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.

scholarly journals A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya

2015 ◽  
Vol XL-7/W3 ◽  
pp. 897-903 ◽  
Author(s):  
M. W. Mwaniki ◽  
M. S. Moeller ◽  
G. Schellmann
Keyword(s):  
Remote Sensing ◽  
Knowledge Base ◽  
Remote Sensing Data ◽  
Factor Loading ◽  
Geological Mapping ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Loading Analysis

Availability of multispectral remote sensing data cheaply and its higher spectral resolution compared to remote sensing data with higher spatial resolution has proved valuable for geological mapping exploitation and mineral mapping. This has benefited applications such as landslide quantification, fault pattern mapping, rock and lineament mapping especially with advanced remote sensing techniques and the use of short wave infrared bands. While Landsat and Aster data have been used to map geology in arid areas and band ratios suiting the application established, mapping in geology in highland regions has been challenging due to vegetation land cover. The aim of this study was to map geology and investigate bands suited for geological applications in a study area containing semi arid and highland characteristics. Therefore, Landsat 7 (ETM+, 2000) and Landsat 8 (OLI, 2014) were compared in determining suitable bands suited for geological mapping in the study area. The methodology consist performing principal component and factor loading analysis, IHS transformation and decorrelation stretch of the FCC with the highest contrast, band rationing and examining FCC with highest contrast, and then performing knowledge base classification. PCA factor loading analysis with emphasis on geological information showed band combination (5, 7, 3) for Landsat 7 and (6, 7, 4) for Landsat 8 had the highest contrast and more contrast was enhanced by performing decorrelation stretch. Band ratio combination (3/2, 5/1, 7/3) for Landsat 7 and (4/3, 6/2, 7/4) for Landsat 8 had more contrast on geologic information and formed the input data in knowledge base classification. Lineament visualisazion was achieved by performing IHS transformation of FCC with highest contrast and its saturation band combined as follows: Landsat 7 (IC1, PC2, saturation band), Landsat 8 (IC1, PC4, saturation band). The results were compared against existing geology maps and were superior and could be used to update the existing maps.

scholarly journals A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 863-870
Author(s):  
L. T. Huang ◽  
W. L. Jiao ◽  
T. F. Long ◽  
C. L. Kang
Keyword(s):  
Remote Sensing ◽  
Linear Relationship ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Normalization Method ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Sentinel 2

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

scholarly journals The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

2021 ◽  
Vol 873 (1) ◽  
pp. 012015
Author(s):  
Zahrah Athirah ◽  
Muhammad Dhery Mahendra
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Volcanic Complex ◽  
Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

scholarly journals Remote Sensing of Evapotranspiration over the Central Arizona Irrigation & Drainage District, USA

2018 ◽  
Author(s):  
Andrew N. French ◽  
Douglas J. Hunsaker ◽  
Lahouari Bounoua ◽  
Arnon Karnieli ◽  
William Luckett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Vegetation Index ◽  
Model Averaging ◽  
Remote Sensing Data ◽  
The Us ◽  
Landsat 7 ◽  
Terra Modis ◽  
Model Variability ◽  
Central Arizona

A remote sensing-based evapotranspiration (ET) study was conducted over the Central Arizona Irrigation and Drainage District (CAIDD), an Arizona agricultural region. ET was assessed means for 137 wheat plots, 183 cotton plots, and 225 alfalfa plots. The remote sensing ET models were the Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), the Two Source Energy Balance (TSEB), and Vegetation Index ET for the US Southwest (VISW). Remote sensing data were principally Landsat 5, supplemented by Landsat 7, MODIS Terra, MODIS Aqua, and ASTER. The models produced similar daily ET for wheat, with 6–8 mm/d mid-season. For cotton and alfalfa daily ET showed greater differences, where TSEB produced largest daily ET, METRIC the least, and VISW in the midrange. Modeled cotton ET at mid-season ranged from 9.5 mm/d (TSEB), to 8 mm/d (VISW), and 6 mm/d (METRIC). For alfalfa ET, values at peak cover ranged from 8 mm/d (TSEB), 6 mm/d (VISW), and 5 mm/d (METRIC). Model bias ranged −10% to +18%. Relative to potential ET, FAO-56 ET, and USDA-SW gravimetric-ET, model variability ranged from negligible to 35% of annual crop water use. Model averaging was found a useful way to consider and reconcile all ET estimates.

scholarly journals Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

2021 ◽  
Vol 24 (3) ◽  
pp. 393-401
Author(s):  
Tengku Zia Ulqodry ◽  
Andreas Eko Aprianto ◽  
Andi Agussalim ◽  
Riris Aryawati ◽  
Afan Absori
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Area Index ◽  
Sensing Data ◽  
Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

scholarly journals A Preliminary Study on Heavy Oil Location in Central Sumatra using Remote Sensing and Geographic Information Sytem

2021 ◽  
Vol 44 (1) ◽  
pp. 39-54
Author(s):  
Suliantara Suliantara ◽  
Tri Muji Susantoro ◽  
Herru Lastiadi Setiawan ◽  
Nurus Firdaus
Keyword(s):  
Remote Sensing ◽  
Heavy Oil ◽  
Vegetation Index ◽  
Oil And Gas ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Landsat 8 ◽  
Sensing Data ◽  
Central Sumatra

Heavy oil which is classifi ed as non conventional oil is the target of exploration in the world. In Indonesia, the potential for heavy oil exploration is quite large, especially in the Central Sumatra basin. This study aims to map the location of potential heavy oil based on remote sensing data and regional gravity data supported by a geographic information system. Landsat 8 OLI satellite data is processed to produce 567 (RGB) color composite images, then further processing is carried out with DEM data to produce fusion images; mapping the vegetation index, clay mineral index, iron oxide index, surface temperature. The gravity data is used for mapping subsurface geological structures. Overlay analysis is carried out on the results of remote sensing data processing and interpretation of surface and subsurface geology. Based on the analysis, it shows that heavy oil fi elds are generally found on the surface and subsurface structures which are relatively identical and located on the edge of the basement high. Based on this analysis, the locations that have the potential for heavy oil and gas traps are on the northeast edge, Dalu-dalu High, the edge of Kampar High, the west edge of Kuantan High, the southwest edge of the Beruk High, the southwest edge of the Sembilan High.

scholarly journals Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

2019 ◽  
Vol 8 (6) ◽  
pp. 248 ◽  
Author(s):  
Imane Bachri ◽  
Mustapha Hakdaoui ◽  
Mohammed Raji ◽  
Ana Cláudia Teodoro ◽  
Abdelmajid Benbouziane
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Lithological Mapping ◽  
Sensing Data

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

scholarly journals REMOTE SENSING DATA IN MUNICIPAL GOVERNMENT

2021 ◽  
Vol 30 (4) ◽  
Author(s):  
Jaroslav Nýdrle
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Information ◽  
Relevant Literature ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Sentinel 2

This article focuses on the issue of using data obtained through remote sensing methods  in the administrative district of the municipality with extended powers of Liberec (the Czech Republic). The first part of the article discusses the question of using Earth remote sensing data for city agendas in general. Then, it presents a questionnaire, created for evaluating the needs of the Liberec municipality. This questionnaire, focusing on the use of remotely sensed data, was created on the basis of a review of relevant literature. Based on the results of the questionnaire, the following spatial information requirements were chosen to be addressed: land surface temperature map - LST (Landsat 8), vegetation index - NDVI (Sentinel 2, Planet Scope), normalized difference water index - NDWI, NDWI 2 (Sentinel 2), normalized difference built-up index - NDBI (Sentinel 2). All data obtained during the creation of this study have become part of the database of the Urban Planning and GIS Department and are available to employees of the City of Liberec.

scholarly journals Application of Remote Sensing Data in Lithological Discrimination of Kerdous Inlier in the Anti Atlas Belt of Morocco

2019 ◽  
Vol 3 (2-2) ◽  
Author(s):  
Amine Jellouli ◽  
Abderrazak El Harti ◽  
Zakaria Adiri ◽  
El Mostafa Bachaoui ◽  
Abderrahmane El Ghmari
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Field Investigation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Lithological Mapping ◽  
Sensing Data ◽  
Color Composite ◽  
Optimum Index Factor

Remote sensing data reveals a great importance for lithological mapping due to their spatial, spectral and radiometric characteristics. Lithological mapping using spatial data is a preliminary and important step to mineral mapping. In this work, several spectral and radiometric transformations methods were applied on Landsat 8 OLI data to enhance lithological units in the study area situated in the Anti Atlas belt. The methods of Optimum Index Factor (OIF), Decorrelation Stretching (DS), Principal Components Analysis (PCA) and Band Ratioing (BR) showed good results for lithological mapping in comparison with the existing geological and field investigation. An RGB color composite of OLI bands 651 was developed for mapping lithological units of the study area by fusing optimum index factor (OIF) and decorrelation stretching methods. furthermore, Band ratios derived from image spectra were applied in two RGB color composites (7+4/2, PC1, PC2)  and (PC1, 7/6, 3/7) providing good discrimination of the lithological units. The Landsat-8 OLI data significantly provided satisfied results for lithological mapping.

Sign in / Sign up

Export Citation Format

Share Document