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

2019 ◽  
Author(s):  
Xiaolong Liu ◽  
Zhengtao Shi ◽  
Guangcai Huang ◽  
Yanchen Bo ◽  
Guangjie Chen
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Dominant Factor ◽  
Natural Disturbances ◽  
Lake Area ◽  
Lake Surface ◽  
Natural Event ◽  
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 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.

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

2020 ◽  
Vol 12 (4) ◽  
pp. 612 ◽  
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.

scholarly journals The global lake area, climate, and population dataset

2020 ◽  
Vol 7 (1) ◽  
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.

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

2007 ◽  
Vol 141 (1-3) ◽  
pp. 131-147 ◽  
Author(s):  
Tyler Wagner ◽  
Patricia A. Soranno ◽  
Kendra Spence Cheruvelil ◽  
William H. Renwick ◽  
Katherine E. Webster ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Area ◽  
Lake Surface ◽  
Inland Lake ◽  
Lake Surface Area

Aquatic macrophyte richness in Danish lakes in relation to alkalinity, transparency, and lake area

2000 ◽  
Vol 57 (10) ◽  
pp. 2022-2031 ◽  
Author(s):  
Ole Vestergaard ◽  
Kaj Sand-Jensen
Keyword(s):  
Species Richness ◽  
Surface Area ◽  
Submerged Macrophytes ◽  
Secchi Depth ◽  
Strong Increase ◽  
Entire Group ◽  
Lake Area ◽  
Lake Surface ◽  
Eutrophic Lakes ◽  
Lake Surface Area

We examined the relationship between environmental factors and the richness of submerged macrophytes species in 73 Danish lakes, which are mainly small, shallow, and have mesotrophic to hypertrophic conditions. We found that mean species richness per lake was only 4.5 in acid lakes of low alkalinity but 12.3 in lakes of high alkalinity due to a greater occurrence of the species-rich group of elodeids. Mean species richness per lake also increased significantly with increasing Secchi depth. No significant relationship between species richness and lake surface area was observed among the entire group of lakes or a subset of eutrophic lakes, as the growth of submerged macrophytes in large lakes may be restricted by wave action in shallow water and light restriction in deep water. In contrast, macrophyte species richness increased with lake surface area in transparent lakes, presumably due to expansion of the area colonised by submerged macrophytes. Thus, the size of the colonised area is a better predictor of species richness than lake surface area. The strong increase in species richness accompanying greater transparency can be accounted for by the combined effect of higher colonised area and higher habitat richness along gradients of deeper macrophyte growth.

scholarly journals Lake Surface Area Forecasting Using Integrated Satellite-SARIMA-Long-Short-Term Memory Model

2021 ◽  
Author(s):  
Keyvan Soltani ◽  
Arash Azari ◽  
Mohammad Zeynoddin ◽  
Afshin Amiri ◽  
Isa Ebtehaj ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Short Term Memory ◽  
Resource Planning ◽  
The Other ◽  
Lake Surface ◽  
Short Term ◽  
Term Memory ◽  
Lake Surface Area ◽  
Long Short Term Memory

Abstract Lake Water Surface Area (WSA) plays a vital role in environmental preservation and future water resource planning and management. Accurately mapping, monitoring and forecasting Lake WSA changes are of great importance to regulatory agencies. This study used the MODIS satellite images to extract a monthly time series of WSA of two lakes located in Iran from 2001 to 2019. Following a consequence of image and time series preprocessing to obtain the preprocessed lake surface area time series, the outcomes were modeled by the Long-Short-Term Memory (LSTM) deep learning (DL) method, the stochastic Seasonal Auto-Regressive Integrated Moving Average (SARIMA) method and hybridization of these two techniques with the objective of developing WSA forecasts. After separate standardization and normalization of AL TS and reevaluation of the preprocessed data, the SARIMA (1, 0, 0) (0, 1, 1)12 model outperformed sole LSTM models with correlation index of (R) 0.819, mean absolute error (MAE) of 49.425 and mean absolute percentage error (MAPE) of 0.106. On the other hand, the hybridization (stochastic-DL) enhanced the reproduction of the primal statistical properties of WSA data and caused better mediation. However, the other accuracy indices did not change markedly (R 0.819, MAE 49.310, MAPE 0.105). The multi-step preprocessing and reevaluation also caused all LSTM models to produce their best results by less than 12 inputs.

Scaling relationships between lake surface area and catchment area

2020 ◽  
Vol 82 (3) ◽  
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

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

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1056 ◽  
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.

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

2018 ◽  
Vol 11 (5) ◽  
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

Geomorphic Expression of Abrupt Climate Change in Southwestern North America at the Glacial Termination

2002 ◽  
Vol 57 (3) ◽  
pp. 371-381 ◽  
Author(s):  
Roger Y. Anderson ◽  
Bruce D. Allen ◽  
Kirsten M. Menking
Keyword(s):  
North America ◽  
North Atlantic ◽  
Climate Changes ◽  
Lake Area ◽  
Lake Surface ◽  
Atlantic Region ◽  
Beach Ridges ◽  
The North ◽  
Lake Floor ◽  
Lake Surface Area

AbstractEolian and subaqueous landforms composed of gypsum sand provide geomorphic evidence for a wet episode at the termination of glacial climate in southwestern North America. Drying of pluvial Lake Estancia, central New Mexico, occurred after ca. 12,000 14C yr B.P. Thereafter, eolian landforms on the old lake floor, constructed of gypsum sand, were overridden by rising lake water, modified by subaqueous processes, and organized into beach ridges along the lake's eastern shore. Preservation of preexisting eolian landforms in the shallow lake suggests abupt changes in lake level and climate. Available radiocarbon ages suggest that the final highstand recorded by beach ridges may have developed during the Younger Dryas (YD) stade. The beach ridges provide information about lake surface area, which was 45% of the lake area reached during the maximum highstands of the late Pleistocene. A similar proportional response has been reported for YD climate changes outside the North Atlantic region.

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