scholarly journals Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review

2021 ◽  
Author(s):  
Khushbu Maurya ◽  
Seema Mahajan ◽  
Nilima Chaube
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Image Classification ◽  
Digital Image ◽  
Mangrove Ecosystem ◽  
Mangrove Forests ◽  
Classification Techniques ◽  
Knowledge Based ◽  
Object Based ◽  
Remote Sensing Techniques

AbstractMangrove forests are considered to be the most productive ecosystem yet vanishing rapidly over the world. They are mostly found in the intertidal zone and sheltered by the seacoast. Mangroves have potential socio-economic benefits such as protecting the shoreline from storm and soil erosion, flood and flow control, acting as a carbon sink, provides a fertile breeding ground for marine species and fauna. It also acts as a source of income by providing various forest products. Restoration and conservation of mangrove forests remain a big challenge due to the large and inaccessible areas covered by mangroves forests which makes field assessment difficult and time-consuming. Remote sensing along with various digital image classification approaches seem to be promising in providing better and accurate results in mapping and monitoring the mangroves ecosystem. This review paper aims to provide a comprehensive summary of the work undertaken, and addresses various remote sensing techniques applied for mapping and monitoring of the mangrove ecosystem, and summarize their potential and limitation. For that various digital image classification techniques are analyzed and compared based on the type of image used with its spectral resolution, spatial resolution, and other related image features along with the accuracy of the classification to derive specific class information related to mangroves. The digital image classification techniques used for mangrove mapping and monitoring in various studies can be classified into pixel-based, object-based, and knowledge-based classifiers. The various satellite image data analyzed are ranged from light detection and ranging (LiDAR), hyperspectral and multispectral optical imagery, synthetic aperture radar (SAR), and aerial imagery. Supervised state of the art machine learning/deep machine learning algorithms which use both pixel-based and object-based approaches and can be combined with the knowledge-based approach are widely used for classification purpose, due to the recent development and evolution in these techniques. There is a huge future scope to study the performance of these classification techniques in combination with various high spatial and spectral resolution optical imageries, SAR and LiDAR, and also with multi-sensor, multiresolution, and temporal data.

scholarly journals Land Cover Classification in Mangrove Ecosystems Based on VHR Satellite Data and Machine Learning—An Upscaling Approach

2020 ◽  
Vol 12 (17) ◽  
pp. 2684 ◽  
Author(s):  
Neda Bihamta Toosi ◽  
Ali Reza Soffianian ◽  
Sima Fakheran ◽  
Saeied Pourmanafi ◽  
Christian Ginzler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Data ◽  
Mangrove Ecosystem ◽  
Limiting Factor ◽  
Mangrove Forests ◽  
Tidal Zone ◽  
Mangrove Ecosystems ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Mangrove forests grow in the inter-tidal areas along coastlines, rivers, and tidal lands. They are highly productive ecosystems and provide numerous ecological and economic goods and services for humans. In order to develop programs for applying guided conservation and enhancing ecosystem management, accurate and regularly updated maps on their distribution, extent, and species composition are needed. Recent advances in remote sensing techniques have made it possible to gather the required information about mangrove ecosystems. Since costs are a limiting factor in generating land cover maps, the latest remote sensing techniques are advantageous. In this study, we investigated the potential of combining Sentinel-2 and Worldview-2 data to classify eight land cover classes in a mangrove ecosystem in Iran with an area of 768 km2. The upscaling approach comprises (i) extraction of reflectance values from Worldview-2 images, (ii) segmentation based on spectral and spatial features, and (iii) wall-to-wall prediction of the land cover based on Sentinel-2 images. We used an upscaling approach to minimize the costs of commercial satellite images for collecting reference data and to focus on freely available satellite data for mapping land cover classes of mangrove ecosystems. The approach resulted in a 65.5% overall accuracy and a kappa coefficient of 0.63, and it produced the highest accuracies for deep water and closed mangrove canopy cover. Mapping accuracies improved with this approach, resulting in medium overall accuracy even though the user’s accuracy of some classes, such as tidal zone and shallow water, was low. Conservation and sustainable management in these ecosystems can be improved in the future.

Digital Image Classification Techniques

2019 ◽  
pp. 162-187
Author(s):  
Utkarsh Shrivastav ◽  
Sanjay Kumar Singh
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Ct Scan ◽  
Image Classification ◽  
Digital Image ◽  
Digital Cameras ◽  
Learning Networks ◽  
Hidden Information ◽  
Classification Techniques

Image classification is a technique to categorize an image in to given classes on the basis of hidden characteristics or features extracted using image processing. With rapidly growing technology, the size of images is growing. Different categories of images may contain different types of hidden information such as x-ray, CT scan, MRI, pathologies images, remote sensing images, satellite images, and natural scene image captured via digital cameras. In this chapter, the authors have surveyed various articles and books and summarized image classification techniques. There are supervised techniques like KNN and SVM, which classify an image into given classes and unsupervised techniques like K-means and ISODATA for classifying image into a group of clusters. For big images, deep learning networks can be employed that are fast and efficient and also compute hidden features automatically.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals Comparison of pixel-based and object-based image classification techniques in extracting information from UAV imagery data

2018 ◽  
Vol 169 ◽  
pp. 012098 ◽  
Author(s):  
H I Sibaruddin ◽  
H Z M Shafri ◽  
B Pradhan ◽  
N A Haron
Keyword(s):  
Image Classification ◽  
Classification Techniques ◽  
Object Based ◽  
Imagery Data

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

2017 ◽  
Vol 129 ◽  
pp. 1-11 ◽  
Author(s):  
Madodomzi Mafanya ◽  
Philemon Tsele ◽  
Joel Botai ◽  
Phetole Manyama ◽  
Barend Swart ◽  
...  
Keyword(s):  
Image Classification ◽  
Plant Invasions ◽  
Aerial Imagery ◽  
Classification Techniques ◽  
Object Based

Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping

2020 ◽  
Vol 56 ◽  
pp. 101059 ◽  
Author(s):  
Mahdi Boroughani ◽  
Sima Pourhashemi ◽  
Hossein Hashemi ◽  
Mahdi Salehi ◽  
Abolghasem Amirahmadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Source Detection ◽  
Dust Source ◽  
Remote Sensing Techniques
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