The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

Roşca, Cristina Florina; Harpa, Gabriela Victoria; Croitoru, Adina‐Eliza; Herbel, Ioana; Imbroane, A.; Burada, Doina Cristina

doi:10.1007/s00704-016-1923-6

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…The spectral response of objects in thermal infrared regions (10.40-2.50 µm for Band 6 for TM and ETM+ sensors, and 10.60-11.19 µm and 11.50-12.51 µm for Bands 10 and 11, respectively, for the TIRS sensor) is stored as digital numbers (DNs) or Quantized and calibrated standard product pixel values (Qcal). Their values range from 0 to 255, and are delivered in an 8-bit (for TM and ETM+) or 16-bit unsigned integer format [43]. Consequently, the preliminary step for calculating surface temperature is to obtain brightness temperature, as stated by USGS [60].…”

Section: Tir Data Conversion To Lstmentioning

confidence: 99%

“…Previous studies have mainly focused on studying differences in ground temperatures as a function of land-use land cover and its evolution at the city level [16,16,20,[33][34][35], temporal trends in urban SUHI in urban areas [18,36,37], the refreshing impact of parks on their surroundings [15,38,39], the evolution of SUHI as a function of day and night [9,34,40,41], the impact of vegetation on LST at the urban scale [12,36,37,42], comparison of surface temperatures and air temperature [8][9][10]20,43,44], the impact of surface temperatures on health [30,41] and transversely at surface temperatures at moderate resolutions (MODIS 1 km) [16,20,[34][35][36]42,45] but, to our knowledge, there are no similar studies such as ours that analyze the thermal monitoring of site redevelopment at such a detailed spatial and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

et al. 2019

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Climate change is a global challenge with multiple consequences. One of its impacts is the increase in heatwave frequency and intensity. The risk is higher for populations living in urban areas, where the highest temperatures are generally identified, due to the urban heat island effect. This phenomenon has recently been taken into account by local elected officials. As a result, developers have decided to use solutions in redevelopment projects to combat high temperatures in urban areas. Consequently, the objective is to study the land-surface temperature evolution of six main urban redevelopments in Lyon, France, from 2000 to 2017. Three of them (the Confluence, Kaplan, and Museum sites) were composed of industrial areas that have undergone major transformations and are now tertiary or residential areas. Two sites have been more lightly transformed, particularly by increasing vegetation to reduce heat stress and urban flooding (Dock and Garibaldi Street). Finally, the Groupama Stadium has been built into agricultural and wooded areas. Changes in vegetation cover (NDVI), water (MNDWI), and moisture (NDMI) content, built areas (NDBI) and bare soil (NDBaI) are also monitored. The results show that the Confluence and Kaplan sites were accompanied by a decrease in surface temperature and an increase in vegetation and moisture, whereas the Groupama Stadium displayed a rise in surface temperature and a decrease in vegetation. On the other hand, the Museum, Dock, and Garibaldi sites did not exhibit clear and uniform trends, although an increase in surface temperature was shown in some statistical tests. The disparity of the results shows the necessity to include a significant amount of vegetation during redevelopment operations in order to reduce heat stress.

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Section: Tir Data Conversion To Lstmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

et al. 2019

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“…Twenty-eight explanatory variables are used to estimate the air temperature over the study area. They have been selected from the literature [22][23][24][25][26]29,[32][33][34][35][36][37][38][39][40][41][42][43][44][45]. The variables belong to various categories and can directly be obtained from the supplying organizations or produced by geomatics or remote sensing ( Table 3).…”

Section: Twenty-eight Explanatory Variables Selected From the Literaturementioning

confidence: 99%

Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns

Alonso

Renard

2019

Urban Science

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With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.

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“…Birçok araştırmacı YYS'nin önemi ve YYS'nin farklı konular üzerindeki etkisini incelemişlerdir. Bu çalışma konularından başlıcaları; kentsel iklim çalışmaları (Arnfield 2003, Karakuş 2019, Karimi et al 2017, Sahana et al 2019, Şekertekin et al 2015a, Şekertekin et al 2015b, Sekertekin et al 2016b, Yavaşlı et al 2018, evapotranspirasyon (Jiang and Weng 2017), yangınların izlenmesi (Prasad et al 2013), endüstriyel alanlardaki sıcak nokta (hot-spot) analizleri (Arslan 2018, Şekertekin et al 2016b), YYS ile biyofiziksel indeks ilişkileri (Balcik 2014, Kumari and Sarma 2017, Roşca et al 2017, Uysal and Polat 2015, su kaynakları yönetimi (Anderson et al 2008), jeotermal araştırmalar (Mia et al 2014, Polat et al 2018, Sekertekin and Arslan 2019, Ulusoy 2016, Yalcin and Kilic Gul 2017 ve bitki gözlem çalışmalarıdır (Weng et al 2004). YYS, meteoroloji istasyonlarının radyans ölçümleriyle hesaplanabilir ancak bu geleneksel yöntem, nokta bazlı ölçümler sunduğu için topoğrafyayı temsil etmez.…”

Section: Introductionunclassified

Işınım Transferi Denklemi Baz Alınarak Yer Yüzey Sıcaklığının Landsat-8 Uydu Verileri ile Haritalanması

Şekertekin¹

2019

Afyon Kocatepe University Journal of Sciences and Engineering

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Bu çalışmanın amacı Landsat-8 uydu verileri ve Işınım Transferi Denklemi (RTE) kullanılarak Yer Yüzey Sıcaklığının (YYS) konumsal olarak elde edilmesidir. Çalışma kapsamında 27 Eylül 2018 tarihli Landsat-8 verisi kullanılmış ve Adana ili Ceyhan ilçe sınırları çalışma alanı olarak seçilmiştir. RTE yöntemi ile YYS çıkarımında atmosferik geçirgenlik (τ λ), aşağı yönlü atmosferik ışınım (L λ ↓) ve yukarı yönlü atmosferik ışınım (L λ ↑) gerekli atmosferik parametrelerdir. Bu parametreler, Amerikan Uzay Ajansı (NASA) tarafından çevrimiçi yayınlanan atmosferik düzeltme parametresi hesaplayıcı tarafından hesaplanmıştır. YYS çıkarımında bir diğer önemli parametre olan yüzey yayınırlığı (ε λ) Normalize Fark Bitki İndeksi (NDVI) kullanılarak hesaplanmıştır. Uygulama sonucunda YYS haritası ERDAS Imagine uzaktan algılama yazılımı yardımıyla oluşturulmuş ve yersel veri olmadığı için aynı gün alınan MODIS YYS verisi ile karşılaştırma işlemi gerçekleştirilmiştir. Karşılaştırma sonucunda korelasyon katsayısı 0.91 ve Karesel Ortalama Hata (KOH) 3.93 K olarak belirlenmiştir. RTE yöntemi kullanılarak hızlı bir şekilde YYS haritalarının üretimi ile zamansal ve konumsal analizler yapılarak bölgesel iklim değişikliği çalışmalarında önemli sonuçlar elde edilecektir.

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The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

Cited by 10 publications

References 37 publications

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns

Işınım Transferi Denklemi Baz Alınarak Yer Yüzey Sıcaklığının Landsat-8 Uydu Verileri ile Haritalanması

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