Wiesel Patrik Gustavo, Schroeder Marcos Henrique, Deprá Bruno, Salgueiro Bianca Junkherr, Barreto Betina Mariela, de Santana Eduardo Rodrigo Ramos, Köhler Andreas, Lobo Eduardo Alcayaga
Environmental Technology Program, University of Santa Cruz Do Sul-UNISC, Santa Cruz do Sul, Rio Grande Do Sul, Brazil.
Veterinary Medicine Course, University of Santa Cruz Do Sul-UNISC, Santa Cruz do Sul, Rio Grande Do Sul, Brazil.
Environ Monit Assess. 2024 Dec 16;197(1):55. doi: 10.1007/s10661-024-13501-5.
The invasive species Hovenia dulcis is considered the main invasive species in the Atlantic Forest, capable of altering environmental conditions at a local scale and provoking profound changes in the composition of the plant community. Combining drone and satellite images can make forest monitoring more efficient, enabling a more targeted and effective response to contain the spread of invasive species. This research aimed to use high-resolution CBERS-4A satellite combined with drone images to detect invasive trees in forested areas of the Atlantic Forest. An object-oriented, supervised automatic classification was performed using the Dzetsaka Classification Tool and the Gaussian Mixture Model method. Additionally, georeferenced orthomosaics obtained by drones, totaling 150 ha, were used to confirm the identification of the invasive species. The entire forest area was surveyed to determine the tree community, where 72 random sample plots, each with a fixed area of 100 m, were established. The calculated indices, such as the Shannon index (H') = 3.65 and uniformity (J') = 78%, demonstrate that the plant community has a high diversity of species. However, the invasive H. dulcis had the highest number of sampled individuals (146), being the species with the highest relative density (9.14) within the community and the second highest in relative frequency (5.10%), coverage importance value (8.85%), and importance value index (7.60%). The methodology employed to identify the invasive species through satellite, and drone images allowed for rapid and precise data collection and quantification of the invasive species, covering an area of 86.44 ha of the forest fragment, which corroborates the field data.
入侵物种枳椇被认为是大西洋森林中的主要入侵物种，它能够在局部尺度上改变环境条件，并引发植物群落组成的深刻变化。结合无人机和卫星图像可以提高森林监测效率，从而能够更有针对性、更有效地应对入侵物种的扩散。本研究旨在利用高分辨率的CBERS-4A卫星结合无人机图像，检测大西洋森林林区中的入侵树木。使用Dzetsaka分类工具和高斯混合模型方法进行了面向对象的监督自动分类。此外，还使用了无人机获得的总计150公顷的地理参考正射镶嵌图，以确认入侵物种的识别。对整个森林区域进行了调查，以确定树木群落，在该区域建立了72个随机样本地块，每个地块的固定面积为100平方米。计算得出的指数，如香农指数（H'）= 3.65和均匀度（J'）= 78%，表明植物群落具有高度的物种多样性。然而，入侵的枳椇的采样个体数量最多（146个），是群落中相对密度最高的物种（9.14），相对频率排名第二（5.10%），覆盖重要值（8.85%）和重要值指数（7.60%）。通过卫星和无人机图像识别入侵物种的方法能够快速、精确地收集数据并对入侵物种进行量化，覆盖了86.44公顷的森林片段，这与实地数据相符。
