scholarly journals Use of GIS in Decision Making for Geotechnical Investigation

2021 ◽  
Author(s):  
Farhan Rafique Khan ◽  
Bhumika Das ◽  
R.K Mishra
Keyword(s):  
Satellite Images ◽  
Preliminary Survey ◽  
Geotechnical Investigation ◽  
Composite Image ◽  
Gis Technique ◽  
Topographic Survey ◽  
Gis Data ◽  
Geological Information ◽  
Landsat 7 ◽  
Human Effort

Geological Information System (GIS) is a tool which is used in different Areas to subside the human effort. The GIS was earlier developed to maintain the geological data of earth, but during the time GIS is used in different areas for research. The purpose of the study is to utilize GIS technique in the field of geotechnical engineering in different work like preliminary survey, availability of digitize Soil data of location, topographic survey. Due to availability of GIS, data can easily digitize according to the geographical coordinates. The satellite imageries of Nagpur city are collected from Earth Explorer a digital platform for researchers to access the satellite images of any Location. This satellite images are Landsat 7 ETM+, these images are later used to form composite image to develop Landuse Landcover map.

scholarly journals Spatio-Temporal Deforestation Measurement Using Automatic Clustering

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Erlyn Wina Rachmawan ◽  
Ali Ridho Barakbah ◽  
Tri Harsono
Keyword(s):  
Experimental Study ◽  
Satellite Images ◽  
New Approach ◽  
Deforestation Rate ◽  
Automatic Interpretation ◽  
Automatic Clustering ◽  
Landsat 7 ◽  
Spatio Temporal ◽  
Temporal Measurement ◽  
World Records

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.

Remote sensing of cyanobacterial blooms in Lake Champlain, USA

2013 ◽  
Vol 13 (5) ◽  
pp. 1402-1409
Author(s):  
Adam Trescott ◽  
Elizabeth Isenstein ◽  
Mi-Hyun Park
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Monitoring Program ◽  
In Situ Monitoring ◽  
Cyanobacterial Blooms ◽  
Lake Champlain ◽  
Initiation And Propagation ◽  
Landsat 7

The objective of this study was to develop cyanobacteria remote sensing models using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) as an alternative to shipboard monitoring efforts in Lake Champlain. The approach allowed for estimation of cyanobacteria directly from satellite images, calibrated and validated with 4 years of in situ monitoring data from Lake Champlain's Long-Term Water Quality and Biological Monitoring Program (LTMP). The resulting stepwise regression model was applied to entire satellite images to provide distribution of cyanobacteria throughout the surface waters of Lake Champlain. The results demonstrate the utility of remote sensing for estimating the distribution of cyanobacteria in inland waters, which can be further used for presenting the initiation and propagation of cyanobacterial blooms in Lake Champlain.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals Informative value of ultra-high resolution satellite images for monitoring the moisture content of the tundra cover

2020 ◽  
pp. 82-95
Author(s):  
S.G. Kornienko
Keyword(s):  
High Resolution ◽  
Moisture Content ◽  
Satellite Images ◽  
Near Infrared ◽  
Yamal Peninsula ◽  
Ground Vegetation ◽  
Ndvi Index ◽  
Fundamental Possibility ◽  
High Resolution Satellite Images ◽  
Landsat 7

The article substantiates the fundamental possibility of using multispectral ultra-high spatial resolution satellite images for monitoring the moisture content of the tundra. The results of the analysis of spectral images from the QuickBird satellite in the area of the construction of the runway in the village Sabetta (the Yamal Peninsula) indicate an obvious relationship between the reflectance factors in the red (ρRED) and near infrared (ρNIR) regions with the types of terrain of varying degrees of drainage. The possibility of assessing changes in the moisture content of the tundra cover using high-resolution images is confirmed by the results of verifying the changes in ρRED, ρNIR and the NDVI index (according to the QuickBird and Ikonos satellites) by comparing with the changes in the NDWI index, which characterizes the cover moisture (according to the Landsat 7, 8 satellites). It is shown that the parameter ρRED is less sensitive, but it has an advantage over ρNIR and NDVI, since it changes unidirectionally with the changes in moisture for any encountered types of surface – from bare ground to developed ground vegetation cover with any real values of the NDVI index.

scholarly journals Restoring SAR images using Effective Image Restoration Approach

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Image Restoration ◽  
Error Rate ◽  
Satellite Images ◽  
Missing Values ◽  
Signal To Noise Ratio ◽  
Edge Matching ◽  
Landsat 7 ◽  
Data Source ◽  
Restoration Method ◽  
Boundary Reconstruction

Landsat 7 Enhanced Thematic Mapper Plus satellite images presents an important data source for many applications related to remote sensing. An effective image restoration method is proposed to fill the missing information in the satellite images. The segmentation of satellite images to find the SLIC Super pixels and then to find the image Segments. The Boundary Reconstruction is performed using Edge Matching to find the area of the missing region. Peak Signal to Noise Ratio and Root Mean Square Error using with boundary reconstruction and without boundary reconstruction to evaluate the quality and the error rate of the satellite images. The results show the capability to predict the missing values accurately in terms of quality, time without need of external information.The values for PSNR has changed from 25 to 90 and RMSE has changed from 180 to 4 in Red Channel of an image.This indicates that quality of the image is high and error rate is less.

scholarly journals Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning

2021 ◽  
Vol 13 (18) ◽  
pp. 3603
Author(s):  
Joaquín Salas ◽  
Pablo Vera ◽  
Marivel Zea-Ortiz ◽  
Elio-Atenogenes Villaseñor ◽  
Dagoberto Pulido ◽  
...  
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Census Data ◽  
Population Census ◽  
Fine Grained ◽  
Vulnerable Communities ◽  
Landsat 7 ◽  
Block Level ◽  
Definition Of

One of the challenges in the fight against poverty is the precise localization and assessment of vulnerable communities’ sprawl. The characterization of vulnerability is traditionally accomplished using nationwide census exercises, a burdensome process that requires field visits by trained personnel. Unfortunately, most countrywide censuses exercises are conducted only sporadically, making it difficult to track the short-term effect of policies to reduce poverty. This paper introduces a definition of vulnerability following UN-Habitat criteria, assesses different CNN machine learning architectures, and establishes a mapping between satellite images and survey data. Starting with the information corresponding to the 2,178,508 residential blocks recorded in the 2010 Mexican census and multispectral Landsat-7 images, multiple CNN architectures are explored. The best performance is obtained with EfficientNet-B3 achieving an area under the ROC and Precision-Recall curves of 0.9421 and 0.9457, respectively. This article shows that publicly available information, in the form of census data and satellite images, along with standard CNN architectures, may be employed as a stepping stone for the countrywide characterization of vulnerability at the residential block level.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016 ◽  
Vol 9 (1) ◽  
pp. 307-321 ◽  
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Normalized Difference Snow Index ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed, e.g. in case of the frequently used normalized-difference snow index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user, but not by reproducible independent information. PRACTISE V.2.1 addresses this important aspect and shows additional improvements. The Matlab-based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilized as in situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs, was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible, and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

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