Watanabe Fernanda Sayuri Yoshino, Alcântara Enner, Rodrigues Thanan Walesza Pequeno, Imai Nilton Nobuhiro, Barbosa Cláudio Clemente Faria, Rotta Luiz Henrique da Silva
Department of Cartography, Sao Paulo State University, Cep. 19060-900, Presidente Prudente, SP 19060-900, Brazil.
Image Processing Division, National Institute for Space Research, São José dos Campos, SP 12227-010, Brazil.
Int J Environ Res Public Health. 2015 Aug 26;12(9):10391-417. doi: 10.3390/ijerph120910391.
Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.
水库是人类建造的人工环境,这些环境的影响尚未完全明确。水体中的滞留时间和高养分可用性会提高富营养化水平。富营养化与浮游植物的初级生产力直接相关。这些生物在环境中起着重要作用。然而,特定物种的高浓度可能会导致公共卫生问题。蓝藻物种会产生毒素,在特定浓度下可导致肝脏和神经系统的严重疾病,甚至可能导致死亡。浮游植物具有可用于识别这些毒素的光活性色素。因此,遥感数据是绘制这些色素从而绘制营养状态图的可行选择。叶绿素a(Chl-a)存在于所有浮游植物物种中。因此,本研究的目的是评估陆地卫星8号上的陆地成像仪(OLI)传感器图像在确定热带水库中Chl-a浓度和估算营养水平方面的性能。使用2014年5月和10月(分别为南半球秋季和南半球春季)进行的两次实地调查数据拟合了经验模型。这些模型应用于2013年5月至2014年10月的OLI图像时间序列。估计的Chl-a浓度用于根据采用这种色素样参数浓度的营养状态指数对营养水平进行分类。Chl-a浓度模型显示出合理的结果,但其性能可能因大气校正而受到影响。因此,营养水平分类也没有获得更好的结果。