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The launch of Luojia 1-01 in June 2018 has increased expectations. LJ1-01 is a nano satellite weighing 20 kg that can obtain high-resolution nocturnal images (130 metres/pixel). The aim of this paper is to analyse, and compare with previous satellites, the new instrument’s capacity to delimit the urbanised area and its efficiency in identifying types of urban landscape (compact, dispersed and urban). The case study is Barcelona Metropolitan Region (3,200 km2, 4.7 million inhabitants).
The objective of this paper is to combine information from various sensors (Modis, Landsat 8 and Sentinel 2) by constructing a set of OLS models of daytime and nighttime LST. These models provide a detailed view of daytime UHI (10 meters) and a robust evaluation of the range of cooling produced during the night. A modelling exercise at 1 meter/pixel of resolution has also been developed, using information from more detailed sensors installed on aircraft in the Barcelona Metropolitan Area.
The increasing availability of satellite information has improved Earth observation applications globally. However, primary satellite information is not as immediate as desirable. Indeed, besides the geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical imagery. Actually, such a contamination is intended as missing information and should be replaced. However, because the most common cloud masking algorithms take advantage by employing thermal images, here the objective is to provide an alternative algorithm suitable for multispectral imagery only. In addition, the work combines a multispectral/multitemporal approach as an effective method to retrieve daytime cloudless and shadow-free optical imagery. Experiment is undertaken upon mid- to low-spatial resolution data from Landsat 5 TM and Landsat 8 OLI, each for a different scene. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over 1 year. The approach relies on a clouds and cloud shadows masking step, based on spectral features, a band-by-band multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase based on automatic selection of the most suitable pixels from the stack. Results have been compared with a recognized masking algorithm approach and tested with uncontaminated image samples for the same scene. Accuracy and spectral features of the results provide high consistency.
The night-lights have been used widespread in scientific contributions, from building human development indices, identifying megalopolis [2] [3] or analyzing the phenomenon of urbanization and sprawl [4], but generally they have not been used to forecast the urbanization in the near future. This paper proposes to study the urbanization impact in China between 1992 and 2013, and models a hypothesis of future scenarios of urbanization (2013-2025). For this purpose, the paper uses DMSP-OLS Nighttime Lights (1992 – 2013). After obtaining a homogeneous series for the whole period 1992- 2013, we proceed to model the spatial dynamics of past urbanization process using the "urbanistic potential" of each of the 13.7 millions of analyzed cells. This model allows to design a probable growth of the urbanization phenomenon between 2013 and 2025 as well to predict a progressive displacement of the urbanization from east coast to mainland and west, in congruence with the current demographic models [5].
This paper proposes the construction of a set of explanatory models of the UHI of the Metropolitan Region of Barcelona (MRB) aimed at assisting planners in taking measures that serve, at the level of territorial and urban planning, to mitigate the effects of climate change. The general objective of the research is to study, using remote sensing techniques as well as "in situ" measurements, how urban design affects in the generation of the Urban Heat Island (UHI), as well as the urban microclimate in general. Specifically, this paper seeks to clarify whether the design of green areas can mitigate the UHI.
The hypothesis is that morphology of public space plays a key role to control UHI. The research methodology consisted in: a) studying the urban and climatic parameters of selected areas; b) analyzing the spatial distribution of the LST using remote sensing technologies (Landsat 8); c) obtaining LST and LSAT through field work, during day and night time; and d) constructing a model of surface and air temperatures as a function of the different types of land cover, combining Remote Sensed data and in situ measurements, for each of the areas of analysis.
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