Remote Sensing of Deformation and Disturbance to Monitor and Assess Infrastructure in Urban Environments

TRUST (Thermal Remote sensing Unmixing for Subpixel Temperature) is a spectral unmixing method developed to provide subpixel abundances and temperatures from radiance images in the thermal domain. By now, this method has been studied in simple study cases, with a low number of endmembers, high spatial resolutions (1 m) and more than 30 spectral bands in the thermal domain. Thus, this article aims to show the applicability of TRUST on a highly challenging study case: the analysis of a heterogeneous urban environment with airborne multispectral (eight thermal bands) images at 8-m resolution. Thus, this study is necessary to generalize the use of TRUST in the analysis of urban thermography. Since TRUST allows linking intrapixel temperatures to specific materials, it appears as a very useful tool to characterize Surface Urban Heat Islands and its dynamics at high spatial resolutions. Moreover, this article presents an improved version of TRUST, called TRUST-DNS (Day and Night Synergy), which takes advantage of daytime and nighttime acquisitions to improve the unmixing performances. In this study, both TRUST and TRUST-DNS were applied on daytime and nighttime airborne thermal images acquired over the center of Madrid during the DESIREX (Dual-use European Security IR Experiment) campaign in 2008. The processed images were obtained with the Aircraft Hyperspectral Scanner (AHS) sensor at 4-m spatial resolution on 4 July. TRUST-DNS appears to be more stable and slightly outperforms TRUST on both day and night images. In addition, TRUST applied on daytime outperforms TRUST on nighttime, illustrating the importance of the temperature contrasts during day for thermal unmixing.

Download Full-text

Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data

Computational Approaches for Urban Environments ◽

10.1007/978-3-319-11469-9_10 ◽

2014 ◽

pp. 237-272 ◽

Cited By ~ 7

Author(s):

Konstantinos Karantzalos

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Remote Sensing Data ◽

Urban Environments ◽

Sensing Data ◽

Recent Advances ◽

2D And 3D

Download Full-text

APPLICATION OF REMOTE SENSING AND GIS MONITORING URBANIZATION IN HO CHI MINH CITY THROUGH IMPERVIOUS SURFACES

Science and Technology Development Journal ◽

10.32508/stdj.v14i1.1868 ◽

2011 ◽

Vol 14 (1) ◽

pp. 65-77

Author(s):

Van Thi Tran

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Good Accuracy ◽

Impervious Surface ◽

Remote Sensing And Gis ◽

Urban Environments ◽

Ho Chi Minh City ◽

Impervious Surfaces ◽

Training Samples ◽

Gis Data

Impervious surface can be used as an indicator in assessing urban environments. In this study, we have used method of remote sensing through the impervious surface to detect urban area in Hochiminh city with good accuracy above 96%. The high accuracy of the measurements come from the application of techniques such as extraction of training samples based on brand ratios, supervised classification in combination with suplement GIS data. This method, in combination with the Landsat image database, can be ultilized in monitoring the development of urbanization in Hochiminh city.

Download Full-text

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

10.5194/hess-2021-283 ◽

2021 ◽

Author(s):

Alby Duarte Rocha ◽

Stenka Vulova ◽

Christiaan van der Tol ◽

Michael Förster ◽

Birgit Kleinschmit

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Urban Areas ◽

Meteorological Data ◽

Urban Environments ◽

Coefficient Of Determination ◽

World Population ◽

Model Errors ◽

Vegetation Fraction ◽

Process Based Models

Abstract. Evapotranspiration (ET) is a fundamental variable to assess water balance and urban heat island effect. ET is deeply dependent on the land cover as it derives mainly from the processes of soil evaporation and plant transpiration. The majority of well-known process-based models based on the Penman-Monteith equation focus on the atmospheric interfaces (e.g. radiation, temperature and humidity), lacking explicit input parameters to describe the land surface. The model Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) accounts for a broad range of surface-atmosphere interactions to predict ET. However, like most modelling approaches, SCOPE assumes a homogeneous vegetated landscape to estimate ET. Urban environments are highly fragmented, exhibiting a blend of pervious and impervious anthropogenic surfaces. Whereas, high-resolution remote sensing (RS) and detailed GIS information to characterise land surfaces is usually available for major cities. Data describing land surface properties were used in this study to develop a method to correct bias in ET predictions caused by the assumption of homogeneous vegetation by process-based models. Two urban sites equipped with eddy flux towers presenting different levels of vegetation fraction and imperviousness located in Berlin, Germany, were used as study cases. The correction factor for urban environments has increased model accuracy significantly, reducing the relative bias in ET predictions from 0.74 to −0.001 and 2.20 to −0.13 for the two sites, respectively, considering the SCOPE model using RS data. Model errors (i.e. RMSE) were also considerably reduced in both sites, from 0.061 to 0.026 and 0.100 to 0.021, while the coefficient of determination (R2) remained similar after the correction, 0.82 and 0.47, respectively. This study presents a novel method to predict hourly urban ET using freely available RS and meteorological data, independently from the flux tower measurements. The presented method can support actions to mitigate climate change in urban areas, where most the world population lives.

Download Full-text

Validation of seasonal time series of remote sensing derived LAI for hydrological modelling

10.5194/egusphere-egu2020-9071 ◽

2020 ◽

Author(s):

Charlotte Wirion ◽

Boud Verbeiren ◽

Sindy Sterckx

Keyword(s):

Remote Sensing ◽

Time Series ◽

Urban Environment ◽

Vegetation Dynamics ◽

Heat Waves ◽

Field Work ◽

Hydrological Modelling ◽

Urban Environments ◽

Urban Vegetation ◽

Area Index

<p>In urban environments, due to climate change urban heat waves are predicted to occur more frequently. Urban vegetation and the linked evapotranspiration rate can play a mitigating role. However, a major challenge in urban hydrological modelling remains the mapping of vegetation dynamics and its role in hydrological processes in particular interception storage and evapotranspiration. Conventional mapping of vegetation usually implies intensive labor and time consuming field work. We explore the potential of different remote sensing sensors (Proba-V, Landsat, Sentinel2, Apex) to characterize the urban vegetation dynamics for hydrological modelling. The here proposed remote sensing sensors show differences in the spectral and spatial resolutions as well as in their revisit time. However, in the urban environment we need a high spatial and spectral resolution to distinguish the urban landcover and a frequent revisit time to capture seasonal vegetation dynamics. Therefore, we propose a combination of different remote sensing sensors to derive leaf area index (LAI) timeseries in the urban environment. To improve the consistency in time series generated from different remote sensing sources a harmonization of the multi-sensor time series is proposed and validated with a multi-resolution validation approach using ground-truthing LAI (BELHARMONY project). The LAI timeseries, derived from the different remote sensing sensors, are then introduced into the hydrological modelling framework for a location- and time- specific assessment of the interception storage and evapotranspiration component. The effect of the sensor differences to the LAI timeseries on the hydrological response is analyzed.</p>

Download Full-text

Sociospatial Applications of Remote Sensing in Urban Environments

Geography Compass ◽

10.1111/j.1749-8198.2008.00106.x ◽

2008 ◽

Vol 2 (3) ◽

pp. 728-743 ◽

Cited By ~ 35

Author(s):

Jay D. Gatrell ◽

Ryan R. Jensen

Keyword(s):

Remote Sensing ◽

Urban Environments

Download Full-text

Time Series Analysis of Land Cover Using Remote Sensing Technology

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8739 ◽

2020 ◽

Vol 17 (2) ◽

pp. 902-910

Author(s):

Ahmad Taufiq Hosni ◽

Suharto Teriman ◽

Nor Aizam Adnan ◽

Muhamad Asri Abdullah Kamar

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Urban Areas ◽

Management Plan ◽

Urban Environments ◽

Lulc Changes ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Development Area ◽

Landsat Satellite

The observation of land use/land cover (LULC) is essential as it allows humans to investigate the alteration of land which occurs over a period of time. This is to allow mankind to have a proper management of the earth resources and well planned development. One way to observe LULC is by using remote sensing technology since it provides continuous data monitoring of the earth’s surface. This study is carried out at Bandar Meru Raya, Ipoh, Perak and aimed to quantify the LULC changes, especially in urban areas which occurred within two decades from 1995 to 2005 and from 2005 to 2015. Maximum likelihood supervised classification was performed on three Landsat satellite imageries using ERDAS Imagine 2014 software. The images were classified into 4 general LULC categories namely forest, development area, green area, and water bodies. The results indicated that a considerable amount of forested area was decreasing by 183.12 hectares within the last 20 years while development area gained a total of 157.12 hectares. This LULC changes showed a serious loss of trees and this sort of change tends to affect the nature’s ecosystem. Therefore, by quantifying the loss of forest area will enable authorities to oversee and plan a better management of trees for future in urban environments. Such management plan is necessary in order to maintain the importance of trees towards nature and community.

Download Full-text

Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services

Remote Sensing ◽

10.3390/rs12020329 ◽

2020 ◽

Vol 12 (2) ◽

pp. 329 ◽

Cited By ~ 4

Author(s):

Elena Barbierato ◽

Iacopo Bernetti ◽

Irene Capecchi ◽

Claudio Saragosa

Keyword(s):

Remote Sensing ◽

Ecosystem Services ◽

Urban Forest ◽

Adaptation Strategy ◽

Urban Environments ◽

Urban Green ◽

Mitigation And Adaptation ◽

Urban Greenery ◽

Street View ◽

Forest Ecosystem Services

There is an urgent need for holistic tools to assess the health impacts of climate change mitigation and adaptation policies relating to increasing public green spaces. Urban vegetation provides numerous ecosystem services on a local scale and is therefore a potential adaptation strategy that can be used in an era of global warming to offset the increasing impacts of human activity on urban environments. In this study, we propose a set of urban green ecological metrics that can be used to evaluate urban green ecosystem services. The metrics were derived from two complementary surveys: a traditional remote sensing survey of multispectral images and Laser Imaging Detection and Ranging (LiDAR) data, and a survey using proximate sensing through images made available by the Google Street View database. In accordance with previous studies, two classes of metrics were calculated: greenery at lower and higher elevations than building facades. In the last phase of the work, the metrics were applied to city blocks, and a spatially constrained clustering methodology was employed. Homogeneous areas were identified in relation to the urban greenery characteristics. The proposed methodology represents the development of a geographic information system that can be used by public administrators and urban green designers to create and maintain urban public forests.

Download Full-text