scholarly journals Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1377
Author(s):  
Weifang Shi ◽  
Nan Wang ◽  
Aixuan Xin ◽  
Linglan Liu ◽  
Jiaqi Hou ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Path Analysis ◽  
Temperature Variation ◽  
Direct Effect ◽  
Air Temperature ◽  
Urban Areas ◽  
Support Vector ◽  
Urban Air ◽  
Air Temperatures

Mitigating high air temperatures and heat waves is vital for decreasing air pollution and protecting public health. To improve understanding of microscale urban air temperature variation, this paper performed measurements of air temperature and relative humidity in a field of Wuhan City in the afternoon of hot summer days, and used path analysis and genetic support vector regression (SVR) to quantify the independent influences of land cover and humidity on air temperature variation. The path analysis shows that most effect of the land cover is mediated through relative humidity difference, more than four times as much as the direct effect, and that the direct effect of relative humidity difference is nearly six times that of land cover, even larger than the total effect of the land cover. The SVR simulation illustrates that land cover and relative humidity independently contribute 16.3% and 83.7%, on average, to the rise of the air temperature over the land without vegetation in the study site. An alternative strategy of increasing the humidity artificially is proposed to reduce high air temperatures in urban areas. The study would provide scientific support for the regulation of the microclimate and the mitigation of the high air temperature in urban areas.

The Footprint of Urban Areas on Global Climate as Characterized by MODIS

2005 ◽  
Vol 18 (10) ◽  
pp. 1551-1565 ◽  
Author(s):  
Menglin Jin ◽  
Robert E. Dickinson ◽  
Da Zhang
Keyword(s):  
Land Cover ◽  
Skin Temperature ◽  
Urban Areas ◽  
Climate Models ◽  
Global Analysis ◽  
The United States ◽  
Climate Impact ◽  
Surface Temperatures ◽  
Air Temperatures ◽  
Seasonal And Diurnal Variations

Abstract One mechanism for climate change is the collected impact of changes in land cover or land use. Such changes are especially significant in urban areas where much of the world’s population lives. Satellite observations provide a basis for characterizing the physical modifications that result from urbanization. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the National Aeronautics and Space Administration (NASA) Terra satellite measures surface spectral albedos, thermal emissivities, and radiative temperatures. A better understanding of these measurements should improve our knowledge of the climate impact of urbanization as well as our ability to specify the parameters needed by climate models to compute the impacts of urbanization. For this purpose, it is useful to contrast urban areas with neighboring nonurban surfaces with regard to their radiative surface temperatures, emissivities, and albedos. Among these properties, surface temperatures have been most extensively studied previously in the context of the “urban heat island” (UHI). Nevertheless, except for a few detailed studies, the UHI has mostly been characterized in terms of surface air temperatures. To provide a global analysis, the zonal average of these properties are presented here measured over urban areas versus neighboring nonurban areas. Furthermore, individual cities are examined to illustrate the variations of these variables with land cover under different climate conditions [e.g., in Beijing, New York, and Phoenix (a desert city of the United States)]. Satellite-measured skin temperatures are related to the surface air temperatures but do not necessarily have the same seasonal and diurnal variations, since they are more coupled to surface energy exchange processes and less to the overlying atmospheric column. Consequently, the UHI effects from skin temperature are shown to be pronounced at both daytime and nighttime, rather than at night as previously suggested from surface air temperature measurements. In addition, urban areas are characterized by albedos much lower than those of croplands and deciduous forests in summer but similar to those of forests in winter. Thus, urban surfaces can be distinguished from nonurban surfaces through use of a proposed index formed by multiplying skin temperature by albedo.

INFLUENCE OF TEMPERATURE AND HUMIDITY ON THE EMERGENCE BEHAVIOUR OF ANAX JUNIUS (ODONATA: AESHNIDAE)

1973 ◽  
Vol 105 (7) ◽  
pp. 975-984 ◽  
Author(s):  
Robert Trottier
Keyword(s):  
Relative Humidity ◽  
Water Temperature ◽  
Air Temperature ◽  
Water Surface ◽  
Average Speed ◽  
Anax Junius ◽  
Air Temperatures ◽  
Stage 4 ◽  
Temperature And Humidity ◽  
Three Stages

AbstractEmergence from the water of Anax junius Drury normally occurred after sunset. The onset was affected independently by water temperature and air temperature; low water temperature and high air temperature delayed the onset of emergence. In the field, the net vrtical distance travelled above the water, before ecdysis, was positively correlated with air temperature. In the laboratory, the vertical distance travelled above the water was greatest when air and water temperatures were approximately the same. The average speed of climbing to the first resting position above the water surface was faster at high than low water temperature, but the average speed of climbing from there to the final position, where ecdysis occurred, was reduced due to the effects of air temperature and humidity. Air temperatures below 12.6 °C were found to retard ecdysis and larvae returned to the water and emerged early the following day making the final process of emergence and ecdysis diurnal instead of nocturnal. The duration of ecdysis was shorter at high than low air temperatures and only the first three stages, as arbitrarily defined, were longer at low than high relative humidity; stage 4, shortened with low relative humidity. This study shows that A. Junius, emerging from the water is affected at first by the temperature experienced when submerged, but it becomes gradually and cumulatively affected by air temperature and humidity while climbing to the ecdysial position and moulting.

scholarly journals A COMPARATIVE ASSESSMENT OF EFFICACY OF SUPER RESOLVED AIRBORNE HYPERSPECTRAL OUTPUTS IN URBAN MATERIAL AND LAND COVER INFORMATION EXTRACTION

2018 ◽  
Vol XLII-5 ◽  
pp. 653-658 ◽  
Author(s):  
K. Mishra ◽  
A. Siddiqui ◽  
V. Kumar
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Spatial Information ◽  
Confusion Matrix ◽  
Spectral Characteristics ◽  
Support Vector ◽  
Spectral Angle Mapper

<p><strong>Abstract.</strong> Urban areas despite being heterogeneous in nature are characterized as mixed pixels in medium to coarse resolution imagery which renders their mapping as highly inaccurate. A detailed classification of urban areas therefore needs both high spatial and spectral resolution marking the essentiality of different satellite data. Hyperspectral sensors with more than 200 contiguous bands over a narrow bandwidth of 1&amp;ndash;10<span class="thinspace"></span>nm can distinguish identical land use classes. However, such sensors possess low spatial resolution. As the exchange of rich spectral and spatial information is difficult at hardware level resolution enhancement techniques like super resolution (SR) hold the key. SR preserves the spectral characteristics and enables feature visualization at a higher spatial scale. Two SR algorithms: Anchored Neighbourhood Regression (ANR) and Sparse Regression and Natural Prior (SRP) have been executed on an airborne hyperspectral scene of Advanced Visible/Near Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for the mixed environment centred on Kankaria Lake in the city of Ahmedabad thereby bringing down the spatial resolution from 8.1<span class="thinspace"></span>m to 4.05<span class="thinspace"></span>m. The generated super resolved outputs have been then used to map ten urban material and land cover classes identified in the study area using supervised Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods. Visual comparison and accuracy assessment on the basis of confusion matrix and Pearson’s Kappa coefficient revealed that SRP super-resolved output classified using radial basis function (RBF) kernel based SVM is the best outcome thereby highlighting the superiority of SR over simple scaling up and resampling approaches.</p>

scholarly journals WATER-CONTAINING ABILITY OF COTTON LEAVES UNDER DIFFERENT TEMPERATURE CONDITIONS

2019 ◽  
pp. 164-166
Author(s):  
Alisher Muradullaevich Muradullaev
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Water Holding Capacity ◽  
Relative Resistance ◽  
Cotton Variety ◽  
Temperature Conditions ◽  
Control Option ◽  
Air Temperatures ◽  
Water Holding

This article presents the results of studies on the change of the water-holding ability in leaves of some varieties and lines of cotton under the influence of various high air temperatures (I control option from +24.6 to + 35.4 °C; II option - from +36.4 to +45, 1 °C; III option - from +39.5 to + 48.4 °C). At a high air temperature of + 48.4 °C, the water-holding capacity of the leaves in varieties Surkhan-14, Istiklol-14, Bukhara-102 was 26.2; 24.6; 26.4%, which indicates the relative resistance of these varieties to high air temperatures. KEYWORDS: cotton, variety, line, high air temperature, relative humidity, water holding ability.

scholarly journals Variation of Frost Roughness on a Flat Plate Under Forced Convection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Tongxin Zhang ◽  
Dennis L. O’Neal ◽  
Stephen T. McClain
Keyword(s):  
Relative Humidity ◽  
Surface Temperature ◽  
Air Temperature ◽  
Environmental Conditions ◽  
Mean Square ◽  
Empirical Correlations ◽  
Testing Conditions ◽  
Air Temperatures ◽  
Photogrammetric Method ◽  
Grain Roughness

Abstract Experiments were conducted on a cold flat aluminum plate to characterize the variation of frost roughness over both time and location on the surfaces. The testing conditions included air temperatures from 8 to 16 °C, wall temperatures from −20 to −10 °C, relative humidities from 60 to 80%, and air velocities from 0.5 to 2.5 m/s. Each test lasted 2 h. A 3D photogrammetric method was employed to measure the variation in frost root-mean-square height and skewness by location and time. These data were used to develop the equivalent sand-grain roughness for the frost at different locations and time. The experimental results showed that frost roughness varied by location and changed with time. For the environmental conditions in this study, relative humidity and air temperature were the most important factors determining changes in the peak frost roughness. For example, at an air temperature of 12 °C and a surface temperature of −15 °C, the frost roughness peaked at about 40 min for a relative humidity of 80% and 90 min for a relative humidity of 60%. Empirical correlations were provided to describe the relationships between the environmental conditions and the appearance of the peak frost roughness.

scholarly journals A Novel Class-Specific Object-Based Method for Urban Change Detection Using High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 87 (4) ◽  
pp. 249-262
Author(s):  
Ting Bai ◽  
Kaimin Sun ◽  
Wenzhuo Li ◽  
Deren Li ◽  
Yepei Chen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Support Vector ◽  
Change Type ◽  
Optimal Classifier ◽  
Object Based ◽  
Specific Object

A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data.

scholarly journals Future scenarios of thermal bioclimatic conditions in a humid tropical city under urban development

2018 ◽  
Vol 13 (5) ◽  
pp. 1
Author(s):  
Vicente De Paulo Rodrigues da Silva ◽  
Joel Silva Santos ◽  
Eduardo Rodrigues Viana de Lima ◽  
Romildo Morant de Holanda ◽  
Enio Pereira de Sousa ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Urban Areas ◽  
Kendall Test ◽  
Future Scenarios ◽  
Rapid Urbanization ◽  
Mann Kendall Test ◽  
Bioclimatic Conditions ◽  
The City

Urbanization modifies the heat balance in urban areas and has negative effects on landscape, aesthetics, energy efficiency, human health and the inhabitants’ quality of life. This work evaluated future scenarios of bioclimatic conditions for João Pessoa, a humid tropical city in Northeast Brazil. The scenarios were determined based on trends in air temperature, relative humidity and wind speed for the time period from 1968 to 2015. The study was performed for two distinct periods of three months each (dry and wet seasons) using data from weather stations equipped with thermo-hygrometers and cup anemometers located in nine representative areas of the city. Trends in air temperature, relative humidity, wind speed, and effective temperature index (ET index) time series were evaluated using the Mann-Kendall test. Results indicated that the air temperature showed an increasing trend of 0.34°C/decade, whereas the relative humidity showed a decreasing trend of 0.49%/decade and the wind speed values ranged from 1.3 ms-1 to 3.80 ms-1. These trends are statistically significant according to the Mann-Kendall test (p<0.05). The air temperature increased between the 1980s and 2010s, which corresponds to a period of rapid urbanization of the city. Future environmental conditions in João Pessoa will be determined in accordance with the urbanization processes.

scholarly journals Cool Pavement Strategies for Urban Heat Island Mitigation in Suburban Phoenix, Arizona

2019 ◽  
Vol 11 (16) ◽  
pp. 4452 ◽  
Author(s):  
Sushobhan Sen ◽  
Jeffery Roesler ◽  
Benjamin Ruddell ◽  
Ariane Middel
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Urban Areas ◽  
Meteorological Data ◽  
Heat Island ◽  
Land Use Patterns ◽  
Reflective Coating ◽  
Air Temperatures ◽  
Artificial Surfaces ◽  
Urban Heat

Urban areas are characterized by a large proportion of artificial surfaces, such as concrete and asphalt, which absorb and store more heat than natural vegetation, leading to the Urban Heat Island (UHI) effect. Cool pavements, walls, and roofs have been suggested as a solution to mitigate UHI, but their effectiveness depends on local land-use patterns and surrounding urban forms. Meteorological data was collected using a mobile platform in the Power Ranch community of Gilbert, Arizona in the Phoenix Metropolitan Area, a region that experiences harsh summer temperatures. The warmest hour recorded during data collection was 13 August 2015 at 5:00 p.m., with a far-field air temperature of about 42 ∘ C and a low wind speed of 0.45 m/s from East-Southeast (ESE). An uncoupled pavement-urban canyon Computational Fluid Dynamics (CFD) model was developed and validated to study the microclimate of the area. Five scenarios were studied to investigate the effects of different pavements on UHI, replacing all pavements with surfaces of progressively higher albedo: New asphalt concrete, typical concrete, reflective concrete, making only roofs and walls reflective, and finally replacing all artificial surfaces with a reflective coating. While new asphalt surfaces increased the surrounding 2 m air temperatures by up to 0.5 ∘ C, replacing aged asphalt with typical concrete with higher albedo did not significantly decrease it. Reflective concrete pavements decreased air temperature by 0.2–0.4 ∘ C and reflective roofs and walls by 0.4–0.7 ∘ C, while replacing all roofs, walls, and pavements with a reflective coating led to a more significant decrease, of up to 0.8–1.0 ∘ C. Residences downstream of major collector roads experienced a decreased air temperature at the higher end of these ranges. However, large areas of natural surfaces for this community had a significant effect on downstream air temperatures, which limits the UHI mitigation potential of these strategies.

Automated detection of urban heat islands based on satellite imagery, digital surface models, and a low-cost sensor network                                     

2021 ◽  
Author(s):  
Sebastian Schlögl ◽  
Nico Bader ◽  
Julien Gérard Anet ◽  
Martin Frey ◽  
Curdin Spirig ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Rural Areas ◽  
Urban Areas ◽  
Low Cost ◽  
Urban Heat Islands ◽  
Heat Islands ◽  
Micro Scale ◽  
Air Temperatures ◽  
Urban Heat

&lt;p&gt;Today, more than half of the world&amp;#8217;s population lives in urban areas and the proportion is projected to increase further in the near future. The increased number of heatwaves worldwide caused by the anthropogenic climate change may lead to heat stress and significant economic and ecological damages. Therefore, the growth of urban areas in combination with climate change can increase future mortality rates in cities, given that cities are more vulnerable to heatwaves due to the greater heat storage capacity of artificial surfaces towards higher longwave radiation fluxes.&lt;/p&gt;&lt;p&gt;To detect urban heat islands and resolve the micro-scale air temperature field in an urban environment, a low-cost air temperature network, including 450 sensors, was installed in the Swiss cities of Zurich and Basel in 2019 and 2020. These air temperature data, complemented with further official measurement stations, force a statistical air temperature downscaling model for urban environments, which is used operationally to calculate hourly micro-scale air temperatures in 10 m horizontal resolution. In addition to air temperature measurements from the low-cost sensor network, the model is further forced by albedo, NDVI, and NDBI values generated from the polar-orbiting satellite Sentinel-2, land surface temperatures estimated from Landsat-8, and high-resolution digital surface and elevation models.&lt;/p&gt;&lt;p&gt;Urban heat islands (UHI) are processed averaging hourly air temperatures over an entire year for each grid point, and comparing this average to the overall average in rural areas. UHI effects can then be correlated to high-resolution local climate zone maps and other local factors.&lt;/p&gt;&lt;p&gt;Between 60-80 % of the urban area is modeled with an accuracy below 1 K for an hourly time step indicating that the approach may work well in different cities. However, the outcome may depend on the complexity of the cities. The model error decreases rapidly by increasing the number of spatially distributed sensor data used to train the model, from 0 to 70 sensors, and then plateaus with further increases. An accuracy below 1 K can be expected for more than 50 air temperature measurements within the investigated cities and the surrounding rural areas.&amp;#160;&lt;/p&gt;&lt;p&gt;A strong statistical air temperature model coupled with atmospheric boundary layer models (e.g. PALM-4U, MUKLIMO, FITNAH) will aid to generate highly resolved urban heat island prediction maps that help decision-makers to identify local heat islands easier. This will ensure that financial resources will be invested as efficiently as possible in mitigation actions.&lt;/p&gt;

Using Combination Technique for Land Cover Classification of Optical Multispectral Images

2021 ◽  
Vol 12 (4) ◽  
pp. 22-39
Author(s):  
Keerti Kulkarni ◽  
Vijaya P. A.
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Geographical Area ◽  
Land Cover Classification ◽  
Combination Method ◽  
Support Vector ◽  
Combination Technique ◽  
Land Cover Map

The need for efficient planning of the land is exponentially increasing because of the unplanned human activities, especially in the urban areas. A land cover map gives a detailed report on temporal dynamics of a given geographical area. The land cover map can be obtained by using machine learning classifiers on the raw satellite images. In this work, the authors propose a combination method for the land cover classification. This method combines the outputs of two classifiers, namely, random forests (RF) and support vector machines (SVM), using Dempster-Shafer combination theory (DSCT), also called the theory of evidence. This combination is possible because of the inherent uncertainties associated with the output of each classifier. The experimental results indicate an improved accuracy (89.6%, kappa = 0.86 as versus accuracy of RF [87.31%, kappa = 0.83] and SVM [82.144%, kappa = 0.76]). The results are validated using the normalized difference vegetation index (NDVI), and the overall accuracy (OA) has been used as a comparison basis.

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