Nowadays, global warming has become more interested for scientist, because the global surface temperature has been increased since last century. More than fifty percent of people are living in cities, in this regard, urbanization has become a key factor for global warming. The urban heat island (UHI) refers to the event of higher atmospheric and surface temperatures occurring in cities than in the surrounding rural areas due to urbanization. The annual average air temperature of urban area with almost one million people can be one to three degree warmer than its surroundings. This phenomena can affect societies by increasing summertime, air pollution, air conditioning costs, heat related illness, greenhouse gas emissions and water quality.

Tehran, a capital city of Iran is case study of this research. Additionally, Tehran is one of megacities of the world. A megacity is usually defined as a metropolitan area with a total population in excess of ten million people. Due to rapid urbanization progress that has resulted in significant UHI effect in this area. Furthermore, Tehran houses to almost twenty percent of Iranian people. In this study, new launched Landsat series (Landsat 8) was used for monitoring UHI and retrieving the brightness temperatures and land use/cover types. The Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has former Landsat bands, with three new bands: a deep blue band for coastal/aerosol studies (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment band. The Thermal Infrared Sensor (TIRS) sensor provides two high resolution (near to 30 meters) thermal bands (band 10, 11). These sensors both use corrected signal-to-noise (SNR) radiometric quantized over a 12-bit. Corrected SNR performance cause better determination of land cover type. Moreover, Landsat 8 images incorporate two valuable thermal bands in 10.9 µm and 12.0 µm. These two thermal bands improve estimation of UHI by incorporating split-window methods.

Recently, quantitative models for urban thermal environment and related factors have been studied, for example, the relation between UHI and land cover structure and established corresponding regression equation. Similar works have been done and models of the relation between the surface temperature and various vegetation Indices have been established. In order to monitor the relationship between UHI and land cover indices, this paper tried to employ a quantitative approach for exploring the relationship land surface temperature and common land cover indices and select suitable indices by incorporating supervised Feature Selection (FS) procedures, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI) in two definition, Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BI), Urban Index (UI), Index-based Built-Up Index (IBI) and Enhanced Built-Up and Bareness Index (EBBI). In this regards, the objectives of this research are to develop a non-linear analysis model for urban thermal environment by employing Support Vector Regression (SVR) method and Multivariate Regression (MR) algorithms. In addition, providing the hazard map for Tehran city is also one of the byproducts of proposed methods for managing and mitigating UHI effects. 

The seismic vulnerability analysis of urban environments is an operational issue that concerns the comprehensive knowledge of both building structural features and soils geophysical parameters, especially when considering areas that are prone to hydrogeological and seismic disasters. The protection of such environments, together with the population growth and the urbanization processes, requires a multi-disciplinary approach aiming at providing both an effective assessment of urban resources and synthetic parameters for managing post crisis events, restoration activities and search & rescue operations. Within such a framework, airborne Light Detection and Ranging (LiDAR) and Hyperspectral sensors have demonstrated to be powerful remote sensing instruments, whose jointly use allow providing meaningful parameters to describe both the topographic settings of urbanized areas and the buildings properties, in terms of geometrical, spectral and structural features. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and Hyperspectral measurements are provided to support the seismic vulnerability assessment of urban seismic areas. The digital elevation model as well as the building height and the shape of the observed area are gathered by using airborne LiDAR measurements. Spectral and structural information of urban buildings are provided through the supervised classification of IMSpectorV10E VNIR (wavelength range between 400 and 1000nm subdivided into 503 bands) measurements acquired by the IPERGEO sensor. The objective is to combine the different products provided by LiDAR and Hyperspectral image processing analysis within a Geographic Information System (GIS) platform, to evaluate the intrinsic properties of buildings (e.g. perimeter, covered area, height and type of roofs) together with the topographic features of the surrounding area (e.g. the surface height and slope) for providing synthetic parameters and thematic maps useful for seismic assessment and mitigation purposes, such as: (i) the identification of steep slope areas, (ii) the analysis of building roof typology for supporting the evaluation of structural load conditions, (iii) the detection of critical structures (e.g. asbestos buildings), (iv) the identification of primary roads (in terms of escape or access routes) for supporting search and rescue operations, (v) the analysis of main road conditions after building collapses. Meaningful experimental results, gathered for the historical center of Cosenza city (Italy), allow demonstrating the benefits of the proposed approach for both seismic assessment and mitigation purposes.

The present work is supported and funded by Ministero dell'Università, dell'Istruzione e della Ricerca (MIUR) under the project PON01-02710 "MASSIMO" - "Monitoraggio in Area Sismica di SIstemi MOnumentali".

In Chittagong city, landslide phenomena is the most burning issue which causes great problems to the life and properties and it is increasing day by day and becoming one of the main problems of city life. On 11 June 2007, a massive landslide happened in Chittagong City Corporation (CCC) area, a large number of foothill settlements and slums were demolished; more than 90 people died and huge resource destruction took place. It is therefore essential to analyze the landslide susceptibility for CCC area to prepare mitigation strategies as well as assessing the impacts of climate change. To assess community susceptibility of landslide hazard, a landslide susceptibility index map has been prepared using analytical hierarchy process (AHP) model based on geographic information system (GIS) and remote sensing (RS) and its susceptibility is analyzed through community vulnerability assessment tool (CVAT). The major findings of the research are 27% of total CCC area which is susceptible to landslide hazard and whereas 6.5 sq.km areas are found very highly susceptible. The landslide susceptible areas of CCC have also been analyzed in respect of physical, social, economic, environmental and critical facilities and it is found that the overall CCC area is highly susceptible to landslide hazard. So the findings of the research can be utilized to prioritize risk mitigation investments, measures to strengthen the emergency preparedness and response mechanisms for reducing the losses and damages due to future landslide events.

Desertification is one of the major threats to the environment and the global community. Changing climate, deforestation, changing agriculture methods and demand of resources are the important causes for the desertification. Due to dependency of human life on land, monitoring and mitigating the desertification effect is getting more attention over the years. The main objective of the study was to assess the natural factors impact on desertification process on a regional level by using the remotely sensed parameters like TRMM-precipitation, MODIS-evapotranspiration, MODIS-net primary productivity, and ASTER-DEM with the aid of AHP-GIS model. From the above mentioned sensor parameters, the indices like aridity, rainfall, rainfall use efficiency, NPP and slope are prepared for 2000-2012. TRMM data were downscaled to 1km spatial resolution to match the other parameters spatial scale for analysis. All the parameters were classified using the frequency distribution. Based on the classes the ranks were assigned and the weight of the parameters assigned by the expert’s opinions based on questionnaire survey. We converted each data into an annual scale for time series analysis. From the 12 year average data analysis, results showed that, 9.22% of the area is characterized by highly sensitive to desertification which is the southeast part of the state. Most of the area comes under the moderate sensitive area (83.45%) and 4.83% comes under the low sensitive area. The very low sensitive area exhibits only 2.5 %, which is the hilly region of the study area. We discuss the climate parameters, vegetation and topography parameters impact on desertification in the study area.

Forest cover dynamics and disturbance can be tracked using a pixel based time-series analysis of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) backscatter and coherence data. In particular, derived features from pixel trajectories in time can be a powerful tool to map changes in tropical forest, where deforestation and forest degradation occur driven by a series of processes such as fire, selective logging, subsistence agriculture and complete clearance of forest due to large scale deforestation. The research presents results from tropical forest environments in Cameroon, Republic of Congo and Indonesia.  Several SAR data with different frequency and resolution were tested including ENVISAT ASAR, ALOS PALSAR and TanDEM-X. Furthermore, the analysis was undertaken on both TanDEM-X backscatter and coherence at HH polarization. Multi-temporal coherence was employed due to its sensitivity to the upper canopy volume, which causes decorrelation as a function of the amount of vegetation (e.g. disturbance event).

A pixel trajectory is defined as a set of values of all resolution elements (backscatter or coherence) at the same row and column position in the stack of images. The stack is generated by multi-resolution analysis (MRA) at a number of spatial resolutions, enabling analysis in the combined time and space domains. Analysis of the trajectories over an area by means of a set of parameters (features) that characterize its time evolution can give insight on the nature and changes of landcover. The following set of trajectory features was computed: running ratios with respect to a baseline year, linear fitting (trend), coefficient of determination (goodness of fit), dispersion around trend, maximum change relative to mean (swing) and statistics of first derivative (variance, kurtosis). These features are designed to detect in each pixel trajectory the presence of a linear trend, the stationary of the distribution around the linear regression, the occurrence of intermittent events, and the dynamic range of the changes.

In recent years, due to the expansion of the vehicles transportation system and concerns about the lack of accurate calculation of vehicle weight, a system that is able to calculate the vehicle's weight at any moment, it seems necessary. Given that the transportation electronic management is related to the location and movement data of vehicle, the information about movement, speed and time, traveled path, the weight sensors and fuel for the quick and timely decisions are required. Therefore the design and implementation of modern systems for monitoring and control of these devices to make quick decisions and plan codified is essential. In this paper firstly, the different ways of measuring the vehicle weight and the problems of each them has been described then the weight sensor device which is equipped with a AVL system, and its application in urban management (Waste collection) has been described, and finally, the advantages of this devise has been proposed.

Remote sensing has been widely used in vegetation-dynamics monitoring. Many studies have used data acquired by multispectral sensors, such as the Landsat TM sensor, due to their high spatial resolution (30 m). However, during the growing season, the temporal resolution (16 day) cannot capture rapid changes of vegetation. Meanwhile, coarse-spectral-resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have high-frequency temporal information that can catch the details of landscape changes. In this research, we proposed a data-fusion approach that lead us to merge the MODIS and Landsat TM data to create a dataset of vegetation dynamics with both a high spatial resolution and a fine temporal resolution. The Comanche and Faith Ranches, located in west Texas, were chosen for this study. The MODIS product was used as a regionally consistent reference dataset to correct the Landsat imagery. Based on this new dataset, NDVI time-series curves from 2004 to 2011 were calculated with the MODIS 13 Vegetation Dataset. One random sample of red-band images was tested and compared with MODIS data. A high correlation coefficient 0.907 and RMSE 0.0245 was found.

At the request of the authors, the proceedings paper [1] will be retracted. Four coauthors, Maurício Zacharias Moreira, Eloisa Domingues, Fernando Peres Dias and Luiz Roberto de Campos Jacintho, do not endorse the content in the paper presented by the main author. We apologize to our readership for any inconvenience caused.

Reference

1. Macedo, R.; Santos, J.; Dias, F.; Moreira, M.; Jacintho, L.; Domingues, E. Mapping of Land Use and Land Cover on Brazil. In Proceedings of the 1st International Electronic Conference on Remote Sensing, 22 June–5 July 2015; Sciforum Electronic Conference Series, 2015, Volume 1, d007; doi:10.3390/ecrs-1-d007.

Retraction of the Proceedings Paper: https://sciforum.net/conference/ecrs-1/paper/2834