Remote Sensing Data, Landsat 8 Oli / Tirs For Identification of Evapotranspiration Spatial Distribution in Sub DAS Cisangkuy West Java Indonesia

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.

Download Full-text

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-897-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 897-903 ◽

Cited By ~ 24

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.

Download Full-text

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060248 ◽

2019 ◽

Vol 8 (6) ◽

pp. 248 ◽

Cited By ~ 7

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.

Download Full-text

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 ◽

10.3390/environments6070085 ◽

2019 ◽

Vol 6 (7) ◽

pp. 85 ◽

Cited By ~ 4

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.

Download Full-text

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

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.2-2.265 ◽

2019 ◽

Vol 3 (2-2) ◽

Cited By ~ 1

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.

Download Full-text

Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020058 ◽

2021 ◽

Vol 10 (2) ◽

pp. 58

Author(s):

Muhammad Fawad Akbar Khan ◽

Khan Muhammad ◽

Shahid Bashir ◽

Shahab Ud Din ◽

Muhammad Hanif

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Learning Algorithms ◽

Remote Sensing Data ◽

Kappa Coefficient ◽

Machine Learning Algorithms ◽

Landsat 8 ◽

Sensing Data ◽

Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

Download Full-text

Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

Journal of Geography and Geology ◽

10.5539/jgg.v10n1p1 ◽

2017 ◽

Vol 10 (1) ◽

pp. 1 ◽

Cited By ~ 4

Author(s):

Clement Kwang ◽

Edward Matthew Osei Jnr ◽

Adwoa Sarpong Amoah

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Remote Sensing Data ◽

Unsupervised Classification ◽

Water Bodies ◽

Water Extraction ◽

Landsat 8 ◽

Sensing Data ◽

Optimal Classifier ◽

Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

Download Full-text

A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i1.pp360-371 ◽

2018 ◽

Vol 8 (1) ◽

pp. 360

Author(s):

Ratih Dewanti Dimyati ◽

Projo Danoedoro ◽

Hartono Hartono ◽

Kustiyo Kustiyo

Keyword(s):

Remote Sensing ◽

Cloud Cover ◽

National Development ◽

Remote Sensing Data ◽

Landsat 8 ◽

Sensing Data ◽

Tile Size ◽

Mosaic Image ◽

Clear Area ◽

Mosaic Images

The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively.

Download Full-text

Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

10.5194/egusphere-egu21-7895 ◽

2021 ◽

Author(s):

Amine Jellouli ◽

Abderrazak El Harti ◽

Zakaria Adiri ◽

Mohcine Chakouri ◽

Jaouad El Hachimi ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Radar Data ◽

Ore Deposits ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Alos Palsar ◽

Geological Map ◽

Color Composite ◽

Manual Extraction

Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0&#176;), NE - SW (45&#176;) and E - W (90&#176;) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.Keywords Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas

Download Full-text

Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽

10.3390/w12020417 ◽

2020 ◽

Vol 12 (2) ◽

pp. 417 ◽

Cited By ~ 2

Author(s):

Mohamed Abdelkareem ◽

Fathy Abdalla ◽

Samar Y. Mohamed ◽

Farouk El-Baz

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Volcanic Activity ◽

Remote Sensing Data ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Alos Palsar ◽

Sensing Data ◽

The Past ◽

Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

Download Full-text