Lake areas monitoring based on unmixing mixed pixels united with double-edge extraction

Qilu Lake is one of the nine plateau lakes in Yunnan Province, China. In recent years, under the influence of extreme climate and human activities, the area of Qilu Lake has shrunk significantly, the water level has dropped, and the problem of water shortage has become increasingly serious. Based on the Landsat and MODIS image data from 2000 to 2020, this study applied the ESTARFM spatiotemporal fusion model to unify the data images used in the study to February, used three kinds of water body indexes, selected the water body index most suitable for the study area to extract relevant information, and analyzed the spatiotemporal change characteristics of Qilu Lake area in the last 20 years. The results showed that: (1) Based on the ESTARFM model, the Landsat and MODIS data on 18 January 2020, the Landsat and MODIS data on 9 May 2020, and the MODIS data on the date to be predicted (February 13) were fused to obtain the Landsat image data of the predicted date, which met the accuracy requirements; (2) Taking 2005 as an example, the NDWI, MNDWI, and AWEIsh indexes were used to extract the water body with the precisions of 99.0%, 99.6% and 98.6%, respectively, and then the MNDWI water body index was selected to extract the lake area; (3) In the past 20 years, the overall area of Qilu Lake has shown a downward trend, with the area reduced by 0.7132 km2. From 2000 to 2010, the lake area was relatively stable, fluctuating up and down around 36 km2. From 2010 to 2015, the lake area decreased sharply, with a change rate of −40%. After 2015, the lake area gradually increased; (4) The spatial change of Qilu Lake area mainly occurred in the southwest and west, which decreased by 0.44 km2 and 0.49 km2, respectively, and there were small fluctuations in other directions. In the past two decades, the shape index of Qilu Lake has shown a downward trend as a whole; the contour of the lake tends to be simplified, the contour is complex and stable from 2000 to 2010, and the shape index decreases from 2.17 to 1.74 from 2010 to 2020; (5) The change in the Qilu Lake area is positively correlated with the change in the water level. Polynomial models with different times were selected as the model for retrieving water level elevation from the Qilu Lake water surface area, with a highest correlation coefficient of 0.9259. The temporal and spatial changes of the Qilu Lake area in the last 20 years are the result of the joint action of natural factors and socio-economic factors. According to the analysis, the annual average temperature, annual precipitation, annual average sunshine hours, and population density are the main driving forces leading to the change. In the future, the government and relevant researchers should strengthen real-time monitoring and regular research, formulate and optimize emergency plans to deal with changes in the ecological environment of lakes, and promote the sustainable development of the ecological environment and social economy of the basin.

show abstract

“…NDWI, MNDWI, and AWEI sh are the most commonly used water indexes. The calculation formula is as follows [10,11]:…”

Section: Extracting Lake Area By Water Body Index Methodsmentioning

confidence: 99%

“…In recent years, a series of achievements have been made in monitoring, extraction methods, and water quality index inversion, etc. [10][11][12][13][14][15][16]. At present, the main remote sensing data used are multi-spectral data such as Sentinel, AHVRR, SPOT, Landsat, ASTER, and resource satellites [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China

Wang

Liu

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…Lake area changes are highly sensitive to both climate change and human activities [1]. Therefore, mapping lakes and detecting their area changes accurately and rapidly are of great significance to understand the relevance of lake variations to climate changes and human activities, and they are also crucial to developing, utilizing and protecting of lake water resources [2].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Zhang et al used the multi-endmember linear spectral unmixing technique to estimate the lake fraction in each mixed pixel of MODIS imagery, and develop an algorithm to locate spatially the water body within mixed pixels [9]. To improve the accuracy and applicability of decomposition of mixed pixels, a model combing doubleedge extraction with decomposition of mixed pixels was proposed, and attested by computing lake areas of northwestern China using AVHRR imagery [2]. More recently, Liu et al improved linear mixture model and applied it to extract water body based on MODIS reflectance images, and it indicated that the final result has a high accuracy with the impact of shade corrected [8].…”

Section: Introductionmentioning

confidence: 99%

Lake Surface Area Estimation Method based on Error Mechanism Model for Satellite Imagery and Its Application in Ebinur Lake

Ru¹,

Zhao²,

Xiong³

2015

IJSIP

View full text Add to dashboard Cite

show abstract

“…With the climate change and human activities, especially the intensification of the unreasonable human activities, lake shrinking became one of the major environmental problems in some regions. Thus, it's very important to make real-time estimation of lake areas, which is meaningful for the sustainable development, utilization and protection of lakes [3].…”

Section: Introductionmentioning

confidence: 99%

Difference in Lake Water Area Derived from Different Resolution Imagery

Ru¹,

Zhang²,

Sun³

et al. 2016

IJSIP

View full text Add to dashboard Cite

show abstract

Lake areas monitoring based on unmixing mixed pixels united with double-edge extraction

Cited by 3 publications

References 4 publications

Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China

Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China

Lake Surface Area Estimation Method based on Error Mechanism Model for Satellite Imagery and Its Application in Ebinur Lake

Difference in Lake Water Area Derived from Different Resolution Imagery

Contact Info

Product

Resources

About