Object-based automatic terrain shadow removal from SNPP/VIIRS flood maps

Li, Sanmei; Sun, Donglian; Goldberg, Mitchell; Sjoberg, Bill

doi:10.1080/01431161.2015.1103918

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…The physical basis for flood detection with the VIIRS imagery is the spectral characteristics in the VIIRS visible (VIS), near infrared (NIR), and short-wave infrared (SWIR) channels. The main algorithms include t decision-tree method by integrating the reflectance in the VIIRS imager channels and a set of spectral indices to classify water surface from vegetation, bare land, and snow/ice surface [19][20][21]30], a geometry-based cloud-shadow algorithm to separate cloud shadows from VIIRS flood maps [28], an object-based terrain-shadow algorithm to discriminate terrain shadows from VIIRS flood maps [29], and a dynamic nearest neighboring searching (DNNS) method to calculate water fractions [21]. The derived water fractions are then compared with a water reference map at normal conditions to identify flooding water.…”

Section: Viirs Flood Detectionmentioning

confidence: 99%

“…Most recently, the VIIRS (Visible Infrared Imaging Radiometer Suite) [27], onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and the Joint Polar Satellite System (JPSS) and now as the new NOAA series, succeeds the MODIS for flood mapping. The VIIRS imagery with a 375 m spatial resolution in the short-wave IR bands and wide 3000 km swath width have demonstrated advantages for flood detection [28][29][30]. Since the imagers onboard the operational and environmental satellites are usually optical sensors, which can be contaminated by clouds.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the Catastrophic Asia Floods and Potentially Affected Population in Summer 2020 Using VIIRS Flood Products

Goldberg²,

Sjoberg³

et al. 2020

Remote Sensing

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Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.

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Section: Viirs Flood Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the Catastrophic Asia Floods and Potentially Affected Population in Summer 2020 Using VIIRS Flood Products

Goldberg²,

Sjoberg³

et al. 2020

Remote Sensing

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“…The key algorithms supporting the software package include a water detection algorithm based on decision-tree techniques to extract water surface from vegetation, bare land and snow/ice surface [8,9], a geometry-based cloud-shadow removal algorithm to remove cloud shadows from VIIRS flood maps [23], an object-based terrain-shadow removal algorithm to remove terrain shadows from VIIRS flood maps [24], and a dynamic nearest neighboring searching method to retrieve water fractions [10]. The retrieved water fractions are then compared with a water reference map produced from a MODIS global 250-m water mask (MOD44 W) and water layer in the 30-m National Land Cover Dataset in the USA to determine flooding water.…”

Section: Viirs Flood Mappingmentioning

confidence: 99%

“…These new features make the VIIRS imagery very attractive for near real-time flood detection. Under the support of the NOAA JPSS program office, near real-time flood maps have been derived from the VIIRS [23][24][25][26]. The SNPP is thus able to provide a flood detection capability to support the National Weather Services (NWS), the U.S. Army Corps of Engineering (USACE), the Federal Emergency Management Agency (FEMA), the U.S. Geological Survey (USGS), and local agencies.…”

Section: Introductionmentioning

confidence: 99%

Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

Goldberg¹,

Goodman

et al. 2018

Remote Sensing

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Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.

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“…This greatly affects geometrical properties and appearance of the object to be detected [3]. Shadow can cause inaccuracy in some applications, such as the applications for plant leaves segmentation [4], license plate recognition [5], gait recognition [6,7], analysis of remote-sensing images [8,9], classification of objects from video surveillance system [10], underwater object detection [11] and clustering [12].…”

Section: Introductionmentioning

confidence: 99%

Development of Algorithm to Reduce Shadow on Digital Image

Ooi¹,

Ibrahim²

2016

IJIGSP

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Abstract-In this paper, two shadow reduction algorithms have been proposed and implemented using CIE Lab color space. The task of performing shadow reduction is done by executing shadow detection, shadow removal and lastly shadow edge correction in a sequential order. The first proposed algorithm is implemented based on pixel illumination and color information meanwhile the second algorithm is carried out via thresholding of one or more CIE Lab color space channels. The outputs from both proposed algorithms are compared in terms of shadow detection accuracy and required processing period. The proposed methods shown some promising results.

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Object-based automatic terrain shadow removal from SNPP/VIIRS flood maps

Cited by 16 publications

References 11 publications

Assessment of the Catastrophic Asia Floods and Potentially Affected Population in Summer 2020 Using VIIRS Flood Products

Assessment of the Catastrophic Asia Floods and Potentially Affected Population in Summer 2020 Using VIIRS Flood Products

Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

Development of Algorithm to Reduce Shadow on Digital Image

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