Analysis of optimal thresholds for identification of open water using MODIS-derived spectral indices for two coastal wetland systems in Mexico

Colditz, René R.; Souza, Carlos Troche; Vázquez, Berenice; Wickel, A. J.; Ressl, Rainer

doi:10.1016/j.jag.2018.03.008

Cited by 17 publications

(18 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Except in the visible bands and band 5 of Sentinel-2 MSI, the turbid water in Lake Taihu have similar spectral signals as clear open water (Figure 10), where reflectance decreases with increasing wavelength [76]. Therefore, water with low reflectance clusters in NIR, SWIR1 and SWIR2 bands (Figure 10), have often been used for water mapping studies [40,77].…”

Section: Algal Bloom Extractions Among Spectral Bandsmentioning

confidence: 99%

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

Zhu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Algal bloom is a serious global issue for inland waters, posing pose a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monitor algal blooms in marine and large inland waters. However, such images typically are of coarse resolution, limiting their utility to map algal blooms in small inland waters. We developed a new method to map the spatial extent of algal blooms using Sentinel-2 MSI and Landsat OLI images with higher spatial resolution but lower temporal resolution based on the concept of Local Indicator of Spatial Association. The mapping results was applied to measure the duration and frequency of algal blooms in Lake Taihu from 2017 to 2020. Our results show that the developed methodology is able to extract the spatial distribution of moderate algal blooms using near-infrared and red-edge bands (band 6, 7, 8 and 8a of Sentinel-2 MSI images or band 5 of Landsat OLI images) by comparison with MODIS FAI data (R2 = 0.888 for Sentinel-2 MSI and R2 = 0.85 for Landsat OLI, P < 0.05). However, the temporal resolution of combined Landsat OLI and Sentinel-2 MSI images (i.e., up to 2-3 days) is insufficient to monitor algal blooms during the summer time in Lake Taihu due to cloud effects and rapid algal change. Our research has benefits for the management of small inland waters with complex water conditions.

show abstract

Section: Algal Bloom Extractions Among Spectral Bandsmentioning

confidence: 99%

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

Zhu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Among various methods for remote sensing extraction of surface water extent, the threshold method is the simplest and most efficient method for batch processing of big data [32,33,36,48]. However, regardless of which remote sensing index is used (such as vegetation index, moisture index, and single-band), the threshold of water is inconsistent and has great uncertainty in space and time.…”

Section: The Theory Of Ndvi Identifying Watermentioning

confidence: 99%

“…These datasets are committed to providing rapid mapping of water bodies in a specific area and serving more actual social needs, rather than studying the changes of global surface water over the long term and serving climate change and ecosystem researches. Though indexes such as the Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI), supervised classification, or even an automation method are useful for extraction of local surface water with low temporal frequency [32][33][34][35][36][37], they are not applicable for large-scale water mapping with high spatial and temporal resolution. The spectral characteristics of surface water vary in time and space on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

Han

Niu

2020

Remote Sensing

View full text Add to dashboard Cite

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

show abstract

“…Satellite images provide useful information for monitoring some climatic and hydrological variables with different spatial resolutions and revisit frequencies. For example, they provide information about water [1,2], area covered by snow [3,4], different land uses [5] in water resource systems and urban physical characteristics [6]. This paper focuses on the analyses of wetland surface-water mapping.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Monthly Dynamics of Surface Water in Wetlands from Satellite and Secondary Hydro-Climatological Data

et al. 2021

View full text Add to dashboard Cite

Satellites produce valuable information for studying the surface water in wetlands, but in many cases the period covered, the spatial resolution and/or the revisit frequency is not enough to produce long historical series. In this paper we propose a novel method which uses regression models that include climatic and hydrological variables to complete the satellite information. We used this method in the Lagunas de Ruidera wetland (Spain). We approached the monthly dynamic of the surface water for a long period (1984–2015). Information from LANDSAT (30-m resolution) and MODIS (250-m resolution) satellites were tested but, due to the size of some lagoons, only the LANDSAT approach produced satisfactory results. An ensemble of regression models based on hydro-climatological explanatory variables was defined to complete the gaps in the monthly surface water. It showed a root mean squared error of around 476 pixels (0.4 Km2) in the cross-validation analysis. Our analysis showed that the explanatory variables with a more significant participation in the regression ensemble are the aquifer discharge, the effective precipitation and the surface water from the previous month. From January to June, the mean surface water in Lagunas de Ruidera is around 4.3 Km2. In summer a reduction of around 13% of the surface water can be observed, which is recovered during the autumn.

show abstract

Analysis of optimal thresholds for identification of open water using MODIS-derived spectral indices for two coastal wetland systems in Mexico

Cited by 17 publications

References 37 publications

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

Estimation of the Monthly Dynamics of Surface Water in Wetlands from Satellite and Secondary Hydro-Climatological Data

Contact Info

Product

Resources

About