佛罗里达湾水质参数时空变化的遥感研究

M. Gholizadeh, A. Melesse
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引用次数: 16

摘要

本研究基于大气校正数据,对佛罗里达湾水质的生物物理参数进行了研究。本研究的主要目的是利用遥感、GIS数据和统计技术,对浑浊度、叶绿素-a、总磷和总氮4个水质参数的时空变化进行监测和评价。为此,两个日期2000年的陆地卫星专题数据映射器(TM)(2月13日),2007年(1月31日),和一个陆地卫星操作日期陆地成像仪(奥利)2015年(1月5日)在旱季,和两个日期2000年TM数据(8月7日),2007(9月28日),和一个2015年奥利数据日期(9月2日)在雨季佛罗里达州南部的亚热带气候,被用来评估时间和空间模式和维度研究了参数在佛罗里达湾,美国。研究参数的同时观测数据来自20个监测站,并用于模型的开发和验证。利用从蓝色到近红外波段的光学波段及所有可能的波段比,探讨了水体反射率与观测数据的关系。通过使用逐步多元线性回归(MLR)建立了估计chl-a和浊度浓度的预测模型,并给出了旱季的高测定系数(chl-a的R2=0.86,浊度的R2=0.84)和雨季的中等测定系数(chl-a的R2=0.66,浊度的R2=0.63)。总磷和总氮的值与chl-a和浊度浓度以及一些波段及其比值相关。利用Landsat TM和OLI以及地面数据的最佳拟合多元线性回归模型估算了总磷和全氮,结果表明,旱季(总磷R2=0.74,全氮R2=0.82)和雨季(总磷R2=0.69,全氮R2=0.82)的决定系数较高。MLR模型对佛罗里达湾水质参数的时空变化具有较好的预测可信度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing
In this study, the bio-physical parameters associated with water quality of Florida Bay were investigated based on atmospherically corrected data. The principal objective of this study was to monitor and assess the spatial and temporal changes of four water quality parameters: turbidity, chlorophyll-a (chl-a), total phosphate, and total nitrogen (TN), using the application of integrated remote sensing, GIS data, and statistical techniques. For this purpose, two dates of Landsat Thematic Mapper (TM) data in 2000 (February 13), 2007 (January 31), and one date of Landsat Operational Land Imager (OLI) in 2015 (January 5) in the dry season, and two dates of TM data in 2000 (August 7), 2007 (September 28), and one date of OLI data in 2015 (September 2) in the wet season of the subtropical climate of South Florida, were used to assess temporal and spatial patterns and dimensions of studied parameters in Florida Bay, USA. The simultaneous observed data of four studied parameters were obtained from 20 monitoring stations and were used for the development and validation of the models. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of waterbody and observed data. The predictive models to estimate chl-a and turbidity concentrations were developed through the use of stepwise multiple linear regression (MLR) and gave high coefficients of determination in dry season (R2=0.86 for chl-a and R2=0.84 for turbidity) and moderate coefficients of determination in wet season (R2=0.66 for chl-a and R2=0.63 for turbidity). Values for total phosphate and TN were correlated with chl-a and turbidity concentration and some bands and their ratios. Total phosphate and TN were estimated using best-fit multiple linear regression models as a function of Landsat TM and OLI, and ground data and showed a high coefficient of determination in dry season (R2=0.74 for total phosphate and R2=0.82 for TN) and in wet season (R2=0.69 for total phosphate and R2=0.82 for TN). The MLR models showed a good trustiness to monitor and predict the spatiotemporal variations of the studied water quality parameters in Florida Bay.
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