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

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...

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Veröffentlicht in:Water (Basel) 2023-05, Vol.15 (10), p.1800
Hauptverfasser: Wang, Ziyuan, Liu, Xingyue, Li, Wei, He, Shuqiang, Zheng, Tingdan
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description 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. Acco
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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.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w15101800</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Agricultural production ; Analysis ; Annual precipitation ; atmospheric precipitation ; basins ; Case studies ; China ; Climate ; Climate change ; Contours ; Correlation coefficient ; Correlation coefficients ; Dams ; Drainage ; Economic factors ; Emergency plans ; humans ; Hydrology ; Information processing ; Lakes ; Landsat ; Methods ; MODIS ; Polynomials ; Population density ; Precipitation ; Remote sensing ; Satellite imagery ; Satellites ; Seasons ; Shape ; Social factors ; Socioeconomic factors ; Socioeconomics ; solar radiation ; Spatial analysis ; Spatial variations ; surface area ; surface water ; Sustainable development ; temperature ; Water ; Water bodies ; Water level fluctuations ; Water levels ; Water shortages</subject><ispartof>Water (Basel), 2023-05, Vol.15 (10), p.1800</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-a14ff2eaf87e9b1d3b3836240d4fd6ac22a2c220de12418658cbfbc8a9c0099f3</citedby><cites>FETCH-LOGICAL-c364t-a14ff2eaf87e9b1d3b3836240d4fd6ac22a2c220de12418658cbfbc8a9c0099f3</cites><orcidid>0000-0002-4512-511X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Wang, Ziyuan</creatorcontrib><creatorcontrib>Liu, Xingyue</creatorcontrib><creatorcontrib>Li, Wei</creatorcontrib><creatorcontrib>He, Shuqiang</creatorcontrib><creatorcontrib>Zheng, Tingdan</creatorcontrib><title>Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China</title><title>Water (Basel)</title><description>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.</description><subject>Accuracy</subject><subject>Agricultural production</subject><subject>Analysis</subject><subject>Annual precipitation</subject><subject>atmospheric precipitation</subject><subject>basins</subject><subject>Case studies</subject><subject>China</subject><subject>Climate</subject><subject>Climate change</subject><subject>Contours</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Dams</subject><subject>Drainage</subject><subject>Economic factors</subject><subject>Emergency plans</subject><subject>humans</subject><subject>Hydrology</subject><subject>Information processing</subject><subject>Lakes</subject><subject>Landsat</subject><subject>Methods</subject><subject>MODIS</subject><subject>Polynomials</subject><subject>Population density</subject><subject>Precipitation</subject><subject>Remote sensing</subject><subject>Satellite imagery</subject><subject>Satellites</subject><subject>Seasons</subject><subject>Shape</subject><subject>Social factors</subject><subject>Socioeconomic factors</subject><subject>Socioeconomics</subject><subject>solar radiation</subject><subject>Spatial analysis</subject><subject>Spatial variations</subject><subject>surface area</subject><subject>surface water</subject><subject>Sustainable development</subject><subject>temperature</subject><subject>Water</subject><subject>Water bodies</subject><subject>Water level fluctuations</subject><subject>Water levels</subject><subject>Water shortages</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdUU2LFDEQDeKCy-we_AcBLwrOmq_uTry1w37BLKvOKHhqatIVN2tPMibdytz94WYYEbEKqh5V71VBFSHPObuQ0rA3P3nFGdeMPSGngjVyrpTiT__Bz8h5zo-smDJaV-yU_FrjdhcTDBRCT1c7GH3BnyH5gmKgbYBhn32m0dElfEPaJgT6DjL2tLTHB6SXq3X78eqO3sUeh7e0pYvSpatx6vcH1Qc_TEepD_TLFAIE-j7FHz5YfE0XDz7AGTlxMGQ8_5Nn5NPV5XpxM1_eX98u2uXcylqNc-DKOYHgdINmw3u5kVrWQrFeub4GKwSIEliPXCiu60rbjdtYDcYyZoyTM_LyOHeX4vcJ89htfbY4DBAwTrmTvJJcGambQn3xH_UxTqkcI3dCc6NMLcvyGbk4sr7CgJ0PLo4JbPEet97GgM6XettUQpmm0gfBq6PApphzQtftkt9C2necdYcfdn9_KH8DSn2LSg</recordid><startdate>20230509</startdate><enddate>20230509</enddate><creator>Wang, Ziyuan</creator><creator>Liu, Xingyue</creator><creator>Li, Wei</creator><creator>He, Shuqiang</creator><creator>Zheng, Tingdan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-4512-511X</orcidid></search><sort><creationdate>20230509</creationdate><title>Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China</title><author>Wang, Ziyuan ; Liu, Xingyue ; Li, Wei ; He, Shuqiang ; Zheng, Tingdan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-a14ff2eaf87e9b1d3b3836240d4fd6ac22a2c220de12418658cbfbc8a9c0099f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Agricultural production</topic><topic>Analysis</topic><topic>Annual precipitation</topic><topic>atmospheric precipitation</topic><topic>basins</topic><topic>Case studies</topic><topic>China</topic><topic>Climate</topic><topic>Climate change</topic><topic>Contours</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Dams</topic><topic>Drainage</topic><topic>Economic factors</topic><topic>Emergency plans</topic><topic>humans</topic><topic>Hydrology</topic><topic>Information processing</topic><topic>Lakes</topic><topic>Landsat</topic><topic>Methods</topic><topic>MODIS</topic><topic>Polynomials</topic><topic>Population density</topic><topic>Precipitation</topic><topic>Remote sensing</topic><topic>Satellite imagery</topic><topic>Satellites</topic><topic>Seasons</topic><topic>Shape</topic><topic>Social factors</topic><topic>Socioeconomic factors</topic><topic>Socioeconomics</topic><topic>solar radiation</topic><topic>Spatial analysis</topic><topic>Spatial variations</topic><topic>surface area</topic><topic>surface water</topic><topic>Sustainable development</topic><topic>temperature</topic><topic>Water</topic><topic>Water bodies</topic><topic>Water level fluctuations</topic><topic>Water levels</topic><topic>Water shortages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Ziyuan</creatorcontrib><creatorcontrib>Liu, Xingyue</creatorcontrib><creatorcontrib>Li, Wei</creatorcontrib><creatorcontrib>He, Shuqiang</creatorcontrib><creatorcontrib>Zheng, Tingdan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Ziyuan</au><au>Liu, Xingyue</au><au>Li, Wei</au><au>He, Shuqiang</au><au>Zheng, Tingdan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China</atitle><jtitle>Water (Basel)</jtitle><date>2023-05-09</date><risdate>2023</risdate><volume>15</volume><issue>10</issue><spage>1800</spage><pages>1800-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w15101800</doi><orcidid>https://orcid.org/0000-0002-4512-511X</orcidid><oa>free_for_read</oa></addata></record>
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source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute
subjects Accuracy
Agricultural production
Analysis
Annual precipitation
atmospheric precipitation
basins
Case studies
China
Climate
Climate change
Contours
Correlation coefficient
Correlation coefficients
Dams
Drainage
Economic factors
Emergency plans
humans
Hydrology
Information processing
Lakes
Landsat
Methods
MODIS
Polynomials
Population density
Precipitation
Remote sensing
Satellite imagery
Satellites
Seasons
Shape
Social factors
Socioeconomic factors
Socioeconomics
solar radiation
Spatial analysis
Spatial variations
surface area
surface water
Sustainable development
temperature
Water
Water bodies
Water level fluctuations
Water levels
Water shortages
title Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China
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