Visible Atmospherically Resistant Index Research Articles

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).

Field evaluation of the effect of Aspergillus niger on lettuce growth using conventional measurements and a high-throughput phenotyping method based on aerial images.

Plant microbiome engineering is a promising tool to unlock crop productivity potential and exceed the yield obtained with conventional chemical inputs. We studied the effect of Aspergillus niger inoculation on in-field lettuce (Lactuca sativa) growth in soils with limiting and non-limiting P concentrations. Lettuce plants originating from inoculated seeds showed increased plant diameter (6.9%), number of leaves (8.1%), fresh weight (23.9%), and chlorophyll content (3.8%) as compared to non-inoculated ones. Inoculation of the seedling substrate just before transplanting was equally efficient to seed inoculation, while application of a granular formulation at transplanting did not perform well. Plant response to P addition was observed only up to 150 kg P2O5 ha-1, but A. niger inoculation allowed further increments in all vegetative parameters. We also employed a high-throughput phenotyping method based on aerial images, which allowed us to detect changes in plants due to A. niger inoculation. The visible atmospherically resistant index (VARI) produced an accurate prediction model for chlorophyll content, suggesting this method might be used to large-scale surveys of croplands inoculated with beneficial microorganisms. Our findings demonstrate that A. niger inoculation surpasses the yield obtained with conventional chemical inputs, allowing productivity gains not reached by just increasing P doses.

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.

A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval

Net photosynthesis rate (Pn) can be used to characterize the health status of plants and their ability to accumulate organic matter. In this study, remotely sensed vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) were retrieved to build regression models to estimate rapeseed canopy Pn. Multi-source unmanned aerial vehicle (UAV) remote sensing data collected from seedling stage rapeseed were used in this study. The results showed that Pn was significantly related to traditional VIs and SIF (R2 = 0.52, p < 0.01). A quadratic polynomial regression model built using the normalized difference vegetation index performed the best on the inversion of Pn (R2 = 0.63, RMSE = 2.56, NRMSE = 0.18). Moreover, this study coupled SIF with traditional VIs by mathematical operations. The composite indices obtained by multiplication resulted in increased correlations. The inversion model established using SIF × VARI (visible atmospherically resistant index) achieved the best overall performance with 0.14 increase in R2 (0.54–0.68) and 0.48 decrease in RMSE (2.87–2.39) compared to SIF, 0.13 increase in R2 (0.55–0.68) and 0.45 decrease in RMSE (2.84–2.39) compared to VARI. Therefore, a novel composite index obtained from the multiplication operation of individual indices improved Pn retrieval of seedling rapeseed from remotely sensed UAV data. The results from this study indicate that the novel composite index has the potential for improving the accuracy of growth status monitoring compared with traditional indices.

Improving Sustainable Vegetation Indices Processing on Low-Cost Architectures

The development of embedded systems in sustainable precision agriculture has provided an important benefit in terms of processing time and accuracy of results, which has influenced the revolution in this field of research. This paper presents a study on vegetation monitoring algorithms based on Normalized Green-Red Difference Index (NGRDI) and Visible Atmospherically Resistant Index (VARI) in agricultural areas using embedded systems. These algorithms include processing and pre-processing to increase the accuracy of sustainability monitoring. The proposed algorithm was evaluated on a real database in the Souss Massa region in Morocco. The collection of data was based on unmanned aerial vehicles images hand data using four different agricultural products. The results in terms of processing time have been implemented on several architectures: Desktop, Odroid XU4, Jetson Nano, and Raspberry. However, this paper introduces a thorough study of the Hardware/Software Co-Design approach to choose the most suitable system for our proposed algorithm that responds to the different temporal and architectural constraints. The evaluation proved that we could process 311 frames/s in the case of low resolution, which gives real-time processing for agricultural field monitoring applications. The evaluation of the proposed algorithm on several architectures has shown that the low-cost XU4 card gives the best results in terms of processing time, power consumption, and computation flexibility.

Development of colour sensor based low-cost hand-held device for crop nitrogen management

In this study a low-cost sensor based device was developed for the measurement of Chlorophyll content in leaves. This device consisted of Arduino based red-green-blue (RGB) sensor, battery and outer casing. It measures the reflectance of the green pigment chlorophyll from the leaves when the light from the LED is allowed to fall on the plant leaf surface. The greenness of the leaves is an indicator of the chlorophyll content in the leaves & thus indicates the plant health & vigour. RGB values obtained from developed device were used to calculate vegetation indices such as Green Ratio vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI) and Normalized Pigment Chlorophyll Ratio Index(NPCI). Vegetation indices derived from the developed device was compared with Chlorophyll content meter (CCM) readings obtained from CCM-200for tomato crop grown under protected cultivation conditions. VARI and GRVI obtained with developed sensor were positively correlated with the CCM-200 readings with an R2 of 0.85 and 0.86, respectively. As VARI and GRVI showed positive correlation with the CCM-200 readings thus it can be used to indicate crop health. Also as developed hand held device is low cost (Rs. 3500 only) it can be suitable for small holder farmers for nutrient management.

Fenotipagem de alto rendimento por análise de imagens RGB e multiespectral de genótipos em milho doce

ABSTRACT Sweet corn (Zea mays subsp. saccharata) is mainly intended for industrial processing. Optimizing time and costs during plant breeding is fundamental. An alternative is the use of high-throughput phenotyping (HTP) indirect associated with agronomic traits and chlorophyll contents. This study aimed to (i) verify whether HTP by digital images is useful for screening sweet corn genotypes and (ii) investigate the correlations between the traits evaluated by conventional methods and those obtained from images. Ten traits were evaluated in seven S3 populations of sweet corn and in two commercial hybrids, three traits by classical phenotyping and the others by HTP based on RGB (red, green, blue) and multispectral imaging analysis. The data were submitted to the analyses of variance and Scott-Knott test. In addition, a phenotypic correlation graph was plotted. The hybrids were more productive than the S3 populations, showing an efficient evaluation. The traits extracted using HTP and classical phenotyping showed a high degree of association. HTP was efficient in identifying sweet corn genotypes with higher and lower yield. The vegetative canopy area (VCA), normalized difference vegetation index (NDVI), and visible atmospherically resistant index (VARI) indices were strongly associated with grain yield.

A Hybrid Vegetation Detection Framework: Integrating Vegetation Indices and Convolutional Neural Network

Vegetation inspection and monitoring is a time-consuming task. In the era of industrial revolution 4.0 (IR 4.0), unmanned aerial vehicles (UAV), commercially known as drones, are in demand, being adopted for vegetation inspection and monitoring activities. However, most off-the-shelf drones are least favoured by vegetation maintenance departments for on-site inspection due to limited spectral bands camera restricting advanced vegetation analysis. Most of these drones are normally equipped with a normal red, green, and blue (RGB) camera. Additional spectral bands are found to produce more accurate analysis during vegetation inspection, but at the cost of advanced camera functionalities, such as multispectral camera. Vegetation indices (VI) is a technique to maximize detection sensitivity related to vegetation characteristics while minimizing other factors which are not categorised otherwise. The emergence of machine learning has slowly influenced the existing vegetation analysis technique in order to improve detection accuracy. This study focuses on exploring VI techniques in identifying vegetation objects. The selected VIs investigated are Visible Atmospheric Resistant Index (VARI), Green Leaf Index (GLI), and Vegetation Index Green (VIgreen). The chosen machine learning technique is You Only Look Once (YOLO), which is a clever convolutional neural network (CNN) offering object detection in real time. The CNN model has a symmetrical structure along the direction of the tensor flow. Several series of data collection have been conducted at identified locations to obtain aerial images. The proposed hybrid methods were tested on captured aerial images to observe vegetation detection performance. Segmentation in image analysis is a process to divide the targeted pixels for further detection testing. Based on our findings, more than 70% of the vegetation objects in the images were accurately detected, which reduces the misdetection issue faced by previous VI techniques. On the other hand, hybrid segmentation methods perform best with the combination of VARI and YOLO at 84% detection accuracy.

Evaluating the Spectral and Physiological Responses of Grapevines (Vitis vinifera L.) to Heat and Water Stresses under Different Vineyard Cooling and Irrigation Strategies

Heat stress (HS) and water stress (WS) pose severe threats to viticulture, and effective management solutions to counter their effects on grapevine performance must be examined. In this study, we evaluated the physiological and spectral responses of Vitis vinifera L. cv. Sauvignon blanc to individual (HS) and combined (HS + WS) stress under four different cooling and irrigation strategies. The treatments were: standard drip irrigation (SI), extra drip irrigation (SI+), extra sprinklers irrigation (SPRI), and sustained deficit irrigation (SDI; 50% of SI). Compared to the other treatments, in the early stages after the occurrence of HS, the vine water status of SPRI and SI+ improved, with high stomatal conductance (gs) (SPRI) and stem water potential (Ψstem; SPRI and SI+). All the physiological indicators measured were significantly lower after the end of HS in the SDI treatment. We also identified the spectral response of grapevine to HS and combined HS and WS (resulting from SDI). Consistent with the physiological analysis, the proximal spectral responses of leaves identified SPRI and SI+ as putative cooling strategies to minimize vine HS. The vines undergoing combined stress (SDI) showed greenness amelioration 10 days after stress, as revealed by the greenness vegetation indices (VIs), i.e., Green Index (GI), Normalized Difference Greenness Vegetation Index (NDGI), and Visible Atmospherically Resistant Index (VARI). However, their physiological recovery was not achieved within this time, as shown by the Simple Ratio Index (SRI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and TCARI/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI). A three-step band selection process allowed the identification of the spectral traits’ responsive to HS and combined stress, i.e., 1336–1340 nm, 1967–1971 nm, and 600–604 nm.

USING UAVs/DRONES AND VEGETATION INDICES IN THE VISIBLE SPECTRUM TO MONITORING AGRICULTURAL LANDS

Unmanned Aerial Vehicles UAVs or Drones have made great progress in the field of aerial surveys to study vegetation and farmland. The research focuses on developing smart systems for managing agricultural fields, thus facilitating decision-making, increasing agricultural productivity, improving profitability and protecting the environment. The paper highlights the ability of drones to distinguish agricultural land intended for cultivation and classified as deserted or cultivated or in the germination stage. For the first time in the Nineveh governorate, a Phantom 4 DJI UAV images were used, in addition to using the spatialized Pix4Dfielde program to process these images. Four types of the standard agricultural indices that rely on the visible spectrum have been used (Visible Atmospherically Resistant Index (VARI), Triangular Greenness Index (TGI), Synthetic Normalized Differences Vegetation Index (S-NDVI) and Visible Difference Vegetation Index (VDVI)) to test UAVs images and to categorize different types of agricultural land. The results showed that when using the S-NDVI and VDVI indicators, the values 0.16 and 0.14 appeared respectively in certain areas, which indicates the presence and integrity of vegetation cover, unlike other regions, whose indicators showed 0.010 and -0.004, respectively, which indicate that the plant has a bad condition or its absence at all. All results finding in this research reflect and confirm the validity of using UAVs images for agricultural field management and development.

Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest

The evaluation of vegetation cover after post-fire treatments of burned lands is important for forest managers to restore soil quality and plant biodiversity in burned ecosystems. Unfortunately, this evaluation may be time consuming and expensive, requiring much fieldwork for surveys. The use of remote sensing, which makes these evaluation activities quicker and easier, have rarely been carried out in the Mediterranean forests, subjected to wildfire and post-fire stabilization techniques. To fill this gap, this study evaluates the feasibility of satellite (using LANDSAT8 images) and drone surveys to evaluate changes in vegetation cover and composition after wildfire and two hillslope stabilization treatments (log erosion barriers, LEBs, and contour-felled log debris, CFDs) in a forest of Central Eastern Spain. Surveys by drone were able to detect the variability of vegetation cover among burned and unburned areas through the Visible Atmospherically Resistant Index (VARI), but gave unrealistic results when the effectiveness of a post-fire treatment must be evaluated. LANDSAT8 images may be instead misleading to evaluate the changes in land cover after wildfire and post-fire treatments, due to the lack of correlation between VARI and vegetation cover. The spatial analysis has shown that: (i) the post-fire restoration strategy of landscape managers that have prioritized steeper slopes for treatments was successful; (ii) vegetation growth, at least in the experimental conditions, played a limited influence on soil surface conditions, since no significant increases in terrain roughness were detected in treated areas.

Assessment of Vineyard Water Status by Multispectral and RGB Imagery Obtained from an Unmanned Aerial Vehicle

Multispectral and conventional cameras (red, green, blue [RGB] imager) onboard unmanned aerial vehicles (UAVs) provide very high spatial, temporal, and spectral resolution data. To evaluate the capacity of these techniques to assess vineyard water status, we carried out a study in a cv. Monastrell vineyard located in southeastern Spain in 2018 and 2019. Several irrigation strategies were applied, including different water quality and quantity regimes. Flights were performed using conventional and multispectral cameras mounted on the UAV throughout the growth cycle. Several visible and multispectral vegetation indices (VIs) were determined from the images with only vegetation (without soil and shadows, among others). Stem water potential was measured by pressure chamber, and the water stress integral (Sψ) was obtained during the season. Simple linear regression models that used VIs and green cover canopy (GCC) to predict Sψ were tested. The results indicate that visible VIs best correlated with Sψ. The green leaf index (GLI), visible atmospherically resistant index (VARI), and GCC showed the best fits in 2018, with R² = 0.8, 0.72, and 0.73, respectively. When the best model developed with the 2018 data was applied to the 2019 data set, the model fit poorly. This suggests that on-ground measurements of vine stress must be taken each growing season to redevelop a model that predicts water stress from UAV-based imaging.

Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

Site- and time-specific early weed control is able to reduce herbicide use in maize - a case study

Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. This study combined site-specific and time-specific weed control methods to explore their collective benefits for precision weed control. Using the AlertInf model, a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for the UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop, and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (&gt;1%, &gt;5%, and &gt;10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen’s Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the amount reduction ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and timespecific weed control might significantly reduce herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results. Highlights - Efficacy of UAVs and emergence predictive models for weed control have been confirmed. - Combination of time-specific and site-specific weed control provides optimal results. - Use of timely prescription maps can substantially reduce herbicide use.

Urban tree analysis using unmanned aerial vehicle (uav) images and object-based classification (case study: university of indonesia campus)

This paper aims to analyze the urban trees located in University of Indonesia campus using UAV image and Object-based Image Analysis (OBIA). Herein, DJI Phantom 4 Pro was flown at 90 meter height to take image above the study area with spatial resolution of 2.4 cm/pixel. The image from UAV then processed using Agisoft Photoscan and underwent geometric correction. The image containing red, green and blue (RGB) bands then segmented with multi-resolution algorithm. Four Vegetation Indices (VIs) namely Normalized Green-red Difference Index (NGRDI), Visible Atmospherically Resistant Index (VARI), Visible-band Difference Vegetation Index (VDVI) and Red-Green Ratio Index (RGRI) were used to develop rule sets for land use land cover (LULC) classification. Vegetation class was separated from LULC image to be further analysed with ArcGIS using information from ground truth observation. Final product is urban tree map containing tree names and LULC classes.

Generating the Baseline in the Early Detection of Bud Rot and Red Ring Disease in Oil Palms by Geospatial Technologies

Oil palm cultivation in Ecuador is important for the agricultural sector. As a result of it, the country generates sources of employment in some of the most vulnerable zones; it contributes 0.89% of the gross domestic product and 4.35% of the agricultural gross domestic product. In 2017, a value of USD $252 million was generated by exports, and palm contributed 4.53% of the agricultural gross domestic product (GDP). It is estimated that 125,000 hectares of palm were lost in the Republic of Ecuador due to Red Ring Disease (RRD) and specifically Bud Rot (BR). The current study aimed to generate an early detection of BR and RRD in oil palm. Image acquisition has been performed using Remotely Piloted Aircraft System (RPAS) with Red, Green, and Blue (RGB) cannons, and multispectral cameras, in study areas with and without the presence of the given disease. Hereby, we proposed two phases. In phase A, a drone flight has been conducted for processing and georeferencing. This allowed to obtain an orthomosaic that serves as input for obtaining several vegetation indices of the healthy crop. The data and products obtained from this phase served as a baseline to perform comparisons with plantations affected by BR and RRD and to differentiate the palm varieties that are used by palm growers. In phase B, the same process has been applied three times with an interval of 15 days in an affected plot, in order to identify the symptoms and the progress of them. A validation for the diseases detection has been performed in the field, by taking Global Positioning System (GPS) points of the palms that presented symptoms of BR and RRD, through direct observation by field experts. The inputs obtained in each monitoring allowed to analyze the spatial behavior of the diseases. The values of the vegetation indices obtained from Phase A and B aimed to establish the differences between healthy and diseased palms, with the purpose of generating the baseline of early responses of BR and RRD conditions. However, the best vegetation index to detect the BR was the Visible Atmospherically Resistant Index (VARI).

Detection of Italian Ryegrass in Wheat and Prediction of Competitive Interactions Using Remote-Sensing and Machine-Learning Techniques

Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions.

Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms

Several vegetation indices have been developed, with the normalized difference vegetation index (NDVI) been the most studied and commonly used. To generate an NDVI map, a relatively high-cost multispectral sensor is required; but currently, most UAVs are equipped with low-cost RGB cameras. For that reason, other indices that utilize RGB data have been developed to generate maps similar to NDVI and minimize the data acquisition cost, such as the triangular greenness index (TGI) and the visible atmospheric resistant index (VARI). However, several studies found that these indices cannot be recommended as reliable general-purpose crop health indicators. This study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms). Three experiments were conducted to create and validate the proposed index. First, the NDVI values generated from a multispectral camera were compared with the NDVI values generated by a hyperspectral camera. In the second experiment, the vNDVI formula was created using a genetic algorithm. The third experiment validates the proposed vNDVI, generated from two uncalibrated RGB cameras, in three different crops (citrus, grapes, and sugarcane). The proposed vNDVI proved to be highly accurate on estimating NDVI values by just using RGB cameras, with an overall mean percentage error of 6.89% and a mean average error of 0.052 in all three crops, providing a low-cost alternative for remote sensing and plant phenotyping.