Matches in SemOpenAlex for { <https://semopenalex.org/work/W2900996968> ?p ?o ?g. }
- W2900996968 endingPage "1616" @default.
- W2900996968 startingPage "1616" @default.
- W2900996968 abstract "Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km2 in 1989 and a minimum of 44 km2 in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development." @default.
- W2900996968 created "2018-11-29" @default.
- W2900996968 creator A5000699866 @default.
- W2900996968 creator A5020492804 @default.
- W2900996968 creator A5031625880 @default.
- W2900996968 creator A5050392359 @default.
- W2900996968 creator A5069110910 @default.
- W2900996968 creator A5079462791 @default.
- W2900996968 date "2018-11-09" @default.
- W2900996968 modified "2023-10-17" @default.
- W2900996968 title "Analysis of the Dynamic Changes of the Baiyangdian Lake Surface Based on a Complex Water Extraction Method" @default.
- W2900996968 cites W1715542629 @default.
- W2900996968 cites W1966808625 @default.
- W2900996968 cites W1969333417 @default.
- W2900996968 cites W1970269224 @default.
- W2900996968 cites W1994652972 @default.
- W2900996968 cites W2007300776 @default.
- W2900996968 cites W2020514916 @default.
- W2900996968 cites W2056079229 @default.
- W2900996968 cites W2084364057 @default.
- W2900996968 cites W2179843978 @default.
- W2900996968 cites W2308510182 @default.
- W2900996968 cites W2502041161 @default.
- W2900996968 cites W2518149467 @default.
- W2900996968 cites W2560167313 @default.
- W2900996968 cites W2567500890 @default.
- W2900996968 cites W2588382265 @default.
- W2900996968 cites W2593542173 @default.
- W2900996968 cites W2595185511 @default.
- W2900996968 cites W2597873148 @default.
- W2900996968 cites W2604853093 @default.
- W2900996968 cites W2617773476 @default.
- W2900996968 cites W2734634937 @default.
- W2900996968 cites W2752403911 @default.
- W2900996968 cites W2758326418 @default.
- W2900996968 cites W2777136247 @default.
- W2900996968 cites W2790944589 @default.
- W2900996968 cites W2800115553 @default.
- W2900996968 cites W2804170736 @default.
- W2900996968 cites W2810684977 @default.
- W2900996968 doi "https://doi.org/10.3390/w10111616" @default.
- W2900996968 hasPublicationYear "2018" @default.
- W2900996968 type Work @default.
- W2900996968 sameAs 2900996968 @default.
- W2900996968 citedByCount "27" @default.
- W2900996968 countsByYear W29009969682019 @default.
- W2900996968 countsByYear W29009969682020 @default.
- W2900996968 countsByYear W29009969682021 @default.
- W2900996968 countsByYear W29009969682022 @default.
- W2900996968 countsByYear W29009969682023 @default.
- W2900996968 crossrefType "journal-article" @default.
- W2900996968 hasAuthorship W2900996968A5000699866 @default.
- W2900996968 hasAuthorship W2900996968A5020492804 @default.
- W2900996968 hasAuthorship W2900996968A5031625880 @default.
- W2900996968 hasAuthorship W2900996968A5050392359 @default.
- W2900996968 hasAuthorship W2900996968A5069110910 @default.
- W2900996968 hasAuthorship W2900996968A5079462791 @default.
- W2900996968 hasBestOaLocation W29009969681 @default.
- W2900996968 hasConcept C127313418 @default.
- W2900996968 hasConcept C1284942 @default.
- W2900996968 hasConcept C132651083 @default.
- W2900996968 hasConcept C151730666 @default.
- W2900996968 hasConcept C185592680 @default.
- W2900996968 hasConcept C187320778 @default.
- W2900996968 hasConcept C18903297 @default.
- W2900996968 hasConcept C205649164 @default.
- W2900996968 hasConcept C2776608160 @default.
- W2900996968 hasConcept C2778395939 @default.
- W2900996968 hasConcept C39432304 @default.
- W2900996968 hasConcept C43617362 @default.
- W2900996968 hasConcept C4725764 @default.
- W2900996968 hasConcept C58640448 @default.
- W2900996968 hasConcept C62649853 @default.
- W2900996968 hasConcept C76886044 @default.
- W2900996968 hasConcept C8625798 @default.
- W2900996968 hasConcept C86803240 @default.
- W2900996968 hasConcept C87717796 @default.
- W2900996968 hasConceptScore W2900996968C127313418 @default.
- W2900996968 hasConceptScore W2900996968C1284942 @default.
- W2900996968 hasConceptScore W2900996968C132651083 @default.
- W2900996968 hasConceptScore W2900996968C151730666 @default.
- W2900996968 hasConceptScore W2900996968C185592680 @default.
- W2900996968 hasConceptScore W2900996968C187320778 @default.
- W2900996968 hasConceptScore W2900996968C18903297 @default.
- W2900996968 hasConceptScore W2900996968C205649164 @default.
- W2900996968 hasConceptScore W2900996968C2776608160 @default.
- W2900996968 hasConceptScore W2900996968C2778395939 @default.
- W2900996968 hasConceptScore W2900996968C39432304 @default.
- W2900996968 hasConceptScore W2900996968C43617362 @default.
- W2900996968 hasConceptScore W2900996968C4725764 @default.
- W2900996968 hasConceptScore W2900996968C58640448 @default.
- W2900996968 hasConceptScore W2900996968C62649853 @default.
- W2900996968 hasConceptScore W2900996968C76886044 @default.
- W2900996968 hasConceptScore W2900996968C8625798 @default.
- W2900996968 hasConceptScore W2900996968C86803240 @default.
- W2900996968 hasConceptScore W2900996968C87717796 @default.
- W2900996968 hasFunder F4320321001 @default.
- W2900996968 hasFunder F4320323659 @default.