Abstract

Sustainable management of biodiversity benefit from cost-effectively multi-temporal classification schemes afforded by remote sensing techniques. This study compared classification accuracies of woody plant species (n = 27) and three coexisting land cover types using dry and wet seasons data. Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN), were applied to Sentinel-2A and SPOT-6 images. The results showed higher overall classification accuracies for wet season data (65%–72%) for both images and classifiers (DNN, RF and SVM), compared to dry season classification (52%–59%). Near infrared region bands, available in both Sentinel-2A and SPOT-6 imagery, produced high performance for both wet (83%) and dry (80%) seasons. Overall, the findings shows potential of multispectral remote-sensing for woody plant species diversity in different seasons. Such a study should be extended to higher frequency species diversity classification, to capture dynamics that may manifest at short time intervals of the year.

摘要

生物多样性的可持续管理受益于遥感技术提供的具有成本效益的多时间分类方案。本研究比较了木本植物物种的分类准确度 ( n = 27) 和使用旱季和雨季数据的三种共存的土地覆盖类型。随机森林 (RF)、支持向量机 (SVM) 和深度神经网络 (DNN) 被应用于 Sentinel-2A 和 SPOT-6 图像。结果表明，与旱季分类 (52%–59%) 相比，图像和分类器（DNN、RF 和 SVM）的雨季数据总体分类准确度更高（65%–72%）。Sentinel-2A 和 SPOT-6 图像中都提供的近红外区域波段在潮湿 (83%) 和干燥 (80%) 季节都产生了高性能。总体而言，研究结果显示了多光谱遥感对不同季节木本植物物种多样性的潜力。这样的研究应该扩展到更高频率的物种多样性分类，以捕捉可能在一年中的短时间间隔内出现的动态。