Ahmed Shara, Nicholson Catherine E, Muto Paul, Perry Justin J, Dean John R
Department of Applied Sciences, Northumbria University, Ellison Building, Newcastle upon Tyne, United Kingdom.
Natural England, Lancaster House, Hampshire Court, Newcastle upon Tyne, United Kingdom.
PLoS One. 2021 Nov 15;16(11):e0260056. doi: 10.1371/journal.pone.0260056. eCollection 2021.
An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.
利用搭载多光谱图像（MSI）相机的无人机，通过应用航空光谱技术对一片古老的半天然林地（ASNW）进行了调查。使用主成分分析（PCA）开发了一种新型归一化差异光谱指数（NDSI）算法。然后将这种新型NDSI与一种简单的阈值分割方法相结合，用于识别本地树种以及林地的整体健康状况。采用这种新方法，可以在7.4公顷（73,934平方米）的ASNW冠层水平上识别树木，其中橡树占53%，白桦占37%，空地占9%，死树占1%。通过一项具有统计学有效性的地面实地研究证实了无人机获取数据的准确性，该研究识别出橡树占47%，白桦占46%，死树占7.4%。这种简单的创新方法，使用配备MSI相机的低成本多旋翼无人机，部署迅速，在离地面约100米的高度飞行，能在22分钟内提供可用的高分辨率（5.3厘米/像素）数据，可使用现成的基于PC的软件进行查询以识别树种。此外，它还能对林地健康状况进行全面监测，并有可能为未来的林地更新策略提供参考。
