Remote Sensing (RS)
Samaneh Bagheri; Mahmoud Soorghali; Hassan Emami
Abstract
Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems ...
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Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems is utilized to evaluate changes and create thematic maps for monitoring diverse plant cover. Today, RS indices are widely used in research projects in specialized fields, such as vegetation health, stress assessment, plant development rate, and plant greenness, to evaluate vegetation health, stress types, and plant illnesses. Hyperspectral imagery, particularly from the red and near-infrared bands in the electromagnetic spectrum (690-740 nm), has been widely used to derive vegetation indices. This project intends to monitor the forest risk regions of a segment of northern Iran's forests in 2020 using a combination of various indices produced by RS data and a geographic information system (GIS). Prisma hyperspectral images were used to assess the health of forests in Northern Iran's Rudsar, Ramsar, and Tonkabon forests, focusing on water stress, insufficient growth, plant pests, diseases, and greenness. Forest areas are divided into five risk-acceptance regions using RS indices, and the data is analyzed using various GIS weighting methods to determine the remaining dangerous forest regions.
2- Methodology
The study utilized twelve plant indices from three categories (greenness, growth, leaf pigments, and leaf surface moisture) and four other individual vegetation indices using various techniques. Based on this, the study selected sixteen forest risk-taking maps from five classes with varying risk-taking potential, weighted the layers using hierarchical analysis, and generated a final map based on the obtained weights. When the average results of combined and individual indices were compared with the classification map, it was discovered that the combined indices were more accurate than the individual indices. Existing composite indices are categorized into three broad groups: plant greenness, leaf pigment, and productivity of water or light usage in the vegetation canopy. The three primary characteristics each possess multiple indices that can be combined to provide crucial insights into forest health.
3- Results and discussion
The study reveals that when combined with appropriate indices, combined indices can provide high accuracy in the risk assessment of forest areas in the north of the country. In contrast, an incorrect combination can result in low-accuracy outcomes. The study found that the combined indices had a 11% error in two high-risk forest areas, while individual indices had a nearly double error of 21%. The use of composite indices significantly reduces the inaccuracy of calculating forest risk regions by 50% and enhances the accuracy of monitoring these areas. Furthermore, when the combined indices were examined independently, the findings revealed that the combination of the VCN and VCNW indices yielded the maximum accuracy. These compounds are highly effective in assessing the health of vegetation, assessing plant stress, and determining plant water content. On the other hand, the combined indexes from RC were less accurate than the previous combination, with the highest accuracy levels being SIPI, NDII, NDWI, and WBI. These synthetic substances are utilized in the fields of plant health and stress assessment. The accuracy of SIPI, NDII, NDWI, WBI1, PRI1, and RGRI is significantly reduced when combined with the NC index. The combination's low accuracy may be due to the NDVI index's limitations, as it is primarily used to detect vegetation presence or absence and does not detect plant health or stress. The study presents the first results from research on plant stress in northern Iranian forests using Prisma hyperspectral data. Hyperspectral data is chosen for its superior spatial, spectral, and radiometric resolution, making it ideal for studying dynamic ecosystems in the current research region. Hyperspectral RS allows for non-destructive monitoring of leaf pigments like chlorophyll, carotenoids, and anthocyanin content, crucial indicators of vegetation health. Therefore, the recommendation is to employ a combination of indices with diverse approaches in hyperspectral images instead of using individual indices for monitoring vegetation usage.
4- Conclusion:
Forest health monitoring is a crucial aspect of forest management programs, and utilizing RS techniques and data can be highly beneficial in this field. The study compared the accuracy of combined indices and individual indices using the classification map, revealing that combined indices were more precise. In addition, the results showed that in almost two high-risk classes of the forest area, the combined indicators have an error of 11% and the individual indicators have an error of almost twice their error, 21%. Therefore, composite indices significantly reduce forest risk area estimation errors by 50% and improve accuracy. Therefore, it's recommended to use a combination of indices with different approaches in hyperspectral images instead of individual indices for monitoring vegetation usage.
