Application of hyperspectral camera in wetland monitoring

2024-06-18 16:47

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As an important ecosystem on earth, the ecological value of wetland is immeasurable. However, the problem of wetland loss and degradation is becoming more and more serious, which is influenced by natural factors and closely related to human activities. In this context, the use of hyperspectral remote sensing technology to study and protect wetlands is particularly important. Hyperspectral remote sensing technology is a technology that can obtain continuous and fine spectral information of surface objects. Compared with the traditional multi-spectral remote sensing technology, it can provide more abundant and finer spectral feature information of ground objects.


understand and give play to the advantages of hyperspectral remote sensing technology

Hyperspectral remote sensing technology is one of the major technological breakthroughs made by mankind in earth observation in the past 20 years, and it is the frontier technology of current remote sensing. The diversity, complexity, dynamic change and other characteristics of wetlands provide important research objects for hyperspectral remote sensing. If the new hyperspectral remote sensing technologies and methods summarized and developed from wetland research can be promoted in other fields, it will improve people's understanding and application level of hyperspectral remote sensing technology.


the main application areas of hyperspectrum in wetland monitoring and classification


1. Wetland type classification:


Hyperspectral remote sensing technology can achieve accurate classification of wetland types according to the spectral characteristics of wetland vegetation and water. This helps us to understand the distribution and type of wetlands more accurately and provide a scientific basis for wetland conservation and management.


2. Wetland vegetation identification:


Wetland vegetation is an important part of wetland ecosystem. Hyperspectral remote sensing technology can identify different types of wetland vegetation, such as marsh vegetation and aquatic vegetation, and monitor their growth status. This is of great significance for protecting wetland biodiversity and maintaining the stability of wetland ecosystem.


3. Wetland water quality monitoring:


Wetland water quality is an important factor affecting wetland ecosystem health. Hyperspectral remote sensing technology can realize the real-time monitoring and evaluation of wetland water quality by analyzing the spectral characteristics of water, reflecting water quality parameters such as chlorophyll, suspended matter and transparency.


4. Inversion of biochemical parameters of wetland vegetation:


Hyperspectral remote sensing technology can reverse the biochemical parameters of wetland vegetation, such as chlorophyll content and leaf area index. These parameters can reflect the growth status and health status of wetland vegetation, and are of great significance for assessing the health status of wetland ecosystem and predicting the change trend of wetland.


5. Estimation of wetland soil parameters:


Wetland soil is the foundation of wetland ecosystem. Hyperspectral remote sensing technology can estimate soil organic matter content and water content by analyzing soil spectral characteristics. These parameters are of great value for understanding wetland soil quality and assessing wetland ecosystem health.


3. Hyperspectral remote sensing technology process


Hyperspectral remote sensing for wetland monitoring and classification, the technical process can be divided into: hyperspectral data acquisition, data preprocessing, image information extraction, wetland information thematic mapping, thematic analysis. The resulting thematic map data can provide abundant geographic information for researchers to carry out targeted research in the field of wetlands.


Hyperspectral application in wetland monitoring


The UAV hyperspectral camera of Hangzhou Color Spectrum was used to collect flight data of a wetland


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The data is preprocessed as shown in the figure below


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Conduct NDVI data analysis


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Water quality model inversion and data fitting


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