scholarly journals Derivation of Spatially Distributed Thermal Comfort Levels in Jordan as Investigated From Remote Sensing, GIS Tools and Computational Methods

2021 ◽  
Author(s):  
ibrahim M. oroud
Keyword(s):  
Remote Sensing ◽  
Thermal Comfort ◽  
Remote Sensing Data ◽  
Dead Sea ◽  
Discrete Point ◽  
Jordan Valley ◽  
Comfort Levels ◽  
Gis Tools ◽  
Wide Range ◽  
Elevation Data

Abstract Thermal comfort is usually calculated using discrete point measurements. This procedure is not suitable to study thermal comfort for inhabited areas with rugged terrains where climate gradient is high. The wide availability of remote sensing data and GIS tools have revolutionized data management, processing and visualization. The present paper implemented digital elevation data, GIS tools and a computational algorithm to generate spatially continuous maps of climatological elements which were employed to derive thermal comfort levels across Jordan. Results show detailed information of the spatial distribution of the degree of thermal comfort in winter and summer across the country which cannot be resolved using discrete point measurements. It is shown that the mountainous areas in the country, where most urban centers are situated, experience “slightly warm” to “warm” indoor apparent temperatures in summer. The Jordan Valley and the desert experience high indoor apparent temperatures in summer. Cold conditions prevail over most parts of the country, with the heating degree days ranging from 2100 in the southern mountains to values close to zero near the Dead Sea area. The presented procedure demonstrated that the very low levels of ambient vapor pressure is an important atmospheric forcing contributing to the widespread cold conditions prevailing over the desert areas in winter. The efficiency of direct evaporative cooling systems to achieve thermal comfort in the various parts of the country is investigated. The procedure presented can be used over regional scales with different levels of spatial resolutions for a wide range of climatological studies.

scholarly journals Modeling Mean Radiant Temperature Distribution in Urban Landscapes Using DART

2021 ◽  
Vol 13 (8) ◽  
pp. 1443
Author(s):  
Maria Angela Dissegna ◽  
Tiangang Yin ◽  
Hao Wu ◽  
Nicolas Lauret ◽  
Shanshan Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Comfort ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Outdoor Thermal Comfort ◽  
Transfer Model ◽  
Mean Radiant Temperature ◽  
Area Index ◽  
Sensing Data ◽  
Radiant Temperature

The microclimatic conditions of the urban environment influence significantly the thermal comfort of human beings. One of the main human biometeorology parameters of thermal comfort is the Mean Radiant Temperature (Tmrt), which quantifies effective radiative flux reaching a human body. Simulation tools have proven useful to analyze the radiative behavior of an urban space and its impact on the inhabitants. We present a new method to produce detailed modeling of Tmrt spatial distribution using the 3-D Discrete Anisotropic Radiation Transfer model (DART). Our approach is capable to simulate Tmrt at different scales and under a range of parameters including the urban pattern, surface material of ground, walls, roofs, and properties of the vegetation (coverage, shape, spectral signature, Leaf Area Index and Leaf Area Density). The main advantages of our method are found in (1) the fine treatment of radiation in both short-wave and long-wave domains, (2) detailed specification of optical properties of urban surface materials and of vegetation, (3) precise representation of the vegetation component, and (4) capability to assimilate 3-D inputs derived from multisource remote sensing data. We illustrate and provide a first evaluation of the method in Singapore, a tropical city experiencing strong Urban Heat Island effect (UHI) and seeking to enhance the outdoor thermal comfort. The comparison between DART modelled and field estimated Tmrt shows good agreement in our study site under clear-sky condition over a time period from 10:00 to 19:00 (R2 = 0.9697, RMSE = 3.3249). The use of a 3-D radiative transfer model shows promising capability to study urban microclimate and outdoor thermal comfort with increasing landscape details, and to build linkage to remote sensing data. Our methodology has the potential to contribute towards optimizing climate-sensitive urban design when combined with the appropriate tools.

Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Biswajeet Pradhan
Keyword(s):  
Remote Sensing ◽  
Process Model ◽  
Remote Sensing Data ◽  
Groundwater Potential ◽  
Remotely Sensed Data ◽  
Drainage Pattern ◽  
Ancillary Data ◽  
Sensing Data ◽  
Gis Techniques ◽  
Gis Tools

AbstractThis paper summarizes the findings of groundwater potential zonation mapping at the Bharangi River basin, Thane district, Maharastra, India, using Satty’s Analytical Hierarchal Process model with the aid of GIS tools and remote sensing data. To meet the objectives, remotely sensed data were used in extracting lineaments, faults and drainage pattern which influence the groundwater sources to the aquifer. The digitally processed satellite images were subsequently combined in a GIS with ancillary data such as topographical (slope, drainage), geological (litho types and lineaments), hydrogeomorphology and constructed into a spatial database using GIS and image processing tools. In this study, six thematic layers were used for groundwater potential analysis. Each thematic layer’s weight was determined, and groundwater potential indices were calculated using groundwater conditions. The present study has demonstrated the capabilities of remote sensing and GIS techniques in the demarcation of different groundwater potential zones for hard rock basaltic basin.

scholarly journals Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics

2019 ◽  
Vol 11 (8) ◽  
pp. 943 ◽  
Author(s):  
Alessio Domeneghetti ◽  
Guy J.-P. Schumann ◽  
Angelica Tarpanelli
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Flood Mapping ◽  
Special Issue ◽  
Sensing Data ◽  
Wide Range ◽  
Flood Monitoring ◽  
Novel Processing ◽  
Flood Dynamics ◽  
Processing Techniques

This Special Issue is a collection of papers that focus on the use of remote sensing data and describe methods for flood monitoring and mapping. These articles span a wide range of topics; present novel processing techniques and review methods; and discuss limitations and challenges. This preface provides a brief overview of the content.

Tsunami Assessment and Evacuation Analysis Using Remote Sensing for Tianya District of Sanya City, China

2019 ◽  
Vol 13 (05n06) ◽  
pp. 1941003
Author(s):  
Jingming Hou ◽  
Zhiyuan Ren ◽  
Peitao Wang ◽  
Juncheng Wang ◽  
Yi Gao
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Tsunami Inundation ◽  
Sensing Data ◽  
Inundation Area ◽  
Nighttime Lights ◽  
Elevation Data ◽  
Landsat Satellite

Tsunami is one of the world’s most dangerous marine disaster. In this paper, freely available remote sensing data are applied to study the hazard, vulnerability, and evacuation in the event that a tsunami strikes the district of Tianya in the city of Sanya. Tsunami inundation is calculated using a tsunami numerical model, and the tsunami vulnerability and evacuation in the inundation area are analyzed. Aster Global Digital Elevation Model elevation data are applied to provide input data for the tsunami numerical model and thus obtain tsunami inundation areas, while they are also used to study the surface slope for evacuation. Landsat satellite imagery is used to analyze land–water borders and land cover in both hazard assessment and evacuation analysis. Visible Infrared Imaging Radiometer Suite nighttime lights data provide information of the socioeconomic activity for vulnerability analysis. The analysis results show that the remote sensing data is suitable for tsunami assessment and evacuation analysis of China’s county-level region. We can get a general understanding about tsunami vulnerability and evacuation situation. One kind of remote sensing data can accomplish several tasks, avoiding the error caused by different source data. Remote sensing can play an important role in tsunami assessment.

scholarly journals Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

2013 ◽  
Vol 6 (4) ◽  
pp. 1061-1078 ◽  
Author(s):  
G. Picard ◽  
L. Brucker ◽  
A. Roy ◽  
F. Dupont ◽  
M. Fily ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Microwave Emission ◽  
Alpine Regions ◽  
Wide Range ◽  
Dense Media ◽  
Wet Snow ◽  
Snow Conditions

Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

scholarly journals Assessment of soil erosion using two spatial approaches: RUSLE and SWAT Model

2021 ◽  
Vol 234 ◽  
pp. 00082
Author(s):  
Soufiane Taia ◽  
Lamia Erraioui ◽  
Noella Claire Mbrenga ◽  
Jamal Chao ◽  
Bouabid El Mansouri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Degradation ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Climatic Data ◽  
Scale Analysis ◽  
Gis Tools ◽  
Major Factors ◽  
Specific Degradation ◽  
Priority Action

In this paper, we attempted to review the erosion in the Ouergha watershed by applying two spatial approaches. The Ouergha watershed has an area of around 7300 km² representing approximately 18.2% of the Sebou basin of which it is the main tributary. In order to develop the erosion map using the SWAT model, it was important to prepare a large spatial database describing basin proprieties, furthermore, the daily hydro-climatic data. This model integrates MUSLE equation for the estimation of specific degradation. In addition, the estimation of erosion through SWAT was consolidated by constructing an erosion mapping through RUSLE method. This method was applied following an approach based on the use of remote sensing data and GIS tools to produce the major factors involved in the erosive process and their integration into RUSLE. The results obtained, in cartographic form, make it possible to target areas that require priority action for a larger-scale analysis, with a view to finding appropriate solutions to fight against erosion and protect the natural environment. Soil degradation in the Ouergha watershed is around 27 ton/ha/year (SWAT_MUSLE) and 25 ton/ha/year (RUSLE). Average sediment yield was estimated for Al Wahda dam of 10.4 Million tons.

Landslide Hazard Zoning for Highway in China Base on Remote Sensing and GIS

2011 ◽  
Vol 368-373 ◽  
pp. 1051-1057
Author(s):  
Fu Chun Wang ◽  
Ming Jian Dou ◽  
Wei Wei
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landslide Hazard ◽  
Annual Rainfall ◽  
Expert Estimation ◽  
Hazard Zoning ◽  
Digital Elevation ◽  
Elevation Data ◽  
Calculation Results ◽  
Cover Ratio

Eight factors such as slope, incision density, incision depth, average annual rainfall, average annual >50mm rainfall days, soil types, river density, and vegetation cover ratio were used to quantify the Landslide Predict Index for highway in China. Using digital elevation data, rainfall data, thematic map of soil and river, remote sensing data of Spot/vegetation NVDI, these factors were calculated base on spatial analysis, hydrologic analysis, geostatistical analysis supported by ArcInfo9 software. The factors weights were confirmed by applying of expert estimation. The calculation results indicate that a highly spatial heterogeneity exists in the landslide Predict index for highway in China. Considering the maximum and minimum value of the index, the Landslide Predict Index for highway is divided into five levels. The landslide hazard zoning for highway is carried out base on the Landslide Predict Index mainly, then the landslide hazard zoning map for highway in China is formed.

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