Modeling the artificial lake-surface area change in arid agro-ecosystem: A case study in the newly reclaimed area, Egypt

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.

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Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

Remote Sensing ◽

10.3390/rs12040612 ◽

2020 ◽

Vol 12 (4) ◽

pp. 612 ◽

Cited By ~ 3

Author(s):

Xiaolong Liu ◽

Zhengtao Shi ◽

Guangcai Huang ◽

Yanchen Bo ◽

Guangjie Chen

Keyword(s):

Time Series ◽

Surface Area ◽

Yunnan Province ◽

Dominant Factor ◽

Natural Disturbances ◽

Lake Area ◽

Lake Surface ◽

Inland Lake ◽

Lake Surface Area ◽

Surface Area Change

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.

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Scaling relationships between lake surface area and catchment area

Aquatic Sciences ◽

10.1007/s00027-020-00726-y ◽

2020 ◽

Vol 82 (3) ◽

Cited By ~ 2

Author(s):

Jonathan A. Walter ◽

Rachel Fleck ◽

Michael L. Pace ◽

Grace M. Wilkinson

Keyword(s):

Surface Area ◽

Catchment Area ◽

Lake Surface ◽

Scaling Relationships ◽

Lake Surface Area

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Simplified Lake Surface Area Method for the Minimum Ecological Water Level of Lakes and Wetlands

Water ◽

10.3390/w10081056 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1056 ◽

Cited By ~ 6

Author(s):

Songpu Shang ◽

Songhao Shang

Keyword(s):

Water Level ◽

Surface Area ◽

Lake Level ◽

Lake Ecosystem ◽

Lake Surface ◽

Area Method ◽

Ecosystem Protection ◽

Lake Morphology ◽

Lake Surface Area ◽

Ecological Water Level

The determination of the rational minimum ecological water level is the base for the protection of ecosystems in shrinking lakes and wetlands. Based on the lake surface area method, a simplified lake surface area method was proposed to define the minimum ecological lake level from the lake level-logarithm of the surface area curve. The curve slope at the minimum ecological lake level is the ratio of the maximum lake storage to the maximum surface area. For most practical cases when the curve cannot be expressed as a simple analytical function, the minimum ecological lake level can be determined numerically using the weighted sum method for an equivalent multi-objective optimization model that balances ecosystem protection and water use. This method requires fewer data of lake morphology and is simple to compute. Therefore, it is more convenient to use this method in the assessment of the ecological lake level. The proposed method was used to determine the minimum ecological water level for one freshwater lake, one saltwater lake, and one wetland in China. The results can be used in the lake ecosystem protection planning and the rational use of water resources in the lake or wetland basins.

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Dynamic surface tension responses to surface area change of mixed solutions of a protein and small or medium-sized organic molecules

Colloids and Surfaces A Physicochemical and Engineering Aspects ◽

10.1016/0927-7757(96)03532-7 ◽

1996 ◽

Vol 114 ◽

pp. 99-111 ◽

Cited By ~ 13

Author(s):

P. Chen ◽

Z. Policova ◽

S.S. Susnar ◽

C.R. Pace-Asciak ◽

P.M. Demin ◽

...

Keyword(s):

Surface Tension ◽

Surface Area ◽

Organic Molecules ◽

Dynamic Surface Tension ◽

Dynamic Surface ◽

Area Change ◽

Mixed Solutions ◽

Surface Area Change

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Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

Arabian Journal of Geosciences ◽

10.1007/s12517-018-3416-7 ◽

2018 ◽

Vol 11 (5) ◽

Cited By ~ 3

Author(s):

Hickmat Hossen ◽

Mona G. Ibrahim ◽

Wael Elham Mahmod ◽

Abdelazim Negm ◽

Kazuo Nadaoka ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Surface Area ◽

Lake Surface ◽

Lake Surface Area

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The global lake area, climate, and population dataset

Scientific Data ◽

10.1038/s41597-020-0517-4 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 3

Author(s):

Michael F. Meyer ◽

Stephanie G. Labou ◽

Alli N. Cramer ◽

Matthew R. Brousil ◽

Bradley T. Luff

Keyword(s):

Surface Area ◽

Population Data ◽

Water Quantity ◽

Lake Area ◽

Lake Surface ◽

Global Trends ◽

Spatial And Temporal Scales ◽

Water Abundance ◽

Multiple Data ◽

Lake Surface Area

Abstract An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42 + million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area and water quantity at local, regional, and global scales.

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