A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

Firozjaei, Mohammad Karimi; Fathololoumi, Solmaz; Alavipanah, Seyed Kazem; Kiavarz, Majid; Vaezi, Ali Reza; Biswas, Asim

doi:10.1016/j.rse.2020.111746

Cited by 43 publications

(23 citation statements)

References 67 publications

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“…on the USES at different dates can reduce the accuracy of modeling the effect of COVID-19 lockdown on the USES. To overcome this challenge, the use of normalization methods to reduce the impact of climatic conditions on the USES can be useful ( Firozjaei et al, 2020a ; Weng et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities

Firozjaei

Fathololomi

Kiavarz

et al. 2021

Journal of Environmental Management

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The COVID-19 pandemic has caused unprecedent negative impacts on our society, however, evidences show a reduction of anthropogenic pressures on the environment. Due to the high importance of environmental conditions on human life quality, it is crucial to model the impact of COVID-19 lockdown on environmental conditions. Consequently, the objective of this study was to model the impact of COVID-19 lockdown on the urban surface ecological status (USES). To this end, the Landsat-8 images of Milan for three pre-lockdown dates (Feb 13, 2018 (MD1), April 18, 2018 (MD2) and Feb 3, 2020 (MD3)) and one date over the lockdown (April 14, 2020 (MD4)), and Wuhan for three pre-lockdown dates (Dec 17, 2017 (WD1), March 23, 2018 (WD2) and Dec 7, 2019 (WD3)) and one lockdown date (Feb 9, 2020 (WD4)) were used. First, pressure-state-response (PSR) framework parameters including index-based built-up index (IBI), vegetation cover (VC), vegetation health index (VHI), land surface temperature (LST) and Wetness were calculated. Second, by combining the PSR framework parameters based on comprehensive ecological evaluation index (CEEI), the USES were modeled on different dates. Thirdly, the USES during the COVID-19 lockdown was compared with the USES for pre-lockdown. The mean (standard deviation) of CEEI for Milan on MD1, MD2, MD3 and MD4 were 0.52 (0.12), 0.60 (0.19), 0.57 (0.13) and 0.45 (0.16), respectively. Also, these values for Wuhan on WD1, WD2, WD3 and WD4 were 0.63 (0.14), 0.67 (0.15), 0.60 (0.13) and 0.57 (0.13), respectively. Due to the lockdowns, the mean CEEI of built-up, bare soil and green spaces for Milan and Wuhan decreased by [0.18, 0.02, 0.08], [0.13, 0.06, 0.05], respectively. During the lockdown period, the USES improved substantially due to the reduction of anthropogenic activities in the urban environment.

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Section: Discussionmentioning

confidence: 99%

Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities

Firozjaei

Fathololomi

Kiavarz

et al. 2021

Journal of Environmental Management

Self Cite

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“…In other words, if we focus on other statistical indices, such as MSE or RMSE u , then the Taylor diagram cannot display them because Equation () does not hold. However, for many applications, we need to focus on statistical indices other than those in the Taylor diagram, such as RMSE u and NSE in hydroclimatic models (Dembele et al ., 2020; Firozjaei et al ., 2020).…”

Section: Decomposition Of Taylor Diagrammentioning

confidence: 99%

Decompositions of Taylor diagram and DISO performance criteria

Zhou

Chen

et al. 2021

Intl Journal of Climatology

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Taylor diagram has been frequently used to evaluate climate or hydrology models or data. A Taylor diagram summarizes three frequently used metrics including correlation coefficient (CC), standard deviation (STD), and centred root mean square error (RMSEc). Although these three metrics are relevant metrics for some applications, in some cases, additional indicators are needed, which calls for a new method. This study firstly addressed the short comments about the distance between indices of simulation and observation (DISO) described in a previous study. Secondly, the number of statistical metrics of DISO is extended from 3 to more than 3. The statistical metrics of the expanded DISO are more flexible than the Taylor diagram which uses the three fixed metrics. Thirdly, the current work compares the Taylor diagram with the expanded DISO in terms of their theoretical bases, revealing the advantage of DISO in terms of its flexibility in the selection of different types of metrics, and its suitability as an effective single metric to express a model's or dataset's overall quality. The power and flexibility of the expanded DISO are discussed.

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“…Natural features such as vegetation and water bodies have been replaced by impervious materials and buildings. These impervious surfaces with high thermal capacity led to a significant changing trend in land Menghang Liu and Haitao Ma are with the Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and surface temperature (LST), which has altered the original urban energy balance [4][5][6][7]. This transition poses a threat to social and environmental issues, such as human health [8], energy consumption [9], heat waves [10], air pollution [11], and climate change [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Drivers of Land Surface Temperature Based on Multisource Data: A Spatial Econometric Perspective

Liu¹,

Ma²,

Bai³

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

Cited by 43 publications

References 67 publications

Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities

Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities

Decompositions of Taylor diagram and DISO performance criteria

Understanding the Drivers of Land Surface Temperature Based on Multisource Data: A Spatial Econometric Perspective

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