A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran

Qureshi, Salman; Alavipanah, Seyed Kazem; Konyushkova, M. V.; Mijani, Naeim; Fathololomi, Solmaz; Firozjaei, Mohammad Karimi; Homaee, Mehdi; Hamzeh, Saeid; Kakroodi, A.A.

doi:10.3390/rs12182989

Cited by 69 publications

(40 citation statements)

References 54 publications

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“…In recent years, the RSEI has grown in popularity and been frequently adopted for urban eco-environment quality assessment in individual cities such as Pingtan Comprehensive Pilot Zone [19] in China, regions such as the Beijing-Tianjin-Hebei region [20], the Guangdong-Hong Kong-Macau Bay area [21], the Chaohu basin [22,23], and the Beijing-Hangzhou Canal coast [24], and even at a national scale [25]. It has also been applied by international researchers, for example, to Gaomishan City in Iran [26], the Samara region of Russia [27], as well as different cities in the United States [28] and Europe [29]. These studies usually calculated annual RSEI from individual remote-sensing images and then performed a multi-year analysis of the change in eco-environmental conditions [30].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Zhang

Yang

et al. 2021

IJGI

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The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.

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Section: Introductionmentioning

confidence: 99%

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Zhang

Yang

et al. 2021

IJGI

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“…In recent times, it has become effective method to obtain data at a large-scale spatial and temporal coverage for investigating the spatiotemporal changes in vegetation, land-use/land cover and LST (Berger et al, 2017;Li et al, 2013;Tomlinson et al, 2011). Moreover, it is increasingly helps to assess biological, physical and other features in this planet (Li et al, 2020;Qureshi et al, 2020). LST is an essential constituent of climate system which is influenced prominently by interactions of the atmosphere and earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variability of land surface temperature in north-western Ethiopia

Bayable

Alemu²

2021

Environ Sci Pollut Res

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The aggravating deforestation, industrialization and urbanization are increasingly becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal discrepancies in Land Surface Temperature (LST) and the link with elevation in Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) was used. The pixel-based linear regression model was employed to explore the spatiotemporal discrepancies of LST changes pixel-wise.Furthermore, Sen's slope and Mann-Kendall were used for determining the extent of temporal shifts of the areal average LST and evaluating trends in areal average LST values, respectively.Coefficient of Variation (CV) was calculated to examine spatial and temporal discrepancies in seasonal and annual LST for each pixel. The distribution of average seasonal LST spatially ranged from 43.45-16.62℃, 39.89-14.59℃, 50.39-21.102℃ and 43.164-20.39℃ for autumn (September-November), summer (June-August), spring (March-May) and winter (December-February) seasons, respectively. The seasonal LST CV varied from1.096-10.72%, 0.7-11.06%, 1.29-14.76% and 2.19-10.35% for average autumn, summer, spring and winter seasons, respectively. The seasonal spatial LST trend varied from -0.7-0.16, -0.4-0.224, 0.6-0.19 and -0.6-0.32 for average autumn, summer, spring and winter seasons, respectively. Besides, the annual spatial LST slope varied from -0.58-0.17. An insignificantly declining trend in LST shown at 0.036℃ yr -1 , 0.041℃ yr -1 , 0.074℃ yr -1 , 0.005℃ yr -1 in autumn, summer, spring and winter seasons (P<0.05), respectively. Moreover, the annual variations of mean LST decreased insignificantly at 0.046℃ yr -1 . Generally, the LST is tremendously variable in space and time and negatively correlated with an elevation.

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“…Field sampling requires a considerable amount of time and effort and may not be cost-effective for the long-term monitoring practices of a large study area [ 43 ]. Remote-sensing techniques are increasingly being used as rapid, cost-effective, and nondestructive approaches to modeling soil properties at a large scale [ 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Hikouei

Kim

Mishra

2021

Sensors

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Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran

Cited by 69 publications

References 54 publications

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Spatiotemporal variability of land surface temperature in north-western Ethiopia

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

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