Green Normalized Difference Vegetation Index Research Articles

Monitoring drought response and chlorophyll content in Quercus by consumer-grade, near-infrared (NIR) camera: a comparison with reflectance spectroscopy

A user-friendly and affordable broad-band digital Near Infrared (NIR) camera (Canon PowerShot S110 NIR) was compared with a narrow-band reflectance spectrometer (USB2000, Ocean Optics) at leaf scale for monitoring changes in response to drought of three ecologically contrasting Quercus species (Q. robur, Q. pubescens, and Q. ilex). We aimed to (a) compare vegetation indices (VIs; that is: NDVI, Normalized Difference Vegetation Index; GNDVI, Green NDVI and NIRv, near-infrared reflectance of vegetation) retrieved by NIR-camera and spectrometer in order to test the reliability of a simple, low-cost, and rapid setup for widespread field applications; (b) to assess if NIR-camera VIs might be used to quantify water stress in oak seedlings; and (c) to track changes in leaf chlorophyll content. The study was carried out during a water stress test on 1-year-old seedlings in a greenhouse. The camera detected plant status in response to drought with results highly comparable to the visible/NIR (VIS/NIR) spectrometer (by calibration and standard geometry). Consistency between VIs and morpho-physiological traits was higher in Q. robur, the most drought-sensitive among the three species. Chlorophyll content was estimated with a high goodness-of-fit by VIs or reflectance bands in the visible range. Overall, NDVI performed better than GNDVI and NIRv, and VIs performed better than single bands. Looking forward, NIR-camera VIs are adequate for the early monitoring of drought stress in oak seedlings (or small trees) in the post-planting phase or in nursery settings, thus offering a new, reliable alternative for when costs are crucial, such as in the context of restoration programs.

Eksplorasi Pendugaan Hara Daun Tanaman Kelapa Sawit Menggunakan Pesawat Tanpa Awak dan Kamera Multispektral

Rekomendasi pemupukan umumnya disusun setiap tahun untuk mengkoreksi kebutuhan hara tanaman melalui kegiatan pemupukan. Hara daun dalam penyusunan rekomendasi pemupukan merupakan bagian kecil dari beberapa parameter yang digunakan. Beberapa peneliti telah melakukan prediksi hara daun tanaman kelapa sawit memanfaatkan data satelit multispektral dan pengukuran spectroradiometer dengan hasil yang bervariasi. Penelitian prediksi hara daun kelapa sawit dengan pesawat tanpa awak dan kamera multispektral ini dilakukan karena belum ada kajian mengenai penggunaan teknologi tersebut untuk prediksi hara daun kelapa sawit. Tujuan penelitian ini adalah membandingkan performa pendugaan hara daun dari berbagai variasi komposisi variabel berupa 3 saluran dan beberapa indeks vegetasi, serta untuk menentukan analisis regresi terbaik. Band hijau, merah, dan inframerah dekat dari kamera multispektral Mapir Survey 3, serta indeks vegetasi simple ratio, normalized difference vegetation index, dan green NDVI digunakan sebagai variabel bebas dalam analisis regresi sederhana, regresi polinomial, regresi berganda dari variabel terseleksi dengan teknik recursive feature elimination dengan metode regresi linear dan random forest, dan regresi berganda polinomial dengan variabel tidak bebas (respons) berupa analisis daun dari 20 sampel yang meliputi hara N, P, K, Ca, Mg, dan B. Model penduga terbaik dari penelitian ini adalah model regresi berganda polinomial dari variabel terseleksi menggunakan teknik RFE metode random forest. Variabel bebas hanya mampu menduga hara daun N, P, K, dan Mg dengan nilai R2 sebesar 0,9415 hingga 0,9991, Adjusted R2 sebesar 0,7223 hingga 0,9837, serta nilai residual standard error (RSE) sebesar 0,0045 hingga 0,0340.

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.

UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation

The use of unmanned aerial vehicles’ (UAV) remotely sensed data in crop evaluation is revolutionizing the field of plant phenotyping. This study was conducted to (1) develop protocol to predict maize streak virus (MSV) and grain yield using UAV-derived multispectral data; and (2) identify the suitable predictor variables and ideal phenological stages for MSV and grain yield prediction. Twenty-five maize varieties were evaluated under artificial MSV inoculation. Manual scoring and multispectral imaging measurements were performed at mid-vegetative, flowering and mid-grain filling stages. UAV-derived data were acquired in the multispectral bands of Green (0.53–0.57 μm), Red (0.64–0.68 μm), Red-edge (0.73–0.74 μm) and Near-Infrared (0.77–0.81 μm). Eight vegetation indices were determined: NDVI (normalized difference vegetation index), NDVIred-edge, GNDVI (green normalized difference vegetation index), SR (simple ratio), CIgreen (green chlorophyll index), CIred-edge (red-edge chlorophyll index), SAVI (soil-adjusted vegetation index) and OSAVI (optimized SAVI). Finally, predictions of MSV and grain yield were performed with 36 models using multiple regression, decision trees and linear regression. Frequently selected variables for MSV prediction were Green band at vegetative (61.5%), Red band at vegetative (68.4%) and flowering (80.4%), and GNDVI at mid-vegetative (88.7%). The best MSV predictors were GNDVI (r = 0.84; RMSE = 0.85), CIgreen (r = 0.83; RMSE = 0.86) and Red band (r = 0.77; RMSE = 0.99) measured at mid-vegetative stage. Six out of 36 models were selected as ideal for predicting maize grain yield: RF-REF-NIRF (r = 0.69; RMSE = 0.65); NDVIREG-GNDVIG (r = 0.74; RMSE = 0.56); RV-NIRV (r = 0.84; RMSE = 0.37); and the tree with the largest correlations are RV-NIRV-RF (r = 0.86; RMSE = 0.32); GNDVIV-OSAVIV (r = 0.84; RMSE = 0.36); GV-RV-NIRV (r = 0.84; RMSE = 0.35); the last two of which were at mid-vegetative stage. We conclude that UAV-based multispectral remote sensing is a reliable tool for phenotyping MSV disease and grain yield prediction, and mid-vegetative appear to be the most ideal phenological stage for MSV and grain yield prediction.

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

AbstractThe current challenge of corn (Zea mays L.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super‐traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha–1 and high, 180 kg ha–1). At full bloom, the spectral variables green (550 nm), red (660 nm), red‐edge (735 nm), and near‐infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red‐edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil‐adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red‐edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield.

Effects of riparian vegetation on evapotranspiration processes and water quality of small plain streams

Riparian vegetation provides a wide range of ecosystem services including water quality regulation, flooding avoidance, and the natural control of stream temperatures. Changes in land use and the increase of anthropogenic activities in the proximity of rivers affect the growth and development of riparian buffers. The aim of the work was to assess the effects of riparian vegetation on the evapotranspiration processes and the water quality of small plain streams using remote sensing resources and in-situ monitoring. The area of study considered two neighboring basins of small plain streams (Glavacioc and Ilfov) from Romania. The data obtained from in-situ monitoring performed in 2019 was used in the Penman-Monteith algorithm to calculate the potential evapotranspiration (PET) based on a series of specific parameters. The Green Normalized Difference Vegetation Index (GNDVI), computed from LANDSAT 8 images, has been used to assess the vegetation status at the basin scale. The GNDVI fluctuations were linked to the variations in precipitation regimes and land use modifications. Remote sensing data have shown changes in GNDVI between June and September 2019 in both riparian areas resulting in an intense spread of cattail (Typha spp.). Surface waters arriving at the entrance control sections had a basic pH and were loaded with nitrates and ammonium ion (NH4+), which had decreased at the downstream section after passing the cattail phytosociological associations and the riparian patches, thus acting as buffer areas. The current anthropogenic pressures on the riparian areas might have degenerative effects on the integrity of the vegetation structure. Thus, for effective and sustainable management, it is essential to identify the main stressors and to assess to what extent they affect the riparian ecosystem as a whole.

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN.

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Identification and quantification of potassium (K+) deficiency in maize plants using an unmanned aerial vehicle and visible / near-infrared semi-professional digital camera

ABSTRACT Brazil is one of the largest producers of maize worldwide. However, this production is threatened due to low soil fertility, especially low levels of potassium (K+). K+ is one of the most important nutrients in plant metabolism, acting on enzymatic activation and also on photosynthetic processes. The identification of its deficiency by using traditional methods is difficult with regard to timely restoration of the nutrient to adequate levels. Therefore, the use of low-cost modified cameras attached to Unmanned Aerial Vehicles (UAVs) are important tools for agricultural monitoring. Nevertheless, there are no reports of studies with the purpose of evaluating the monitoring of K+ deficiency in maize crops using multispectral images captured from UAVs. Therefore, this study aimed at exploring the possibility of identifying K+ deficiency and quantifying the nutrient leaf content by using a Vegetation Index (VI). The experiment was carried out at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation). The experimental plots were constantly managed in order to obtain different conditions of K+ availability to plants, achieving levels that ranged from severe deficiency to an adequate nutrient level. The following treatments were established: severe potassium deficiency (SPD), moderate potassium deficiency (MPD) and adequate supply of potassium (ASP). The evaluations were performed in the Brazilian maize crop referred to as ‘safrinha, at the V7, V12 and R3 developmental stages, with image capture covering the visible and near-infrared region, using two Fujifilm IS PRO digital cameras attached to an UAV. In these development stages, leaves were collected to determine tissue K+ concentration. The images were radiometrically corrected with the support of calibration targets and reference values, using an Fieldspec 3 Jr. spectroradiometer. The VIs comprised the ratio among the red, green and infrared spectral bands, that is, green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), ratio between infrared and green (GRVI), ratio between green and infrared (GNIR), ratio between red and infrared (RNIR) and ratio between infrared and red (RVI). Regarding all the treatments assessed, the results showed that foliar K+ was statistically different. The VIs were efficient only in differentiating SPD and ASP treatments at all development stages evaluated. However, none were statistically significant for MPD. The linear regressions showed a high coefficient of determination (R 2) and low root mean square error (RMSE) value; the best prediction of K+ concentration obtained was at V12 for regressions with these VIs: GRVI (R 2 = 0.79, RMSE 4.50 g kg−1) and RVI (R 2 = 0.71, RMSE 4.39 g kg−1). The grain yield values showed that SPD caused an average reduction of 5,645.90 kg ha−1 in relation to the ASP. Considering MPD, the grain yield was 1,242.00 kg ha−1 lower in comparison with ASP. In conclusion, estimating foliar K+ content and identifying its deficiency in maize crops based on the VIs of multispectral images from cameras attached to UAVs is possible, which ensures agility to these evaluations in a non-destructive manner, improving efficiency of K+ fertilization and providing farmers with a new tool.

Stand Delineation of Pinus sylvestris L. Plantations Suffering Decline Processes Based on Biophysical Tree Crown Variables: A Necessary Tool for Adaptive Silviculture

Many planted Pinus forests are severely affected by defoliation and mortality processes caused by pests and droughts. The mapping of forest tree crown variables (e.g., leaf area index and pigments) is particularly useful in stand delineation for the management of declining forests. This work explores the potential of integrating multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data for stand delineation based on selected tree crown variables in Pinus sylvestris plantations in southern Spain. Needle pigments (chlorophyll and carotenes) and leaf area index (LAI) were quantified. Eight vegetation indices and ALS-derived metrics were produced, and seven predictors were selected to estimate and map tree crown variables using a Random Forest method and Gini index. Chlorophylls a and b (Chla and Chlb) were significantly higher in the non-defoliated and moderately defoliated trees than in severely defoliated trees (F = 14.02, p < 0.001 for Chla; F = 13.09, p < 0.001 for Chlb). A similar response was observed for carotenoids (Car) (F = 14.13, p < 0.001). The LAI also showed significant differences among the defoliation levels (F = 26.5, p < 0.001). The model for the chlorophyll a pigment used two vegetation indices, Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI); three WV-2 band metrics, and three ALS metrics. The model built to describe the tree Chlb content used similar variables. The defoliation classification model was established with a single vegetation index, Green Normalized Difference Vegetation Index (GNDVI); two metrics of the blue band, and two ALS metrics. The pigment contents models provided R2 values of 0.87 (Chla, RMSE = 12.98%), 0.74 (Chlb, RMSE = 10.39%), and 0.88 (Car, RMSE = 10.05%). The cross-validated confusion matrix achieved a high overall classification accuracy (84.05%) and Kappa index (0.76). Defoliation and Chla showed the validation values for segmentations and, therefore, in the generation of the stand delineation. A total of 104 stands were delineated, ranging from 6.96 to 54.62 ha (average stand area = 16.26 ha). The distribution map of the predicted severity values in the P. sylvestris plantations showed a mosaic of severity patterns at the stand and individual tree scales. Overall, the findings of this work underscore the potential of WV-2 and ALS data integration for the assessment of stand delineation based on tree health status. The derived cartography is a relevant tool for developing adaptive silvicultural practices to reduce Pinus sylvestris mortality in planted forests at risk due to climate change.

Estimation of forage biomass and vegetation cover in grasslands using UAV imagery.

Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass [FM] and dry biomass [DM], respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.

Estimating maize lethal necrosis (MLN) severity in Kenya using multispectral high-resolution data

Maize lethal necrosis (MLN) is a severe disease in maize that significantly reduces yields by up to 90% in maize-growing regions such as Kenya and other countries in Africa. The disease causes chlorotic mottling of leaves and severe stunting which leads to plant death. The spread of MLN in the maize-growing regions of Kenya has intensified since the first outbreak was reported in September 2011. In this study, the RapidEye (5 m) imagery was combined with field-based data of MLN severity to map three MLN severity levels in Bomet County, Kenya. Two RapidEye images were acquired during maize stem elongation and inflorescence stages, respectively, and thirty spectral indices for each RapidEye time step were computed. A two-step random forest (RF) classification algorithm was used to firstly create a maize field mask and to predict the MLN severity levels (mild, moderate, and high). The RF algorithm yielded an overall accuracy of 91.0%, representing high model performance in predicting the MLN severity levels in a complex cropping system. The normalized difference red edge index (NDRE) was highly sensitive to MLN detection and demonstrated the ability to detect MLN-caused crop stress earlier than the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI). These results confirm the potential of the RapidEye sensor and machine learning to detect crop disease infestation rates and for use in MLN monitoring in fragmented agro-ecological landscapes.

Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions?

HighlightsVegetation indices (NDVI, GNDVI, and SAVI) extracted from high-resolution satellite imagery were significantly associated with vegetation indices extracted from UAV imagery.High-resolution satellite data can be used to predict maize yield at breeding plot scale.Breeding plot sizes and the variability between maize genotypes may be associated with prediction accuracies.Abstract. The recent availability of high spatial and temporal resolution satellite imagery has widened its applications in agriculture. Plant breeding and genetics programs are currently adopting unmanned aerial vehicle (UAV) based imagery data as a complement to ground data collection. With breeding trials across multiple geographic locations, UAV imaging is not always convenient. Hence, we anticipate that, similar to UAV imaging, phenotyping of individual test plots from high-resolution satellite imagery may also provide value to plant genetics and breeding programs. In this study, high spatial resolution satellite imagery (~38 to 48 cm pixel-1) was compared to imagery acquired using a UAV for its ability to phenotype maize grown in two-row and six-row breeding plots. Statistics (mean, median, sum) of color (red, green, blue), near-infrared, and vegetation indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and soil adjusted vegetation index (SAVI) were extracted from imagery from both sources (UAV and satellite) for comparison at three time points. In general, a strong correlation between satellite and UAV imagery extracted NDVI, GNDVI, and SAVI features (especially with mean and median statistics, p < 0.001) was observed at different time points. The correlation of both UAV and satellite image features with yield potential was maximum (p < 0.001) at the third time point (milk/dough growth stages). For example, Pearson’s correlation coefficients between mean NDVI, GNDVI, and SAVI features with yield potential were 0.52, 0.54, and 0.51 for data derived from UAV imagery, and 0.34, 0.41, and 0.40 for data derived from satellite imagery, respectively. Machine learning algorithms, including least absolute shrinkage and selection operator (Lasso) regression, were evaluated for yield prediction using vegetation index features that were significantly correlated with observed yield. The relationship between satellite imagery with crop performance can be a function of plot size in addition to crop variability. Nevertheless, with the ongoing improvement of satellite technologies, there is a possibility for the integration of satellite data into breeding programs, thus improving phenotyping efficiencies. Keywords: Image processing, Machine learning, Plant breeding, Statistical analysis, Unmanned aerial vehicles.

Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery

ABSTRACT This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images’ T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10−5. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

The rapid assessment of maize yields in a smallholder farming system is important for understanding its spatial and temporal variability and for timely agronomic decision-support. We assessed the predictability of maize grain yield using unmanned aerial/air vehicle (UAV)-derived vegetation indices (VI) with (out) biophysical variables on smallholder farms. High-resolution imageries were acquired with UAV-borne multispectral sensor at four and eight weeks after sowing (WAS) on 31 farmer managed fields (FMFs) and 12 nearby nutrient omission trials (NOTs) sown with two genotypes (hybrid and open-pollinated maize) across five locations within the core maize region of Nigeria. Acquired multispectral imageries were post-processed into three VIs, normalized difference VI (NDVI), normalized difference red-edge (NDRE), and green-normalized difference VI (GNDVI) while plant height (Ht) and percent canopy cover (CC) were measured within georeferenced plot locations. Result shows that the nutrient status had a significant effect on the grain yield (and variability) in NOTs, with a maximum grain yield of 9.3 t/ha, compared to 5.4 t/ha in FMFs. Generally, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r < 0.02, p > 0.1), but significant correlations were observed at 8WAS (r ≤ 0.3; p < 0.001). Ht was positively correlated with grain yield at 4WAS (r = 0.5, R2 = 0.25, p < 0.001) and more strongly at 8WAS (r = 0.7, R2 = 0.55, p < 0.001), while the relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMFs (separately), predictability of grain yield from UAV-derived VIs was generally low (R2 ≤ 0.24); however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥ 0.62, Root Mean Square Error of Prediction, RMSEP ≤ 0.35) in NOTs but not in FMFs. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer managed fields where various confounding agronomic factors can amplify noise-signal ratio.

Mapping spatial variability of foliar nitrogen and carbon in Indian tropical moist deciduous sal (Shorea robusta) forest using machine learning algorithms and Sentinel-2 data

ABSTRACT Foliar Nitrogen (N) and Carbon (C) are two vital leaf biochemical components that can indicate forest health. In this study, the spatial distribution of foliar N and C was mapped using Sentinel-2 data in a tropical moist deciduous sal (Shorea robusta) forest of northwest Himalayan foothills of India. Empirical relationships were established between satellite data-derived spectral indices, band reflectance and ground measured foliar N and C using machine learning algorithm (MLA). Performance of MLAs viz. Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM) were assessed. Using all the independent spectral variables, RF performed better than ANN and SVM in explaining the variation in foliar N and C. RF was further used to optimize the independent variables for identifying the best predictor variables. It was found that foliar N is strongly related to shortwave infrared-1 (SWIR-1) band and Normalized Difference Red Edge Index (NDRE). Foliar C was found to be strongly related to SWIR-1 band and spectral vegetation indices: Green Normalized Difference Vegetation Index (GNDVI), Green Red Vegetation Index (GRVI), Green Chlorophyll Index (GCI), and Ratio of Modified Chlorophyll Absorption in Reflectance Index and Optimized Soil Adjusted Vegetation Index (MCARI/OSAVI). Using these best predictor variables, the spatial variability of foliar N and C was mapped using RF algorithm. On validation of the predicted N and C with ground measured foliar N and C, R 2 of 0.85 (RMSE = 0.04%) for N and R 2 of 0.86 (RMSE = 0.26%) for C were observed. It can be concluded that MLAs have great potential to map the spatial variability of foliar N and C in the tropical forests using the broad bands of Sentinel-2 imagery.

Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves

Cucurbits plants are very susceptible to fungal diseases as powdery mildew (PM) infection. Currently, the PM early detection in the field is the cutting-edge research. The objectives of our study were to assess visible and near-infrared spectroscopy of normal and infected leaves for early detection of PM in Cucurbita pepo L. plants. Samples for spectral analysis were taken three days a week (Monday, Wednesday, and Friday) after the first true leaves appeared. The reflectance spectra of leaves were collected using an HR4000CG-UV-NIR spectrometer (Ocean Optics) with a fiber optics probe. Vegetative indices were used for discrimination between infected and healthy plants. The calculated vegetation indices (green normalized difference vegetation index, triangular greenness index, single-band reflectance index, simple ratio indices, and anthocyanins reflectance index) showed the highest sensitivity to differentiate healthy, infected plants at a symptomless stage, first symptoms, and diseased plants. The best prediction on early detection in sampling days were structure-independent pigment index and red-edge reflection point with an R of 0.211 and 0.1893, respectively. Our study shows the efficacy of the identification based on reflectance spectra for an early distinction of PM disease and could be used in cucurbits plants with similar characteristics on leaves.

Evaluation of lawn color and chlorophyll concentration using hyperspectral index

A study was carried out at a subtropical zone of southern west China to test if hyperspectral methods could be used to measure turf canopy chlorophyll concentration, and to analyse correlation between green normalized difference vegetation index (GNDVI) values. Turf chlorophyll concentration, canopy hyperspectral reflectance, and visual rating were measured. The results showed that canopy hyperspectral reflectance value was more sensitive to the turfgrass chlorophyll concentration difference than visual rating both among turfgrass species. Visual rating value and hyperspectral index were both highly correlated with canopy chlorophyll concentration. Regression analysis indicated a strong linear correlation between GNDV and visual rating value. Hyperspectral technique can provide an improved method to evaluate turfgrass chlorophyll concentration and to quantify turfgrass color.

Remote Sensing Analysis of Agricultural Drone

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

VEGETATION INDEX BASED IN UNMANNED AERIAL VEHICLE (UAV) TO IMPROVE THE MANAGEMENT OF INVASIVE PLANTS IN PROTECTED AREAS, SOUTHERN BRAZIL

Abstract. The biological invasion is considered the second largest global threat to the maintenance and conservation of natural ecosystems biodiversity. Strategies and actions that guide the control and monitoring of invasive species in protected areas are still a challenge on the management of these areas. Remote sensing is potential tool to detect and monitoring these species, gaining a timeline scale and allowing the adoption of more effective control methods. In this study, search to evaluate the vegetation index potential by using multispectral images acquired by UAV as a support on detection and monitoring of invasive plants in Quarta Colônia State Park located on the Brazil’s southern region. A sampling area with a density of invasive plants above 80% was evaluated, with predominance of Psidium guajava and Ligustrum lucidum, generating a large data set from the extracted indexes. Among the evaluated index, the ones that showed the most potential in this study were Green Normalized Difference Vegetation Index (GNDVI), Plant Senescence Reflectance Index (PSRI) and Red Green Ratio Index (RGRI). Believe us that the use of UAVs platforms will be an important tool for the management of invasive species in protected areas.