Vegetation Coverage Monitoring Based on Hyperspectral Remote Sensing Technology

In remote sensing technology, vegetation indices are commonly used methods for extracting vegetation information. Vegetation indices are characteristic quantities calculated from the reflectance of visible and near-infrared bands, reflecting the degree and growth status of surface vegetation cover. Commonly used vegetation indices include the Naturalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), and the VI (Virtualization Index).

Vegetation cover change monitoring methods based on remote sensing technology

1. Vegetation index change method

The vegetation index change method compares vegetation indices in multiple remote sensing images and calculates the changes in these indices to determine the state of vegetation cover changes. By analyzing the changes in vegetation indices, it is possible to determine the growth and withering of vegetation, as well as changes in vegetation cover.

2. Classification and Variation Method

The classification-based change method uses remote sensing images for classification to reflect changes in vegetation cover. This method obtains information on changes and types of vegetation cover area by classifying and analyzing remote sensing images, and its monitoring effect is more accurate than the vegetation index change method.

Basic process of vegetation cover monitoring

1. Data Acquisition

The first step in using remote sensing technology for vegetation cover monitoring is acquiring appropriate data. Remote sensing data can come from remote sensing satellites, drones, or other remote sensing platforms. Satellite data typically has a large spatial coverage area and a smaller temporal resolution, while drone data has a smaller spatial coverage area and a higher temporal resolution. Different types of data can be selected depending on specific needs. Furthermore, factors such as the band combination and resolution of the data must be considered to ensure that the data provides effective vegetation information.

2. Data Preprocessing

After acquiring remote sensing data, a series of preprocessing operations are required to obtain data usable for vegetation cover monitoring. Preprocessing operations include cloud removal, atmospheric correction, and radiometric calibration. For cloud removal, cloud detection algorithms can be used to remove cloud-covered areas, or cloud image fusion technology can be used to stitch together cloud-covered and cloudless images. Atmospheric correction can be performed using atmospheric correction models to eliminate atmospheric effects and reduce errors. In addition, radiometric calibration is necessary to convert the remote sensing data into physical quantities.

3. Vegetation Index Calculation

Vegetation indices are important indicators for monitoring vegetation cover. Commonly used vegetation indices include the Normalized Difference Vegetation Index (NDVI) and the Difference Vegetation Index (DVI).

Vegetation indices can be calculated using different bands of remote sensing data according to specified formulas. For example, NDVI can be obtained by calculating the ratio of infrared to visible light bands. The calculated vegetation indices can reflect the status of vegetation cover, thus enabling vegetation cover monitoring.

4. Data Interpretation and Analysis

After calculating the vegetation index, the data needs to be interpreted and analyzed to obtain more information about vegetation cover. Interpretation and analysis can employ methods such as image classification and change detection. Image classification can divide different pixels in a remote sensing image into different categories, such as vegetation, water bodies, and buildings. Change detection can compare remote sensing images from different time periods to identify changes in vegetation cover. Through interpretation and analysis, information such as the spatial distribution and changing trends of vegetation cover can be obtained, providing a basis for decision-making in vegetation management and ecological protection.

5. Result verification and accuracy evaluation

To verify the accuracy of monitoring results, result verification and precision evaluation are necessary. Verification can be conducted through field surveys and sampling, comparing the consistency between the field survey results and the remote sensing monitoring results. Precision evaluation can utilize specialized software for spatial statistical analysis, calculating precision indicators of the monitoring results, such as accuracy and precision. Through result verification and precision evaluation, the reliability of remote sensing monitoring can be assessed, providing a reference for subsequent monitoring work.

△ Distribution of vegetation cover in Xi'an City in March 2023

CAS Xiguang Aerospace - Vegetation Remote Sensing Monitoring

The average vegetation coverage in Xi'an during spring and summer shows a clear seasonal trend, indicating that Xi'an has a good natural ecological environment.Regional statistical results show that vegetation growth is relatively stable in the Qinling Mountains and the Weihe River alluvial plain in the southern mountainous area; vegetation growth trends are significant along the hills and loess plateaus; and vegetation coverage is low in newly built urban areas and major development zones. Landscape pattern analysis shows that the density and number of vegetation patches in Xi'an are relatively high, indicating that Xi'an's resources are being continuously developed and utilized. 

△ Distribution of vegetation cover in Xi'an City in August 2023

The application of remote sensing technology in vegetation cover change monitoring has greatly improved monitoring efficiency and accuracy. With the continuous development and advancement of remote sensing technology, new technologies and methods will continue to emerge, providing more accurate and comprehensive information for vegetation cover change monitoring. With the assistance of remote sensing technology, our understanding and protection of the ecological environment will become more precise and efficient.