Measuring rangeland vegetation with high resolution airborne videography in the blue-near infrared spectral region

An airborne video system was used to investigate the visible and near-infrared (NIR) spectral properties of soil and vegetation features across a range of common arid landscape types. The four-camera system was equipped with filters of 25mm bandwidth centred on 450nm ('blue'), 550nm ('green'), 650nm ('red') and 770nm ('NIR'). The aim was to determine what vegetation properties could be detected by combining data from the blue part of the spectrum with the green, red and NIR range, thereby utilizing information contained in the first channel of Landsat Thematic Mapper (TM) (450-520nm). Adding information from the blue end of the spectrum did not assist in discriminating between green vegetation and dry vegetation or green vegetation and bare soil. This separation is best done with a red/NIR ratio. Neither was the blue band an improvement over the PD54 red-green perpendicular distance index in distinguishing between soil and vegetation, irrespective of phenological condition. The blue band can help separate soil from dry vegetation when combined with the sum of brightness values in the red and green bands in a perpendicular distance index. These properties of the spectral dataspace lead to a sequential classification procedure by which airborne videography data can be used to measure vegetation components which are much slower to assess with conventional ground-based methods. Videography has great potential for rapidly verifying or calibrating vegetation cover indices derivedfrom satellite data. Vegetation cover derived from classifying high resolution video data acquired from a heterogeneous floodplain area correlated well with vegetation indices computed from contemporary and co-registered TM data. The most effective indices for measuring vegetation cover with TM data are the PD54 index, brightness in the red band and a perpendicular index based on the sum of the red-green bands and the blue band. However, multiple regression indicates that the addition of a red/NIR ratio as an additional predictor of cover does not greatly improve the performance of these indices.     

Normalized sensitivity measures for leaf area index estimation using three-band spectral vegetation indices

A great number of spectral vegetation indices (SVIs) have been developed to estimate key biophysical parameters such as leaf area index (LAI). Considerable interest is often given to the local optimization, performance analysis and sensitivity of each spectral band and SVI for LAI estimation given that several confounding factors are present. In this regard, inclusion of shortwave infrared (SWIR) reflectance in traditionally near-infrared (NIR)-red (R)-based SVIs has played a great role for local optimization and increased sensitivity of SVIs to LAI. This study presents the enhanced and normalized sensitivity functions for evaluating (1) the sensitivity of each spectral band and SVI to LAI and (2) the generic performance analysis of empirical model to estimate LAI based on the SVIs. Several alternatives for three-band (NIR-R-SWIR) SVI modifications have been recommended and proven to be simplistic and unbiased way of local optimization.     

Winter wheat vegetation indices calculated from combinations of seven spectral bands

Spectral reflectance data were obtained for winter wheat over a full growing season. Four irrigation treatments, applied to six genotypes, provided a variety of crop growth conditions. Leaf area index, green ground cover, total wet and total dry phytomass, and leaf phytomass measurements were taken monthly during the winter and biweekly during the spring. Reflectance measurements were made with a radiometer having three visible, two near-IR and two mid-IR bands. Vegetation indices, calculated from various band combinations, were linearly related to the five plant parameters. Of the 1240 vegetation indices formed, ratio indices had the higher (0.79–0.86) coefficients of determination (r²) than N-space greenness (0.61–0.81) when related to the plant parameters. The commonly used IR/red ratio produced considerably lower r² values than many of the other ratio indices. The mid-IR bands appeared more frequently in the ratio indices than in the greenness indices. The results show the relative merits of the seven bands, when combined into vegetation indices, to estimate various plant parameters.     

Performance evaluation of spectral vegetation indices using a statistical sensitivity function

A great number of spectral vegetation indices (VIs) have been developed to estimate biophysical parameters of vegetation. Traditional techniques for evaluating the performance of VIs are regression-based statistics, such as the coefficient of determination and root mean square error. These statistics, however, are not capable of quantifying the detailed relationship between VIs and biophysical parameters because the sensitivity of a VI is usually a function of the biophysical parameter instead of a constant. To better quantify this relationship, we developed a “sensitivity function” for measuring the sensitivity of a VI to biophysical parameters. The sensitivity function is defined as the first derivative of the regression function, divided by the standard error of the dependent variable prediction. The function elucidates the change in sensitivity over the range of the biophysical parameter. The Student's t- or z-statistic can be used to test the significance of VI sensitivity. Additionally, we developed a “relative sensitivity function” that compares the sensitivities of two VIs when the biophysical parameters are unavailable.     

Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices

In this study, the response of vegetation indices (VIs) to the seasonal patterns and spatial distribution of the major vegetation types encountered in the Brazilian Cerrado was investigated. The Cerrado represents the second largest biome in South America and is the most severely threatened biome as a result of rapid land conversions. Our goal was to assess the capability of VIs to effectively monitor the Cerrado and to discriminate among the major types of Cerrado vegetation. A full hydrologic year (1995) of composited AVHRR, local area coverage (LAC) data was converted to Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) values. Temporal extracts were then made over the major Cerrado vegetation communities. Both the NDVI and SAVI temporal profiles corresponded well to the phenological patterns of the natural and converted vegetation formations and depicted three major categories encompassing the savanna formations and pasture sites, the forested areas, and the agricultural crops. Secondary differences in the NDVI and SAVI temporal responses were found to be related to their unique interactions with sun-sensor viewing geometries. An assessment of the functional behaviour of the VIs confirmed SAVI responds primarily to NIR variations, while the NDVI showed a strong dependence on the red reflectance. Based on these results, we expect operational use of the MODIS Enhanced Vegetation Index (EVI) to provide improved discrimination and monitoring capability of the significant Cerrado vegetation types.     

Response of spectral vegetation indices to soil moisture in grasslands and shrublands

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.     

Mapping leaf area index through spectral vegetation indices in a subtropical watershed

Leaf area index (LAI) has been associated with vegetation productivity and evapotranspiration in mathematical models. At a regional level LAI can be estimated with enough accuracy through spectral vegetation indices (SVIs), derived from remote sensing imagery. However, there are few studies showing LAI–SVI relationships in subtropical regions. The aim of this work was to examine the relationship between LAI and SVIs in a subtropical rural watershed (in Piracicaba, State of Sa?o Paulo, Brazil), for different land covers, and to use the best relationship to generate a LAI map for the watershed. LAI was measured with a LAI-2000 instrument in 32 plots on the field in areas of sugar cane, pasture, corn, eucalypt, and riparian forest. The SVIs studied were Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI), calculated from Landsat-7 ETM+ data. The results showed LAI values ranging from 0.47 to 4.48. LAI–SVI relationships were similar for all vegetation types, and the potential model gave the best fit. It was observed that LAI–NDVI correlation (r ²=0.72) was not statistically different from LAI–SR correlation (r ²=0.70). The worst correlation was obtained by LAI–SAVI (r ²=0.56). A map was generated for the study area using the LAI–NDVI relationship. This was the first LAI map for the region.     

A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery

Based on very high resolution satellite images, object-based classification methods can be used to produce large scale maps for forest management. These new products require a method to derive quantitative information about the accuracy and precision of delineated boundaries. This assessment would complement any measure of thematic accuracy derived from the confusion matrix. This study aims to assess the positional quality of the boundaries between different landscape units produced by automated segmentation of IKONOS and SPOT-5 satellite images over temperate forests. A robust method was developed to assess the accuracy and the precision of the forest boundaries, respectively measured by the bias and the standard deviation. The two main sources of positional error, namely residual parallax and automatic segmentation, were independently assessed. Positional errors caused by the residual parallax were quantified using a 3D model. Forest stand boundaries generated by automatic segmentation were compared to corresponding visual delineations. The results showed that residual parallax was the major source of positive bias (area overestimation) along forest/non-forest boundaries and depended on the interactions between forest stand patterns and sensor viewing angles. Due mainly to tree shade, the automatic segmentation also produced a positive bias on forest areas, which remained under 1 m for both IKONOS-2 and SPOT-5 images. Standard deviation did not increase linearly with pixel size and was influenced by the nature of the boundary. Production of 1:20,000 scale forest maps from very high resolution satellite data clearly requires acquisition of near nadir imagery or knowledge of landscape object height for true orthorectification. In these cases, IKONOS-2 segmentation outputs were found to correspond with 1:20,000 scale map specification, and SPOT-5 imagery with 1:30,000 scale.     

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.     

Critical assessment of vegetation indices from AVHRR in a semi-arid environment

The most frequently used vegetation index (VI), the Normalized Difference Vegetation Index (NDVI) and its variants introduced recently to correct for atmospheric and soil optical response such as Global Environment Monitoring Index (GEMI) and Modified Soil-Adjusted Vegetation Index (MSAVI) are evaluated over a Sahelian region. The usefulness and limitations of the various vegetation indices are discussed, with special attention to cloud contamination and green vegetation detection from space. The HAPEX Sahel database is used as a test case to compare these indices in arid and semi-arid environments. Selected sites are characterized by sparse vegetation cover and day-to-day variability in atmospheric composition. Simulated indices values behaviour at the surface level shows that these VIs were all sensitive to the presence of green vegetation but were affected differently by changes in soil colour and brightness. We showed that GEMI is less sensitive to atmospheric variations than both NDVI and MSAVI since it exhibits a high atmospheric transmissivity over its entire range for various atmospheric aerosol loadings and water vapour contents. These results were first tested on a vegetation gradient, and secondly evaluated on a transect which encompasses various soils formations. On the vegetation gradient, it was found that GEMI computed from measurements at the top of the atmosphere is invariable from one day to the next. On the bare soils transect, MSAVI calculated at the surface level, has shown a great insensitivity to soil optical responses modifications, while GEMI exhibits from space noticeable variability in this bright soil context. Finally, it was illustrated that GEMI exhibits interesting properties for cloud detection because of the strong decrease of its value on cloudy pixels.     

Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices

Early drought detection and impact assessment on the amount of pasture biomass are important in Mongolia, whose economy strongly depends on livestock production. The country's large area and a lack of information on grass availability due to the sparseness of biomass-observing and/or meteorological stations make it difficult to optimize nomadic livestock output in the Mongolian dry climate. The application of a new satellite-based method for drought detection and for assessment of wild biomass in Mongolia was investigated. Measurements of biomass at an experimental station in a semi-dry steppe ecosystem during 1985–1997 were compared with the Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices. The results showed the indices can be used as proxies for biomass production estimation (biomass anomaly, BA) applying the following equation BA=43.201+0.881 VHI (R ²=0.658).     

Patterned wafer inspection by high resolution spectral estimation techniques

A new self-reference signal processing technique is proposed for detecting the location of irregularities and defects in a periodic two-dimensional signal or image. Using high-resolution spectral estimation algorithms, the proposed technique first extracts the period and structure of repeated patterns from the image. Then a defect-free reference image for comparison with the actual image is produced. Since the technique acquires all the information needed from a single image (in contrast to most existing methods), there is no need for a database image, a scaling or alignment procedure or any a priori knowledge about the repetition period of the patterns.Potential application fields for the proposed method range from the area of wafer and mask defect inspection, which includes inspection of memory chips, shift registers, switched capacitors, CCD arrays, and LCD displays to other areas that deal with repeated structures, such as crystallography. Some results of applying the proposed technique to real images from microlithography are presented.     

Validation of LAI and assessment of winter wheat status using spectral data and vegetation indices from SPOT VEGETATION and simulated PROBA-V images

Due to the information gap between the VEGETATION sensors and Sentinel-3 mission, the Belgian state decided to build a small satellite, Project for Onboard Autonomy-Vegetation (PROBA-V), to ensure the continuity of the data record for vegetation studies. In this study, simulated PROBA-V data generated by the Landsat Thematic Mapper (TM) were used to evaluate the potential of this mission to assess winter wheat status. The root mean square error (RMSE) of PROBA-V's leaf area index (LAI), which was generated using the exponential method and the interpolation method, is 0.33 and 0.96 for March 2011 and 1.40 and 3.33 for May 2011, respectively. Système Pour l'Observation de la Terre (SPOT) VEGETATION's LAI does not show a significant relationship with the reference LAI values except for the LAI values during the stem elongation 100% phenological stage generated using the exponential method (correlation coefficient, r = 0.91; p = 0.01). For the tillering and stem elongation 100% phenological stages, linear regression models for the fraction of absorbed photosynthetically active radiation (FAPAR) with PROBA-V's normalized difference vegetation index (NDVI) were developed (coefficient of determination, R ², of 0.94 and 0.88). Exponential models for LAI (R ² of 0.91 and 0.93) and fresh weight of above-ground biomass (AGBf) (R ² of 0.90 and 0.93) with PROBA-V's near-infrared (NIR) and visible and near-infrared bands (VNIR B2) were developed accordingly. The assessment of winter wheat status showed that the highest and the lowest values of PROBA-V's simulated data (SD), i.e. NDVI, normalized difference water index (NDWI), and LAI of Field 2 and Field 4, correspond to the ground-measured biometric parameters.     

A comparative evaluation of spectral vegetation indices for the estimation of biophysical characteristics of Mediterranean semi-deciduous shrub communities

Human civilizations have made intensive use of the Mediterranean Basin for millennia, resulting in a profound impact on the natural ecosystems. Nowadays, the drier zones with degraded soils are covered with drought semi-deciduous plant communities. The aim of this study was to compare the ability of various spectral vegetation indices (VIs) computed from a Landsat Thematic Mapper (TM) image to discriminate different vegetation biophysical parameters of these communities and thus to assess their capability to monitor long-term vegetation changes. It was found that although semi-deciduous plants display a spectral behaviour similar to that of semi-arid vegetation, VIs can estimate not only vegetation abundance but also changes in vegetation structure. Normalized difference VIs are highly correlated to vegetation amount-related parameters while soil-related VIs respond basically to canopy structural parameters. Our results indicate that satellite imagery can be used to map biomass in Mediterranean scrubland regions, and show that it has the potential to discriminate types of semi-deciduous shrub communities with different crown architectures.     

Comparison of different vegetation indices for the remote assessment of green leaf area index of crops

Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI_Green, the CI_Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m²/m²). However, the CI_Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m²/m²). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI_Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).     

The performance of vegetation indices for operational monitoring of CORINE vegetation types

Vegetation monitoring has been performed using remotely sensed images to secure food production, prevent fires, and protect natural ecosystems. Recent satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide frequent wide-scale coverage in multiple areas of the spectrum, allowing the estimation of a wide range of specialized vegetation indices (VIs), each offering several advantages. It is not, however, clear which VI performs better during operational monitoring of wide-scale vegetation patches, such as CORINE Land Cover (CLC) classes. The aim of this work was to investigate the performance of several VIs in operational monitoring of vegetation condition of CLC vegetation types, using Terra MODIS data. Comparison among the VIs within each CLC class was conducted using the sensitivity ratio, a statistical measure that has not been used to compare VIs and does not require calibration curves between each VI and a biophysical parameter. In addition, the VI’s sensitivity to factors such as the aspect, viewing angle, signal saturation, and partial cloud cover was estimated with correlation analysis in order to identify their operational monitoring ability. Results indicate the enhanced vegetation index as superior for monitoring vegetation condition among CLC types, but not always optimum in performance tests for operational monitoring.     

Forecasting crop production using satellite-based vegetation health indices in Kansas,USA

This article shows the results of early crop yield prediction from remote-sensing data. The study was carried out in Kansas, USA. The methodology proposed allows the estimation of winter wheat (WW), sorghum and corn yields 3–4 months before harvest. The procedure uses the vegetation health (VH) indices (vegetation condition index (VCI) and temperature condition index (TCI)) computed for each pixel and week over a 21-year period (1985–2005) from the Advanced Very High Resolution Radiometer (AVHRR) data. Over this period, a strong correlation was found between crop yield and VH indices during the weather-related critical period of crop development, which controls much final crop productivity. The 3-month advanced yield forecasts were independently compared with official agricultural statistics, showing that the estimation errors for WW, sorghum and corn were 8%, 6% and 3%, respectively. Implementing the 3–4 months lead forecast in operational practice will aid farmers to mitigate weather vagaries using irrigation, diseases/insects control, application of fertilizers and so on during a growing season and will help decision-makers to regulate marketing strategies, import/export and price policies and address food security issues.     

Posidonia oceanica genetic and biometry mapping through high-resolution satellite spectral vegetation indices and sea-truth calibration

In the framework of Posidonia oceanica (PO) preservation activities, a small-scale restoration pilot project was implemented in 2005 at a Santa Marinella site to replace the loss of this important species of seagrass in this zone of the central Tyrrhenian coast via an innovative transplantation approach. In this context, taking into account the recent advances in the fields of high-resolution (HR) satellite/airborne remote-sensing and genetics laboratory analysis techniques, we propose this integrated methodology for monitoring changes in transplanted meadows in regard to perspective to provide support in the assessment of the entire local PO and seagrass population dynamic. According to specific information requirements in terms of radiometric and spectral/spatial resolution, the multispectral data currently available from the QuickBird polar satellite’s four-band (red, green, blue visible and near-infrared) HR sensor were exploited for methodology implementation using a practical ‘image-based’ approach to account for atmospheric and water column turbidity typical of this mid-coastal Mediterranean region. First, the extents and types of seagrass cover were suitably mapped, and then also the distributions of specific vegetation parameters related to PO dynamics and health were assessed by exploiting the remotely sensed satellite-derived radiance signals and point sea-truth calibration measurements of the bio-genetic parameters. In particular, we implemented maps of leaf area index, genetic similarity, and density Giraud indices corresponding to distributions of PO patches using multivariate and data-mining models (artificial neural network) based on appropriately preprocessed radiometric and auxiliary (bathymetry) input variables.     

Assessing spectral indices to estimate the fraction of photosynthetically active radiation absorbed by the vegetation canopy

ABSTRACT

The fraction of absorbed photosynthetically active radiation (FPAR) by the vegetation canopy (FPAR_canopy) is an important parameter for vegetation productivity estimation using remote-sensing data. FPAR_canopy is widely estimated using many different spectral vegetation indices (VIs), especially the simple ratio vegetation index (SR) and normalized difference vegetation index (NDVI). However, there have been few studies into which VIs are most suitable for this estimation or into their sensitivities to the leaf area index and the observation geometry of remote-sensing data, which are very important for the accurate estimation of FPAR_canopy based on the plant growth stage and satellite imagery. In this study, nine main VIs calculated from field-measured spectra were evaluated and it was found that the SR and NDVI underestimated and overestimated FPAR_canopy, respectively. It was also found that the enhanced vegetation index produced lesser errors and a higher agreement than other broadband VIs used to estimate FPAR_canopy. Among all the selected VIs, the photochemical reflectance index (PRI) turned out to have the lowest root mean square error of 0.17. The SR produced the highest errors (about 0.37) and lowest index of agreement (about 0.50) compared to the measured values of FPAR_canopy. Except for carotenoid reflectance index (CRI), FPAR_canopy estimated by VIs are evidently sensitive to the leaf area index (LAI), especially for FPAR_canopy (SR), which are also most sensitive to solar zenith angles (SZA). SR, CRI, PRI, and EVI have remarked variations with view zenith angles. Our study shows that FPAR_canopy can be simply and accurately estimated using the most suitable VIs – i.e. EVI and PRI – with broadband and hyperspectral remote-sensing data, respectively, and that the nadir reflectance or nadir bidirectional reflectance distribution function adjusted reflectance should be used to calculate these VIs.     

Study of vegetation spectral anomaly behaviour in a porphyry copper mine area based on hyperspectral indices

ABSTRACT

Hyperspectral remote sensing is economical and fast, and it can reveal detailed spectral information of plants. Hence, hyperspectral data are used in this study to analyse the spectral anomaly behaviours of vegetation in porphyry copper mine areas. This analytical method is used to compare the leaf spectra and relative differences among the vegetation indices; then, the correlation coefficients were computed between the soil copper content and vegetation index of Quercus spinosa leaves at both the leaf scale and the canopy scale in the Chundu mine area with different geological backgrounds. Lastly, this study adopts hyperspectral data for the level slicing of vegetation anomalies in the Chundu mine area. The results showed that leaf spectra in the orebody and background area differed greatly, especially in the infrared band (750 nm – 1300 nm); moreover, some indices like the normalized water index (NWI) and normalized difference water index (NDWI) of Quercus spinosa and Lamellosa leaves are sensitive to changes in the geological background. Compared with the canopy, the leaf hyperspectral indices of Quercus spinosa in Chundu can better reflect soil cuprum (Cu) anomaly. In addition, the NWI and NDWI of Quercus spinosa are significantly correlated with the soil Cu content at both the canopy scale and the leaf scale. Consequently, the results of the vegetation anomaly level slicing can adequately reflect the plant anomalies from ore bodies and nearby areas, thereby providing a new ore-finding method for areas with a high degree of vegetation coverage.