scholarly journals ZAB RIVER (IRAQ) SINUOSITY AND MEANDERING ANALYSIS BASED ON THE REMOTE SENSING DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 91-95
Author(s):  
B. Kalantar ◽  
M. H. Ameen ◽  
H. J. Jumaah ◽  
S. J. Jumaah ◽  
A. A. Halin
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Geographic Information Systems ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sinuosity Index ◽  
River Region ◽  
Landsat 7 ◽  
Landsat 5 Tm ◽  
River Migration

Abstract. This work studies the meandering and change of paths along the Zab River in Iraq. Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 (2-sets) images were acquired from the years 1989, 1999, 2015 and 2019, respectively, which were used together with Remote sensing and Geographic Information Systems (GIS) techniques to study the changes. To determine the river/stream shape, the Sinuosity Index was calculated to classify Zab River segments into either the straight, sinuous or meandering class. Our findings via image analysis show coarse river migration and that most river segments fall into the two classes of sinuous and meander. In addition, it seems that the east bank of the Zab River region of the basin has extremely shifted where the river passes near the Kirkuk governorate.

scholarly journals ON THE INFORMATION CONTENT OF SPECTRAL CHANNELS AND NDVI OF THE LANDSAT 5 TM LANDSAT 7 ETM+ REMOTE SENSING SATELLITE FOR ASSESSING THE STAGES OF FOREST OVERGROWTH OF ABANDONED AGRICULTURAL LANDS

ÈKOBIOTEH ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 178-185
Author(s):  
I.R. Tuktamyshev ◽  
◽  
P.S. Shirokikh ◽  
R.Y. Mullagulov ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
The Urals ◽  
Landsat 7 ◽  
Landsat 5 Tm ◽  
The Difference

Abandoned arable land is a widespread phenomenon in land use. Methods based on the use of remote sensing data are most suitable for studying and monitoring farmlands overgrown with forest. Multispectral satellite images and vegetation indices can reflect the difference at certain stages of the successional development of fallow vegetation. The aim of the work is to evaluate the informative value of individual channels of medium-resolution images of Landsat satellites and the normalized difference vegetation index (NDVI) for identifying vegetation areas at various stages of reforestation succession on abandoned arable land in the zone of distribution of broad-leaved forests in the Urals. As the source material we used 30 georeferenced relevés of different overgrowth stages made in 2012, and 9 cloudless Landsat 5 TM and Landsat 7 ETM+ images for the period from April to October 2011. Using the data, NDVI and values of three spectral bands (Red, NIR, Thermal) were calculated for the relevé points. The most informative when dividing the stages of reforestation on abandoned fields in the zone of distribution of broad-leaved forests in the Urals were the NDVI vegetation index and the surface temperature estimated by the thermal channel. In addition, the red band can be useful for identifying the initial stage of succession.

scholarly journals Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

2011 ◽  
Vol 8 (1) ◽  
pp. 1125-1159
Author(s):  
J. Cristóbal ◽  
R. Poyatos ◽  
M. Ninyerola ◽  
P. Llorens ◽  
X. Pons
Keyword(s):  
Remote Sensing ◽  
Estimation Error ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Climate Modelling ◽  
Mountain Area ◽  
Flow Measurements ◽  
Sensing Data ◽  
Landsat 7 ◽  
Landsat 5 Tm

Abstract. Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (TERRA/AQUA MODIS and Landsat-5 TM/Landsat-7 ETM+) and combining three different approaches to calculate the B parameter. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30%. The poor agreement obtained using MODIS data reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue.

scholarly journals Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

2011 ◽  
Vol 15 (5) ◽  
pp. 1563-1575 ◽  
Author(s):  
J. Cristóbal ◽  
R. Poyatos ◽  
M. Ninyerola ◽  
P. Llorens ◽  
X. Pons
Keyword(s):  
Remote Sensing ◽  
Estimation Error ◽  
Remote Sensing Data ◽  
Geographical Information ◽  
Climate Modelling ◽  
Mountain Area ◽  
Flow Measurements ◽  
Sensing Data ◽  
Landsat 7 ◽  
Landsat 5 Tm

Abstract. Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day−1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day−1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue. Finally, implementing regional GIS-based climate models as inputs in ETd retrieval have has provided good results, making possible to compute ETd at regional scales.

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 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 ASSESSMENT OF THE ANTHROPOGENIC LOAD OF URBANIZED AREAS BASED ON EARTH REMOTE SENSING DATA

2021 ◽  
Vol 2 (6) ◽  
pp. 53-58
Author(s):  
S.A. Yeprintsev ◽  
◽  
, S.A. Kurolap ◽  
O.V. Klepikov ◽  
, S.V. Shekoyan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Negative Impact ◽  
Remote Sensing Data ◽  
Good Alternative ◽  
Quality Parameters ◽  
Anthropogenic Load ◽  
Landsat 8 ◽  
Central Russia ◽  
Landsat 7

The high anthropogenic load characteristic of urban settlements entails the need for constant monitoring of factors that can potentially have a negative impact on the quality of the environment and the health of the population. Ground-based research methods used for spatial zoning of urbanized territories according to the level of anthropogenic load entail significant time costs, which, despite the high accuracy, significantly reduces their effectiveness. Remote sensing technologies have become a good alternative to ground-based methods. To assess the anthropogenic load of the cities of Central Russia (Voronezh, Lipetsk, Belgorod), an archive of multi-channel satellite images obtained from Landsat-7 and Landsat-8 satellites has been created. The satellite images are grouped into three periods (2001, 2016 and 2020). The processing of satellite images of the studied cities of Central Russia, as well as suburban areas, was carried out in the Scanex Image Processor software package. Spatial assessment of the ratio of the areas of anthropogenic-altered territories and the natural framework was made by determining the value of NDVI within cities and suburban ten-kilometer zones. For the analysis of satellite images of the above-mentioned time periods, equal areas of territories were allocated, where the NDVI indicators of the studied urbanized territories of the cities of Voronezh, Lipetsk, Belgorod, as well as suburban tenkilometer zones with subsequent spatial geoinformation zoning of territories according to this indicator were calculated. The obtained results made it possible to study a number of environmental quality parameters (the level of anthropogenic load, the natural framework of the territory, hydrological objects), as well as their dynamics over a twenty-year period.

scholarly journals KARSTIC LANDSCAPE STUDY BASED ON REMOTE SENSING DATA: THE CASE OF KSIROMERO REGION, AITOLOAKARNANIA - WESTERN GREECE

2017 ◽  
Vol 43 (3) ◽  
pp. 1582
Author(s):  
M. Golubović Deliganni ◽  
I. Parcharidis ◽  
K. Pavlopoulos
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Ad Hoc ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Sensing Data ◽  
Medium Resolution ◽  
Landsat 5 Tm ◽  
Karst Landforms ◽  
Western Greece

The aim of this study is to investigate and recognize karst landforms in the area of Ksiromero (Aitoloakarnania, Western Greece) based on medium resolution remote sensing data. In order to highlight karstic structures appropriate and innovative methodologies of image analysis have been developed, applied and compared. In particular, the original Landsat 5 TM bands have been, first, ad-hoc stretched and then processed to obtain the so-called Tasseled Cap Features and the Principal Component images. Finally, a comparative study between the two methods has been carried out.

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

2017 ◽  
Vol 6 (1) ◽  
pp. 2246-2252 ◽  
Author(s):  
Ajay Roy ◽  
◽  
Anjali Jivani ◽  
Bhuvan Parekh ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Landsat 7

scholarly journals Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Salt Marsh ◽  
Bulk Density ◽  
Remote Sensing Data ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Marsh Soil ◽  
Landsat 7 ◽  
Salt Marsh Soil

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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