Search Results (108)

There is growing interest in the use of herbicide for the silvicultural practice of tree thinning (i.e., chemical thinning or e-thinning) in New Zealand. Potential benefits of this approach include improved stability of the standing crop in high winds, and safer and lower-cost operations, particularly in steep or remote terrain. As uptake grows, tools for monitoring treatment effectiveness, particularly during the early stages of stress, will become increasingly important. This study evaluated the use of UAV-based multispectral and hyperspectral imagery to detect early herbicide-induced stress in a nine-year-old radiata pine (Pinus radiata D. Don) plantation, based on temporal changes in crown spectral signatures following treatment with metsulfuron-methyl. A staggered-treatment design was used, in which herbicide was applied to a subset of trees in six blocks over several weeks. This staggered design allowed a single UAV acquisition to capture imagery of trees at varying stages of herbicide response, with treated trees ranging from 13 to 47 days after treatment (DAT). Visual canopy assessments were carried out to validate the onset of visible symptoms. Spectral changes either preceded or coincided with the development of significant visible canopy symptoms, which started at 25 DAT. Classification models developed using narrow band hyperspectral indices (NBHI) allowed robust discrimination of treated and non-treated trees as early as 13 DAT (F1 score = 0.73), with stronger results observed at 18 DAT (F1 score = 0.78). Models that used multispectral indices were able to classify treatments with a similar accuracy from 18 DAT (F1 score = 0.78). Across both sensors, pigment-sensitive indices, particularly variants of the Photochemical Reflectance Index, consistently featured among the top predictors at all time points. These findings address a key knowledge gap by demonstrating practical, remote sensing-based solutions for monitoring and characterising herbicide-induced stress in field-grown radiata pine. The 13-to-18 DAT early detection window provides an operational baseline and a target for future research seeking to refine UAV-based detection of chemical thinning. Full article

Global challenges such as climate change and population growth require improvements in crop monitoring models. To address these issues, this study advances the identification of potato crop phenological stages using satellite remote sensing, a field where cereals have been the primary focus. We introduce a methodology using Sentinel-1 (S1) and Sentinel-2 (S2) time series data to pinpoint critical phenological stages—emergence, canopy closure, flowering, senescence onset, and harvest timing—at the field scale. Our approach utilizes analysis of NDVI, fAPAR, and IRECI2 from S2, alongside VH and VV polarizations from S1, informed by domain knowledge of the spectral and morphological responses of potato crops. We propose the integration of NDVI and VH indices, NDVI_VH, to improve stage detection accuracy. Comparative analysis with ground-observed stages validated the method’s effectiveness, with NDVI proving to be one of the most informative indices, achieving RMSEs of 12 and 14 days for emergence and closure, and 17 days for the onset of senescence. The integrated NDVI_VH approach complemented NDVI, particularly in harvest and flowering stages, where VH enhanced accuracy, achieving an overall R² value of 0.80. The study demonstrates the potential of combining SAR and optical data for post-season crop phenology analysis, providing insights that can inform the development of new methods and strategies to enhance on-season crop monitoring and yield forecasting. Full article

Extreme climate events are becoming more intense, and how coastal mangroves respond to the alternating intense cyclones and severe droughts is less understood, which challenges the sustainability of the ecosystem services they provide to coastal communities. To address this, we analyzed spatiotemporal dynamics of coastal mangroves in a Caribbean island in response to major hurricanes in 2017, which followed a severe multi-year drought in 2014–2015, using multiple indices derived from multispectral optical images. We further explored the roles of hurricane forces, local hydro-geomorphic environment, and rainfall dynamics in the damage and the following recovery. In addition to the hurricane forces, such as gusty wind and rainfall, the local hydro-geomorphic environment largely determines the spatial variations of damage. Lower-lying, flatter, and wetter mangrove areas sustained more damage, possibly due to prolonged inundation susceptibility and tall canopy configurations. Recovery is mainly limited by the severity of damage. However, sufficient rainfall gradually becomes important to facilitate the recovery. While the pre-hurricane severe drought (2014–2015) largely degraded the mangroves at dry sites, the drought after the hurricanes exacerbated the hurricane damage and retarded the recovery. We also found that the spectral distance and the mangrove vegetation index revealed slower and more spatiotemporally heterogenous mangrove recovery than indices of greenness, implying they are better measures for monitoring mangroves’ response to disturbance. Six years after the disturbance, the greenness of mangroves near the hurricane landfall reached 84% of the pre-hurricane values. However, the mangrove vegetation index showed that healthy mangrove coverage was only 10%, in comparison to 76% before the disturbance. The sluggish recovery at this site with the severest damage may be associated with the loss of pre-established seedlings and the difficulty to have new ones established, thus human efforts are in need to restore the system. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

Since its first detection in 2013, Xylella fastidiosa subsp. pauca (Xfp) has caused a devastating Olive Quick Decline Syndrome (OQDS) outbreak in Southern Italy. Effective disease surveillance and treatment strategies are urgently needed to mitigate its impact. This study investigates the short-term (1.5 years) effects of thymol-based treatments on infected olive trees of the susceptible cultivar Cellina di Nardò in two orchards in Salento, Apulia region. Twenty trees per trial received a 3% thymol solution either alone or encapsulated in a cellulose nanoparticle carrier. Over two years, sap flux density and canopy-transmitted solar radiation were monitored using TreeTalker sensors, and spectral greenness indices were calculated. Xfp cell concentrations in plant tissues were quantified via qPCR. Neither thymol treatment halted disease progression nor significantly reduced bacterial load, though the Xfp cell concentration reduction increased over time in the preventive trial. Symptomatic trees exhibited increased sap flux density, though the treatment mitigated this effect in the curative trial. Greenness indices remained lower in infected trees, but the response to symptom severity was delayed. These findings underscore the need for longer-term studies, investigation of synergistic effects with other phytocompounds, and integration of real-time sensor data into adaptive disease management protocols. Full article

Monitoring vineyard diseases such as downy mildew (Plasmopara viticola) is important for viticulture, enabling an early intervention and optimized disease management. This is crucial for disease monitoring, and the use of high-spatial-resolution multispectral data from unmanned aerial vehicles (UAVs) can allow to for a better understanding of disease progression. This study explores the application of UAV-based multispectral data for monitoring downy mildew infection in vineyards through multi-temporal analysis. This study was conducted in a vineyard plot in the Vinho Verde region (Portugal), where 84 grapevines were monitored, half of which received phytosanitary treatments while the other half were left untreated in this way during the growing season. Seven UAV flights were performed across different phenological stages to assess the effects of infection using spectral bands, vegetation indices, and morphometric parameters. The results indicate that downy mildew affects canopy area, height, and volume, restricting the vegetative growth. Spectral analysis reveals that infected grapevines show increased reflectance in the visible and red-edge bands and a progressive decline in near-infrared (NIR) reflectance. Several vegetation indices demonstrated a suitable response to the infection, with some of them being capable of detecting early-stage symptoms, while vegetation indices using red edge and NIR allowed us to track disease progression. These results highlight the potential of UAV-based multi-temporal remote sensing as a tool for vineyard disease monitoring, supporting precision viticulture and the assessment of phytosanitary treatment effectiveness. Full article

The temperature-based crop water stress index (CWSI) is the most robust metric among precise techniques that assess the severity of crop water stress, particularly in susceptible crops like maize. This study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands. This study sought to predict a foliar temperature-derived maize CWSI as a proxy for crop water stress using UAV-acquired spectral variables together with random forest regression throughout the vegetative and reproductive growth stages. The CWSI was derived after computing the non-water-stress baseline (NWSB) and non-transpiration baseline (NTB) using the field-measured canopy temperature, air temperature, and humidity data during the vegetative growth stages (V5, V10, and V14) and the reproductive growth stage (R1 stage). The results showed that the CWSI (CWSI < 0.3) could be estimated to an R² of 0.86, RMSE of 0.12, and MAE of 0.10 for the 5th vegetative stage; an R² of 0.85, RMSE of 0.03, and MAE of 0.02 for the 10th vegetative stage; an R² of 0.85, RMSE of 0.05, and MAE of 0.04 for the 14th vegetative stage; and an R² of 0.82, RMSE of 0.09, and MAE of 0.08 for the 1st reproductive stage. The Red, RedEdge, NIR, and TIR UAV-bands and their associated indices (CCCI, MTCI, GNDVI, NDRE, Red, TIR) were the most influential variables across all the growth stages. The vegetative V10 stage exhibited the most optimal prediction accuracies (RMSE = 0.03, MAE = 0.02), with the Red band being the most influential predictor variable. Unmanned aerial vehicles are essential for collecting data on the small and fragmented croplands predominant in southern Africa. The procedure facilitates determining crop water stress at different phenological stages to develop timeous response interventions, acting as an early warning system for crops. Full article

Monitoring the dust retention content in grassland plants around open-pit coal mines is of significant importance for environmental pollution monitoring and the development of dust control strategies. This paper focuses on the HulunBuir grassland in the Inner Mongolia Autonomous Region, China. UAV-borne hyperspectral data and measured dust retention content in plant canopies are used as data sources. The spectral response characteristics of canopy dust retention are analyzed, and four types of optimized spectral indices are constructed, including the difference index (DI), ratio index (RI), normalized difference index (NDI), and inverse difference index (IDI). The spectral index with the highest absolute value of the correlation coefficient with the canopy dust retention is selected as the feature variable for each spectral index. In addition, machine learning methods such as the partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) methods are used to develop models for the inversion of canopy dust retention. The results show that as the dust retention content increases, the canopy reflectance in the visible wavelength initially increases and then decreases, while the reflectance in the near-infrared wavelength gradually decreases. The spectral reflectance values at different dust retention levels exhibit significant differences in the 400–420 nm, 579–698 nm, and 714–1000 nm ranges. The four types of spectral indices constructed exhibit high correlations with the canopy dust retention content, and the spectral index with the highest absolute value of the correlation coefficient is composed of near-infrared bands. The dust retention inversion model established using the RF method is more accurate than those established using the PLSR and SVM methods and yields a higher prediction accuracy. The high canopy dust retention areas are mainly distributed within 900 m of the mining area, and the dust retention gradually decreases with distance. In addition, with increasing dust retention, the fractional vegetation cover (FVC) decreases. The results of this study provide a theoretical basis and technical support for monitoring dust retention in grassland plant canopies and for dust control measures. Full article

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (g_s), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (g_s and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm²), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale. Full article

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVI_RB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVI_RB was designed. GRVI_RB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVI_RB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales. Full article

A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R²) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) the most sensitive wavelengths were 640–714 nm at WJQ, 450–650 nm and 750–950 nm at SKS, and 450–700 nm and 750–950 nm at FJJ; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest. Full article

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies. Full article

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. Full article

Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type). Full article

Invasive species have historically disrupted environments by outcompeting, displacing, and extirpating native species, resulting in significant environmental and economic damage. Developing approaches to detect the presence of invasive species, favorable habitats for their establishment, and predicting their potential spread are underutilized management strategies to effectively protect the environment and the economy. Spotted lanternfly (SLF, Lycorma delicatula) is a phloem-feeding planthopper native to China that poses a severe threat to horticultural and forest products in the United States. Tools are being developed to contain the spread and damage caused by SLF; however, methods to rapidly detect novel infestations or low-density populations are lacking. Vegetation spectroscopy is an approach that can represent vegetation health through changes in the reflectance and absorption of radiation based on plant physiochemical status. Here, we hypothesize that SLF infestations change the spectral and chemical characteristics of tree canopies. To test this hypothesis, we used a full range spectroradiometer to sample canopy foliage of silver maple (Acer saccharinum) and red maple (Acer rubrum) trees in a common garden in Berks County, Pennsylvania that were exposed to varying levels of SLF infestation. Foliar spectral profiles separated between SLF infestation levels, and the magnitude of separation was greater for the zero-SLF control compared with higher infestation levels. We found the red-edge and portions of the NIR and SWIR regions were most strongly related to SLF infestation densities and that corresponding changes in vegetation indexes related to levels of chlorophyll were influenced by SLF infestations, although we found no change in foliar levels of chlorophyll. We found no influence of SLF densities on levels of primary metabolites (i.e., pigments, nonstructural carbohydrates, carbon, and nitrogen), but did find an increase in the phenolic compound ferulic acid in response to increasing SLF infestations; this response was only in red maple, suggesting a possible species-specific response related to SLF feeding. By identifying changes in spectral and chemical properties of canopy leaves in response to SLF infestation, we can link them together to potentially better understand how trees respond to SLF feeding pressure and more rapidly identify SLF infestations. Full article