Predicting the ground-level pollutants concentrations and identifying the influencing factors using machine learning, wavelet transformation, and remote sensing techniques

2021 ◽  
pp. 101064
Author(s):  
Zohre Ebrahimi-Khusfi ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
Mohamad Kazemi ◽  
Ali Reza Nafarzadegan
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Influencing Factors ◽  
Wavelet Transformation ◽  
Ground Level ◽  
Remote Sensing Techniques

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

Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods

2020 ◽  
Vol 143 (1-2) ◽  
pp. 713-735 ◽  
Author(s):  
Keyvan Soltani ◽  
Afshin Amiri ◽  
Mohammad Zeynoddin ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Lake Surface ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Surface Areas ◽  
Remote Sensing Techniques

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 Machine learning and remote sensing techniques applied to estimate soil indicators – Review

2022 ◽  
Vol 135 ◽  
pp. 108517
Author(s):  
Freddy A. Diaz-Gonzalez ◽  
Jose Vuelvas ◽  
Carlos A. Correa ◽  
Victoria E. Vallejo ◽  
D. Patino
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Techniques ◽  
Soil Indicators

scholarly journals Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5

2018 ◽  
Vol 242 ◽  
pp. 1417-1426 ◽  
Author(s):  
Yongming Xu ◽  
Hung Chak Ho ◽  
Man Sing Wong ◽  
Chengbin Deng ◽  
Yuan Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Learning Techniques

Method for predicting soil salinity concentrations in croplands based on machine learning and remote sensing techniques

2019 ◽  
Vol 13 (03) ◽  
pp. 1
Author(s):  
Nurmemet Erkin ◽  
Lei Zhu ◽  
Haibin Gu ◽  
Alimujiang Tusiyiti
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Salinity ◽  
Remote Sensing Techniques

scholarly journals Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3950 ◽  
Author(s):  
Kun Cai ◽  
Qiushuang Zhang ◽  
Shenshen Li ◽  
Yujing Li ◽  
Wei Ge
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Temporal Variations ◽  
Ozone Monitoring Instrument ◽  
Economic Zone ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Concentrations ◽  
Remote Sensing Techniques

The Chengdu–Chongqing Economic Zone (CCEZ), which is located in southwestern China, is the fourth largest economic zone in China. The rapid economic development of this area has resulted in many environmental problems, including extremely high concentrations of nitrogen dioxide (NO2) and fine particulate matter (PM2.5). However, current ground observations lack spatial and temporal coverage. In this study, satellite remote sensing techniques were used to analyze the variation in NO2 and PM2.5 from 2005 to 2015 in the CCEZ. The Ozone Monitoring Instrument (OMI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product were used to retrieve tropospheric NO2 vertical columns and estimate ground-level PM2.5 concentrations, respectively. Geographically, high NO2 concentrations were mainly located in the northwest of Chengdu and southeast of Chongqing. However, high PM2.5 concentrations were mainly located in the center areas of the basin. The seasonal average NO2 and PM2.5 concentrations were both highest in winter and lowest in summer. The seasonal average NO2 and PM2.5 were as high as 749.33 × 1013 molecules·cm−2 and 132.39 µg·m−3 in winter 2010, respectively. Over 11 years, the annual average NO2 and PM2.5 values in the CCEZ increased initially and then decreased, with 2011 as the inflection point. In 2007, the concentration of NO2 reached its lowest value since 2005, which was 230.15 × 1013 molecules·cm−2, and in 2015, the concentration of PM2.5 reached its lowest value since 2005, which was 26.43 µg·m−3. Our study demonstrates the potential use of satellite remote sensing to compensate for the lack of ground-observed data when quantitatively analyzing the spatial–temporal variations in regional air quality.

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. 

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