Vegetation Index Measurements Research Articles

Assessing the accuracy of an infrared-converted drone camera with Orange-Cyan-NIR filter for vegetation and environmental monitoring

Drones equipped with cameras sensitive to near-infrared wavelengths are increasingly being used in environmental assessment studies and in agriculture. These cameras are sensitive to vegetation cover, extent of eutrophication in water bodies, and aspects of crops such as growth vigour, biomass and potential yield. Single-sensor (‘RGB’) cameras with modified spectral filters that allow for capturing near-infrared wavelengths offer a low-cost alternative to multi-sensor multispectral cameras or spectrometers. However, some studies point to lower measurement accuracies by such infrared converted sensors. So, to what extent can infrared converted cameras be used to quantify vegetation condition? This case study compared vegetation indices calculated from infrared converted camera imagery to those measured by a drone-borne multispectral camera and a handheld NDVI meter, as captured over soybean and potato fields. The study found that infrared converted camera derived NDVI was consistently lower over vegetation than NDVI measured from the multispectral and handheld sensors. Further, all considered indices of the infrared converted camera displayed relative underestimation at high index values, but over estimation at low index values. The study builds on previous case studies with similar results by further evaluating the reflectance patterns of the individual image bands to find possible reasons for the discrepancy in vegetation index measurements. There is good agreement between the near-infrared bands of the respective sensors (r=0.87), but the respective red bands have weak correlation (r=−0.03). We discuss possible reasons for the lower vegetation index measurements observed by the infrared converted camera, noting broad band sensitivities, and differing central wavelengths, which may have caused overestimated reflectance in the red band. All processing and analysis were executed with open-source software, and source code is made available to support reproducible research.

Comparative evaluation of vegetation greenness trends over circumpolar Arctic tundra using multi-sensors satellite datasets

ABSTRACT The circumpolar arctic tundra, located at Earth’s highest latitudes, is extremely sensitive to climate warming. Studies on arctic greening, based on satellite data and field measurements, show discrepancies due to differences in spatial resolution across datasets (e.g., Landsat 30-m, MODIS 250-m, and AVHRR GIMMS 8 km). Research on scale effects has been limited, mostly focusing on small areas rather than the entire 7.11 million km² arctic tundra. Our study addresses this by mapping scale effects across the entire tundra using Normalized Difference Vegetation Index (NDVI) measurements. Findings reveal: (1) Landsat data provides detailed spatial trends, identifying 18.7% of the area as significantly greening, whereas GIMMS data detects more browning due to spectral mixing; (2) GIMMS underestimates the greening to browning ratio at 2.2:1, compared to Landsat and MODIS ratios of 14.1:1 and 15.1:1, respectively; (3) Over 93% agreement exists between Landsat and MODIS or GIMMS trends, with discrepancies in limited areas. This highlights the importance of high-resolution data and field studies for accurately understanding vegetation trends across the arctic tundra.

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

Pendugaan Cadangan Karbon Dengan Citra Sentinel-2B dan Terrestrial Laser Scanner Di Kawasan Hutan Dengan Tujuan Khusus (KHDTK) Pendidikan dan Pelatihan Kehutanan Fakultas Kehutanan Universitas Mulawarman

Measurement of forest carbon on the ground in a large scale is time-consuming and expensive. This research utilized remote sensing with Sentinel-2B image through its Vegetation Index and related those values with those obtained from field measurements and Terrestrial Laser Scanner (TLS). This study specifically compares the forest stand carbon value using TLS and field measurements and link it to the vegetation index. This study transforms the vegetation indices, i.e.: Transformed Vegetation Index (TVI), Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) from Sentinel-2B imagery and relates them with field measurements and TLS. The calculation of carbon in forest stands using TLS revealed an average carbon of 151.35 ton/Ha. In contrast, in the field measurements, carbon was measured at 149.81 ton/Ha, and statistically, there was no difference between these two measurements. The relationship between the vegetation index and field measurements showed the best coefficient of correlation of TVI with (r) = 0.784 and (R2) = 0.524. The relationship between the vegetation index and TLS measurements showed the best coefficient of correlation of TVI with (r) = 0.759 and the coefficient of determination (R2) = 0.577.

Differentiating nutritional and water statuses in Hass avocado plantations through a temporal analysis of vegetation indices computed from aerial RGB images

Maximizing crop production efficiently and sustainably through plant health monitoring is key for global food security. Monitoring large areas with remote sensing technologies such as unmanned aerial vehicles (UAVs) with sensors deals with time and money issues; however, the usage of advanced sensors such as hyperspectral, multispectral and thermal cameras limit their usage among all the stakeholders. In this study we explore different vegetation indices (VIs) extracted from aerial RGB images acquired in different flights to differentiate the nutritional and water statuses of Hass avocado plantations. We used an image processing workflow consisting of image selection through a convolutional neural network (CNN) model, tree crown segmentation, color correction and feature extraction to automate the computation of VIs from RGB images. To compare the performance of VIs in the differentiation of nutritional and water statuses, we proposed a comparison metric called Mean Distance between Vegetation Indices (MDVI), analyzed the evolution of the extracted features, and studied their relationships with gold standard Normalized Difference Vegetation Index (NDVI) measurements. Since the extracted features from each group vary from flight to flight due to multiple factors such as the light intensity of each season and the phenological stage of the plant, the proposed comparison metric leverages the differences between the features extracted from each group, thus reducing these temporal effects. We found that Modified Green Red Vegetation Index (MGRVI) allows a better differentiation of nutritional and water statuses. Furthermore, the correlation coefficients of this VI in the three statuses and NDVI for nitrogen group range between 0.63 and 0.85, indicating a positive strong relationship. The results of this work show that MGRVI has a potential to be used as a correlation variable in studies that only use RGB sensors in order to monitor the nutritional and water status of crops.

The spatial variability of NDVI within a wheat field: Information content and implications for yield and grain protein monitoring.

Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.

Development of an Automated Linear Move Fertigation System for Cotton Using Active Remote Sensing

Optimum nitrogen (N) application is essential to the economic and environmental sustainability of cotton production. Variable-rate N fertigation could potentially help farmers optimize N applications, but current overhead irrigation systems normally lack automated site-specific variable-rate fertigation capabilities. The objective of this study was to develop an automated variable-rate N fertigation based on real-time Normalized Difference Vegetation Index (NDVI) measurements from crop sensors integrated with a lateral move irrigation system. For this purpose, NDVI crop sensors and a flow meter integrated with Arduino microcontrollers were constructed on a linear move fertigation system at the Edisto Research and Education Center in Blackville, South Carolina. A computer program was developed to automatically apply site-specific variable N rates based on real-time NDVI sensor data. The system’s ability to use the NDVI data to prescribe N rates, the flow meter to monitor the flow of N, and a rotary encoder to establish the lateral’s position were evaluated. Results from this study showed that the system could accurately use NDVI data to calculate N rates when compared to hand calculated N rates using a two-sample t-test (p > 0.05). Linear regression analysis showed a strong relationship between flow rates measured using the flow meter and hand calculations (R2 = 0.95), as well as the measured distance travelled using the encoder and the actual distance travelled (R2 = 0.99). This study concludes that N management decisions can be automated using NDVI data from on-the-go handheld GreenSeeker crop sensors. The developed system can provide an alternative N application solution for farmers and researchers.

Leaf and Canopy Traits Associated with Stay-Green Expression Are Closely Related to Yield Components of Wheat Genotypes with Contrasting Tolerance to Water Stress.

The onset and rate of senescence influence key agronomical traits, including grain yield (GY). Our objective was to assess the relationships between stay-green and GY in a set of fourteen spring bread wheat (Triticum aestivum L.) genotypes with contrasting tolerance to water stress. Based on leaf chlorophyll content index (Chl) and normalized vegetation index (NDVI) measurements, the senescence dynamics at leaf and canopy levels, respectively, were quantified. Parameters describing the dynamics of senescence were examined in glasshouse and field experiments under well-watered (WW) and water-limited (WL) regimes, and they included the following stay-green traits: maximum NDVI or Chl near to anthesis (NDVImax, Chlmax), the senescence rate (SR, rate), the area under curve (AreaNDVI, AreaChl), and the time from anthesis to 10 (tonset), 50 (t50, X50) and 90% (t90) senescence. Our results revealed that specific stay-green traits were significantly different among genotypes and water regimes in both glasshouse and field experiments. GY was positively correlated with ttotal (0.42), tonset (0.62) and NDVIdif (0.63). Under WL, NDVIdif and NDVImax correlated with GY (0.66–0.58), but only t50 correlated with GY under WW (0.62), indicating that phenotyping of stay-green trait is a useful tool for tracking the dynamics of senescence in WW and WL environments.

Identification of growth‐related indicators affecting the appearance and protein content of rice grains

AbstractTo facilitate the stable production of rice (Oryza sativa L.) in a changing climate, it is imperative to establish optimal N application rate via growth diagnosis. In this study, field experiments were conducted to identify useful indicators of white‐back grains and protein content of rice. During the reproductive stages in 2018 and 2019, we applied N at different rates and recorded the soil and plant analyzer development (SPAD) readings and normalized difference vegetation index (NDVI) measurements. The white‐back grain percentage and grain protein content were more strongly correlated with SPAD readings and NDVI during the reproductive stage when the 2018 and 2019 data were combined than with the separate data for each year. In the combined 2018 and 2019 data, SPAD readings were the most dominant factor affecting white‐back grain percentage at 4–2 wk before heading, whereas NDVI was the most dominant factor at 1 wk before heading. Similarly, in the combined 2018 and 2019 data, SPAD readings were the most dominant factors for grain protein content on all measurement days, and NDVI was one of the dominant factors at 1 wk before heading. These results suggest that SPAD readings at 4–2 wk before heading and SPAD readings and NDVI at 1 wk before heading are useful indicators for determining the optimal N application rates during the reproductive stage to reduce the occurrence of white‐back grains and to regulate the grain protein content.

Agronomic parameters as indicators of inbred maize nitrogen status

Inbred maize (Zea mays L.) lines are grown for seed in Puerto Rico. In efforts to increase crop yields, some producers may apply nitrogen (N) fertilizer in excess of crop nutrient requirements. The use of cover crops (CC) in rotation can serve as an alternative to continuous cultivation of maize. This experiment was conducted in field plots of Güamaní (fine-loamy over sandy or sandy-skeletal, mixed, superactive, isohyperthermic Torrifluventic Haplustepts) soil in Guayama, Puerto Rico. The cropping systems evaluated were a maize-cowpea [Vigna unguiculata (L.) Walp.] cover crop rotation versus a typical practice of maize fallow (FA), each with five N fertilizer levels. The maize planting sequence was an initial spring 2014 planting, followed by a winter 2014-2015 planting and a final winter 2015-2016 planting. The cowpea cover crop was planted in the summers of 2014, 2015 and 2016. The inbred maize lines were female lines used for commercial hybrid maize production. Mean maize plant densities ranged from 59,391 to 69,182 plants per hectare for the cropping seasons. Indicators of crop N status were the Soil Plant Analysis Development (SPAD-502) Chlorophyll Meter®measuring leaf greenness; the Normalized Difference Vegetation Index (NDVI, measured with GreenSeeker®) and plant height measurements. The application of N fertilizer significantly influenced (p<0.05) leaf greenness (in five out of 13 occasions) and plant height (in seven of 12 occasions) but not NDVI. The use of cover crop significantly influenced leaf greenness (in four out of nine occasions) and plant height (in three out of eight occasions). The agronomic parameters tested did not predict yield or crop N status, thus are not recommended in the decision-making process of N fertilizer management of inbred maize.

Influence of weather shocks and coping strategies on food consumption: Evidence from rural Niger

Weather is an important determinant of household well-being in rural Sub-Saharan Africa. This paper explores the relationship between novel measures of cropping-season weather conditions and household food consumption in rural Niger, and how household coping mechanisms mediate that relationship. We employ a panel logit model to show that the normalised difference vegetation index (NDVI) and extreme heat degree day (EHDD) measures are associated with subjective self-reporting of drought in a panel dataset of 2 264 households. We then show, with a household fixed-effects panel model, that low NDVI and high EHDD measures are associated with significant decreases in household per capita food consumption. Household coping strategies, such as the disbursement of savings, temporary migration of a family member, and the adoption of heat-resistant agricultural technologies, are found to partially mitigate, but not fully alleviate, the negative effects of weather shocks on consumption. More comprehensive coping mechanisms are needed to improve household resilience to weather shocks.

Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments.

A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from −0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment – results from UAS-based high-resolution remote sensing

Abstract. Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epigejos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epigejos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Si m−2), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

A field experiment examined the wheat response following soybean due to urea-N fertilization at sowing (0, 20, 40, and 60 kg N ha−1) and top dressing (0, 30, 60, and 90 kg N ha−1). Normalized difference vegetation index (NDVI) measurements using a GreenSeeker active sensor were taken at different growth stages, at various sensor heights above the canopy, and at different times of day. Both the shoot dry matter yield and the grain yield were not affected by N fertilization at sowing and increased with increasing N rates at top dressing. A maximum economic yield was obtained at 55 kg N ha−1 in top dressing, causing a 33% increase in grain yield and an economic return of US$ 189.50 ha−1. Differences in NDVI were found before and after top-dressing N fertilization, indicating that GreenSeeker was efficient in monitoring wheat N nutrition. NDVI varied depending on the measurement distance, stabilizing from 0.30 to 1.20 m above the canopy. Because of the influence of incident radiation, higher NDVI values were obtained at the beginning and end of the day. The results suggest that for the wheat cultivar Quartzo following soybean under minimum soil disturbance, while there is no need to apply N at sowing, an important increase in grain yield can be obtained with top-dressing N fertilization. In addition, to improve the accuracy in developing N fertilizer recommendation models using GreenSeeker, a consistent protocol for spectral reflectance readings, mainly regarding the time of day, is required.

Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper

Vegetation indices (VIs) can be useful tools to evaluate crop nitrogen (N) status. To be effective, VIs measurements must be related to crop N status. The nitrogen nutrition index (NNI) is a widely accepted parameter of crop N status. The present work evaluates the performance of several VIs to estimate NNI in sweet pepper (Capsicum annuum). The performance of VIs to estimate NNI was evaluated using parameters of linear regression analysis conducted for calibration and validation. Three different sweet pepper crops were grown with combined irrigation and fertigation, in Almería, Spain. In each crop, five different N concentrations in the nutrient solution were frequently applied by drip irrigation. Proximal crop reflectance was measured with Crop Circle ACS470 and GreenSeeker handheld sensors, approximately every ten days, throughout the crops. The relative performance of VIs differed between phenological stages. Relationships of VIs with NNI were strongest in the early fruit growth and flowering stages, and less strong in the vegetative and harvest stages. The green band-based VIs, GNDVI, and GVI, provided the best results for estimating crop NNI in sweet pepper, for individual phenological stages. GNDVI had the best performance in the vegetative, flowering, and harvest stages, and GVI had the best performance in the early fruit growth stage. Some of the VIs evaluated are promising tools to estimate crop N status in sweet pepper and have the potential to contribute to improving crop N management of sweet pepper crops.

In‐season potato yield prediction with active optical sensors

AbstractCrop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha−1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.

Comparison of Trimble GreenSeeker and Crop Circle (Model ACS-210) Reflectance Meters for Assessment of Severity of Late Leaf Spot

ABSTRACT Four field experiments conducted in 2015 were used to examine the relationships among normalized difference vegetation index (NDVI) measurements from two canopy crop sensors and visual estimates of defoliation by late leaf spot (Nothopassalora personata) of peanut (Arachis hypogaea) the predominant foliar disease in this study. For each evaluation, reflectance was measured with each the two meters, and leaf spot severity was measured visually within as short a time as possible. Linear and quadratic regression models were used to characterize the relationships between percent defoliation from late leaf spot and NDVI measured with the GreenSeeker (NDVIGS) and Crop Circle (NDVICC) instruments and the relationships between NDVIGS and NDVICC. NDVIGS decreased with increasing percent defoliation according to linear or quadratic functions in three of the four trials, NDVICC decreased with increasing percent defoliation according to linear functions in three of four trials. In two of the four trials, NDVICC increased with increasing NDVIGS according to quadratic functions, but there was no significant regression for those variables in two trials. In three of the four trials, NDVICC linear regression had a better fit for predicting percent defoliation according to the coefficient of determination (R2). There was no indication for either instrument that the same NDVI reading corresponded with the same level of defoliation across trials. Results indicated that NDVI measurements from the two instruments are not interchangeable.

Estimating Wheat Yield with Normalized Difference Vegetation Index and Fractional Green Canopy Cover

Core Ideas Normalized difference vegetation index and fractional green canopy cover best estimated wheat grain yield. Normalized difference vegetation index and fractional green canopy cover measurements should be conducted prior to Feekes 6 growth stage. Camera height should be adjusted to capture three rows of wheat in the image when using fractional green canopy cover to estimate wheat grain yield. Producers are interested in methods for estimating wheat grain yield at earlier growth stages. Ability to estimate wheat yield early in the growing season could provide producers with the flexibility needed to decide whether to terminate their wheat crop to plant a more profitable alternative crop, such as soybean. The traditional assessment method, counting plants and tillers, is tedious and time consuming. There is a need to create easier methods to estimate wheat yield, such as normalized difference vegetative index (NDVI) or fractional green canopy cover (FGCC). The objective of this study was to compare NDVI, FGCC, and stem count measurements at multiple wheat growth stages to estimate winter wheat grain yield. Trials were established during the 2015–2016 and 2016–2017 growing seasons at two locations in Ohio. Treatment included wheat seeding rates of 0.75, 1.0, 1.5, 2.0, and 2.5 million seeds/acre to mimic a poor to excellent wheat stand. Wheat stand assessment methods of NDVI, FGCC, and stem counts were conducted at Feekes 1 (emergence), Feekes 5 (leaf sheaths strongly erect), and Feekes 6 (first node visible) growth stages. Regression models were used to estimate wheat yield. Normalized difference vegetation index (R2 = 0.49) and FGCC (R2 = 0.45) best estimated wheat grain yield while stem count measurements accounted for 29% of the variability in yield. However, NDVI and FGCC measurements are time sensitive and should be conducted prior to Feekes 6 growth stage, and for FGCC, camera height should be adjusted to capture three rows of wheat in the image.

Impacts of Water Stress on Forest Recovery and Its Interaction with Canopy Height.

Global climate change is leading to an increase in the frequency, intensity, and duration of drought events, which can affect the functioning of forest ecosystems. Because human activities such as afforestation and forest attributes such as canopy height may exhibit considerable spatial differences, such differences may alter the recovery paths of drought-impacted forests. To accurately assess how climate affects forest recovery, a quantitative evaluation on the effects of forest attributes and their possible interaction with the intensity of water stress is required. Here, forest recovery following extreme drought events was analyzed for Yunnan Province, southwest China. The variation in the recovery of forests with different water availability and canopy heights was quantitatively assessed at the regional scale by using canopy height data based on light detection and ranging (LiDAR) measurements, enhanced vegetation index data, and standardized precipitation evapotranspiration index (SPEI) data. Our results indicated that forest recovery was affected by water availability and canopy height. Based on the enhanced vegetation index measures, shorter trees were more likely to recover than taller ones after drought. Further analyses demonstrated that the effect of canopy height on recovery rates after drought also depends on water availability—the effect of canopy height on recovery diminished as water availability increased after drought. Additional analyses revealed that when the water availability exceeded a threshold (SPEI > 0.85), no significant difference in the recovery was found between short and tall trees (p > 0.05). In the context of global climate change, future climate scenarios of RCP2.6 and RCP8.5 showed more frequent water stress in Yunnan by the end of the 21st century. In summary, our results indicated that canopy height casts an important influence on forest recovery and tall trees have greater vulnerability and risk to dieback and mortality from drought. These results may have broad implications for policies and practices of forest management.