Remote sensing model adaptation to very high resolution digital images of urban areas

2002 ◽  
Author(s):  
D. Boldo ◽  
H. Le Men
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Urban Areas ◽  
Digital Images ◽  
Model Adaptation ◽  
Remote Sensing Model ◽  
Sensing Model ◽  
Very High

scholarly journals ANALISIS TOPOLOGI DAN POPULASI PENDUDUK PEMUKIMAN MISKIN MENGGUNAKAN TEKNOLOGI REMOTE SENSING

2012 ◽  
Vol 6 (1) ◽  
pp. 20
Author(s):  
Aniati Murni Arymurthy ◽  
Edina Putri Purwandari
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Global Positioning System ◽  
Urban Areas ◽  
Complex Problem ◽  
Positioning System ◽  
Use Of Technology ◽  
Global Positioning ◽  
Very High

Wilayah perkotaan di Indonesia memiliki karakteristik yang sama dengan wilayah perkotaan di negara-negara berkembang. Beberapa karakteristik tersebut seperti: (1) penurunan fungsi alam dengan berkurangnya ruang hijau atau vegetasi, (2) penumpukan bangunan beratap pada wilayah yang dekat dengan akses transportasi, industri dan pasar, (3) lokasi pemukiman pada zona yang berbahaya karena dekat dengan terminal, sepanjang aliran sungai, sepanjang jalur rel kereta api, dan tempat pembuangan sampah akhir. Keterkaitan antara nilai indeks kemiskinan dengan morfologi fisik dan vegetasi suatu wilayah dapat diketahui dengan pemanfaatan teknologi remote sensing (RS). Keakuratan analisis pemukiman miskin dengan teknologi RS bergantung pada kualitas citra satelit Very High Resolution (VHR) dan kelengkapan dataset Sistem Informasi Geografis (SIG). Teknologi Geospasial yang terintegrasi seperti RS, SIG, dan Global Positioning System (GPS) dapat berkontribusi secara interaktif dalam penilaian, pemahaman dan pemetaan untuk memecahkan masalah pemukiman penduduk yang kompleks di Indonesia. Urban areas in Indonesia have the same characteristics with urban areas in developing countries. Some characteristics such as: (1) decreased of the function of nature with the reduced the number of natural green space or vegetation, (2) accumulation of roofed buildings in the area close to transportation access, industry and market, (3) the location of housing in the dangerous zone as close to the terminal, along the river side, along the railway lines, and the final waste disposal sites. The linkage between poverty index values with the physical morphology and vegetation of an area can be identified by the use of technology and remote sensing (RS). The accuracy of the analysis of poor housing with RS technology relies on the image quality of Very High Resolution (VHR) satellite and the completeness of the dataset Geographic Information Systems (GIS). Geo-spatial technologies are integrated as RS, GIS, and Global Positioning System (GPS) can contribute interactively in the assessment, understanding and mapping to solve the complex problem of residential in Indonesia.

Development of a Three-Source Remote Sensing Model for Estimation of Urban Evapotranspiration (TRU)

2021 ◽  
Author(s):  
Han Chen ◽  
Jinhui Jeanne Huang ◽  
Edward McBean ◽  
Zhiqing Lan ◽  
Junjie Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Latent Heat ◽  
Urban Areas ◽  
Impervious Surface ◽  
Single Source ◽  
Surface Evaporation ◽  
Remote Sensing Model ◽  
Sensing Model ◽  
Source Models ◽  
Impervious Areas

<p>Evapotranspiration (ET) from an urban area and its components are important  when estimating the urban ‘heat island’ effect and the urban hydrological cycle. Multi-source satellite-based ET models for ecosystems (e.g. farmland, forest, and wetland) have been developed and applied, but satellite-based ET model dimensions for urban areas are lacking, especially since all currently available models are designed for single-source schemes. This paper proposes the first Three-source Remote sensing model for Urban areas (TRU) to discriminate between soil evaporation, vegetation transpiration, and impervious surface evaporation. TRU uses a new parameterization scheme, based on the use of a complementary relationship integrating soil surface temperature to estimate soil evaporation. An iterative procedure was developed for decomposing land surface temperature (LST) into component temperatures. Also, the ET for impervious areas was independently delineated using the “patch” approach. The model was tested for 45 cloudless days in Tianjin for 2017-2020 based on 30 m Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM<sup>+</sup>) images. Results indicated the root mean square error (RMSE) of 38.8 W/m<sup>2</sup> and Bias of 9.9 W/m<sup>2</sup> compared with two Eddy Correlation (EC) observations for instantaneous latent heat (LE) simulation and RMSE was 0.087 and Bias was -0.012, compared with stable water isotope measurements for the estimation of the ratio of vegetation latent heat flux to latent heat flux (LE<sub>v</sub>/LE).Comparison with urban single-source models and two-source models for ecosystem suggest TRU provide best accuracy for ET and its components simulation. The spatial pattern suggested impervious surface evaporation exhibited minimal seasonal variation and maintained a very lower level due to limited availability of water. The results emphasized the importance of using land use and land cover (LULC) in urban ET modeling and the necessity to calculate ET as independent of impervious areas. TRU represents a groundbreaking development of multi-source urban satellite-based ET models and facilitates the mapping of urban ET components.</p>

Application of an optical remote sensing model

1995 ◽  
Author(s):  
Charles R. Bostater, Jr. ◽  
Wei-ming Ma ◽  
Ted McNally ◽  
Manuel Gimond ◽  
A. P. Lamb
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Remote Sensing Model ◽  
Sensing Model

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

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