分割。该方法主要使用四种技术：滚球算法和数学形态学运算可增强树冠边界并简化树冠的提取；双峰分布参数估计，以识别间隙，边界和冠中的阴影像素；以及焦点统计数据，用于分析相邻像素。通过将划定的ITC与通过视觉解释手动划定的ITC样本进行比较来验证冠冕检测。另外，为了测试单个物种的光谱在自动描绘的冠中是否守恒，我们将已知物种的自动和手动描绘冠的物种预测准确性进行了比较。我们发现我们的方法可以检测多达80％的ITC。在田野中识别出的具有超过10个冠的7种物种以合理的精度进行了定位（30。5–96％），因为仅使用了WorldView-2频段和纹理特征。使用自动和手动划定方法都获得了相似的分类精度，从而确认了在自动方法中物种的光谱响应得以保留，因此可以在景观尺度上识别物种。我们的方法可能支持热带森林的应用，例如在景观尺度上绘制物种和树冠特征。