scholarly journals Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing

2020 ◽  
Vol 12 (6) ◽  
pp. 984 ◽  
Author(s):  
Christopher E. Soulard ◽  
Jessica J. Walker ◽  
Roy E. Petrakis
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Open Water ◽  
Google Earth ◽  
Measurement Tool ◽  
Dynamic Surface ◽  
Computing Platform ◽  
Flooding Hazard ◽  
Landsat Satellite ◽  
Over Time

Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe.

Anthropogenic and Climatic Factors Differentially Affect Waterbody Area and Connectivity in an Urbanizing Landscape: A Case Study in Zhengzhou, China

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1070
Author(s):  
Chang Liu ◽  
Emily S. Minor ◽  
Megan B. Garfinkel ◽  
Bo Mu ◽  
Guohang Tian
Keyword(s):  
Yellow River ◽  
Climatic Factors ◽  
Landscape Connectivity ◽  
Aquatic Organisms ◽  
Open Water ◽  
Central China ◽  
Water Pipeline ◽  
Landsat Satellite ◽  
The City ◽  
Over Time

Urbanization alters the distribution and characteristics of waterbodies, potentially affecting both the habitat availability and connectivity for aquatic wildlife. We used Landsat satellite imagery to observe temporal and spatial changes in open-water habitats in Zhengzhou, a rapidly growing city in central China. We classified open water into six categories: perennial rivers, seasonal rivers and streams, canals, lakes, ponds, and reservoirs. From 1990 to 2020, in 5-year intervals, we identified, counted, and measured the area of each kind of waterbody, and we used a model selection approach with linear regressions to ask which climate and anthropogenic drivers were associated with these changes. We also used Conefor software to examine how these changes affected the landscape connectivity for waterfowl. Over the study period, lakes and canals were the only waterbody types to show statistically significant changes in surface area, increasing by 712% and 236%, respectively. Changes in lakes and canals were positively correlated with the length of water pipeline in the city. The connectivity of waterbodies fluctuated over the same period, mirroring fluctuations in the perennial Yellow River. Ponds contributed very little to landscape connectivity, and the importance of reservoirs decreased over time. Conversely, canals played an increasingly important role in landscape connectivity over time. Counterintuitively, the connectivity of waterbodies increased in the built-up part of the city. Our results show that urbanization can have unexpected effects—both positive and negative—on the connectivity and area of open-water habitats. These effects are likely to be important for waterfowl and other aquatic organisms.

scholarly journals SPATIOTEMPORAL CHANGE ANALYSIS OF THE PROTECTED AREAS: A CASE STUDY – İĞNEADA FLOODPLAIN FORESTS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 735-740
Author(s):  
M. Toker ◽  
E. Çolak ◽  
F. Sunar
Keyword(s):  
Time Series ◽  
Protected Areas ◽  
National Parks ◽  
National Park ◽  
Spatial Scales ◽  
Google Earth ◽  
Floodplain Forests ◽  
Landsat Satellite ◽  
Time Series Images ◽  
The Impact

Abstract. Protected areas are important with land or water body ecosystems that have biodiversity, flora and fauna species. In Turkey, National Parks are one of the protected areas managed according to the National Parks Law No. 2873. Among them, the İğneada Floodplain Forests National Park, located in İğneada town in the province of Kırklareli, Turkey has been declared as a national park in 2007, and has an importance being a rare ecosystem, which consists of wetland, swamp, lakes and coastal sand dunes. Planning of Protected Areas can be done in a variety of ways, taking into account the balance of protection/use and should follow policies and guidelines. Today, for the sustainability and effective management of forest ecosystems, remote sensing technology provides an effective tool for assessing and monitoring ecosystem health at different temporal and spatial scales. In this study, potential temporal changes in the National Park were analyzed with Landsat satellite time series images using two different methods. First method, the Landtrendr algorithm (Landsat-based Detection of Trends in Disturbance and Recovery) developed for multitemporal satellite data, uses pixel values as input data and analysis them by using regression models to capture, label and map the changes. In this context, Landsat satellite time series images were taken quinquennial between 1987 and 2007 and biennially until 2017 for Landtrendr analysis (i.e. before and after its declaration as a National Park, respectively). As a second approach, the Google Earth Engine (GEE) cloud-based platform, which facilitates access to high-performance computing resources to process large long-term data sets, was used to analyze the impact of land cover changes. The results showed that the area was subjected to various pressures (i.e. due to illegal felling, pollution, etc.) until it was declared as a National park. Although there was general improvement and recovery after the region declared as a Park, it was seen that the sensitive dynamics of the region require continuous monitoring and protection using geo-information technologies.

scholarly journals Recent Changes of Glacial Lakes in the High Mountain Asia and Its Potential Controlling Factors Analysis

2021 ◽  
Vol 13 (18) ◽  
pp. 3757
Author(s):  
Meimei Zhang ◽  
Fang Chen ◽  
Hang Zhao ◽  
Jinxiao Wang ◽  
Ning Wang
Keyword(s):  
Google Earth ◽  
Controlling Factors ◽  
Rapid Expansion ◽  
Expansion Rate ◽  
Glacial Lake ◽  
High Mountain ◽  
Glacial Lakes ◽  
Computing Platform ◽  
Glacier Ice ◽  
Landsat Satellite

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km2 and 1577.38 ± 288.82 km2; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region.

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 A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

2019 ◽  
Vol 23 (2) ◽  
pp. 669-690 ◽  
Author(s):  
Tim Busker ◽  
Ad de Roo ◽  
Emiliano Gelati ◽  
Christian Schwatke ◽  
Marko Adamovic ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Surface Water ◽  
Linear Relationship ◽  
Satellite Altimetry ◽  
Google Earth ◽  
Lake Area ◽  
Lake Volume ◽  
Reservoir Volume ◽  
Lakes And Reservoirs

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use, and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lake and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 137 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh ∕ dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with a R2 > 0.8, an average R2 of 0.91 and a standard deviation of 0.05. For these lakes and for lakes with a nearly constant lake area (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage and (3) a predominant lack of data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the US, Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97 with a standard deviation of 0.041, and a normalized RMSE of 7.42 %. These results show a high potential for measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will not only provide a historical lake and reservoir volume variation record, but will also help to improve our understanding of the behaviour of lakes and reservoirs and their representation in (large-scale) hydrological models.

scholarly journals EXPLOITING SENTINEL-1 SAR TIME SERIES TO DETECT GRASSLANDS IN THE NORTHERN BRAZILIAN AMAZON

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 259-265
Author(s):  
A. F. Carneiro ◽  
W. V. Oliveira ◽  
S. J. S. Sant'Anna ◽  
J. Doblas ◽  
D. V. Vaz
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Google Earth ◽  
Data Availability ◽  
Kappa Index ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Sar Imagery

Abstract. Recent advances in cloud-computing technologies and remote sensing data availability foster the development of studies based on the analysis of optical and SAR imagery time series. In this paper, we assess the potential of Sentinel-1 imagery time series for grassland detection in the northern Brazilian Amazon. We used the Google Earth Engine cloud-computing platform as an alternative to obtain and analyse Sentinel-1 imagery, acquired from 2017 to 2018 over the region of Mojuí dos Campos/PA, Brazil. We extracted several temporal metrics from the imagery time series and used the Random Forest algorithm to perform the classification. In addition, we analysed the time series considering different channels, including the VV and VH polarizations, both separately and in combination, and the CR, RGI and NL indices. We could efficiently discriminate areas of grasslands from forest and agricultural crops using either VH time features or features extracted from the combination of both VV and VH polarizations. The classification map that resulted from the combination of VV and VH data presented the highest accuracy, with an overall accuracy of 95.33% and a 0.93 kappa index. Despite simple, the approach adopted in this paper showed potential to differ grasslands from areas of agriculture and forest in the northern Brazilian Amazon.

scholarly journals Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

2021 ◽  
Vol 10 (8) ◽  
pp. 521
Author(s):  
Adalet Dervisoglu
Keyword(s):  
Surface Area ◽  
Water Surface ◽  
Google Earth ◽  
Ramsar Convention ◽  
Computing Platform ◽  
Surface Areas ◽  
Inland Wetlands ◽  
Area Reduction ◽  
Management Plans ◽  
Landsat Satellite

Ramsar Convention (RC) is the first of modern intergovernmental agreement on the conscious use and conservation of natural resources. It provides a platform for contracting parties working together to develop the best available data, advice, and policy recommendations to increase awareness of the benefits of wetlands in nature and society. Turkey became a party of the RC in 1994, and in the years 1994 to 2013, 14 wetlands that reached the Ramsar criteria were recognized as Ramsar sites (RS). With this study, all inland RS in Turkey from 1985 to 2020 were examined, and changes in the water surface areas were evaluated on the GEE cloud computing platform using Landsat satellite images and the NDWI index. The closest meteorological station data to each RS were evaluated and associated with the surface area changes. The reasons for the changes in these areas, besides the meteorological effects, have been scrutinized using management plans and publications. As a result, inland wetlands decreased at different rates from 1985 to 2020, with a total loss of 31.38% and 21571.0 ha for the spring months. Since the designation dates of RS, the total amount of water surface area reduction was 27.35 %, constituting 17,758.90 ha.

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