Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario

Beaton, Andy D.; Whaley, R. Clint; Corston, K.; Kenny, Frank

doi:10.1016/j.rse.2019.02.011

Cited by 47 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, a new method was developed to estimate lake ice timing over a range of lake sizes by combing MODIS, Landsat Fmask, and an air temperature filter from MERRA-2 to remove outliers. Differing from the previous studies that used a near-infrared (NIR) band [1,15,25] or the combination of both visible and NIR [29,30,38], only the red band was used to remove the impact of changing vegetation. Validation results against in situ observations over lakes of different sizes suggest that the approach performs well for all lakes.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, only the red band was chosen for classification, which is different from the previous studies that used a near-infrared (NIR) band [1,15,25] or the combination of both visible and NIR bands [29,30]. At the scale of MODIS imagery, small lakes are likely to be dominated by mixed pixels that have more than one land cover type.…”

Section: Lake Ice Classification and Ice Fraction Calculationmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

Zhang

Pavelsky

2019

Remote Sensing

View full text Add to dashboard Cite

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km2 and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km2.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Lake Ice Classification and Ice Fraction Calculationmentioning

confidence: 99%

Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

Zhang

Pavelsky

2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In addition, Engram et al (2018) [16] adopted a threshold method on log-transformed data to discriminate bedfast ice and floating ice with the SAR imagery across Arctic Alaska. Along with the above methods, Beaton et al (2019) [17] presented a calibrated thresholds approach to classifying pixels as snow/ice, mixed ice/water or open water using MODIS satellite imagery.…”

Section: Ice Segmentationmentioning

confidence: 99%

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

et al. 2020

View full text Add to dashboard Cite

River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset.

show abstract

“…There are various types of remotely sensed data available to examine drought severity across various regions in the world, such as the advanced very high resolution radiometer (AVHRR) [44,49,50], the moderate resolution imaging spectroradiometer (MODIS) [24,33], Landsat [51,52], or Sentinel [53]. Among these sensors, MODIS has great temporal resolution of 1-2 days with high radiometric resolution image (12 bit) and collects data for 36 spectral bands [54,55]. Whereas, higher spatial resolution data such as Landsat (30 m) is challenged in providing an appropriate number of high-quality images [51,56] and Sentinel data is not sufficient to investigate long-term analysis as it has only been available since 2015 [57].…”

mentioning

confidence: 99%

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Tran

Myint

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001-2018 period, along with the Mann-Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

show abstract

Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario

Cited by 47 publications

References 24 publications

Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Contact Info

Product

Resources

About