Trend Analysis of Las Vegas Land Cover and Temperature Using Remote Sensing

The Las Vegas urban area expanded rapidly during the last two decades. In order to understand the impacts on the environment, it is imperative that the rate and type of urban expansion is determined. Remote sensing is an efficient and effective way to study spatial change in urban areas and Spectral Mixture Analysis (SMA) is a valuable technique to retrieve subpixel landcover information from remote sensing images. In this research, urban growth trends in Las Vegas are studied over the 1990 to 2010 period using images from Landsat 5 Thematic Mapper (TM) and National Agricultural Imagery Program (NAIP). The SMA model of TM pixels is calibrated using high resolution NAIP classified image. The trends of land cover change are related to the land surface temperature trends derived from TM thermal infrared images. The results show that the rate of change of various land covers followed a linear trend in Las Vegas. The largest increase occurred in residential buildings followed by roads and commercial buildings. Some increase in vegetation cover in the form of tree cover and open spaces (grass) is also seen and there is a gradual decrease in barren land and bladed ground. Trend analysis of temperature shows a reduction over the new development areas with increased vegetation cover especially, in the form of golf courses and parks. This research provides a useful insight about the role of vegetation in ameliorating temperature rise in arid urban areas

A Review on Different Modeling Techniques

In this study, the importance of air temperature from different aspects (e.g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio- temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio- temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities

Impacts of Lateral Boundary Condition Resolution in Tropical Urban Climate Modelling for Kuala Lumpur

Choosing the best LBCs is still debated among researchers due to the errors resulted. However, several recommendations have been documented to control the errors propagated by LBCs. One of the recommendations is employing higher resolutions LBCs. In the present, many LBCs are developed with various resolutions; spatially and temporally, for many applications but no claims regarding the best LBCs for tropical climate modelling have yet been documented. Therefore, this study intends to analyse the impacts of lateral boundary condition resolution during numerical downscaling within a tropical city. This study serves as a site-specific investigation to determine the suitable LBCs for the focused study area. Two widely used LBCs with different resolutions were utilized to initiate the Weather Research and Forecasting (WRF) simulation model. The performances of the two LBCs were compared using statistical tests and analyses. The study has found that the LBC with higher resolutions excels the other LBC during inter-monsoon season. Nevertheless, it was identified that both LBCs were able to provide reliable reconstruction of the tropical climate condition of the Kuala Lumpur City as portrayed by similar results obtained. Thus, it is concluded that both LBCs can be employed in numerical downscaling for tropical urban regions similar to the Kuala Lumpur City

Land-cover change monitoring in Obuasi, Ghana: an integration of earth observation, geoinformation systems and stochastic modelling

For over twenty years, Obuasi Municipality, Ghana, has experienced land-cover change arising from gold mining and urbanisation. This project quantified the land-cover changes that have taken place and projected likely future land-cover. An integration of Earth Observation (or EO), Geographical Information Science (or GIS) and Stochastic Modelling was examined. Post-Classification Change Detection employed Landsat TM or ETM+ images from 1986, 2002 and 2008. Subsequently, Markov Chain Analysis projected the land-cover distribution for 2020. Seven broad land-use and land-cover classes were identified and mapped, namely: built-up areas; mine sites; tailing ponds; barren land; forestland; farmland; and, rangeland. The results obtained for the 2008 to 2020 projection revealed a continuous expansion of built-up areas (1.63%), mine sites (0.89%) and farmland (3.4%), and a reduction of forestland (4.17%) and rangeland (2.59%). Despite the advent of very high resolution satellite imagery, this use of EO and GIS technology focussed on low-cost and lower resolution satellite imagery, coupled with Markov Modelling and was found to be beneficial in describing and analysing land-cover change processes in the study area, and was hence potentially useful for strategic planning purposes

Multi-criteria assessment model on environmental ergonomics for decision-making in schoolyards based on remote-sensing and GIS resources

The consequences of global warming have led to an acceleration of action strategies towards efficient and passive renovation work in the building stock. Most existing schoolyards are becoming obsolete with respect to current bioclimatic design patterns, for which a lack of methodological studies and diagnosis mechanisms in outdoor spaces has been identified. This research aims to design a multi-criteria assessment model on environmental ergonomics for the identification of feasible and passive measures that improve comfort conditions in schoolyards. The innovation of this system lies in its basis on weighting data that combines 12 qualitative, quantitative, and graphical parameters by using remote-sensing algorithms and GIS resources, leading to major insights regarding remote information acquisition capabilities for the promotion of bioclimatic actions in schools. The model is applied and tested in 6 representative pilot schools to demonstrate its operation and replicability. An innovative graphic output of results provides a significant research outcome, which contributes towards the visualisation of the diagnosis on environmental ergonomics and identifies potentials and weaknesses for decision-making. The conclusions focus on methodological insights and implications from an integral diagnosis for schoolyards, thereby serving as a decision-support system to identify optimal interventions that would ensure a more appropriate environmental performance.European Commission US-15547Andalusian Government US.20-06 POSTDOC_21_00575Spanish Government PID2021-124539OB-I00European project Horizon 2020-10103650

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales