Monitoring Changes in Urban Cover Using Landsat Satellite Images and Demographical Information

2017 ◽  
pp. 89-103 ◽  
Author(s):  
Prashant K. Srivastava ◽  
Swati Suman ◽  
Smita Pandey
Keyword(s):  
Satellite Images ◽  
Time Span ◽  
Support Vector ◽  
Consumption Ratio ◽  
Governmental Organizations ◽  
Green Cover ◽  
Non Governmental Organizations ◽  
Vector Machines ◽  
Land Consumption ◽  
Landsat Satellite

The monitoring of urban cover is very important for the planner, management, governmental and non-governmental organizations for optimizing the use of urban resources and minimizing the environmental losses. The study here aims at analyzing the changes that occurred in urban green cover over a time span of 1991-2001 using multi-date Landsat satellite images data over the Varanasi district, India and its relation to demographical changes. The Support Vector Machines (SVMs) classifier has been used for image classification. The urbanization indicators such as Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used in order to understand the changes in urban cover and population dynamics. All the analysis indicates significant changes in the urban cover values with increasing population at both spatial and temporal scale.

Monitoring Changes in Urban Cover Using Landsat Satellite Images and Demographical Information

2019 ◽  
pp. 981-995
Author(s):  
Prashant K. Srivastava ◽  
Swati Suman ◽  
Smita Pandey
Keyword(s):  
Satellite Images ◽  
Time Span ◽  
Support Vector ◽  
Consumption Ratio ◽  
Governmental Organizations ◽  
Green Cover ◽  
Non Governmental Organizations ◽  
Vector Machines ◽  
Land Consumption ◽  
Landsat Satellite

The monitoring of urban cover is very important for the planner, management, governmental and non-governmental organizations for optimizing the use of urban resources and minimizing the environmental losses. The study here aims at analyzing the changes that occurred in urban green cover over a time span of 1991-2001 using multi-date Landsat satellite images data over the Varanasi district, India and its relation to demographical changes. The Support Vector Machines (SVMs) classifier has been used for image classification. The urbanization indicators such as Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used in order to understand the changes in urban cover and population dynamics. All the analysis indicates significant changes in the urban cover values with increasing population at both spatial and temporal scale.

scholarly journals Assessment of Interventions in Fuel Management Zones Using Remote Sensing

2020 ◽  
Vol 9 (9) ◽  
pp. 533 ◽  
Author(s):  
Ricardo Afonso ◽  
André Neves ◽  
Carlos Viegas Damásio ◽  
João Moura Pires ◽  
Fernando Birra ◽  
...  
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fuel Management ◽  
K Nearest Neighbors ◽  
Management Zones ◽  
Vector Machines ◽  
Sentinel 2

Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.

scholarly journals Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

2021 ◽  
Vol 15 (4) ◽  
pp. 101-116
Author(s):  
Lamyaa Gamal El-deen Taha ◽  
Rania Elsayed Ibrahim
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
High Resolution ◽  
Random Forest ◽  
Satellite Images ◽  
Support Vector ◽  
Morphological Operations ◽  
Vector Machines ◽  
High Resolution Images ◽  
Very High

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

scholarly journals Satellite image classification and quality parame-ters using ML classifier

2018 ◽  
Vol 7 (1.8) ◽  
pp. 6
Author(s):  
K. Radhika ◽  
S. Varadarajan
Keyword(s):  
Maximum Likelihood ◽  
Satellite Images ◽  
Performance Metrics ◽  
Satellite Image ◽  
Feature Space ◽  
Support Vector ◽  
Vector Machines ◽  
Supervised Classifiers ◽  
Multispectral Satellite Images

Remote sensing images are an important source of information regarding the Earth surface. For many applications like geology, urban planning, forest and land cover/land use, the underlying information from such images is needed. Extraction of this information is usually achieved through a classification process which is one of the most powerful tools in digital image processing. Good classifier is required to extract the information in satellite images. Latest methods used for classification of pixels in multispectral satellite images are supervised classifiers such as Support Vector Machines (SVM), k-Nearest Number (K-NN) and Maximum Likelihood (ML) classifier. SVM may be one-class SVM or multi-class SVM. K-NN is simple technique in high-dimensional feature space. In ML classifier, classification is based on the maximum likelihood of the pixel. The performance metrics for these classifiers are calculated and compared. Totally 200 points have been considered for validation purpose.

ADAPTIVE CHEBYSHEV FUSION OF VEGETATION IMAGERY BASED ON SVM CLASSIFIER

2016 ◽  
Vol 78 (6-11) ◽  
Author(s):  
Zaid Omar ◽  
Nur’Aqilah Hamzah ◽  
Tania Stathaki
Keyword(s):  
Image Fusion ◽  
Satellite Imagery ◽  
Processing Time ◽  
Satellite Images ◽  
Real Life ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Real Hardware ◽  
Image Fusion Method

A novel adaptive image fusion method by using Chebyshev polynomial analysis (CPA), for applications in vegetation satellite imagery, is introduced in this paper. Fusion is a technique that enables the merging of two satellite cameras: panchromatic and multi-spectral, to produce higher quality satellite images to address agricurtural and vegetation issues such as soiling, floods and crop harvesting. Recent studies show Chebyshev polynomials to be effective in image fusion mainly in medium to high noise conditions, as per real-life satellite conditions. However, its application was limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Support vector machines (SVM) is used as a classifying tool to estimate the noise parameters, from which the appropriate CPA degree is utilised to perform image fusion according to a look-up table. Performance evaluation affirms the approach’s ability in reducing the computational complexity to perform fusion. Overall, adaptive CPA fusion is able to optimize an image fusion system’s resources and processing time. It therefore may be suitably incorporated onto real hardware for use on vegetation satellite imagery.    

Non-governmental organizations as key partners in mental health in Europe

2014 ◽  
Vol 11 (01) ◽  
pp. 27-34 ◽  
Author(s):  
A. E. Baumann
Keyword(s):  
Mental Health ◽  
Health Policy ◽  
Policy Development ◽  
Mental Health Problems ◽  
Action Plan ◽  
Mental Health Policy ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
Health Action ◽  
Health Action Plan

SummaryThe shift towards a rights-based approach to health which has taken place over the past decade has strengthened the role of civil society and their organizations in raising and claiming the entitlements of different social groups. It has become obvious that non-governmental organizations (NGOs) are central to any successful multi-stakeholder partnership, and they have become more recognized as key actors in health policy and programme development and implementation. There is a broad spectrum of NGOs active in the area of mental health in Europe which aim to empower people with mental health problems and their families, give them a voice in health policy development and implementation and in service design and delivery, to raise awareness and fight stigma and discrimination, and foster implementation of obligations set by internationally agreed mental health policy documents. With the endorsement of the Mental Health Action Plan 2013-2020 (20) and the European Mental Health Action Plan (19) stakeholders agree to strengthen capacity of service user and family advocacy groups and to secure their participation as partners in activities for mental health promotion, disorder prevention and improving mental health services.

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