ABSTRACT

Using remote-sensing technology to accurately map the composition and distribution of tree species is vital for sustainable forest resource management. Sentinel-2 data with the dense time-series observations enable to identify tree species. However, few studies clarify the differences in classification using Sentinel-2 images in natural forest and planted forest. Two study areas with different forest environments (planted forest and natural forest) were selected to evaluate the potential of Sentinel-2 imagery. Our results show that red-edge band, short-wave infrared (SWIR) band and vegetation indices (VIs), such as Red-Edge Normalized Difference Vegetation Index (ReNDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), have important effects on tree species classification, especially in the growing season. By using two machine learning algorithms (support vector machine [SVM] and random forest [RF]), the results show that the classification accuracy for planted forests (91.27% in SVM and 88.35% in RF) significantly exceeds that of natural forests (84.34% in SVM and 81.03% in RF). This accuracy difference may be related to the spatial heterogeneity inside the forest and the surrounding environmental implications. Although the multi-temporal Sentinel-2 images produce satisfactory accuracy for classifying tree species, further research is needed to improve the classification accuracy.

摘要

使用遥感技术准确绘制树种的组成和分布图对于可持续森林资源管理至关重要。具有密集时间序列观测的 Sentinel-2 数据能够识别树种。然而，很少有研究阐明在天然林和人工林中使用 Sentinel-2 图像进行分类的差异。选择了两个具有不同森林环境（人工林和天然林）的研究区域来评估 Sentinel-2 图像的潜力。我们的结果表明，红边波段、短波红外 (SWIR) 波段和植被指数 (VIs)，如红边归一化植被指数 (ReNDVI)、土壤调整植被指数 (SAVI) 和增强植被指数(EVI)，对树种分类有重要影响，尤其是在生长季节。通过使用两种机器学习算法（支持向量机 [SVM] 和随机森林 [RF]），结果表明，人工林的分类准确率（SVM 为 91.27%，RF 为 88.35%）明显超过天然林（84.34 SVM 中的 % 和 RF 中的 81.03%）。这种精度差异可能与森林内部的空间异质性和周围环境影响有关。尽管多时相 Sentinel-2 图像在树种分类方面产生了令人满意的准确度，但仍需进一步研究以提高分类准确度。这种精度差异可能与森林内部的空间异质性和周围环境影响有关。尽管多时相 Sentinel-2 图像在树种分类方面产生了令人满意的准确度，但需要进一步研究以提高分类准确度。这种精度差异可能与森林内部的空间异质性和周围环境影响有关。尽管多时相 Sentinel-2 图像在树种分类方面产生了令人满意的准确度，但仍需进一步研究以提高分类准确度。