Green Red Vegetation Index Research Articles

Remote sensing of legacy effects of biodiversity on crop performance

With a fast changing climate and the rise of human population there is an increasing concern on food security whilst avoiding environmental damage. The beneficial effects of crop rotation and enhanced sown agro-diversity on yield production and ecosystem functioning have been demonstrated, although their persistence over time and our ability to detect such lasting legacy effects on the following crops have been barely investigated, thus hindering their adoption as an economically sustainable and ecologically sound solution. Here, we address this issue with a biodiversity-ecosystem functioning experiment in an agricultural field where we manipulated the composition and relative proportion of three forage species crossed with two levels of nitrogen fertilization from 2008 to 2011. Two years after the end of the experiment we investigated the potential legacy effects of previous sown diversity over the performance of the following crop and assessed whether these effects are detectable with remote sensing tools mounted in unmanned aerial vehicles (UAV). Previous plant diversity had a strong legacy effect influencing the biomass production of the next cereal crop. For instance, on average a forb-grass mixture increased two times more the leaf biomass production and the leaf area index of the following wheat crop than a legume monoculture. Thus, diversity-interaction models showed that species mixtures had greater legacy effects over crop performance than monocultures, while experimental Nitrogen fertilization did not show legacy effects. The influence of previous plant diversity on the performance of the following crop was detectable by means of a remote sensing index like the Green-Red Vegetation Index. Biodiversity-ecosystem function relationships may result in lasting legacy effects leading to enhanced crop performance with increasing agrodiversity. Lower amounts of fertilizer may be needed by increasing the sown diversity during crop rotation, whose effects can be easily scaled-up and monitored by UAV and remote sensing tools.

飼料作物育種におけるGreen Red Vegetation Index（GRVI）と他のRGB植生指標との比較

牧草品種育成での罹病程度評価および越冬性評価において，一般的なRGBカメラを搭載したUAVで撮影した空撮画像から，GRVIを含む88のRGB植生指標を算出し育種家評点と比較した．罹病程度評価においては，育種家評点とのピアソンの積率相関係数の2乗（R2）の平均が0.5以上の高い値を示したのはGRVI，GmR，GmR_2，MGVRI，VARI，GI，RGRI，ExGR，ExR_2，SAVI，SAVI_2の11植生指標で，その植生指標の多くは，RGBの3色のうちのBによる影響が小さい植生指標であった．越冬性評価において育種家評点と高いR2を示したのはg，ExG，ExG_2，GLI，CIVE，CIVE_2，RGVBI，RGBVIの8植生指標であった．越冬性評価とのR2が高かった8植生指標はRGBの全色を利用する植生指標であった．この他に複数の地域で行われた，エンバク，イタリアンライグラス，スーダングラス，ペレニアルライグラス，アカクローバの育種試験における育種家評点とGRVIの相対指標であるrGとを比較したところ，草勢および冬枯れ程度の評価でR2が高かった．一方，草型，倒伏程度の評価ではR2は低かった．また，形質の種類にかかわらず，育種家の評価自体が難しい試験ではR2が低かった．これらのことからrGでの評価は，上空からの空撮画像において，試験区内における植物体の面積の大きさで評価する形質や，試験区全体の色調の違いで評価する形質に適していると考えられた．

Utilization of unmanned aerial vehicle (UAV) in identifying the characteristics of the riparian ecosystem of the Percut River, North Sumatra Province

The Percut River border area has changed its function. Changes in land use result in a decrease in the area and types of vegetation that grow on river borders. Considering that the area is quite long and inaccessible in some locations, it can identify riparian vegetation by utilizing remote sensing technology. This study aims to identify the characteristics of the Percut river riparian vegetation using a UAV. Eight vegetation indices were used to analyse land cover types in this riparian ecosystem, namely Green-red ratio (GR), Green-red vegetation index (GRVI), RGB-based vegetation index (RGBVI), Visible atmospherically resistant index (VARI), Simple blue-green ratio (BGI2), Excess green index (ExG), Normalized green-blue difference index (NGBDI) and Modified green-red vegetation index (MGRVI). The analysis of the vegetation index based on RGB images shows that not all indices used are good in separating vegetation from other land covers. Analysis of riparian vegetation characteristics based on the RGB index is recommended using the RGBVI index (RGB-based vegetation index).

A comparison of field assessment methods for lucerne inoculation experiments

ABSTRACT Effective and practical measurement methods for assessing field inoculation experiments are needed to identify inoculants that could improve lucerne establishment. In this study, assessment potential of different existing measurement methods (plant height, Dualex 4 Scientific leaf-clip meter, GreenSeeker handheld crop sensor, drone-acquired orthomosaic calculation, yield, nutrient analysis and nodule assessment) were compared across 12 inoculation treatments applied to lucerne (Medicago sativa L.) at 3 sites. F-values were used to compare the potential of different methods to separate inoculation treatments. The handheld GreenSeeker measuring normalised difference vegetation index (NDVI) showed the greatest potential for separating inoculation treatments in fields where lucerne had not previously been cultivated, followed by visible atmospherically resistant index (VARI) and green-red vegetation index (GRVI) from drone-acquired orthomosaics. These methods are non-destructive, low cost, require low labour input, fast, and do not require sample preparation, and thus are efficient measurement methods for disaggregating treatments in field inoculation experiments.

Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.

Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)

Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of Lemna minor in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. The study focuses on a selected section of the Lis River (Portugal), a small river that is often affected by water pollution. The study approach uses spatiotemporal multispectral data from the Sentinel-2 satellite and from 2021 and investigates the potential of remote sensing-based vegetation and water indices (Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), Green Red Vegetation Index (GRVI), Normalized Difference Water Index (NDWI)) for detecting duckweeds’ infestation and its severity. The NDAVI was identified as the vegetation index (VI) that better depicted the presence of duckweeds in the surface of the water course; however, results obtained for the other VIs are also encouraging, with NDVI showing a response that is very similar to NDAVI. Results are promising regarding the ability of remote sensing products to provide insight into the behavior of Lemna minor and to identify problematic sections along small watercourses.

Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China

Refined tobacco plant information extraction is the basis of efficient yield estimation. Tobacco planting in mountainous plateau areas in China is characterized by scattered distribution, uneven growth, and mixed/intercropping crops. Thus, it is difficult to accurately extract information on the tobacco plants. The study area is Beipanjiang topographic fracture area in China, using the smart phantom 4 Pro v2.0 quadrotor unmanned aerial vehicle to collect the images of tobacco planting area in the study area. By screening the visible light band, Excess Green Index, Normalized Green Red Difference Vegetation Index, and Excess Green Minus Excess Red Index were used to obtain the best color index calculation method for tobacco plants. Low-pass filtering was used to enhance tobacco plant information and suppress noise from weeds, corn plants, and rocks. Combined with field measurements of tobacco plant data, the computer interactive interpretation method performed gray-level segmentation on the enhanced image and extracted tobacco plant information. This method is suitable for identifying tobacco plants in mountainous plateau areas. The detection rates of the test and verification areas were 96.61% and 97.69%, and the completeness was 95.66% and 96.53%, respectively. This study can provide fine data support for refined tobacco plantation management in the terrain broken area with large exposed rock area and irregular planting land.

The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level

In this paper, we present an identification of rockfall-injured trees based on multiband images obtained by an unmanned aerial vehicle (UAV). A survey with a multispectral camera was performed on three rockfall sites with versatile tree species (Fagus sylvatica L., Larix decidua Mill., Pinus sylvestris L., Picea abies (L.) Karsten, and Abies alba Mill.) and with different characterizations of rockfalls and rockfall-induced injuries. At one site, rockfall injuries were induced in the same year as the survey. At the second site, they were induced one year after the initial injuries, and at the third site, they were induced six years after the first injuries. At one site, surveys were performed three years in a row. Multiband images were used to extract different vegetation indices (VIs) at the tree crown level and were further studied to see which VIs can identify the injured trees and how successfully. A total of 14 VIs were considered, including individual multispectral bands (green, red, red edge, and near-infrared) by using regression models to differentiate between the injured and uninjured groups for a single year and for three consecutive years. The same model was also used for VI differentiations among the recorded injury groups and size of the injuries. The identification of injured trees based on VIs was possible at the sites where rockfall injuries were induced at least one year before the UAV survey, and they could still be identifiable six years after the initial injuries. At the site where injuries were induced only four months before the UAV survey, the identification of injured trees was not possible. VIs that could explain the largest variability (R2 > 0.3) between injured and uninjured trees were: inverse ratio index (IRVI), green–red vegetation index (GRVI), normalized difference vegetation index (NDVI), normalized ratio index (NRVI), and ratio vegetation index (RVI). RVI was the most successful, explaining 40% of the variance at two sites. R2 values only increased by a few percentages (up to 10%) when the VIs of injured trees were observed over a period of three years and mostly did not change significantly, thus not indicating if the vitality of the trees increased or decreased. Differentiation among the injured groups did not show promising results, while, on the other hand, there was a strong correlation between the VI values (RVI) and the size of the injury according to the basal area of the trees (so-called injury index). Both in the case of broadleaves and conifers at two sites, the R2 achieved a value of 0.82. The presented results indicate that the UAV-acquired multiband images at the tree crown level can be used for surveying rockfall protection forests in order to monitor their vitality, which is crucial for maintaining the protective effect through time and space.

SPATIAL ANALYSIS OF VEGETATION DENSITY CLASSIFICATION IN DETERMINING ENVIRONMENTAL IMPACTS USING UAV IMAGERY

Abstract. Along with remote sensing technology development, vegetation monitoring can be performed using satellite imagery or Unmanned Aerial Vehicle (UAV) data. UAV imagery with a high resolution, between 3–5 cm at an altitude <100 m, is able to present specific land conditions without being affected by the weather. Information related to vegetation density is one of the components in the Environmental Impact Analysis (EIA) study of a proposed project development due to vegetation removal. In this study, information from consumer-grade cameras of a low-cost UAV platform was explored to classify vegetation density using the potential of RGB imagery-based vegetation index (VI). The correlation coefficient (R2) between field observation data and the seven different values of VI demonstrated moderate to strong correlation. The highest linier correlation of 80.16% (R2 = 0.64) was performed by the Green Red Vegetation Index (GRVI). Classification of the vegetation density was established by applying the object-based image analysis method through the combination of supervised machine learning algorithm of Support Vector Machine (SVM) and the GRVI vegetation index. The vegetation density classification consists of very low, low, medium, high, and very high-density classes. The data can be utilized in determining vegetation management efforts from the presence of a proposed project in the EIA study. The use of UAV imagery is considered effective in identifying vegetation density.

Non-invasive Estimation of Foliar Nitrogen Concentration Using Spectral Characteristics of Menthol Mint (Mentha arvensis L.).

Menthol mint (Mentha arvensis L., Family: Lamiaceae), popularly known as corn mint or Japanese mint, is an important industrial crop that is widely grown for its valued essential oil. Nitrogen (N) is an important macro-nutrient and an essential factor for optimizing the yield and quality of crops. Hence, rapid and accurate estimation of the N content is crucial for nutrient diagnosis in plants and to make precise N fertilizer recommendations. Generally, N concentration is estimated by destructive sampling methods; however, an indirect assessment may be possible based on spectral characteristics. This study aimed to compare the foliar N concentration based on non-destructive (reflectance) and destructive (laboratory analyses) methods in menthol mint. Foliar N concentration was measured through the Kjeldahl method and reflectance by Miniature Leaf Spectrometer C-710 (CID Bio-Science). Using reflectance data, several vegetation indices (VIs), that is, normalized difference red edge (NDRE), red edge normalized difference vegetation index (reNDVI), simple ratio (SR), green–red vegetation index (GRVI), canopy chlorophyll content index (CCCI), photochemical reflectance index (PRI), green chlorophyll index (CIGreen), red edge chlorophyll index (CIRed Edge), canopy chlorophyll index (CCI), normalized pigment chlorophyll ratio index (NPCI), and structure insensitive pigment index (SIPI), were developed to determine the foliar N concentration. The highest correlation (r) between VIs and foliar N concentrations was achieved by NDRE (0.89), followed by reNDVI (0.84), SR (0.83), GRVI (0.78), and CCCI (0.76). Among the VIs, the NDRE index has been found to be the most accurate index that can precisely predict the foliar N concentration (R2 = 0.79, RMSE = 0.18). In summary, the N deficiencies faced by the crop during its growth period can be detected effectively by calculating NDRE and reNDVI, which can be used as indicators for recommending precise management strategies for the application of nitrogenous fertilizers.

Evaluation of Land Surface Phenology for Autumn Leaf Color Change Based on Citizen Reports across Japan

Autumn foliage color is an important phenological characteristic associated with climate and appeals to populations as a cultural ecosystem service (CES). Land surface phenology (LSP) analyzed using time-series remotely sensed imagery can facilitate the monitoring of autumn leaf color change (ALCC); however, the monitoring of autumn foliage by LSP approaches is still challenging because of complex spatio-temporal ALCC patterns and observational uncertainty associated with remote sensing sensors. Here, we evaluated the performance of several LSP analysis approaches in estimation of LSP-based ALCCs against the ground-level autumn foliage information obtained from 758 sightseeing (high CES) sites across Japan. The ground information uniquely collected by citizens represented ALCC stages of greening, early, peak, late, and defoliation collected on a daily basis. The ALCC was estimated using a second derivative approach, in which normalized difference vegetation index (NDVI), kernel normalized difference vegetation index (kNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and green red vegetation index (GRVI) were applied based on MODerate resolution Imaging Spectroradiometer (MODIS) MOD09A1 with four (Beck, Elmore, Gu, and Zhang) double logistic smoothing methods in 2020. The results revealed inconsistency in the estimates obtained using different analytical methods; those obtained using EVI with the Beck model estimated the peak stage of the ALCC relatively well, while the estimates obtained using other indices and models had high discrepancies along with uncertainty. Our study provided insights on how the LSP approach can be improved toward mapping the CESs offered by autumn foliage.

Tracking Autumn Photosynthetic Phenology of Alpine Grassland on the Tibetan Plateau with Green-Red Vegetation Index

Tracking Autumn Photosynthetic Phenology of Alpine Grassland on the Tibetan Plateau with Green-Red Vegetation Index

A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology

The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) ( R 2 = 0.98 , p < 0.001 ), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 ( R 2 = 0.97 , p < 0.001 ). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.

Drone technology for identification of healing forest spot at Kampung Cisamaya Mount Ciremai National Park

Reconnecting people to nature through healing activities in the forest ecosystem is important. Various studies have shown that forest ecosystems dominated by tree vegetation have positive impacts on physical and psychological health. Not all locations in the forest ecosystem are suitable for healing forests (HF), but their suitability should be identified. Land slope, vegetation density, and easiness access to the site are some physical parameters of the land which are indicators for the development of HF site. Identification of suitable HF spots can be identified using drone technology and GIS. The research objective was the use of drones equipment in identifying potential sites for HF activities. The research site was Kampung Cisamaya in Mount Ciremai National Park. The type of drone used was the Phantom 4 Pro Obsidian equipped with a 20-megapixel RGB camera. The stages of research activities were data acquisition, processing, and analyzing from drone spatial data. Vegetation density was determined through GRVI (Green-Red Vegetation Index), while drone data analyzed the slope classification by DTM (Digital Terrain Model). The accessibility to the location was analyzed through data from the spatial map of the Kuningan Regency. The results found that the use of drones was effective in evaluating the suitability spots for HF activities. From this study can be concluded that the Cisamaya area was suitable for the development of HF activities due to physical parameters of flat to gentle slopes (0-15%), having dense vegetation, as well as the easiness access by people.

Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle.

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green–red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping.

Dengue is recognized as a major health issue in large urban tropical cities but is also observed in rural areas. In these environments, physical characteristics of the landscape and sociodemographic factors may influence vector populations at small geographic scales, while prior immunity to the four dengue virus serotypes affects incidence. In 2019, a rural northwestern Ecuadorian community, only accessible by river, experienced a dengue outbreak. The village is 2–3 hours by boat away from the nearest population center and comprises both Afro-Ecuadorian and Indigenous Chachi households. We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases. We mapped visible water containers seen in UAV images and calculated both the green-red vegetation index (GRVI) and household proximity to public spaces like schools and meeting areas. To identify risk factors for symptomatic dengue infection, we used mixed-effect logistic regression models to account for the clustering of symptomatic cases within households. We identified 55 dengue cases (9.5% of the population) from 37 households. Cases peaked in June and continued through October. Rural spatial organization helped to explain disease risk. Afro-Ecuadorian (versus Indigenous) households experience more symptomatic dengue (OR = 3.0, 95%CI: 1.3, 6.9). This association was explained by differences in vegetation (measured by GRVI) near the household (OR: 11.3 95% 0.38, 38.0) and proximity to the football field (OR: 13.9, 95% 4.0, 48.4). The integration of UAV mapping with other data streams adds to our understanding of these dynamics.

A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images

Unmanned aerial vehicles (UAVs) equipped with high-resolution multispectral cameras have increasingly been used in urban planning, landscape management, and environmental monitoring as an important complement to traditional satellite remote sensing systems. Interest in urban regeneration projects is on the rise in Korea, and the results of UAV-based urban vegetation analysis are in the spotlight as important data to effectively promote urban regeneration projects. Vegetation indices have been used to obtain vegetation information in a wide area using the multispectral bands of satellites. UAV images have recently been used to obtain vegetation information in a more rapid and precise manner. In this study, multispectral images were acquired using a UAV equipped with a Micasense RedEde MX camera to analyze vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Blue Normalized Difference Vegetation Index (BNDVI), Red Green Blue Vegetation Index (RGBVI), Green Red Vegetation Index (GRVI), and Soil Adjusted Vegetation Index (SAVI). However, in the process of analyzing urban vegetation using the existing vegetation indices, it became clear that the vegetation index values of long-run steel roofing, waterproof coated roofs, and urethane-coated areas are often similar to, or slightly higher than, those of grass. In order to improve the problem of misclassification of vegetation, various equations were tested by combining multispectral bands. Kappa coefficient analysis showed that the squared Red-Blue NDVI index produced the best results when analyzing vegetation reflecting urban land cover. The novel vegetation index developed in this study will be very useful for effective analysis of vegetation in urban areas with various types of land cover, such as long-run steel roofing, waterproof coated roofs, and urethane-coated areas.

A new method for dating the surface exposure age of granite rock walls in the Mont Blanc massif by reflectance spectroscopy

In the high mountain rock walls of the Mont Blanc massif, changes in the granite surface colour are related to its exposure age. The light grey colour of fresh rock surfaces turns orange when is long exposed to weathering. In order to study this colour/age relationship, reflectance spectroscopy was performed on 73 samples, and Terrestrial Cosmogenic Nuclide (TCN) dating was used to obtain their surface exposure age. The standard deviation of the reflectance values was calculated for each wavelength of the visible spectrum to study the behaviour of each spectral region. The aim was to find two colour regions that showed opposite behaviour, and once they are combined, they could provide a representative index of the rock colour changes that are linked to the degree of weathering. As an adaptation of the Green Red Vegetation Index used to measure colour changes in vegetation, the GReen-Infrared GRanite Index (GRIGRI), a normalized difference between the granite 770 nm and 530 nm reflectance values, was developed. The GRIGRI value of a weathered granite surface has a close relationship with its exposure age (R 2 = 0.85). The reflectance spectra of seven samples for which TCN dating failed and two samples for which the TCN age was considered to be an outlier were used to calculate their GRIGRI value to assess the colour-based ages, that were plausible according to rock wall morphology and the TCN exposure ages of the surrounding surfaces. We propose a new method of surface dating for the rock walls of the Mont Blanc massif using reflectance spectroscopy. • Mont Blanc granite surface colour is related to the time that it has been exposed to weathering. • Reflectance spectroscopy was used to quantify granite surface colour. • TCN dating was used to estimate granite surface exposure age. • We use different approaches to explore the relationship between the granite colour and its exposure age. • The ratio between the Green and Infrared reflectance values can be used to estimate granite exposure age.

Assessing the effects of dicamba and 2,4 Dichlorophenoxyacetic acid (2,4D) on soybean through vegetation indices derived from Unmanned Aerial Vehicle (UAV) based RGB imagery

ABSTRACT The increase in agricultural production is facing several challenges with future implications for food security and environmental protection. The aim of this study was to evaluate a remote sensing-based low-cost methodology for assessing the effects of dicamba and 2,4 Dichlorophenoxyacetic acid (2,4D) in a non-tolerant soybean crop. Here, we introduced the application of six vegetation indices (VI) derived from Unmanned Aerial Vehicle (UAV) based Red-Green-Blue (RGB) imagery contrasting with a conventional approach of visual injury criteria classification to estimate soybean plant injury and the effect on grain yield. The results demonstrated the feasibility of Modified Green-Red Vegetation Index (MGRVI) and Excess Green (ExG) strongly correlated with the effects of dicamba and 2,4D in soybean. These VIs discriminated plant injury caused by dicamba and 2,4D up to 5% of the recommended dose. The Lethal Dose 50 (LD50) considering the effect on grain yield was around 13% (72.80 g a.e. ha−1), 55% (552.75 g a.e. ha−1) and 48% (482.40 g a.e. ha−1) for dicamba; 2,4D dimethylamine (DMA) and 2,4D choline (CHO) of the recommended dose, respectively. This study revealed noteworthy limitations for the RGB indices to discriminate between the effects of different formulations of the same herbicide, as for 2,4D DMA and 2,4D CHO. With expectations for the introduction of new genetic soybean events and alongside new synthetic auxin compounds, our results pointed out that the proposed methodology can lead to a protocol for identifying and estimating the damage to the off-target movement from these outcoming herbicides on neighbourhood fields.

Fijiを利用したイネ科牧草条播圃場空撮画像の植生指標・Green Red Vegetation Index(GRVI)解析マニュアル

Fijiを利用したイネ科牧草条播圃場空撮画像の植生指標・Green Red Vegetation Index(GRVI)解析マニュアル