South China Sea Environment Monitoring Using Remote Sensing Techniques

Deng, Wanqian; Xin, Zhang; Luo, Jiancheng; Peng, Yi

doi:10.2112/si95-006.1

Cited by 6 publications

(3 citation statements)

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“…For example, when using B-P neural network model to comprehensively evaluate the quality of ecological environment, it is not necessary to deal with the evaluation indicators, and the evaluation results obtained by using this model are relatively objective. In addition, using B-P neural network model, multiple evaluation indexes can be selected to establish a comprehensive evaluation system of ecological environment quality, but there are problems such as parameter convergence and optimization in the application of B-P neural network algorithm [20].…”

Section: Commonly Used Comprehensive Evaluation Methods Of Ecological...mentioning

confidence: 99%

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Wang

2022

Journal of Robotics

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With the continuous improvement of people’s demand for ecological environment quality, the research on ecological environment monitoring, analysis and evaluation had been paid more and more attention by relevant departments and personnel. Because the images collected by remote sensing technology were many and multi-source, the features extracted from remote sensing images using traditional methods had been difficult to meet the needs of related industry applications. Therefore, this paper made use of the advantages of PIE remote sensing image processing software in data analysis and processing, and put forward the research on ecological environment monitoring, analysis and evaluation methods. Firstly, on the basis of summarizing the concepts and related problems of ecological environment, this paper analyzed the processing methods of remote sensing data sources of ecological environment, and explained the evaluation standards and common methods of ecological environment. Secondly, the composition of PIE remote sensing image processing technology system and its application advantages were described, the common indicators and analysis methods of ecological environment monitoring were given, and the index system and model of ecological environment comprehensive evaluation were established. Finally, through the analysis of experimental cases, the results showed that the ecological environment monitoring analysis and evaluation method proposed in this paper was feasible. Compared with the traditional methods, the method proposed in this paper could objectively evaluate the ecological environment. This paper can not only provide support for the analysis and processing of remote sensing image data, but also provide an important reference for the application of remote sensing technology in the field of ecological environment.

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Section: Commonly Used Comprehensive Evaluation Methods Of Ecological...mentioning

confidence: 99%

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Wang

2022

Journal of Robotics

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“…Previous studies in a meta-analysis of mangrove resource mapping using RS have not yet included the temporal effect of oil spills on mangroves. However, this must be considered for further research to quantify the impacts and recovery of mangroves considering that oil spills are one of the multiple stressors of mangrove ecosystems [27,[112][113][114].…”

Section: Different Approaches In Discriminating Mangroves From Other ...mentioning

confidence: 99%

Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

Pillodar,

Suson,

Aguilos

et al. 2023

Forests

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In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.

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“…Remote sensing technology with a wide monitoring range and strong timeliness has been widely used for investigations, monitoring, analyses and predictions in many fields (such as agriculture, resource management and the environment), especially for China's large landmass [1][2][3]. Among these technologies, the most commonly used data are optical remote sensing imagery [4].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Probability of Acquiring Cloud-Free Imagery in China with AVHRR Cloud Mask Data

Fang

et al. 2021

Atmosphere

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Optical remote sensing data are used widely in many fields (such as agriculture, resource management and the environment), especially for the vast territory of China; however, the application of these data is usually limited by clouds. Although it is valuable to analyze the probability of acquiring cloud-free imagery (PACI), PACI using different sensors at the pixel level across China has not been reported. In this study, the PACI of China was calculated with daily Advanced Very High Resolution Radiometer (AVHRR) cloud mask data from 1990 to 2019. The results showed that (1) PACI varies dramatically in different regions and months in China. The value was larger in autumn and winter, and the largest figure reached 49.55% in October in Inner Mongolia (NM). In contrast, relatively small values occurred in summer, and the minimum value (5.26%) occurred in June in South China (SC). (2) As the climate changes, the PACI has increased significantly throughout the country and most regions, especially in North China (NC), with a growth rate of 1.9% per decade. The results can be used as a reference for selecting appropriate optical sensors and observation times in areas of interest.

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South China Sea Environment Monitoring Using Remote Sensing Techniques

Cited by 6 publications

References 0 publications

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software

Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

Analyzing the Probability of Acquiring Cloud-Free Imagery in China with AVHRR Cloud Mask Data

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