Abstract Increases in liana abundance in tropical forests are pervasive threats to the current and future forest carbon stocks. Never before has the need been more evident for new approaches to detect the presence of liana in ecosystems, given their significance as fingerprints of global environmental change. In this study, we explore the use of longwave infrared reflectance (LWIR, 8–11 μm) as a wavelength region for the classification of liana and tree leaves and compare classification results with those obtained using visible-near infrared reflectance data (VIS-NIR, 0.45–0.95 μm). Twenty sun leaves were collected from each of 14 liana species and 21 tree species located at the canopy or forest edge (n = 700) in Santa Rosa National Park, Costa Rica. LWIR and VIS-NIR reflectance measurements were performed on these leaves using a portable calibrated Fourier Transform Infrared Spectroscopy (FTIR) Agilent ExoScan 4100 and a UniSpec spectral analysis system, respectively. The VIS-NIR and LWIR data were first resampled. Then these two spectral libraries were pre-processed for noise reduction and spectral feature enhancement resulting in three datasets for each spectral region as follows: filtered only, filtered followed by extraction of the first derivative, and continuous wavelet transformation (CWT). Data reduction was then applied to these data sets using principal components analysis (PCA). The outputs obtained from the PCA were used to conduct the supervised classification of liana and tree leaves. In total, 21 classifiers were applied to datasets of training and testing to extract the classification accuracy and agreement for liana and tree leaves. The results suggest that the classification of leaves based on LWIR data can reach accuracy values between 66 and 96% and agreement values between 32 and 92%, regardless of the type of classifier. In contrast, the classification based on VIS-NIR data shows accuracy values between 50 and 70% and agreement values between 0.01 and 40%. The highest classification rates of liana and tree leaves were obtained from datasets pre-processed using the CWT or from the extraction of the first derivative and classified using either random forest, k-nearest neighbor, or support vector machine with radial kernel. The results using the LWIR reflectance highlight the potential of this spectral region for the accurate detection of liana extent in tropical ecosystems. Future studies should consider this potential and test the regional monitoring of lianas.

中文翻译：

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使用可见-近红外和长波红外反射光谱鉴别新热带干林中藤本植物和树叶

摘要 热带森林中藤本植物丰度的增加是对当前和未来森林碳储量的普遍威胁。鉴于藤本植物作为全球环境变化指纹的重要性，对于检测生态系统中藤本植物存在的新方法的需求从未如此明显。在这项研究中，我们探索了使用长波红外反射率（LWIR，8-11 μm）作为藤本植物和树叶分类的波长区域，并将分类结果与使用可见-近红外反射率数据（VIS-NIR）获得的结果进行比较, 0.45–0.95 μm)。从位于哥斯达黎加圣罗莎国家公园的树冠或森林边缘 (n = 700) 的 14 种藤本植物和 21 种树种中的每一种收集了 20 片太阳叶。分别使用便携式校准傅立叶变换红外光谱 (FTIR) Agilent ExoScan 4100 和 UniSpec 光谱分析系统对这些叶片进行 LWIR 和 VIS-NIR 反射率测量。首先对 VIS-NIR 和 LWIR 数据进行重新采样。然后对这两个光谱库进行预处理以进行降噪和光谱特征增强，从而为每个光谱区域生成三个数据集，如下所示：仅过滤、过滤后提取一阶导数和连续小波变换 (CWT)。然后使用主成分分析 (PCA) 将数据简化应用于这些数据集。从 PCA 获得的输出用于进行藤本植物和树叶的监督分类。总共，将 21 个分类器应用于训练和测试数据集，以提取藤本植物和树叶的分类准确性和一致性。结果表明，基于 LWIR 数据的叶子分类可以达到 66% 到 96% 之间的准确度值和 32% 到 92% 之间的一致性值，而不管分类器的类型如何。相比之下，基于 VIS-NIR 数据的分类显示准确度值介于 50% 和 70% 之间，一致性值介于 0.01% 和 40% 之间。藤本植物和树叶的最高分类率是从使用 CWT 预处理的数据集或从一阶导数的提取中获得的，并使用随机森林、k-最近邻或具有径向核的支持向量机进行分类。使用 LWIR 反射率的结果突出了该光谱区域在准确检测热带生态系统中藤本植物范围方面的潜力。未来的研究应该考虑这种潜力并测试藤本植物的区域监测。