Vegetation Health Analysis in the Qinling Mountains Based on Hyperspectral Remote Sensing Technology

With increasing demands for environmental and ecological protection, environmental protection and ecological construction have gradually become a global focus. Forestry development is crucial in the field of ecological construction and environmental protection. Given the significant increase in forest coverage in my country, improving forest quality has become a key focus of current forest management. Current research on forest health in my country is limited to traditional plot surveys, a method that is time-consuming, labor-intensive, and unable to effectively reflect changes in forest health. With advancements in spectral imagers and hyperspectral remote sensing technology, more detailed information about land features can be obtained. Our company uses vegetation indices extracted from hyperspectral images to assess forest health. Based on the spectral characteristics of forest vegetation, we can establish an indicator system for evaluating forest health based on vegetation indices, considering three aspects: vegetation distribution, photosynthetic intensity, and stress caused by pests, diseases, and water.

Vegetation Index (VI)

Vegetation indices (VIs) are calculated by combining the reflectance of ground features across two or more wavelength ranges. These indices can enhance certain characteristics of vegetation. Based on key chemical components that significantly influence vegetation spectral characteristics, such as pigments, nitrogen, water, and carbon, several practical indices have been developed, including greenness, light use efficiency, canopy nitrogen, drought or carbon decay, leaf pigment, and canopy water content. These vegetation indices provide a simple way to measure the quantity and growth status of green vegetation.

Greenness Index

Primarily used to assess and display the distribution of green vegetation and plant growth, it is highly sensitive to chlorophyll content, leaf surface canopy, and canopy structure—all essential substances for photosynthesis. By calculating the Normalized Difference Vegetation Index (NDVI), the growth status of vegetation can be evaluated.

Light utilization index

It is used to measure the efficiency of vegetation in utilizing incident light during photosynthesis. Light utilization efficiency is directly related to vegetation growth rate and photosynthetically active radiation (fAPAR). By assessing and quantifying the light utilization efficiency of vegetation through the red-green ratio index (RG), the development process of vegetation canopy can be estimated. It is also an indicator of leaf productivity and stress, and can be used to evaluate the growth status of vegetation.

Leaf pigment index

These indices are used to measure stress-related pigments in vegetation, primarily anthocyanins, which are mainly found in senescent vegetation. These indices cannot measure chlorophyll. The anthocyanin index I (ARI1) within the leaf pigment indices indicates whether vegetation is dead. ARI1 is particularly sensitive to anthocyanins in leaves; a higher ARI1 indicates a higher anthocyanin content in the plant canopy and a worse living environment.

Forest tree health assessment methods

The system utilizes three vegetation indices: first, greenness, which shows the distribution of surface green vegetation; second, chlorophyll, which indicates the content of carotenoids and anthocyanins; and third, light utilization rate, which indicates the forest growth rate. This allows for the generation of a spatial distribution map of the overall forest health, enabling the detection of pests, diseases, and wilt, as well as the assessment of timber harvest in a specific area.

Subdividing the input image based on the health status of the trees greatly helps in assessing the overall health of the trees within the image. The following image shows the output classification map generated by the tree health analysis tool:

The image shows a schematic diagram of forest tree health classification.

Forest Health Assessment Results

△ Hyperspectral image of XIGUANG-002 satellite (Qinling area)

△ Results of forest health analysis in the Qinling Mountains

The forest health status is graded, with higher numbers indicating healthier trees.

Outlook

Hyperspectral remote sensing satellite data can be used to analyze and evaluate the likelihood of forest fires, crop stress, and vegetation flammability in the Qinling Mountains region. The likelihood of forest fires is associated with greenness index, canopy water content, drought, and carbon decay caused by non-photosynthetic plants. Crop stress is closely related to greenness index, leaf area index, canopy water content, canopy nitrogen content, and light use efficiency. Vegetation suppression is related to changes in spectral reflectance in the red and near-infrared bands of the image.