Mr. Alan Lan Profile

Alan Lan

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Proceedings Article | 23 November 2011 Paper

Mechanism and look-alikes analysis of oil spill monitoring with optical remote sensing

Guoxin Lan, Long Ma, Ying Li, Bingxin Liu

Proceedings Volume 8006, 800628 (2011) https://doi.org/10.1117/12.901912

KEYWORDS: Ocean optics, Remote sensing, Sensors, Optical sensors, Sun, Clouds, Water, Synthetic aperture radar, Satellites, Environmental sensing

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Remote Sensing surveillance constitutes an important component of oil spill disaster management system, but subject to monitoring accuracy and ability, which suffered from resolution, environmental conditions, and look-alikes. So this article aims to provide information of identification and distinguishing of look-alikes for optical sensors, and then improve the monitoring precision. Although limited by monitoring conditions of the atmosphere and night, optical satellite remote sensing can provide the intrinsic spectral information of the film and the background sea, then affords the potentiality for detailed identification of the film thickness, oil type classification (crude/light oil), trends, and sea surface roughness by multi-type data products. This paper focused on optical sensors and indicated that these false targets of sun glint, bottom feature, cloud shadow, suspend bed sediment and surface bioorganic are the main factors for false alarm in optical images. Based on the detailed description of the theory of oil spill detection in optical images, depending on the preliminary summary of the feature of look-alikes in visible-infrared bands, a discriminate criteria and work-flow for slicks identification are proposed. The results are helpful to improve the remote sensing monitoring ability and the contingency planning.

Proceedings Article | 30 October 2009 Paper

Large-sized seaweed monitoring based on MODIS

Long Ma, Ying Li, Guo-xin Lan

Proceedings Volume 7498, 749824 (2009) https://doi.org/10.1117/12.832916

KEYWORDS: MODIS, Reflectivity, Remote sensing, Ocean optics, Water, Mid-IR, Algorithm development, Clouds, Coastal modeling, Lithium

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In recent years, large-sized seaweed, such as ulva lactuca, blooms frequently in coastal water in China, which threatens marine eco-environment. In order to take effective measures, it is important to make operational surveillance. A case of large-sized seaweed blooming (i.e. enteromorpha), occurred in June, 2008, in the sea near Qingdao city, is studied. Seaweed blooming is dynamically monitored using Moderate Resolution Imaging Spectroradiometer (MODIS). After analyzing imaging spectral characteristics of enteromorpha, MODIS band 1 and 2 are used to create a band ratio algorithm for detecting and mapping large-sized seaweed blooming. In addition, chlorophyll-α concentration is inversed based on an empirical model developed using MODIS. Chlorophyll-α concentration maps are derived using multitemporal MODIS data, and chlorophyll-α concentration change is analyzed. Results show that the presented methods are useful to get the dynamic distribution and the growth of large-sized seaweed, and can support contingency planning.

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