College of Resources and Environment, Shanxi Agricultural University, Taiyuan 030031, China.
Sensors (Basel). 2023 Jan 6;23(2):659. doi: 10.3390/s23020659.
Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.
自然保护区是全球生物多样性最丰富的地区之一，快速准确的识别是其管理的必要条件。本研究基于多时相 Sentinel-2 数据集，提出了三种多时相改进植被指数（多时相归一化差值植被指数（MTM-NDQI）、多时相改进差异灌丛草指数（MTM-DSI）和多时相改进比值肖氏指数（MTM-RSI）），以提高中国灵空山自然保护区（LMNR）植被遥感分类精度。这三个指数结合了典型植被指数和多时相遥感数据的优点。通过使用所提出的指数与单时相改进植被指数（单时相改进松栎混交林指数（UTM-DMI））和典型植被指数（如比值植被指数（RVI）、差值植被指数（DVI）和归一化差值植被指数（NDVI））相结合，获得了一个包含 12 月 NDVI、4 月 NDVI、UTM-DMI、MTM-NDQI、MTM-DSI 和 MTM-RSI 的最佳特征集。随机森林分类的总体精度（OA）（98.41%）和最优特征集的 Kappa 系数（0.98）高于时间序列 NDVI（OA=96.03%，Kappa=0.95）、时间序列 RVI（OA=95.56%，Kappa=0.95）和时间序列 DVI（OA=91.27%，Kappa=0.90）。基于最优特征集的快速分类的 OA 和知识决策树的 Kappa 系数分别为 95.56%和 0.95，同时，只有七种植被类型中的三种被略过或错误分类。总体而言，所提出的植被指数在识别保护区植被类型方面具有优势。
