Literature DB >> 17171243

Mapping turbidity in the Charles River, Boston using a high-resolution satellite.

Ferdi L Hellweger1, Will Miller, Kehinde Sarat Oshodi.   

Abstract

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 17171243     DOI: 10.1007/s10661-006-9535-8

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  5 in total

1.  A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes.

Authors:  T Kutser; A Herlevi; K Kallio; H Arst
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

2.  Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes.

Authors:  A G Dekker; R J Vos; S W Peters
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

3.  Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery.

Authors:  C Giardino; M Pepe; P A Brivio; P Ghezzi; E Zilioli
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

4.  Mapping of the water quality of Lake Erken, Sweden, from imaging spectrometry and Landsat Thematic Mapper.

Authors:  C Ostlund; P Flink; N Strömbeck; D Pierson; T Lindell
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

5.  Landsat and limnologically derived water quality data: A perspective.

Authors:  A Howman; D Grobler; P Kempster; A Seed
Journal:  Environ Monit Assess       Date:  1989-08       Impact factor: 2.513

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.