Department of Water Engineering, Vytautas Magnus University, 10 Universiteto Str., Akademija, 53361 Kaunas, Lithuania.
Sensors (Basel). 2022 Dec 28;23(1):303. doi: 10.3390/s23010303.
This paper presents research concerning dewatered areas in the littoral zones of the Kaunas hydropower plant (HPP) reservoir in Lithuania. It is a multipurpose reservoir that is primarily used by two large hydropower plants for power generation. As a result of the peaking operation regime of the Kaunas HPP, the large quantity of water that is subtracted and released into the reservoir by the Kruonis pumped storage hydropower plant (PSP), and the reservoir morphology, i.e., the shallow, gently sloping littoral zone, significant dewatered areas can appear during drawdown operations. This is especially dangerous during the fish spawning period. Therefore, reservoir operation rules are in force that limit the operation of HPPs and secure other reservoir stakeholder needs. There is a lack of knowledge concerning fish spawning locations, how they change, and what areas are dewatered at different stages of HPP operation. This knowledge is crucial for decision-making and efficient reservoir storage management in order to simultaneously increase power generation and protect the environment. Current assessments of the spawning sites are mostly based on studies that were carried out in the 1990s. Surveying fish spawning sites is typically a difficult task that is usually carried out by performing manual bathymetric measurements due to the limitations of sonar in such conditions. A detailed survey of a small (approximately 5 ha) area containing several potential spawning sites was carried out using Unmanned Aerial Vehicles (UAV) equipped with multispectral and conventional RGB cameras. The captured images were processed using photogrammetry and analyzed using various techniques, including machine learning. In order to highlight water and track changes, various indices were calculated and assessed, such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), and Normalized Green-Red Difference Index (NGRDI). High-resolution multispectral images were used to analyze the spectral footprint of aquatic macrophytes, and the possibility of using the results of this study to identify and map potential spawning sites over the entire reservoir (approximately 63.5 km) was evaluated. The aim of the study was to investigate and implement modern surveying techniques to improve usage of reservoir storage during hydropower plant drawdown operations. The experimental results show that thresholding of the NGRDI and supervised classification of the NDWI were the best-performing methods for the shoreline detection in the fish spawning sites.
本文介绍了立陶宛考那斯水电厂（HPP）水库滨海地带脱水区域的研究情况。该水库是一座多用途水库，主要由两座大型水电站用于发电。由于考那斯 HPP 的峰荷运行模式、克鲁尼斯抽水蓄能水电厂（PSP）大量抽水和放水，以及水库形态，即浅而缓倾斜的滨海带，在放水过程中可能会出现大面积的脱水区域。这在鱼类产卵期尤其危险。因此，实施了水库运行规则，限制 HPP 的运行，并确保其他水库利益相关者的需求得到满足。关于鱼类产卵地点、它们如何变化以及在 HPP 运行的不同阶段哪些区域脱水的知识还很缺乏。为了在提高发电的同时保护环境，这些知识对于决策和高效水库存储管理至关重要。目前对产卵地的评估大多基于 20 世纪 90 年代进行的研究。由于声纳在这种条件下的局限性，对鱼类产卵地进行调查通常是一项艰巨的任务，通常需要通过手动进行水深测量来完成。使用配备多光谱和常规 RGB 摄像机的无人机对一个包含几个潜在产卵地的小面积（约 5 公顷）进行了详细调查。捕获的图像通过摄影测量进行处理，并使用各种技术进行分析，包括机器学习。为了突出显示水并跟踪变化，计算并评估了各种指数，例如归一化差异水体指数（NDWI）、归一化差异植被指数（NDVI）、可见大气阻力指数（VARI）和归一化绿-红差值指数（NGRDI）。高分辨率多光谱图像用于分析水生植物的光谱足迹，并评估利用本研究结果识别和绘制整个水库（约 63.5 公里）潜在产卵地的可能性。该研究的目的是调查和实施现代调查技术，以改善水电厂放水期间水库的蓄水利用。实验结果表明，NGRDI 的阈值处理和 NDWI 的监督分类是鱼类产卵地海岸线检测的最佳方法。
