Wetlands and Sustainability

Smardon, Richard C.

doi:10.3390/w6123724

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Wetlands are being drastically converted into non-wetland habitats due to agricultural activities (VOLK et al, 2017;ZOU et al, 2018), urbanization (ALIKHANI; NUMMI; OJALA, 2021), natural processes, and climate change impacts (TINER; LANG; KLEMAS, 2015;MOOMAW et al, 2018). They are among the ecosystems that experience the greatest anthropogenic pressure, even though they play a vital role for humans (SMARDON, 2014;MOOMAW et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Classification of Potential Wetlands using the Random Forest in Google Earth Engine in Geomorphological Units - Rio Grande do Sul, Brazil

Cunha,

Guasselli,

Belloli

et al. 2023

Rev. Bras. Cartogr.

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Wetlands are important and valuable ecosystems in the landscape of the extreme south of Brazil. However, they are among the ecosystems threatened by human pressures and climate change. Monitoring and managing these environments is a challenge due to their high spatial and temporal dynamics. The use of remote sensing techniques, supervised classification, and machine learning algorithms offers a promising opportunity to map and monitor wetlands. The objective of this work is to develop a method to map Potential Wetlands (PW) from the integration of images from different satellites, sensor systems, spectral, topographic, climatological and hydrological features, made available on the Google Earth Engine (GEE) cloud user platform. Supervised classification was performed on the geomorphological units Coastal Plain (CP) and Central Depression (CD) considering two classes: Potential Wetlands, which encompasses all types of wetlands, and No Wetlands, for the remaining land use and occupation classes. The supervised pixel-by-pixel classification, individual for each geomorphological unit, allowed more accurate results consistent with previous, consolidated mapping. The PW were mapped with a global accuracy higher than 88% and consumer and producer accuracy higher than 81% in the Coastal Plain and Central Depression. These results allow us to affirm that the proposed methodology enabled the identification of 22% to 24% increase in potential wetlands in geomorphological regions, from spectral signatures, and pixel-by-pixel supervised classification of PW using different image collections and sensor systems.

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Section: Introductionmentioning

confidence: 99%

Classification of Potential Wetlands using the Random Forest in Google Earth Engine in Geomorphological Units - Rio Grande do Sul, Brazil

Cunha,

Guasselli,

Belloli

et al. 2023

Rev. Bras. Cartogr.

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“…Wetlands are natural resources on which human beings depend for existence, known as the 'kidney of the earth' [1][2][3][4][5][6]. Wetlands perform important ecological functions such as improving water quality, maintaining rich biodiversity [7][8][9], regulating the regional climate through the differences in albedo, heat capacity, roughness, and energy exchange between wetlands and other land use types [10], and affect the regional runoff process [11,12] by reducing or avoiding flood disasters and maintaining a stable water supply [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Attribution Analysis on Areal Change of Main Wetland and Its Influence on Runoff in the Naolihe River Basin

Ding,

Zeng,

Yang

et al. 2023

Water

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Wetlands have powerful runoff regulation functions, which can effectively store and retain surface runoff. The runoff regulation function of wetlands is affected by wetland areas, which affect the capacity of flood control. To explore the law of the area change of the main wetlands of the Naolihe River Basin (MWNRB), the visual interpretation method was used to extract wetlands. To identify the reasons for area changes in the MWNRB, the maximum likelihood method, minimum distance method, and neural network method were used to classify land use types from remote sensing images; the M-K variation point test and Theil-Sen trend analysis were used to test the variation point and calculate the trend of precipitation and temperature series. To clarify the influence of wetland areas on runoff, the Gini coefficient and SRI of runoff were used to calculate runoff temporal inhomogeneity. The results showed that the area of the MWNRB obviously decreased, with 74.5 × 106 m2/year from 1993 to 2008, and increased slowly from 2008 to 2015, with 27.7 × 106 m2/year. From 1993 to 2008, 50.74% and 38.87% of wetlands were transformed into paddy fields and dry fields, respectively. From 2008 to 2015, 61.69% and 7.76% of wetlands were transformed from paddy fields and dry fields, respectively. The temperature of the MWNRB increased slowly by 0.04 °C/year from 1993 to 2008 and increased obviously by 0.16 °C/year from 2008 to 2015. The precipitation decreased by 5.6–8.1 mm/year and increased by 16.6–41.2 mm/year in 1993–2008 and 2008–2015, respectively. Compared with precipitation and temperature, land use change caused by human activities is the main cause of wetland area change. The area change of the MWNRB has a certain influence on the runoff regulation and storage capacity. The Gini coefficient and SRI index increased from 0.002/year (0.008) to 0.023/year from 1993 to 2008 and decreased from 0.046/year (0.045) to 0.161/year from 2008 to 2015, respectively.

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“…The lakeshore zone is the area at the edge of the lake basin, which is adjacent to the surrounding land [19], and has the function of regulating water quality [20], water quantity [21], and groundwater recharge [22] of the lake. In addition, the lakeshore zone maintains the biodiversity of flora and fauna in the area [23,24] and is critical for food security in nearby subsistence agriculture areas [25,26].…”

Section: Introductionmentioning

confidence: 99%

Changes in Ecosystem Service Value in the 1 km Lakeshore Zone of Poyang Lake from 1980 to 2020

Long

Liu

et al. 2021

Land

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Poyang Lake is a typical lake in the middle and lower reaches of the Yangtze River and is the largest freshwater lake in China. The habitat quality of Poyang Lake has been declining in recent years, leading to a series of ecological problems. An ecological risk evaluation, based on land use, is important in order to promote a coordinated development of land use and the ecological environment. In this paper, land use data from the Poyang Lake basin in the corresponding years are interpreted based on the images from the Landsat satellite mission in seven periods from 1980 to 2020. The lake surface and the 1 km lakeshore zone of Poyang Lake are extracted based on the interpreted land use data. Finally, the ecological service value per unit area of the area is measured by combining it with the Chinese terrestrial ecosystem service value equivalent table, and then with the value of each ecological factor and the value of the changes to land use type. The research results show that: (1) from 1980 to 2000, the lake area of Poyang Lake had an overall decreasing trend (the area slightly increased from 1980 to 1990); from 2000 to 2020, the lake area of Poyang Lake gradually increased (the area slightly decreased from 2015 to 2020). (2) The farmland, forest, grassland and desert areas gradually increased and the wetlands gradually decreased over 40 years; the area of the water body gradually increased from 1980 to 2010, and gradually decreased from 2010 to 2020. (3) The ecosystem service value of the lakeshore zone of Poyang Lake fluctuated around 15,000 × 106 Yuan from year to year.

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Wetlands and Sustainability

Cited by 6 publications

References 11 publications

Classification of Potential Wetlands using the Random Forest in Google Earth Engine in Geomorphological Units - Rio Grande do Sul, Brazil

Classification of Potential Wetlands using the Random Forest in Google Earth Engine in Geomorphological Units - Rio Grande do Sul, Brazil

Attribution Analysis on Areal Change of Main Wetland and Its Influence on Runoff in the Naolihe River Basin

Changes in Ecosystem Service Value in the 1 km Lakeshore Zone of Poyang Lake from 1980 to 2020

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