scholarly journals MONITORING OF URBAN AREA WITH SATELLITE IMAGERY

2019 ◽  
Vol 6 (1) ◽  
pp. 86-93
Author(s):  
Marina Plotnikova ◽  
Elena Khlebnikova
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Images ◽  
Automated Detection ◽  
The Earth ◽  
Erdas Imagine ◽  
New Facilities

The problem of identifying changes occurring in the territory of an urban area due to construction of new facilities, renovations and reconstructions using remote sensing of the Earth was considered. Various algorithms for automated detection of changes from different-time satellite images in the ERDAS IMAGINE 2010 program are analyzed in practice. Factors that must be considered when monitoring urban areas are identified.

scholarly journals Remote Sensing of the Earth as a Method of Assessing Environmental Quality of Urban Areas

2020 ◽  
pp. 5-12
Author(s):  
S.A. Yeprintsev ◽  
O.V. Klepikov ◽  
S.V. Shekoyan
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Urban Areas ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Space Images ◽  
The Earth ◽  
Voronezh Region

Introduction: Spatial zoning of an urban area by the level of anthropogenic burden using land-based research methods is very time-consuming. Since the end of the 20th century, the usage of the Earth remote sensing (ERS) techniques has served as their more efficient alternative. The study objectives included geoinformation zoning and evaluation of the level of technogenic changes in the areas according to NDVI (Normalized Difference Vegetation Index) values. Materials and methods: The cities of the Voronezh Region and their suburban ten-kilometer territories were chosen as the study objects. For the spatial analysis of the area of anthropogenically modified territories based on the example of the cities of the Voronezh Region we created an archive of multichannel satellite images taken by the Landsat-7 and Landsat-8 satellites. The data were borrowed from the Website of the US Geological Survey. Space images were grouped by two periods (the years of 2001 and 2016). Depending on NDVI values, territories with high and low anthropogenic burden, natural framework zones, and water bodies were distinguished. Results: We established that the smallest percentage of areas of the natural framework and their poor location was observed in the city of Voronezh. The largest area occupied by the natural framework was identified within the town of Borisoglebsk. This fact is attributed to the sensible policy of ensuring environmental and hygienic safety of the population implemented by the regional and municipal authorities. Discussion: At present, it is still impossible to fully use space monitoring data to assess health risks of technogenic factors; they can only be used simultaneously with ground monitoring that includes instrumental and laboratory monitoring of environmental quality indicators within the framework of the socio-hygienic monitoring. Conclusions: The analysis of changes in the proportion of areas with a high anthropogenic burden relative to the natural framework performed using satellite images taken in 2001 and 2016 showed an increase in the technogenic burden on the urban environment.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals New technology for mineral deposits prediction to identify prospective areas in the Zhezkazgan ore region

2021 ◽  
Vol 15 (2) ◽  
pp. 134-142
Author(s):  
Boris Zeylik ◽  
Yalkunzhan Arshamov ◽  
Refat Baratov ◽  
Alma Bekbotayeva
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
New Technology ◽  
Methodological Approach ◽  
Cost Savings ◽  
Remote Sensing Data ◽  
Ring Structure ◽  
Mineral Deposits ◽  
Erdas Imagine ◽  
Radar Satellite

Purpose. Exploration and predicting the prospective areas in the Zhezkazgan ore region to set up detailed prospecting and evaluation works using new integrated technologies of prediction constructions in the mineral deposits geology. Methods. An integrated methodological approach is used, including methods for deciphering the Earth’s remote sensing (ERS) data, the use of geophysical data and methods of analogy and actualism. All constructions are made in accordance with the principles of shock-explosive tectonics (SET). Prediction constructions are started with the selection of remote sensing data for the studied region and interpretation based on the processing of radar satellite images obtained from the Radarsat-1 satellite. The radar satellite images are processed in the Erdas Imagine software package. Findings. New local prospective areas have been identified, within which it is expected to discover the deposits. Their reserves are to replenish the depleted ore base in the Zhezkazgan region. Area of the gravity maximum 1 (the Near), considered to be the most promising, is located in close proximity to the city of Zhezkazgan; area of the gravity maximum 2 (the Middle); area of the gravity maximum 3 (the Distant-Tabylga); area of the gravity maximum 6 (the Central). A prospective area has been also revealed, overlaid by a loose sediment cover and located inside the Terekty ring structure, as well as the area of a thick stratum of pyritized grey sandstones, which is adjacent to the Sh-2 well drilled to the south of the Zhezkazgan field. Originality. The use of a new prediction technology, in contrast to the known ones, is conditioned by the widespread use of the latest remote information from satellite images, which increases the accuracy of identifying the prospective areas of fields. Practical implications. The new technology for predicting mineral deposits makes it possible to significantly reduce the areas exposed to priority prospecting, which provides significant cost savings.

scholarly journals Use of Satellite Imagery for Pastoral Resources Monitoring in Kossi Province (Burkina Faso)

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Sieza Yssouf ◽  
Gomgnimbou P. K Alain ◽  
Belem Adama ◽  
Serme Idriss
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Burkina Faso ◽  
Satellite Imagery ◽  
Field Data ◽  
Satellite Images ◽  
Temporal Evolution ◽  
Livestock Farming ◽  
Important Place ◽  
Livestock Sector

In Burkina Faso, livestock sector has an important place in the country's economy. Essentially extensive, this livestock farming is characterized by transhumance system, which consists of leading livestock sometimes over long distances in search of good pastures and water.Satellite images from different periods can be used to monitor the evolution of pastoral resources (pasture areas and surface water points) in a given area. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area. The objective of this study was to monitor the spatial and temporal evolution of pastoral resources using remote sensing tools in Kossi province. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area.

scholarly journals BUILDINGS CHANGE DETECTION BASED ON SHAPE MATCHING FOR MULTI-RESOLUTION REMOTE SENSING IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 683-687 ◽  
Author(s):  
M. Abdessetar ◽  
Y. Zhong
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Urban Area ◽  
Damage Assessment ◽  
Euclidean Distance ◽  
Shape Matching ◽  
Satellite Images ◽  
Remote Sensing Imagery ◽  
Multi Scale ◽  
Evolution Study

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object’s shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T<sub>0</sub> to shape T<sub>1</sub> and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

scholarly journals STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION

2012 ◽  
Vol 15 (4) ◽  
pp. 33-47
Author(s):  
Van Thi Tran ◽  
Binh Thi Trinh ◽  
Bao Duong Xuan Ha
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Image ◽  
Regression Function ◽  
Statistical Correlation ◽  
Step Test ◽  
Residential Areas ◽  
Remote Sensing Technology ◽  
Sensing Technology

This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.

scholarly journals MAPPING AND CHANGE DETECTION OF MANGROVES AROUND MUMBAI USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (GIS)

2021 ◽  
Vol 12 (1) ◽  
pp. 26-31
Author(s):  
A. Abhyankar ◽  
T. Sahoo ◽  
B. Seth ◽  
P. Mohapatra ◽  
S. Palai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Supervised Classification ◽  
Satellite Images ◽  
Likelihood Estimator ◽  
Mangrove Area ◽  
Detection Analysis ◽  
Erdas Imagine ◽  
Landcover Classification ◽  
Liss Iii

The study focuses on the mangroves in two districts namely, Mumbai and Mumbai Suburban. Mumbai, a coastal megacity, is a financial capital of the country with high population density. Mumbai is facing depletion of coastal resources due to land scarcity and large developmental projects. Thus, it is important to monitor these resources accurately and protect the stakeholders’ interest. Cloud-free satellite images of IRS P6 LISS III of 2004 and 2013 were procured from National Remote Sensing Centre (NRSC), Hyderabad. Two bands of visible and one band of NIR were utilized for landcover classification. Supervised Classification with Maximum Likelihood Estimator was used for the classification. The images were classified into various landcovers classes namely, Dense Mangroves, Sparse Mangroves and Others. Two software’s namely, ERDAS Imagine and GRAM++ were used for landcover classification and change detection analysis. It was observed that the total mangrove area in Mumbai in 2004 and 2013 was 50.52 square kilometers and 48.7 square kilometers respectively. In the year 2004 and 2013, contribution of sparse mangroves in the study area was 72.31 % and 87.06% respectively.

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