scholarly journals Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015)

2019 ◽  
Vol 11 (11) ◽  
pp. 1323 ◽  
Author(s):  
Xianghong Che ◽  
Min Feng ◽  
Joe Sexton ◽  
Saurabh Channan ◽  
Qing Sun ◽  
...  
Keyword(s):  
Surface Water ◽  
Central Asia ◽  
Learning Algorithm ◽  
Water Area ◽  
Water Bodies ◽  
Ecological Zones ◽  
Hydrological Variability ◽  
Global Mapping ◽  
Water Cover ◽  
Semi Arid

Surface water is of great importance to ecosystems and economies. Crucial to understanding hydrological variability and its relationships to human activities at large scales, open-access satellite datasets and big-data computational methods are now enabling the global mapping of the distribution and changes of inland water over time. A machine-learning algorithm, previously used only to map water at single points in time, was applied over 16 years of the USGS Landsat archive to detect and map surface water over central Asia from 2000 to 2015 at a 30-m, monthly resolution. The resulting dataset had an overall classification accuracy of 99.59% (±0.32% standard error), 98.24% (±1.02%) user’s accuracy, and 87.12% (±3.21%) producer’s accuracy for water class. This study describes the temporal extension of the algorithm and the application of the dataset to present patterns of regional surface water cover and change. The findings indicate that smaller water bodies are dramatically changing in two specific ecological zones: the Kazakh Steppe and the Tian Shan Montane Steppe and Meadows. Both the maximum and minimum extent of water bodies have decreased over the 16-year period, but the rate of decrease of the maxima was double that of the minima. Coverage decreased in each month from April to October, and a significant decrease in water area was found in April and May. These results indicate that the dataset can provide insights into the behavior of surface water across central Asia through time, and that the method can be further developed for regional and global applications.

scholarly journals Downscaling MODIS Surface Reflectance to Improve Water Body Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xianghong Che ◽  
Min Feng ◽  
Hao Jiang ◽  
Jia Song ◽  
Bei Jia
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Terrestrial Ecosystems ◽  
Water Area ◽  
Water Bodies ◽  
Tibet Plateau ◽  
Surface Reflectance ◽  
Discrimination Ability ◽  
Automated Method ◽  
Landsat 7

Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

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 Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

2020 ◽  
Vol 12 (22) ◽  
pp. 3701
Author(s):  
Deepakrishna Somasundaram ◽  
Fangfang Zhang ◽  
Sisira Ediriweera ◽  
Shenglei Wang ◽  
Junsheng Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Sri Lanka ◽  
Anthropogenic Activities ◽  
Water Area ◽  
Temporal Changes ◽  
Water Bodies ◽  
Water Dynamics ◽  
Annual Growth Rate ◽  
Spatial And Temporal Changes

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

scholarly journals An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 431 ◽  
Author(s):  
Kelsey Herndon ◽  
Rebekke Muench ◽  
Emil Cherrington ◽  
Robert Griffin
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Decision Tree ◽  
Water Bodies ◽  
Spectral Indices ◽  
Dynamic Surface ◽  
Tree Methods ◽  
Global Surface ◽  
Decision Tree Methods ◽  
Semi Arid

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

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 Extraction of Water-body Area from High-resolution Landsat Imagery

2018 ◽  
Vol 8 (6) ◽  
pp. 4111
Author(s):  
B. Chandrababu Naik ◽  
B. Anuradha
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Body ◽  
Water Area ◽  
Water Bodies ◽  
Landsat 8 ◽  
Indexing Methods ◽  
Water Index ◽  
Surface Water Body ◽  
Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

scholarly journals The Decrease in Lake Numbers and Areas in Central Asia Investigated Using a Landsat-Derived Water Dataset

2021 ◽  
Vol 13 (5) ◽  
pp. 1032
Author(s):  
Xianghong Che ◽  
Min Feng ◽  
Qing Sun ◽  
Joseph O. Sexton ◽  
Saurabh Channan ◽  
...  
Keyword(s):  
Central Asia ◽  
Temporal Dynamics ◽  
Qaidam Basin ◽  
Anthropogenic Activities ◽  
Water Area ◽  
Aral Sea ◽  
Lake Area ◽  
Human Welfare ◽  
Ecological Zones ◽  
Northern Tibetan Plateau

Although Central Asia has a strong continental climate with a constant moisture deficit and low relative humidity, it is covered by thousands of lakes that are critical to the sustainability of ecosystems and human welfare in the region. Vulnerability to climate change and anthropogenic activities have contributed to dramatic inter-annual and seasonal changes of the lakes. In this study, we explored the high spatio–temporal dynamics of the lakes of Central Asia using the terraPulse™ monthly Landsat-derived surface water extent dataset from 2000 to 2015 and the HydroLAKES dataset. The results identified 9493 lakes and significant linear decreasing trends were identified for both the number (rate: −85 lakes/year, R2: 0.69) and area (rate: −1314.1 km2/year, R2: 0.84) of the lakes in Central Asia between 2000 and 2015. The decrease rate in lake area accounted for 1.41% of the total lake area. About 75% of the investigated lakes (7142 lakes), mainly located in the Kazakh steppe (especially in the north) and the Badghyz and Karabil semi-desert terrestrial ecological zones, experienced a decrease in the water area. Lakes with increasing water area were mainly distributed in the Northern Tibetan Plateau–Kunlun Mountains alpine desert and Qaidam Basin semi-desert zones in the east-south corner of Central Asia. The possible driving factors of lake decreases in Central Asia were explored for the Aral Sea and Tengiz Lake on yearly and monthly time scales. The Aral Sea showed the greatest decrease in the summer months because of increased evaporation and massive irrigation, while the largest decrease for Tengiz Lake was observed in early spring and was linked to decreasing snowmelt.

scholarly journals Monitoring Surface Water Dynamics in the Prairie Pothole Region Using Dual-Polarised Sentinel-1 SAR Time Series

2021 ◽  
Author(s):  
Stefan Schlaffer ◽  
Marco Chini ◽  
Wouter Dorigo ◽  
Simon Plank
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Prairie Pothole Region ◽  
Open Water ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Prairie Pothole ◽  
Small Water ◽  
Small Water Bodies

Abstract. The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors have been widely used to calibrate and validate hydrological models of wetland dynamics. Yet, they are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors, such as the ones on board the Copernicus Sentinel-1 mission, can potentially overcome such limitations. However, water extent retrieval from SAR data is often affected by environmental factors, such as wind on water surfaces. Hence, for reliably monitoring water extent over longer time periods robust retrieval methods are required. The aim of this study was to develop a robust approach for classifying open water extent dynamics in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the light of ancillary data. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of water bodies and the median area per water body were computed. The validation of the retrieved water maps yielded producer’s accuracies between 84 % and 95 % for calm days and between 74 % and 88 % on windy days. User’s accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. Surface water dynamics showed strong intra-annual dynamics especially in the case of small water bodies (< 1 ha). Water area and number of small water bodies decreased from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. During the extremely wet period between the autumn of 2019 and mid-2020, however, 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. However, the area covered by small water bodies was more stable than the area covered by large water bodies. This suggests that large potholes released water faster via the drainage network, while small potholes released water mainly to the atmosphere via evaporation. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations exist related to wind inhibiting correct water extent retrieval and due to the rather low temporal resolution of 12 days over the PPR.

scholarly journals Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2822
Author(s):  
Jiahao Chen ◽  
Tingting Kang ◽  
Shuai Yang ◽  
Jingyi Bu ◽  
Kexin Cao ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Water Area ◽  
Google Earth ◽  
Water Bodies ◽  
Tarim River ◽  
Tarim River Basin ◽  
Open Surface ◽  
Body Area ◽  
The Tarim River Basin

The Tarim River Basin (TRB), located in an arid region, is facing the challenge of increasing water pressure and uncertain impacts of climate change. Many water body identification methods have achieved good results in different application scenarios, but only a few for arid areas. An arid region water detection rule (ARWDR) was proposed by combining vegetation index and water index. Taking computing advantages of the Google Earth Engine (GEE) cloud platform, 56,284 Landsat 5/7/8 optical images in the TRB were used to detect open-surface water bodies and generated a 30-m annual water frequency map from 1992 to 2019. The interannual changes and trends of the water body area were analyzed and the impacts of climatic and anthropogenic drivers on open-surface water body area dynamics were examined. The results show that: (1) ARWDR is suitable for long-term and large-scale water body identification, especially suitable for arid areas lacking vegetation. (2) The permanent water area was 2093.63 km2 and the seasonal water area was 44,242.80 km2, accounting for 4.52% and 95.48% of the total open-surface water area of he TRB, respectively. (3) From 1992 to 2019, the permanent and seasonal water bodies of the TRB all showed an increasing trend, with obvious spatial heterogeneity. (4) Among the effects of human activities and climate change, precipitation has the largest impact on the water area, which can explain 65.3% of the change of water body area. Our findings provide valuable information for the entire TRB’s open-surface water resources planning and management.

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