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 Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

2021 ◽  
Vol 13 (14) ◽  
pp. 7539
Author(s):  
Zaw Naing Tun ◽  
Paul Dargusch ◽  
DJ McMoran ◽  
Clive McAlpine ◽  
Genia Hill
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Local Governments ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Map Algebra ◽  
Change Detection Analysis ◽  
Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

scholarly journals Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

2010 ◽  
Vol 10 (10) ◽  
pp. 2179-2190 ◽  
Author(s):  
F. Tsai ◽  
J.-H. Hwang ◽  
L.-C. Chen ◽  
T.-H. Lin
Keyword(s):  
Spatial Analysis ◽  
Change Detection ◽  
Satellite Images ◽  
Regional Scale ◽  
Typhoon Morakot ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Disaster Assessment ◽  
Southern Taiwan ◽  
Post Disaster

Abstract. On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20° and 40°. In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m3 with an average depth of 40 m.

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 Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 398 ◽  
Author(s):  
Nety Nurda ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Satellite Remote Sensing ◽  
Land Suitability ◽  
Landsat 8 ◽  
Suitability Analysis ◽  
Land Suitability Analysis ◽  
Expert Opinions ◽  
Detection Analysis ◽  
The Government

The objective of this research was to detect changes in forest areas and, subsequently, the potential forest area that can be extended in the South Sumatra province of Indonesia, according to the Indonesian forest resilience classification zones. At first, multispectral satellite remote sensing datasets from Landsat 7 ETM+ and Landsat 8 OLI were classified into four classes, namely urban, vegetation, forest and waterbody to develop Land Use/Land Cover (LULC) maps for the year 2003 and 2018. Secondly, criteria, namely distance from rivers, distance from roads, elevation, LULC and settlements were selected and the reclassified maps were produced from each of the criteria for the land suitability analysis for forest extension. Thirdly, the Analytical Hierarchy Process (AHP) was incorporated to add expert opinions to prioritize the criteria referring to potential areas for forest extension. In the change detection analysis, Tourism Recreation Forest (TRF), Convertible Protection Forest (CPF) and Permanent Production Forest (PPF) forest zones had a decrease of 20%, 13% and 40% in area, respectively, in the forest class from 2003 to 2018. The Limited Production Forest (LPF) zone had large changes and decreased by 72% according to the LULC map. In the AHP method, the influential criteria had higher weights and ranked as settlements, elevation, distance from roads and distance from rivers. CPF, PPF and LPF have an opportunity for extension in the highly suitable classification (30%) and moderately suitable classification (41%) areas, to increase coverage of production forests. Wildlife Reserve Forests (WRFs) have potential for expansion in the highly suitable classification (30%) and moderately suitable classification (52%) areas, to keep biodiversity and ecosystems for wildlife resources. Nature Reserve Forests (NRFs) have an opportunity for extension in the highly suitable classification (39%) and moderately suitable classification (48%) areas, to keep the forests for nature and biodiversity. In case of TRF, there is limited scope to propose a further extension and is required to be managed with collaboration between the government and the community.

The Comparative Study of Remote Sensing Image Classification Method Based on ERDAS

2012 ◽  
Vol 546-547 ◽  
pp. 542-547 ◽  
Author(s):  
Guang Wei Zeng ◽  
Gui Fen Chen ◽  
Chu Nan Li ◽  
Jiao Ye
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Arable Land ◽  
Processing System ◽  
Image Data ◽  
Unsupervised Classification ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Remote Sensing Image Classification ◽  
Erdas Imagine

ERDAS IMAGINE is a remote sensing image processing system developed by the United States.The paper using ERDAS to classified the remote sensing of Land-sat TM image data by supervised classification method and unsupervised classification method, Using the Yushu City remote sensing image of Jilin Province as the trial data, and classified the forest, arable land and water from the remote sensing images, compared the test data of the supervised classification and unsupervised classification method, shows that the supervised classification method can be better to solute the questions "with the spectrum of foreign body" and "synonyms spectrum" than unsupervised classification method, and optimize classification images, improved information extraction accuracy. The application shows the classification result is consistent with the actual situation and it laid the foundation for land to have the rational planning and use.

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 Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction

2020 ◽  
Vol 12 (11) ◽  
pp. 1868 ◽  
Author(s):  
Huihui Dong ◽  
Wenping Ma ◽  
Yue Wu ◽  
Jun Zhang ◽  
Licheng Jiao
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Satellite Images ◽  
Main Idea ◽  
Representation Learning ◽  
Remote Sensing Image ◽  
Generative Adversarial Networks ◽  
Image Domain ◽  
Temporal Prediction ◽  
Feature Representations

Traditional change detection (CD) methods operate in the simple image domain or hand-crafted features, which has less robustness to the inconsistencies (e.g., brightness and noise distribution, etc.) between bitemporal satellite images. Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. To solve this problem, we propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD. The main idea of our algorithm is to transform two satellite images into more consistent feature representations through a self-supervised mechanism without semantic supervision and any additional computations. Based on the transformed feature representations, a better difference image (DI) can be obtained, which reduces the propagated error of DI on the final detection result. In the self-supervised mechanism, the network is asked to identify different sample patches between two temporal images, namely, temporal prediction. By designing the network for the temporal prediction task to imitate the discriminator of generative adversarial networks, the distribution-aware feature representations are automatically captured and the result with powerful robustness can be acquired. Experimental results on real remote sensing data sets show the effectiveness and superiority of our method, improving the detection precision up to 0.94–35.49%.

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