scholarly journals Atmospheric impacts of Indonesian fire emissions: Assessing Remote Sensing Data and Air Quality During 2013 Malaysian Haze

2016 ◽  
Vol 36 ◽  
pp. 176-179
Author(s):  
De Lin Show ◽  
Su-Chin Chang
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Remote Sensing Data ◽  
Fire Emissions ◽  
Sensing Data

Access, Visualization, and Interoperability of Air Quality Remote Sensing Data Sets via the Giovanni Online Tool

2010 ◽  
Vol 3 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Ana I. Prados ◽  
Gregory Leptoukh ◽  
Chris Lynnes ◽  
James Johnson ◽  
Hualan Rui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Online Tool ◽  
Sensing Data

scholarly journals Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

Environments ◽  
2019 ◽  
Vol 6 (7) ◽  
pp. 85 ◽  
Author(s):  
Cesar I. Alvarez-Mendoza ◽  
Ana Claudia Teodoro ◽  
Nelly Torres ◽  
Valeria Vivanco
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Pm10 Concentration ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Terra Modis

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

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