Health assessment of agroforestry areas based on hyperspectral satellite data and vegetation indices

Kom Umbu, located in Aswan Governorate, is situated in southern Egypt, on the upper Nile River, near Lake Nasser. It is an important city and oasis along the Nile, with a well-developed irrigated agriculture sector, primarily cultivating cotton, grains, and economic forests, making it a mixed agricultural region. Due to differences in leaf and canopy structure, traditional single vegetation indices cannot simultaneously cover forestry and agriculture; therefore, a combination of indices is necessary for assessment.

Project Introduction

Recently, the Xi'an Aerospace Team of the Chinese Academy of Sciences used a hyperspectral satellite to image the Kom Umbu region of Aswan Governorate, Egypt (Figure 1). Within the band coverage of the hyperspectral data, the Enhanced Vegetation Index was used to screen and mask crops, and to assess crop greenness and chlorophyll content; the Anthocyanin Reflectance Index 2 was used to assess leaf anthocyanin content; and the Red-to-Green Ratio Index was used to represent light use efficiency and forest growth rate. Index maps and agricultural and forestry health maps combining the above indices were created, which can be used to assess the occurrence of pest and disease stress, and also to assess the region's timber harvest. The input images were divided into nine categories based on crop health, which helps to assess the overall crop health status within the imagery.

△ Figure 1. True-color fused image of XIGUANG-003 overlaid with Google base map

△Figure 2 Enhanced Vegetation Index

The Enhanced Vegetation Index (EVI) is an optimized vegetation index designed to improve the responsiveness to vegetation and enhance vegetation monitoring capabilities.It is based on the Normalized Difference Vegetation Index (NDVI) and improves upon it by using a more rigorous band combination and algorithm to eliminate some of the variations in irradiance conditions related to solar angle, topography, cloud/shadow and atmospheric conditions, thereby enhancing the responsiveness of the EVI to vegetation.

The EVI calculation formula is: EVI = (NIR - Red) / (NIR + 6*Red - 7.5*Blue + 1), where NIR is the near-infrared reflectance, Red is the red light reflectance, and Blue is the blue light reflectance. By adjusting the weights and coefficients of these bands, EVI can better reflect the fine structure and health status of vegetation.

The advantage of EVI lies in its sensitivity to changes in canopy structure, including leaf area index (LAI), canopy type, vegetation phase, and canopy structure. EVI can better extract biophysical parameters of vegetation, such as chlorophyll content and biomass, thereby improving the accuracy of monitoring vegetation growth status and changes.

EVI has a wide range of applications, including agriculture, forestry, ecology, and environmental science. In agriculture, EVI can be used to monitor crop growth, assess crop nutrient status, and predict crop yield. In forestry, EVI can be used to monitor forest health and assess forest structure and ecological function. In ecology and environmental science, EVI can be used to monitor and assess environmental issues such as vegetation cover change, land degradation, and urbanization.

△Figure 3 Anthocyanin reflectance index 2

Anthocyanin Reflectance Index 2 (ARI2) is a remote sensing index used to monitor the anthocyanin content in vegetation.It is an improvement on the anthocyanin reflectance index 1 (ARI1), which improves the response to anthocyanin content by adjusting the band combination and algorithm.

The formula for calculating ARI2 is: ARI2 = (NIR - 0.57*Red - 0.2*Green) / (NIR + 0.88*Red + 0.2*Green), where NIR is the near-infrared reflectance, Red is the red light reflectance, and Green is the green light reflectance. Compared to ARI1, ARI2 has an increased weight in the red light band and a decreased weight in the green light band, thereby improving the sensitivity to anthocyanin content.

The advantage of ARI2 lies in its responsiveness to anthocyanin content in vegetation canopy. Anthocyanins are pigments present in low concentrations in plants but have a significant impact on plant growth and health. By monitoring changes in ARI2 values, changes in anthocyanin content within the vegetation canopy can be understood, thereby inferring the growth status and stress conditions of the vegetation.

The applications of ARI2 primarily encompass fields such as ecology, environmental science, and agriculture. In ecology, ARI2 can be used to monitor the health and productivity of ecosystems such as forests and grasslands. In environmental science, ARI2 can be used to assess the impacts of environmental issues such as soil quality, pollution, and climate change on vegetation. In agriculture, ARI2 can be used to monitor crop growth, assess crop stress levels, and determine yield.

△Figure 4 Red-Green Ratio Index

The Red-Green Ratio Index is a remote sensing index used to monitor the health status and growth trends of vegetation.The index is calculated by comparing the reflectance of the red and green light bands, and the formula is: Red-Green Ratio Index = Red / Green.

Red light typically reflects the chlorophyll content of plants, while green light reflects their overall structure and health. Therefore, by calculating the red-to-green ratio index, we can understand the growth status and health level of vegetation.

The red-to-green ratio index (R-Green Ratio) is advantageous due to its simplicity, ease of calculation, and ability to quickly obtain information on vegetation growth. However, its limitation lies in its relatively low sensitivity to canopy structure and environmental factors, potentially failing to accurately reflect vegetation stress and other detailed information. While the R-Green Ratio is a convenient remote sensing index capable of rapidly acquiring information on vegetation growth, it may not accurately reflect vegetation stress and other detailed information. In practical applications, it is necessary to combine it with other remote sensing indices and field survey data to comprehensively understand the growth status and health level of vegetation.

△Figure 5 Crop health

By observing the crop health distribution in Figure 5, it was found that the health of some areas was generally higher than that of the surrounding areas. These areas were mainly located in areas relatively far from the city and close to the Nile River and its tributaries, which also confirmed the irrigation agriculture model in the region that was limited by water resources.

△Figure 6. Crop health in hot and cold zones

The vegetation index map in Figure 2-5 can reflect the distribution of crop growth and health, and has a certain visualization effect. However, for indices with excessively large or small differences between their upper and lower limits, the visualization will be affected. Therefore, we sampled the crop health index raster data into point data according to the grid.

Next, Getis-Ord Gi* statistics are calculated for each feature in the point dataset. The Gi* statistics are derived from the obtained z-score and p-value. For statistically significant positive z-scores, the higher the z-score, the tighter the clustering of high-value (hot spots). For statistically significant negative z-scores, the lower the z-score, the tighter the clustering of low-value (cold spots), and the p-value is used to determine the confidence level of the result.

Through the above calculations, we can obtain the locations where high- or low-value elements cluster in space, and identify cold and hot zones where crop health is significantly lower or higher than that of the surrounding areas, thus displaying the agricultural health of the region in a more intuitive way.

Further, it will be possible to vectorize the hot and cold zones, and further analyze the shape of the regions, the distances between them, and the relationship between the regions and rivers and cities based on the vector boundaries of the hot and cold zones, so as to explore the local agricultural development model in depth.