scholarly journals Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image

Author(s):  
Salah Abdul Hameed Saleh Ghada Hasan
2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.


Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.


Author(s):  
Nguyen Quang Tuan ◽  
Do Thi Viet Huong ◽  
Doan Ngoc Nguyen Phong ◽  
Nguyen Dinh Van

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.


Author(s):  
Nguyen Nhu Hung ◽  
Tran Van Anh ◽  
Pham Quang Vinh ◽  
Nguyen Thanh Binh ◽  
Vu Van Hoang

PM10 (Particulate matter 10 is a dust with aerodynamic diameters of 0.001 ÷ 10μm) is one of the air pollutants affecting human health. In this study, we conducted a modeling study to identify PM10 dust in the air by using Landsat 8 OLI satellite image, along with PM10 ground-measured data using the machine DustTrak II . Conduct regression analysis to determine the correlation model. Here, we used 16 in-situ measurement points. In that, 10 points were used to determine the regression function and 6 other points were used to test the regression model. Results were evaluated based on correlation coefficient (R) and Root Mean Square Error (RMSE) between measured and calculated data.


2020 ◽  
Vol 167 ◽  
pp. 987-993 ◽  
Author(s):  
Amit Kumar Rai ◽  
Nirupama Mandal ◽  
Akansha Singh ◽  
Krishna Kant Singh

2015 ◽  
Vol 25 (44) ◽  
pp. 221-236
Author(s):  
Marcos Esdras Leite ◽  
Lucimar Sales Dias ◽  
André Medeiros Rocha

O presente trabalho consiste no diagnóstico do uso e ocupação do solo no entorno da Barragem Bico da Pedra, no município de Janaúba-MG, por meio de imagens de satélites do satélite Landsat 8 - sensor OLI, além de incursões em campo e levantamento bibliográfico sobre o tema estudado. As análises e avaliações realizadas indicam a presença do número de edificações no entorno da barragem do Bico da Pedra, construída em 1979, sob responsabilidade da Companhia de Desenvolvimento dos Vales do São Francisco e Parnaíba - CODEVASF. O reservatório foi construído com a finalidade de abastecimento humano da população dos municípios de Janaúba e Nova Porteirinha-MG, abastecimento do Perímetro de Irrigação do Gorutuba e perenização do rio Gorutuba, que outrora se encontrava em regime intermitente devido às condições ambientais da região em estudo. A partir de levantamento de dados históricos, foi possível detectar um aumento nos números de edificações motivado por diversos fatores, como a ausência de fiscalização e a prática do turismo. As consequências oriundas dessa ocupação podem gerar impactos ambientais negativos, tais como, desmatamento da Área de Preservação Permanente - APP, redução da biodiversidade, erosão do solo e poluição do corpo hídrico. Vale ressaltar que muitos desses impactos já são observados no entorno da Barragem Bico da Pedra. Portanto, este estudo, a partir do uso das Geotecnologias, traz informações sobre a forma e o processo de construção de imóveis residenciais no entorno da barragem do Bico da Pedra, bem como das consequências dessa ocupação. Palavras chaves: Ocupação, barragem, Degradação, Imagem de satélite, Sensoriamento Remoto      AbstractThe present work consists on the diagnosis of the land use on the neighborhood of Bico da Pedra dam, in the municipality of Janaúba-MG, through images of satellite Landsat 8 - OLI sensor and incursions in the field and survey literature on the subject studied. The analysis and evaluations done indicates the increasement on the number of buildings close to Bico da Pedra dam, constructed in 1979, by responsibility of Development Company of the Valleys São Francisco e Parnaíba (CODEVASF). The water tank was constructed to fill up the people that live in Janaúba and Nova Porteirinha, provide water to Perimeter of Irrigation of Gorutuba e evergreening of Gorutuba river that once before was in intermittent regime caused by environmentals conditions of the region. From survey of historical data it was possible to detect an increase in the numbers of buildings caused by several factors, such as lack of supervision and the practice of tourism. The consequences of that occupation can cause negatives environmental impacts: deforestation of Permanent Preservation Area, reduction on biodiversity, land erosion and hydrous body pollution. It is important to emphasize that a lot of these impacts are already observed next to the Bico da Pedra dam, as well as the consequences of this occupation. Keywords: Occupation, watertank, degradation, Satellite Image, Remote Sensing 


Environments ◽  
2019 ◽  
Vol 6 (7) ◽  
pp. 85 ◽  
Author(s):  
Cesar I. Alvarez-Mendoza ◽  
Ana Claudia Teodoro ◽  
Nelly Torres ◽  
Valeria Vivanco

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.


Sign in / Sign up

Export Citation Format

Share Document