Home Journals TS News Application of Machine Learning in Remote Sensing Signal Processing for Environmental Monitoring

JOURNAL METRICS

Impact Factor (JCR) 2023: 1.2 ℹImpact Factor (JCR):

The JCR provides quantitative tools for ranking, evaluating, categorizing, and comparing journals. The impact factor is one of these; it is a measure of the frequency with which the “average article” in a journal has been cited in a particular year or period. The annual JCR impact factor is a ratio between citations and recent citable items published. Thus, the impact factor of a journal is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years.

5-Year Impact Factor: 1.2 ℹ5-Year Impact Factor:

A 5-Year Impact Factor shows the long-term citation trend for a journal. This is calculated differently from the Journal Impact Factor, so it is not simply an average of the Impact Factors in the time period. The Impact Factor itself is based only on Web of Science Core Collection citation data from the last three years and thus reflects only recent impact. The Journal Impact Factor is the average number of times articles from the journal published in the past two years have been cited in the Journal Citation Reports year.

qqtu_pian_20240428144739.png

Application of Machine Learning in Remote Sensing Signal Processing for Environmental Monitoring

Mar

15

2025

Application of Machine Learning in Remote Sensing Signal Processing for Environmental Monitoring

As climatic and land cover change and its effects on environment become more widely recognized, environmental monitoring is becoming a more crucial concern. It is now possible for remote sensing to uncover patterns and trends in both space and time according to the advanced spatial and temporal details provided by current earth observation satellites. Here, machine learning techniques have proven to be an effective way to take into account the complexity and nonlinearity present in nature and connect remote sensing data to pertinent environmental factors. Intelligent decision-making and enhanced signal analysis were made possible by the combination of digital signal processing and machine learning, which led to advancements in signal processing. Rarely seen potential to develop innovative and effective digital signal processing services that evaluate and extract useful information from digital signals is presented by the simultaneous use of ML and digital signage.

These findings suggest that the accuracy of the support vector machine algorithm is higher than that of other algorithms. The learning's findings showed that accurate classification maps that can be utilized as basis data can be produced by integrating classification algorithms with remote sensing in change monitoring determination. This is because machine learning algorithms attain higher accuracy than traditional classifiers because of their capacity to understand intricate patterns, adapt to data, and constantly improve. Therefore, their use was encouraged for decision-makers. The varied nature of the remote sensing instruments presents another significant obstacle in the classification and identification process. This also affects the efficiency and efficacy of the data from remote sensing. When practical applications are the key focus, multi-modal datasets from the growing sensing and extra secondary datasets can be used to improve the methodologies used to analyze the remote sensing data. The problem facing consultants in the environmental sector has changed from having too little data to coping with too much data due to the growth in sensor and platform availability. Effective policies for sustainable land use in conjunction with economic development are necessary to address the growing burden of land use. Land use maps can be used to analyze local and global indicators, which can provide information on sustainable development is progressing. The growing availability of remote sensing data has led to the emergence of machine learning, particularly deep learning, which has opened up new avenues for understanding and quantifying environmental changes brought about by natural disasters and human activity.

The goal of this special issue is to promote the use of machine learning algorithms in environmental monitoring systems that rely on remote sensing. Also especially welcoming the methodological contributions that enhance the robustness and dependability of the outcomes through creative breakthroughs and fresh machine learning methodologies.

Potential topics include but are not limited to the following:

Ecological remote sensing using multisensor data merging and machine learning
Combining environmental monitoring with machine learning and remote sensing
Benchmarks and obstacles for deep learning techniques in environmental remote sensing
Monitoring the aquatic environment with remote sensing: present state and problems
An examination of machine learning for handling data from remote sensing
Machine learning algorithms for remote sensing uses: a framework
Precipitation estimation using machine learning methods and remote sensing
Utilizing machine learning and remote sensing together to identify anomalies in brightness
Data from remote sensing using an unsupervised machine learning method
Acquisition and evaluate of large-scale remote sensing data using deep learning
Using remote sensing and machine learning methods to monitor shorelines.