Urban LULC Change Detection and Mapping Spatial Variations of Aurangabad City Using IRS LISS-III Temporal Datasets and Supervised Classification Approach

2018 ◽  
pp. 369-386 ◽  
Author(s):  
Ajay D. Nagne ◽  
Amol D. Vibhute ◽  
Rajesh K. Dhumal ◽  
Karbhari V. Kale ◽  
S. C. Mehrotra
Keyword(s):  
Change Detection ◽  
Supervised Classification ◽  
Spatial Variations ◽  
Lulc Change ◽  
Classification Approach ◽  
Liss Iii

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.

scholarly journals Land Use/Land Cover Change Detection using Geoinformatics in Gurugram District, Haryana, India

2019 ◽  
Vol 8 (2) ◽  
pp. 3753-3755
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Agricultural Land ◽  
Water Bodies ◽  
Time Range ◽  
Barren Land ◽  
Irs P6 ◽  
Significant Extension ◽  
Liss Iii ◽  
Irs P6 Liss Iv

The district Gurugram in the state Haryana has seen significant extension & development during the last few years. In this paper, the change in land-use/cover has been estimated with time range of 2007 - 2017 and the change detection was quantified. The land-use/cover data generated through satellite imagery has been classified into five major classes i.e., (i) Built-up land (ii) Water Bodies (iii) Barren Land (iv) Agricultural Land (v) Vegetation. The investigation was helped out through Geoinformatics approach by using IRS-P6- LISS-III sensor of 2007 and IRS-P6-LISS-IV sensor of 2017. Observing of land-use/spread mirrored that changes were more noteworthy in degree over the time range of 10 years in the land under various classes. The most sensational changes are the increase in built-up land and barren land. Apart from this decrease in agricultural, water bodies and vegetation cover area also. Results demonstrates an expansive change in the territory of various land use classifications amid the period from 2007 to 2017.The agriculture land covering an area of about 55.27% in 2007 reduced to 43.42% in 2017. The built up area increased from 15.97 % in 2007 to 30.23 in 2017. The barren land area increased from 6.45 % in 2007 to 16.97 in 2017 The Water bodies decreased from 4.65 % in 2007 to 1.05 % in 2017. The vegetation area has also decreased from 17.66 % in 2007 to 8.33 % in 2017. Urban extension and various anthropogenic exercises have brought genuine misfortunes of agricultural land, vegetation and water bodies.

scholarly journals Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)

2022 ◽  
Vol 14 (2) ◽  
pp. 317
Author(s):  
Andy Hardy ◽  
Gregory Oakes ◽  
Juma Hassan ◽  
Yussuf Yussuf
Keyword(s):  
Vector Control ◽  
Malaria Vector ◽  
Supervised Classification ◽  
Region Growing ◽  
Malaria Vector Control ◽  
Larval Habitats ◽  
Classification Approach ◽  
Spatial Intelligence ◽  
Supervised Image Classification ◽  
Complex Features

Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes.

scholarly journals Change Detection in Water reservoir using Image Fusion and Supervised Classifier

2021 ◽  
pp. 324-330
Author(s):  
Kiran Khandarkar ◽  
Dr. Sharvari Tamne
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Classification System ◽  
Supervised Classification ◽  
Water Reservoir ◽  
Input Image ◽  
Sar Images ◽  
The Mean ◽  
Log Ratio

The research provides a method for improving change detection in SAR images using a fusion object and a supervised classification system. To remove noise from the input image, we use the DnCNN denoising approach. The data from the first image is then processed with the mean ratio operator. The log ratio operator is used to process the second image. These two images are fused together using SWT-based image fusion, and the output is sent to a supervise classifier for change detection.

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