Summertime Microscale Assessment and Prediction of Urban Thermal Comfort Zone Using Remote-Sensing Techniques for Kuwait
暂无分享,去创建一个
[1] I. N. Hidayati,et al. Analysis of urban heat island and urban ecological quality based on remote sensing imagery transformation in semarang city , 2022, IOP Conference Series: Earth and Environmental Science.
[2] Zullyadini A. Rahaman,et al. Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms , 2022, Energy and Buildings.
[3] A. Hossain,et al. Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh , 2022, Geomatics, Natural Hazards and Risk.
[4] Zullyadini A. Rahaman,et al. Modelling the mpacts of land use/land cover changing pattern on urban thermal characteristics n Kuwait , 2022, Sustainable Cities and Society.
[5] Aaron S. Bernstein,et al. Climate change and health in Kuwait: temperature and mortality projections under different climatic scenarios , 2022, Environmental Research Letters.
[6] Xiangming Xiao,et al. Contribution of urban functional zones to the spatial distribution of urban thermal environment , 2022, Building and Environment.
[7] P. Zhang,et al. Rapid urbanization and climate change significantly contribute to worsening urban human thermal comfort: A national 183-city, 26-year study in China , 2022, Urban Climate.
[8] G. Feyisa,et al. Detection of Land Use/Land Cover and Land Surface Temperature change in the Suha Watershed, north-western highlands of Ethiopia. , 2022, Environmental Challenges.
[9] Muhammad Tauhidur Rahman,et al. Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms , 2022, Building and Environment.
[10] S. Triyadi,et al. Effect of high-rise buildings on the surrounding thermal environment , 2022, Building and Environment.
[11] T. Biggs,et al. Land use land cover changes and their impacts on surface-atmosphere interactions in Brazil: A systematic review. , 2021, The Science of the total environment.
[12] M. Kamruzzaman,et al. Spatiotemporal distribution of drought and its possible associations with ENSO indices in Bangladesh , 2021, Arabian Journal of Geosciences.
[13] Md. Soumik Sikdar,et al. Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area , 2021, Environmental Challenges.
[14] Steven M. de Jong,et al. Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[15] A. Kafy,et al. Assessment of urban thermal field variance index and defining the relationship between land cover and surface temperature in Chattogram city: A remote sensing and statistical approach , 2021 .
[16] A. Kafy,et al. Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh , 2021 .
[17] Han Soo Lee,et al. Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model , 2021, Sustainability.
[18] Md. Shahinoor Rahman,et al. Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh , 2021 .
[19] E. Vaz,et al. Urban Sprawl and Growth Prediction for Lagos Using GlobeLand30 Data and Cellular Automata Model , 2020, Sci.
[20] Prafull Singh,et al. Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India , 2020 .
[21] Tarig A. Ali,et al. Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates , 2020, Remote. Sens..
[22] Yuei-An Liou,et al. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review , 2020, Remote. Sens..
[23] Liujun Zhu,et al. The seasonal and annual impacts of landscape patterns on the urban thermal comfort using Landsat , 2020 .
[24] T. Al-Awadhi,et al. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques , 2020, Land Use Policy.
[25] Hatim O. Sharif,et al. Land use/land cover change along the Eastern Coast of the UAE and its impact on flooding risk , 2020, Geomatics, Natural Hazards and Risk.
[26] Prasad S. Thenkabail,et al. Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud , 2019, GIScience & Remote Sensing.
[27] Ali Jamali,et al. Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran , 2019, SN Applied Sciences.
[28] A. Dewan,et al. Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region , 2019, Sustainability.
[29] C. Schröder,et al. Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8. , 2019, Journal of environmental management.
[30] M. Ranagalage,et al. Land-Use/Land-Cover Changes and Their Impact on Surface Urban Heat Islands: Case Study of Kandy City, Sri Lanka , 2019, Climate.
[31] José Claudio Mura,et al. A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images , 2019, Remote. Sens..
[32] Han Soo Lee,et al. Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton , 2019, Sustainability.
[33] José Cristóbal Riquelme Santos,et al. A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks , 2019, Remote. Sens..
[34] Mirela G. Tulbure,et al. Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics , 2018, Remote Sensing of Environment.
[35] Peijun Du,et al. A review of supervised object-based land-cover image classification , 2017 .
[36] Prafull Singh,et al. Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate , 2017 .
[37] S. Frank,et al. Physiological thermal limits predict differential responses of bees to urban heat-island effects , 2017, Biology Letters.
[38] T. Kershaw,et al. Utilising green and bluespace to mitigate urban heat island intensity. , 2017, The Science of the total environment.
[39] G. N. Nsofor,et al. Toward achieving a sustainable management: characterization of land use/land cover in Sokoto Rima floodplain, Nigeria , 2017, Environment, Development and Sustainability.
[40] M. S. Mondal,et al. Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results , 2016 .
[41] Lizhe Wang,et al. A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery , 2016, Remote. Sens..
[42] Ali Hosseini,et al. Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/ Land Cover in Tehran , 2016 .
[43] M. Lyons,et al. An Investigation into the Lifestyle, Health Habits and Risk Factors of Young Adults , 2015, International journal of environmental research and public health.
[44] Biswajeet Pradhan,et al. A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS , 2015 .
[45] F. Haghighat,et al. Indoor thermal condition in urban heat island: Comparison of the artificial neural network and regression methods prediction , 2014 .
[46] Lee,et al. The application of a prediction model on land surface temperature using Artificial Neural Network and Scenario , 2014 .
[47] Xiaoma Li,et al. Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures , 2013, Landscape Ecology.
[48] R. Pontius,et al. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment , 2011 .
[49] Jungho Im,et al. Support vector machines in remote sensing: A review , 2011 .
[50] Kevin W. Manning,et al. The Integrated WRF/Urban Modeling System: Development, Evaluation, and Applications to Urban Environmental Problems , 2010 .
[51] D. Sailor. A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment , 2011 .
[52] Lily Parshall,et al. Mitigation of the heat island effect in urban New Jersey , 2005 .
[53] J. Al-awadhi,et al. Impact of gravel quarrying on the desert environment of Kuwait , 2001 .
[54] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[55] O. Mangasarian,et al. Robust linear programming discrimination of two linearly inseparable sets , 1992 .