scholarly journals Estimating inundation of small waterbodies with sub‐pixel analysis of Landsat imagery: long‐term trends in surface water area and evaluation of common drought indices

2020 ◽  
Author(s):  
Ibrahima Sall ◽  
Christopher J. Jarchow ◽  
Brent H. Sigafus ◽  
Lisa A. Eby ◽  
Michael J. Forzley ◽  
...  
Keyword(s):  
Surface Water ◽  
Landsat Imagery ◽  
Water Area ◽  
Drought Indices ◽  
Small Waterbodies ◽  
Long Term Trends ◽  
Pixel Analysis

Remote Sensing of Surface Water Dynamics Between 2015 and 2020 in the Prairie Pothole Region 

2021 ◽  
Author(s):  
Stefan Schlaffer ◽  
Marco Chini ◽  
Wouter Dorigo
Keyword(s):  
Surface Water ◽  
Prairie Pothole Region ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Drought Indices ◽  
Prairie Pothole ◽  
Small Water ◽  
Wide Range ◽  
Small Water Bodies

<p>The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (< 1 ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.</p>

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

2020 ◽  
Vol 12 (17) ◽  
pp. 2675
Author(s):  
Qianqian Han ◽  
Zhenguo Niu
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Temporal Parameter ◽  
Modis Ndvi ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Spatio Temporal ◽  
Global Surface ◽  
Global Water

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.

scholarly journals Changes in Net Global Surface Water Area Since 1985

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 69
Author(s):  
Zahra Kalantari ◽  
Sonia Borja ◽  
Georgia Destouni
Keyword(s):  
Surface Water ◽  
Ecosystem Functioning ◽  
Water Allocation ◽  
Land Use Changes ◽  
Water Area ◽  
Aral Sea ◽  
Surface Water Resources ◽  
Global Surface ◽  
Spatial And Temporal Characteristics

Spatial and temporal characteristics of surface water resources (e.g., extension, connectivity, seasonality) are key elements in water allocation, climate and hydrological regulation, ecosystem functioning, and the food-energy-water nexus. Changes in surface water area due to losses/gains to land could strongly affect these processes on different scales. Previous findings on changes in the Earth’s surface water area are contradictory. Based on water–land year classification datasets, we estimated global surface water area changes between 1985–2000 and 2001–2015. We found a net global gain in surface water of 100,454 km2, attributable to a large net gain in seasonal water (83,329 km2) and a small net gain in permanent water (17,125 km2). In general, net changes were highly heterogeneous in space, with local exceptions of clear drying and wetting trends, e.g., the Aral Sea and Quill Lakes, respectively. These findings raise multiple questions as to why seasonal water gains dominate and how different intertwined drivers (e.g., hydroclimate and human-induced water–land use changes) shape the distribution of the Earth’s surface water. Understanding these long-term changes is essential to predicting water-related pressures and prioritizing management decisions.

Parameterization of surface water temperature and long-term trends in Europe’s fourth largest lake shows recent and rapid warming in winter

Limnologica ◽  
2020 ◽  
Vol 82 ◽  
pp. 125777 ◽  
Author(s):  
Burak Öğlü ◽  
Tõnu Möls ◽  
Tanel Kaart ◽  
Fabien Cremona ◽  
Külli Kangur
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Surface Water Temperature ◽  
Long Term Trends

scholarly journals ANALYSIS OF THE SHORELINE POSITION EXTRACTED FROM LANDSAT TM AND ETM+ IMAGERY

2015 ◽  
Vol XL-7/W3 ◽  
pp. 991-998 ◽  
Author(s):  
E. Sánchez-García ◽  
J. E. Pardo-Pascual ◽  
A. Balaguer-Beser ◽  
J. Almonacid-Caballer
Keyword(s):  
Statistical Tests ◽  
Landsat Imagery ◽  
Temporal Analysis ◽  
Long Term Trends ◽  
Annual Means ◽  
Run Up ◽  
Incident Waves ◽  
Intrinsic Error ◽  
Average Position

A statistical analysis of the results obtained by the tool SELI (Shoreline Extraction from Landsat Imagery) is made in order to characterise the medium and long term period changes occurring on beaches. The analysis is based on the hypothesis that intraannual shifts of coastline positions hover around an average position, which would be significant when trying to set these medium and long term trends. Fluctuations around this average are understood as the effect of short-term changes -variations related to sea level, wave run-up, and the immediate morphological beach profile settings of the incident waves- whilst the alterations of the average position will obey changes relating to the global sedimentary harmony of the analysed beach segment. The goal of this study is to assess the validity of extracted Landsat shorelines knowing whether the intrinsic error could alter the position of the computed mean annual shoreline or if it is balanced out between the successive averaged images. Two periods are stablished for the temporal analysis in the area according to the availability of other data taken from high precision sources. Statistical tests performed to compare samples (Landsat versus high accuracy) indicate that the two sources of data provide similar information regarding annual means; coastal behaviour and dynamics, thereby verifying Landsat shorelines as useful data for evolutionary studies.

scholarly journals Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3010 ◽  
Author(s):  
Ruimeng Wang ◽  
Haoming Xia ◽  
Yaochen Qin ◽  
Wenhui Niu ◽  
Li Pan ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Body ◽  
Vegetation Index ◽  
Water Area ◽  
Google Earth ◽  
Body Area ◽  
Hetao Plain ◽  
Google Earth Engine

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

Constructing Mediterranean wetland open water dynamics using a new 18-year MODIS-derived surface water fraction dataset

2020 ◽  
Author(s):  
Linlin Li ◽  
Anton Vrieling ◽  
Andrew Skidmore ◽  
Tiejun Wang
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Temporal Dynamics ◽  
Open Water ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Water Fraction ◽  
Wide Range

<p>Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies despite the ecological importance of smaller-sized wetland systems. In this paper, we constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. These water bodies has a wide range of size, specifically 0.01 km<sup>2</sup> and larger. We then compared the water extent time series derived from MODIS SWF with those derived from a Landsat-based dataset. Results showed that MODIS- and Landsat-derived water extent time series showed a high correlation (r = 0.81) for more dynamic water bodies. Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (<1 km<sup>2</sup>) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.</p>

scholarly journals Satellite Imageries and Field Data of Macrophytes Reveal a Regime Shift of a Tropical Lake (Lake Ziway, Ethiopia)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 396
Author(s):  
Yohannes Tefera Damtew ◽  
Boud Verbeiren ◽  
Aymere Awoke ◽  
Ludwig Triest
Keyword(s):  
Surface Water ◽  
Water Level ◽  
Regime Shift ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Aquatic Vegetation ◽  
Meteorological Data ◽  
Water Area ◽  
Ethiopian Rift

Lake Ziway is one of the largest freshwater lakes located in the central Ethiopian rift valley. The lake shoreline is dominated by macrophytes which play an important role in immobilizing run-off pollution, stabilize sediments and support biodiversity. Monitoring the spatio-temporal changes of great lakes requires standardized methods. The aim of this study was to assess the current and long-term trends of macrophyte distribution, surface water area and the water level of Lake Ziway using remote sensing images from 1986 to 2016 with additional hydro-meteorological data. A supervised image classification with classification enhancement using Normalized Difference Aquatic Vegetation Index (NDAVI) and Normalized Difference Vegetation Index (NDVI) was applied. The classification based on NDAVI revealed eight target classes which were identified with an overall producer’s accuracy of 79.6%. Contemporary open water and macrophyte fringes occupied most of the study area with a total area of 407.4 km2 and 60.1 km2, respectively. The findings also revealed a regime shift in the mean water level of the lake and a decline in macrophyte distribution. The long-term water surface area of Lake Ziway also decreased between 1986 and 2016. The changes in water level could be explained by climate variability in the region and strong anthropogenic disturbance. A decline in water level was also associated with lowered surface water area, lakeward retreated macrophyte fringes and enhanced landward encroachment of mudflats, and resulted in a succession of macrophytes with semi-terrestrial vegetations.

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