Plant Senescence Reflectance Index Research Articles

A statistical approach to map saline and fluoride (F) affected areas using optical and microwave remote sensing data simulations: A case study in Palacode Taluk, Tamil Nadu, India

This research presents an approach for the indirect assessment of soil contamination by soil salinity and Fluoride (F) in varying environmental conditions, including both dry and wet seasons. The research was conducted in Dharmapuri District, utilizing Landsat 8 and Sentinel-1 data for analysis. The region exhibits a diverse soil regime, encompassing both dry-state and irrigated agriculture. The Modified Salinity Index (MSI) and Plant Senescence Reflectance Index (PSRI) were extracted from Landsat 8 imagery to assess contamination in dry conditions. Simultaneously, the Simplified Hallikainen's model and the Inverse of Dual-Polarization Soil Vegetation Index (DPSVI−1) were obtained from Synthetic Aperture Radar (SAR) data to investigate contamination in wet conditions. The correlation coefficient (CC) analysis was performed between optical data simulations (MSI and PSRI) and synthetic aperture radar (SAR) data simulations (Simplified Hallikainen's model and DPSVI−1), yielded R2 values of 0.90 and 0.92 in the dry and wet seasons, respectively, indicating a high degree of accuracy and reliability. From the correlation coefficient (CC) analysis, approximately 23% (173 km2) of the area exhibited negative correlation was identified as F affected regions and 54% (378 km2) of the areas exhibited positive correlation was identified as saline-affected regions in dry season. Similarly, approximately 41% (288 km2) of the area was identified as F affected regions and 46% (322 km2) of the areas was identified as saline-affected regions in wet season. The findings were verified through ground measurements of soil electrical conductivity (EC) and F using a confusion matrix. The results indicate an overall accuracy of 83% and 84%, accompanied by kappa coefficient measures of 0.79 and 0.83, for salinity and F, respectively, during the dry season. Likewise, in the wet season, there is a recorded overall accuracy of 86% and 88%, with corresponding kappa coefficients of 0.81 and 0.85 for salinity and F, respectively. This implies that the proposed methodology has a higher potential for mapping saline and F-affected areas in both wet and dry conditions. Hence, the methodology outlined in this study provides crucial information for local authorities, farmers, and policymakers, enabling informed decision-making and proactive measures.

Detection of pine wood nematode infestation using hyperspectral drone images

The pine wood nematode (PWN) is one of the most threatening pests of pine forests in China. An essential management response involves felling and removing the infested trees before the PWN can spread over large areas, but this requires early detection. In this study, we analyzed the temporal changes in the spectral signatures of healthy and infested trees throughout the entire growing season (May to October) using unmanned aerial vehicle (UAV) hyperspectral images (HSI). To explore the potential of these seasonal spectral changes in detecting PWN, we investigated the separability of healthy and infested trees using both absolute values and changes in values. A random forest (RF) classifier was applied to separate healthy and infested trees based on reflectances, derivatives, vegetation indices (VIs), and their seasonal change values. We obtained the following results: (1) the seasonal changes of tree crown spectra following a PWN infestation were similar to those observed using absolute spectral values, though the latter performed better in early detection; (2) in the early stage of infestation (June and July), the green and red-edge regions contained the most sensitive bands, whereas the near-infrared portion played a more important role in later stages; (3) derivatives achieved higher overall accuracy (OA) than the reflectance in the early stage of PWN infestation (June and July); (4) in June, the Pigment Specific Simple Ratio (PSSR) had the highest separability (OA = 0.77) for PWN infestation, followed by the Plant Senescence Reflectance Index (PSRI) and the Normalized Difference Red-edge Index (NDRE), both with a OA of 0.70. In July, PSRI, Red Edge Normalized Difference Vegetation Index (RENDVI), and the Modified Red Edge Simple Ratio (MRESR) had the highest OA of 0.77. These VIs were calculated based on the red-edge regions. In August, RENDVI achieved the best accuracy (0.87), followed by the Normalized Difference Vegetation Index (NDVI, OA = 0.83) and the Plant Stress Index (PSI, OA = 0.80). In September and October, PSRI, PSI, the Greenness Index (GI), and RENDVI showed the strongest separability (0.87 to 0.90). Similar results were obtained using change values of VIs. In June, RENDVI, PSI, and NDVI showed the highest accuracy (OA = 0.73). In July, MRESR had the best accuracy (0.77), followed by RENDVI (0.73) and PSI (0.73). In the late stage of PWN infestation, NDVI, PSRI, PSI, RENDVI, GI, and PSSR obtained the highest accuracies (0.83 to 0.93), surpassing the accuracy achieved using absolute spectra; (5) in RF classification model, seasonal variation values derived from reflectances, derivatives, and VIs performed well in the late stages of the PWN infestation (September and October) but were not as good as absolute values in the early and middle stages (June to August). Our study explored the potential of seasonal changes in UAV spectra for early detection of forest pests.

Detection of Fungal Diseases in Lettuce by VIR-NIR Spectroscopy in Aquaponics.

One of the main challenges facing the development of aquaponics is disease control, due on one hand to the fact that plants cannot be treated with chemicals because they can lead to mortality in cultured fish. The aim of this study was to apply the visible-near-infrared spectroscopy and vegetation index approach to test aquaponically cultivated lettuce (Lactuca sativa L.) infected with different fungal pathogens (Aspergillus niger, Fusarium oxysporum, and Alternaria alternata). The lettuces on the third leaf formation were placed in tanks (with dimensions 1 m/0.50 m/0.35 m) filled up with water from the aquaponics system every second day. In this study, we included reference fungal strains Aspergillus niger NBIMCC 3252, Fusarium oxysporum NBIMCC 125, and Alternaria alternata NBIMCC 109. Diffuse reflectance spectra of the leaves of lettuce were measured directly on the plants using a USB4000 spectrometer in the 450-1100 nm wavelength range. In near-infrared spectral range, the reflectance values of infected leaves are lower than those of the control, which indicates that some changes in cell structures occurred as a result of the fungal infection. All three investigated pathogens had a statistically significant effect on leaf water content and water band index. Vegetative indices such as Chlorophyll Absorption in Reflectance Index (CARI), Modified chlorophyll absorption in reflectance index (MCARI), Plant Senescence Reflectance Index (PSRI), Red Edge Index (REI2), Red Edge Index (REI3), and Water band index (WBI) were found to be effective in distinguishing infected plants from healthy ones, with WBI demonstrating the greatest reliability.

Proximal remote sensing and gross primary productivity in a temperate salt marsh

Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network.

Analyzing the recovery mechanisms of patchy degradation and its response to mowing and plateau pika disturbances in alpine meadow

The alpine meadow is susceptible to degradation triggered by overgrazing and rodent outbreaks, leading to uneven patches of degradation. In this regard, we conducted a study that involved the implementation of nine combined treatments utilizing a two-factor randomized block design. The treatments were established based on three levels of plateau pika densities and three levels of simulated grazing intensity (mowing), and were replicated three times, resulting in 27 test plots. Hyperspectral images were used to map healthy meadow (HM), long-term vegetated patch (LVP), and short-term vegetated patch (SVP) and characterize their spectral properties. Four vegetation indices, including modified red edge simple ratio (MRESR), photochemical reflectance index (PRI), anthocyanin reflectance index (ARI), and plant senescence reflectance index (PSRI), were used to measure the recovery rate of various patches. In order to measure the resilience of various patches, the ratio of the LVP to degraded patch coverage (RLDC) and ratio of the SVP to degraded patch coverage (RSDC) were calculated from nadir-viewing drone images of the test plots. The research findings indicate that moderate mowing negatively affects the vegetative recovery rate of grass in healthy meadow. A moderate density of plateau pika facilitates plant growth in various types of patches. The joint action of plateau pika and mowing amplifies the negative effects of both factors while neutralizing their positive effects. SVP plants improve community resilience by shortening their life cycle and sprouting in cracks of soil crust. SVPs exhibit high resilience and can withstand local severe interference by plateau pika and grazing activities. LVPs require a stable environment for healthy meadow and cannot adapt to the disturbance of plateau pika at any density. Under the joint interferences of plateau pika and mowing, the SVPs could not be replaced by the LVPs. Therefore, the restoration and management of patchily degraded meadow must consider enhancing the resilience of plant patches and maintaining the long-term and stable survival of the restored grassland.

Land cover changes, biomass loss, and predictive causes of massive dieback of a mangrove plantation in Lampung, Sumatra

A new massive dieback case of Avicennia marina was observed in the area of the Sunda Strait, which started in 2020, and the cause is still unknown. This research was conducted to report the rate of mangrove degradation, measure biomass loss, and predict the causes of the dieback. A sequential imagery analysis (2019–2022) was conducted to interpret the area changes using a research area of 200 ha from the total 528.69-ha mangrove, using the plant senescence reflectance index (PSRI) and normalized difference vegetation index (NDVI). Field data were collected before and during the dieback event (2020 and 2021) at seven research stations including, live mangroves, partial dieback, and full dieback sites. Biomass loss was measured as standing stock volume (SSV) and total biomass carbon (TBC). A literature study was conducted to predict the cause of the dieback. Based on the results, the mangrove dieback caused a rapid and significant loss of healthy stands (results of the PSRI) and very-high-density stands (results of the NDVI). The rate of healthy stand loss was 13.43 ha month−1 (during July–October 2020) and that of very-high-density stand loss was 14.99 ha month−1 (October 2020–April 2021), which affected 126.62 ha or 24% of the total area (last measurement in January 2022). The SSV before dieback was 118.70 (±46.1) m3 ha−1, but within 14 months, it decreased to 20.8 (±6.0), 79.80 (±52.3), and only 1.0 (±1.0) m3 ha−1 for live, partial dieback, and full dieback stands, respectively. The TBC of live mangroves (before dieback) was 51.6 (±24.4) Mg C ha−1, but within the same period, it changed to 30.70 (±3.80), 69.50 (±14.6), and 51.60 (±9.4) Mg C ha−1 for live, partial dieback, and full dieback stands, respectively. Based on the literature study and field observation, the dieback was probably related to less flushing and long-term inundation by the decrease in rainfall intensity, in addition to nitrogen (NO3−N and NO2−N) enrichment, an over-supply of N from fishpond wastes. Sustainable silvofishery may be introduced to ameliorate water and soil quality.

Enhanced Herbicidal Action of Clopyralid in the Form of a Supramolecular Complex with a Gemini Surfactant

Surfactants are often added to herbicidal formulations to improve the delivery of the herbicide into plants. In this study a new herbicidal formulation was formed based on the clopyralid with 0.01% gemini surfactant hexanediyl-1,6-bis(dimethylcetylammonium bromide) (16-6-16) as an adjuvant. The increase in the efficiency of the formulation was associated with the formation of a supramolecular surfactant–herbicide complex (SMC), which has improved wetting properties, provides high clopyralid concentration on the leaf surface, and has higher penetrating ability compared to surfactant-free clopyralid solutions. Comparison of the herbicidal action of clopyralid–16-6-16 SMC with two commercial formulations of the same concentration of clopyralid was performed using digital phenotyping of the model weed plant cocklebur (Xanthium strumarium). Based on the spectral indices NDVI (normalized differential vegetation index) and PSRI (plant senescence reflectance index) and key morphological indexes of the leaf angle, plant height, and leaf area, we showed that clopyralid formulations strongly affected the plants and that the strongest and most durable effect was exerted by the clopyralid–16-6-16 SMC formulation.

Linking random forest and auxiliary factors for extracting the major economic forests in the mountainous areas of southwestern Yunnan Province, China

Forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County, which is a mountainous area with rich biodiversity and is witnessing rapid development of economic forests in Yunnan province of China. The results obtained using the RF–AFs model were compared with those obtained using the random forest model without AFs (RF). The results were as follows: (1) The kappa coefficient for extracting the first–level land use obtained using the RF model was 0.9531. Lancang County is dominated by forests, accounting for 73.76% of the total area. (2) After parameter optimization, the RF–AFs model yielded the highest accuracy in the extraction of the second–level forests, with a kappa coefficient value of 0.9493, which was 14.69% higher than that of the RF model. Thus, the RF–AFs model is more suitable for the precise extraction of economic forests. (3) The evaluation results of the factors’ importance of the RF–AFs model showed that the cumulative importance values of SCs such as temperature (TEM), elevation (EL), precipitation (PRE) and VIs such as plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), transformed soil–adjusted vegetation index (TSAVI) was 76.09%, indicating that they were the main factors for the extraction of economic forests. (4) Economic forests are dominated by Simao pines in Lancang County, which are mainly distributed in the central, southwestern and northern regions, accounting for 31.37% of forests area. The proportion of tea plantations, eucalyptus, and rubber trees is 9.05%, 6.71%, and 3.05% of forests area, respectively. The RF–AFs model is conducive for precisely extracting the economic forests and is thus of great significance in studying the ecological and environmental effects of economic forests, performing forestry management, and maintaining regional ecological security.

Estimation of Maize Yield and Protein Content under Different Density and N Rate Conditions Based on UAV Multi-Spectral Images

In the field of precision agriculture research, it is very important to monitor crop growth in time so as to effectively conduct field diagnosis and management and accurately predict yield and quality. In this experiment, the relationship between the vegetation index of Zhengdan 958 and Suyu 41 and their yield and quality when reducing N application (25 and 50% N reduction compared to local conventional N application rate) under low, medium and high planting densities (60,000, 75,000 and 90,000 plants·ha−1) during 2018–2020 was investigated using multispectral images obtained from UAV monitoring. The results showed that under different density treatments, the normalized vegetation index (NDVI) and ratio vegetation index (RVI) decreased with the decrease in nitrogen application, while the plant senescence reflectance index (PSRI) increased. Through principal component analysis (PCA) and subordinate function analysis, the comprehensive score of each treatment can reflect the maize yield and total protein content under each treatment. Based on the vegetation index, predictive models of maize yield and protein content were established. The best prediction period for grain yield and protein content were physiological maturity and 35 days after silking (R4), respectively. The R2 of the predictive models are greater than 0.734 and 0.769, respectively. Multi-period and multi-vegetation indexes can better monitor crop growth and help agricultural field management.

Estimating Biophysical Parameters of Native Grasslands Using Spectral Data Derived from Close Range Hyperspectral and Satellite Data

Estimating biophysical parameters of native grassland enables management changes that affect ecological processes and economic benefits. Although multiple hyperspectral studies were focused on native grasslands, just a few compare data at different scales and among ecoregions. In this study, we compared data collected at different spectral and spatial scales and among Canadian Prairie ecoregions. Field observations indicate that the Fescue Ecoregion grasslands has specific dominant species, while the Moist-Mixed and Mixed Ecoregions share similar dominant species, which is important in determining parameters such as leaf area index (LAI) and canopy height. Hyperspectral measurements showed a specific signature for the Fescue Ecoregion, due to denser canopies, while the Moist-Mixed and Mixed Ecoregions showed similar spectral characteristics to each other. The correlation between biophysical parameters and spectral indices reveals the importance of LAI, since it was significantly correlated with all spectral indices analyzed. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and the Plant Senescence Reflectance Index (PSRI) showed significant correlations with biophysical parameters. The comparison results indicated the PSRI being overestimated at all sites (satellite data) and NDVI underestimated at all sites. Finally, the satellite-derived LAI showed a significant positive relationship with the field-measured LAI.

Differentiation of Six Grassland/Forage Types in Three Canadian Ecoregions Based on Spectral Characteristics

Differentiation of grassland/forage types and accurate estimates of their location and extent are important for understanding their ecological processes and for applying appropriate management practices. We are aiming to reveal the different spectral characteristics of six grassland/forage land covers in three ecoregions located in the Canadian Prairies, based on field data and satellite images. Three spectral indices representing productivity (Normalized Difference Vegetation Index (NDVI)), moisture content (Normalized Difference Moisture Index (NDMI)), and plant photosynthetic activity (Plant Senescence Reflectance Index (PSRI)) were used for comparison of means, comparison of coefficient of variation (CV), and analysis of variance (ANOVA). The results indicated that different grassland types show distinguishable spectral characteristics in the Moist-Mixed and Mixed Ecoregions, while it was not possible to differentiate the classes in the Fescue Ecoregion. To further investigate the within-sites and between-sites heterogeneity, we calculated the CV in a 3 × 3 window and placed them in comparative triangles to demonstrate their potential separability. Results indicated that the triangles based on the CV offered greater class separability in the Fescue Ecoregion and in the Mixed Ecoregion.

Hyperspectral vegetation indices offer insights for determining economically optimal time of harvest in Mentha arvensis

Continual crop monitoring and harvesting at optimum time to ensure maximum economic returns is a huge challenge for farmers having small landholdings in Indo-Gangetic plains where Mentha arvensis is a popular cash crop. The present study evaluates different hyperspectral indices for phenotyping the canopy of M. arvensis winter crop (cv. CIM-Kranti) for detection of optimum harvest time using ground-based plant traits. Field experiments were conducted during mid-crop growth stage to late crop growth period for two consecutive years using hyperspectral camera (400–940 nm) to acquire images representing the spectral reflectance of crop canopies. Hyperspectral vegetation indices (HVIs) derived from these images were further evaluated with an aim to identify those hyperspectral indices showing a strong correlation to measured crop parameters. Out of the 18 HVIs, 4 HVI’s i.e. photochemical reflectance index (PRI), carotenoids reflectance index (CRIr), hyperspectral normalized difference vegetation index (HNDVI) and plant senescence reflectance index (PSRI) were recognized as potential predictors for sucker traits (weight and length) and thereby indicators of optimum harvesting time for M. arvensis winter crop (cv. CIM-Kranti) cultivated primarily to produce suckers for crop propagation. Our results show promise for the development of non-destructive methods for the detection of optimum harvest time using crop reflectance. This study holds importance for the menthol mint farmers, who cultivate the crop for suckers during winter months for additional income generation.

Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

BackgroundHigh-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial.ResultsThe results show that 40–42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of rs = 0.50–0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI).ConclusionsThe proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.

Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery

Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there is little research on the monitoring of areca yellow leaf disease. PlanetScope imagery can achieve daily global coverage at a high spatial resolution (3 m) and is thus suitable for the high-precision monitoring of plant pest and disease. In this paper, PlanetScope images were employed to extract spectral features commonly used in disease, pest and vegetation growth monitoring for primary models. In this paper, 13 spectral features commonly used in vegetation growth and pest monitoring were selected to form the initial feature space, followed by the implementation of the Correlation Analysis (CA) and independent t-testing to optimize the feature space. Then, the Random Forest (RF), Backward Propagation Neural Network (BPNN) and AdaBoost algorithms based on feature space optimization to construct double-classification (healthy, diseased) monitoring models for the areca yellow leaf disease. The results indicated that the green, blue and red bands, and plant senescence reflectance index (PSRI) and enhanced vegetation index (EVI) exhibited highly significant differences and strong correlations with healthy and diseased samples. The RF model exhibits the highest overall recognition accuracy for areca yellow leaf disease (88.24%), 2.95% and 20.59% higher than the BPNN and AdaBoost models, respectively. The commission and omission errors were lowest with the RF model for both healthy and diseased samples. This model also exhibited the highest Kappa coefficient at 0.765. Our results exhibit the feasible application of PlanetScope imagery for the regional large-scale monitoring of areca yellow leaf disease, with the RF method identified as the most suitable for this task. Our study provides a reference for the monitoring, a rapid assessment of the area affected and the management planning of the disease in the agricultural and forestry industries.

Role of epicuticular wax in the regulation of plant growth and health as measured by spectral indices

ABSTRACT Using remote sensing technology for exploring the trait of interest can provide better results without damaging the plants and are comparatively economic. Traditional methods are available for quantification of different pigments and chemicals present in plants, yet these methods do not allow repeated measurements on the same plant throughout development. The reflected values from plant surfaces are a direct representation of plant physiology including plant morphological factors. The hyperspectral imaging indices for vegetation and water-stressed canopies provided a better indication of each genotype proficiency, thus improving their selection efficiency. The presence of epicuticular wax (EW) influences the reflectance from leaf surface which depends on the presence of leaf pigments including carotenoids, photosynthetic light use efficiency biochemical structures, and water content as they absorb the incident light necessary for photosynthesis. The results obtained suggested decrease carotenoid reflectance index (CRI) and photochemical reflectance index (PRI) values for high wax lines indicating the low concentration of stress-related pigments thus improving plant health and extended maturation. The high waxy lines decreased for plant senescence reflectance index (PSRI) and reduce canopy stress at grain filling and maturation growth stages. A positive correlation between high epicuticular wax (EW) and yield was found confirming previous study. A positive correlation between high epicuticular wax (EW) lines and yield indicated its important role in preventing yield losses under drought conditions.

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.

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning.

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

Аналіз характеристик та обґрунтування індексів рослинності

In this article there has been given the analyzing of non-fiction sources in order to find out the popular indexes of the vegetable control parameters. There are such parameters as chlorophyll presence, water containing, the efficiency of a light use, nitrogen and carbon presence, foliage pigmentation.  The chlorophyll presence control is conducted by such indexes as the difference vegetation index, the global environmental monitoring index, the infrared percentage vegetation index, the modified simple ratio, the modified Soil adjusted vegetation index 2, the non-linear index, the normalized difference vegetation index, the optimized soil adjusted vegetation index, the renormalized difference vegetation index, the soil adjusted vegetation index, the simple ratio etc. The control of water content can be based on the moisture stress index, the normalized difference infrared index, the normalized difference water index, the normalized multi-band drought index, the water band index. There is the photochemical reflectance index, the structure insensitive pigment index, the red green ratio index for the efficiency control of the light use. The nitrogen presence is controlled by the normalized differential nitrogen index. The carbon presence can be detected by the implementing of cellulose absorption index, the lignin cellulose absorption index, the normalized difference lignin index, the plant senescence reflectance index. The foliage pigmentations can be controlled by the means of the anthocyanin reflectance index 1, the anthocyanin reflectance index 2, the carotenoid reflectance index 1, and the carotenoid reflectance index 2. The specificity of the presented indexes is described; the calculation formulas and its main straits are proposed.

Evaluating annual spruce budworm defoliation using change detection of vegetation indices calculated from satellite hyperspectral imagery

During spruce budworm (SBW; Choristoneura fumiferana (Clem.)) outbreaks, defoliation causes severe tree damage and mortality to spruce-balsam fir (Picea spp.-Abies balsamea (L.) Mill.) forests. Annual defoliation thematic maps provide vital information for forest protection planning against outbreaks, but such data are challenging to accurately collect over large areas during a short (2–3 week) period when damaged foliage peaks. Current operational methods for estimating annual defoliation are aerial surveys, through visual observations by professionals from small aircraft, defining broad defoliation classes which are subjective and spatially can be inaccurate. Remote sensing studies estimating annual defoliation are limited due to lack of ground verification data and difficulty of capturing spectral changes specific to the short detection period. Only one study has used satellite hyperspectral imagery to detect annual SBW defoliation, classifying just two classes of defoliation and using inaccurate aerial survey results for training and validation. In this paper, we introduce a new method to estimate annual SBW defoliation in three classes (light ≤ 30%, moderate 30–70%, and severe ≥ 70%), using change detection of vegetation indices (VIs) derived from satellite (EO-1 Hyperion) hyperspectral imagery acquired before and after annual defoliation. Random Forest and Support Vector Machine classifiers were used to classify defoliation, and intensive field measurements of branch-level defoliation per plot were used for accuracy assessment. Field plots, with spatially correlated defoliation within stands, were used to derive an additional 624 plots to obtain sufficient and independent datasets for training and validating the detection results. The top four VIs for detecting annual defoliation, based on Random Forest Total Variable Importance Score and Spearman's rank correlations, were Plant Senescence Reflectance Index, Normalized Difference Water Index, Moisture Stress Index, and Enhanced Vegetation Index. The spectral regions of VIs used to classify defoliation included visible, near-infrared, red-edge, and shortwave infrared. Accuracies of the light, moderate, and severe defoliation classes using our method ranged from 55–85%, 40–76%, and 40–80%, respectively, which were significantly higher than the 32% accuracy of aerial survey defoliation data for the plots.

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.