Edge Normalized Difference Vegetation Index Research Articles

Efficient vegetation indices for phenotyping of abiotic stress tolerance in tea plant (Camellia sinensis (L.) Kuntze)

Early non-destructive detection of stress effect is crucial for efficient breeding strategies and germplasm characterization. Recently developed hyperspectral technologies allow to perform fast real-time phenotyping through reflectance-based vegetation indices. However, efficiency of these vegetation indices has to be validated for each crop in different environment. The aim of this study was to reveal efficient vegetation indices for phenotyping of abiotic stress (cold, freezing and nitrogen deficiency) response in tea plant. Among 31 studied VIs, few indices were efficient to distinguish tolerant and susceptible tea plants under abiotic stress: ZMI (Zarco-Tejada & Miller Index), VREI1,2,3 (Vogelmann Red Edge Indices), RENDVI (Red Edge Normalized Difference Vegetation Index), CTR1 and CTR2 (Carter Indices). Most of these indices are calculated based on reflectance in near-infrared area at 705–760 nm, indicating this range as promising for tea germplasm characterization under abiotic stresses. Tolerant tea plants showed the following values under freezing: ZMI ≥1.90, VREI1 ≥ 1.40, RENDVI ≥0.38, Ctr1 ≤ 1.74. The leaf N-content was positively correlated (Pearson's) with the following indices ZMI, VREI1, RENDVI, while negatively correlated with CTR, and VREI2,3. These results will be useful for tea germplasm management, genomics and breeding research aimed at abiotic stress tolerance of tea plant.

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping capability to achieve this goal. The system consists of a multispectral crop growth sensor, signal conditioning module, and truss-type sensor bracket with self-vibration damping capability. The commercial finite element analysis software ABAQUS 6.10 was used to conduct modal and dynamic simulation analyses of the sensor bracket, which indicate that the truss-type sensor bracket can damp vibrations effectively. The p-values (least significant differences) of crop canopy DNRE (red edge normalized difference vegetation index) under different operating speeds (1.5, 3, and 4.5 km/h) are 0.454, 0.703, 0.81, and 0.838, respectively, for four different crop growth stages. In a comparative experiment between the proposed monitoring system and two similar vehicle-mounted sensors (CropSpec and GreenSeeker RT 200) for measuring agronomic parameters at different stages of crop growth, the proposed monitoring system yielded R2 values of 0.8757, 0.7194, and 0.795, respectively, and RMSE values of 0.7157, 2.2341, and 2.0952, respectively, in the tillering stage, jointing stage, and tillering and jointing stage, outperforming the other two sensors.

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.

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines.

Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M3 generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of "a" resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of "b" alleles in Lr9 and Lr10-except for one line with allele "a" in Lr9 and three mutant lines with allele "a" in Lr10-showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with "b" alleles in Lr1, Lr2a, Lr10, and Lr17 and "a" alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the "a" alleles of Lr3 and Lr9.

Groth’s algorithm to detect the possible presence of landmines using changes in the reflection of plants

Abstract. Post-conflict reconstruction includes the removal of land mines and remnants of war. The CSIR conducted field experiments to determine the impact of TNT in the soil on plants as a possible means to detect landmines. All the leaf clip readings were done using a spectrometer to determine reflection and absorption of light at one micrometre intervals between 350 to 2500 micrometre. Laboratory analysis such as UPLC qTOF MS indicated that the TNT have an influence on the plants. Several indices such as Modified Red Edge Normalized Difference Vegetation Index (mNDVI705), Red Edge Position (REP) and Moisture Stress Index (MSI) did not show any significant differences between control plants and experimental plants with different TNT concentrations. Groth’s pattern matching algorithm designed to match several photographs of the same part of the universe. A set of triangles using dominant stars are created for each photograph and matched using an error band. If the selected triangles from the two photographs fit within the error band, then they are from the same section of the universe. Bands for the Pléiades instruments were simulated using the data from the spectrometer for each plant. The reflectance value of the band and the normalised midpoint wavelength of each Pléiades band were used to construct the triangles. The control plant triangle is then matched with the experimental plant and if the triangles do not match, then the effect of TNT on the plant is significant. The initial results with the control plants and experimental plants are positive.

Dynamic subcanopy leaf traits drive resistance of net primary production across a disturbance severity gradient

Across the globe, the forest carbon sink is increasingly vulnerable to an expanding array of low- to moderate-severity disturbances. However, some forest ecosystems exhibit functional resistance (i.e., the capacity of ecosystems to continue functioning as usual) following disturbances such as extreme weather events and insect or fungal pathogen outbreaks. Unlike severe disturbances (e.g., stand-replacing wildfires), moderate severity disturbances do not always result in near-term declines in forest production because of the potential for compensatory growth, including enhanced subcanopy production. Community-wide shifts in subcanopy plant functional traits, prompted by disturbance-driven environmental change, may play a key mechanistic role in resisting declines in net primary production (NPP) up to thresholds of canopy loss. However, the temporal dynamics of these shifts, as well as the upper limits of disturbance for which subcanopy production can compensate, remain poorly characterized. In this study, we leverage a 4-year dataset from an experimental forest disturbance in northern Michigan to assess subcanopy community trait shifts as well as their utility in predicting ecosystem NPP resistance across a wide range of implemented disturbance severities. Through mechanical girdling of stems, we achieved a gradient of severity from 0% (i.e., control) to 45, 65, and 85% targeted gross canopy defoliation, replicated across four landscape ecosystems broadly representative of the Upper Great Lakes ecoregion. We found that three of four examined subcanopy community weighted mean (CWM) traits including leaf photosynthetic rate (p = 0.04), stomatal conductance (p = 0.07), and the red edge normalized difference vegetation index (p < 0.0001) shifted rapidly following disturbance but before widespread changes in subcanopy light environment triggered by canopy tree mortality. Surprisingly, stimulated subcanopy production fully compensated for upper canopy losses across our gradient of experimental severities, achieving complete resistance (i.e., no significant interannual differences from control) of whole ecosystem NPP even in the 85% disturbance treatment. Additionally, we identified a probable mechanistic switch from nutrient-driven to light-driven trait shifts as disturbance progressed. Our findings suggest that remotely sensed traits such as the red edge normalized difference vegetation index (reNDVI) could be particularly sensitive and robust predictors of production response to disturbance, even across compositionally diverse forests. The potential of leaf spectral indices to predict post-disturbance functional resistance is promising given the capabilities of airborne to satellite remote sensing. We conclude that dynamic functional trait shifts following disturbance can be used to predict production response across a wide range of disturbance severities.

Mapping Forage Biomass and Quality of the Inner Mongolia Grasslands by Combining Field Measurements and Sentinel-2 Observations

Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CFarea), and crude protein per area (CParea) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R2 = 0.67, NRMSE = 12%), CFarea (R2 = 0.59, NRMSE = 14%), and CParea (R2 = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R785-900 and R2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R2 = 0.53; NRMSE = 14%; CFarea, R2 = 0.40, NRMSE = 17%; CParea, R2 = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CFarea, and CParea all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management.

Mapping dead understoreyBuxus hyrcana Pojarkusing Sentinel-2 and Sentinel-1 data

AbstractThe Hyrcanian Forests comprise a continuous 800-km belt of mostly deciduous broadleaf forests and are considered as Iran’s most important vegetation region in terms of density, canopy cover and species diversity. One of the few evergreen species of the Hyrcanian Forests is the box tree (Buxus), which is seriously threatened by box blight disease and box tree moth outbreaks. Therefore, information on the spatial distribution of intact and infested box trees is essential for recovery monitoring, control treatment and management. To address this critical knowledge gap, we integrated a genetic algorithm (GA) with a support vector machine (SVM) ensemble classification based on the combination of leaf-off optical Sentinel-2 and radar Sentinel-1 data to map the spatial distribution of box tree mortality. We additionally considered the overstorey species composition to account for a potential impact of overstory stand composition on the spectral signature of understorey defoliation. We consequently defined target classes based on the combination of dominant overstorey trees (using two measures including the relative frequency and the diameter at breast height) and two defoliation levels of box trees (including dead and healthy box trees). Our classification workflow applied a GA to simultaneously derive optimal vegetation indices (VIs) and tuning parameters of the SVM. Then the distribution of box tree defoliation was mapped by an SVM ensemble with bagging using GA-optimized VIs and radar data. The GA results revealed that normalized difference vegetation index, red edge normalized difference vegetation index and green normalized difference vegetation index were appropriate for box tree defoliation mapping. An additional comparison of GA-SVM (using GA-optimized VIs and tuning parameters) with a simple SVM (using all VIs and user-based tuning parameters) showed that our suggested workflow performs notably better than the simple SVM (overall accuracy of 0.79 vs 0.74). Incorporating Sentinel-1 data to GA-SVM, marginally improved the performance of the model (overall accuracy: 0.80). The SVM ensemble model using Sentinel-2 and -1 data yielded high accuracies and low uncertainties in mapping of box tree defoliation. The results showed that infested box trees were mostly located at low elevations, low slope and facing north. We conclude that mortality of evergreen understorey tree species can be mapped with good accuracies using freely available satellite data if a suitable work-flow is applied.

Non-invasive Estimation of Foliar Nitrogen Concentration Using Spectral Characteristics of Menthol Mint (Mentha arvensis L.).

Menthol mint (Mentha arvensis L., Family: Lamiaceae), popularly known as corn mint or Japanese mint, is an important industrial crop that is widely grown for its valued essential oil. Nitrogen (N) is an important macro-nutrient and an essential factor for optimizing the yield and quality of crops. Hence, rapid and accurate estimation of the N content is crucial for nutrient diagnosis in plants and to make precise N fertilizer recommendations. Generally, N concentration is estimated by destructive sampling methods; however, an indirect assessment may be possible based on spectral characteristics. This study aimed to compare the foliar N concentration based on non-destructive (reflectance) and destructive (laboratory analyses) methods in menthol mint. Foliar N concentration was measured through the Kjeldahl method and reflectance by Miniature Leaf Spectrometer C-710 (CID Bio-Science). Using reflectance data, several vegetation indices (VIs), that is, normalized difference red edge (NDRE), red edge normalized difference vegetation index (reNDVI), simple ratio (SR), green–red vegetation index (GRVI), canopy chlorophyll content index (CCCI), photochemical reflectance index (PRI), green chlorophyll index (CIGreen), red edge chlorophyll index (CIRed Edge), canopy chlorophyll index (CCI), normalized pigment chlorophyll ratio index (NPCI), and structure insensitive pigment index (SIPI), were developed to determine the foliar N concentration. The highest correlation (r) between VIs and foliar N concentrations was achieved by NDRE (0.89), followed by reNDVI (0.84), SR (0.83), GRVI (0.78), and CCCI (0.76). Among the VIs, the NDRE index has been found to be the most accurate index that can precisely predict the foliar N concentration (R2 = 0.79, RMSE = 0.18). In summary, the N deficiencies faced by the crop during its growth period can be detected effectively by calculating NDRE and reNDVI, which can be used as indicators for recommending precise management strategies for the application of nitrogenous fertilizers.

Initiation of Unmanned Aerial System (UAS) Phenotyping of Plant Height and Canopy Width in Guar

The use of the Unmanned Aerial System (UAS) has attracted scientific attention because of its potential to generate high-throughput phenotyping data. The application of UAS to guar phenotyping remains limited. Guar is multi-purpose legume species. India and Pakistan are the world’s top guar producers. The U.S. is the world guar largest market with an import value of >$1 billion annually. The objective of this study was to test the feasibility of UAS phenotyping of plant height and canopy width in guar. The UAS data were collected from a field plot of 10 guar accessions on July 7, 2021, and September 27, 2021. The study was organized in a Randomized Complete Block Design (RCBD) with 3 blocks. A total of 23 Vegetation Indices (VIs) were computed. The analysis of variance showed significant genotypic effects on plant weight (p on plant height (p most VIs were significant for both flights (p Vegetation Index (NDVI) and Red Edge Normalized Difference Vegetation Index (NDRE) were significantly and highly correlated with plant height (r = 0.74) and canopy width (r = 0.68). The results will be of interest in developing high throughput phenotyping approach for guar breeding.

Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions

Tomato is among important vegetable crops cultivated in different climates; however, heat stress can greatly affect fruit quality and overall yield. Crop reflectance measurements based on ground reflectance sensor data are reliable indicators of crop tolerance to abiotic stresses. Here, we report on using non-destructive spectral vegetation indices to monitor yield traits of 10 tomato genotypes transplanted on three different dates (Aug. 2, Sept. 3 and Oct. 1) during 2019 growing season in the Riyadh region. The ten genotypes comprised six commercial cultivars–(Pearson Improved, Strain B, Valentine, Marmande VF, Super Strain B, and Pearson early) ––and four local Saudi cultivars (Al-Ahsa, Al-Qatif, Hail and Najran). Spectral reflectance data were utilized using a FieldSpec 3 spectroradiometer in the range of 350–2500 nm to calculate nine vegetation indices (VIs): Normalized Water Band Index (NWBI), Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red Edge Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index (SAVI), Red Edge Normalized Difference Vegetation Index (RENDVI), Renormalized Difference Vegetation Index (RDVI), and Normalized Difference Nitrogen Index (NDNI). VIs and yield parameters (total fruit yield, harvest index) revealed that second transplanting date was optimal for all the genotypes. Valentine showed the best growth performance followed by Najran, Hail, Super Strain B and finally Pearson early. For all the three transplanting dates, Valentine recorded the highest total fruit yield. Additionally, some genotypes had no significant differences in the VIs values or the total fruit yield between the second and third transplanting dates. This study indicated that yield parameters could be linked to rapid, non-destructive hyperspectral reflectance data to predict tomato production under heat stress.

Aboveground biomass estimates over Brazilian savannas using hyperspectral metrics and machine learning models: experiences with Hyperion/EO-1

ABSTRACT We investigated the potential of hyperspectral remote sensing to estimate aboveground biomass (AGB) over the Brazilian savannas (Cerrado), the second-largest source of carbon emissions in Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) image was collected in the dry season at the Ecological Station of Águas Emendadas (ESAE). In order to estimate the AGB, we evaluated the performance of five machine learning models (Classification and Regression Trees – CART; Cubist – CB, Partial Least Squares Regression – PLS; Random Forest – RF; and Support Vector Machine – SVM) and four sets of metrics (reflectance, narrowband vegetation indices – VIs; absorption band parameters; and the combination of these attributes). The lowest root mean square error (RMSE) was obtained for RF using VIs (29%) and a combination of metrics (28%). For VIs, RF differed from CUB, PLS and SVM at 5% significance level. From cross-validation results, the RMSE was 26.36% for grasslands, 35.04% for open savannas, and 24.85% for dense savannas. The RF model with VIs had the most stable predictive performance across the models, as indicated by small variations in RMSE from CART to SVM. The five most important ranked VIs in the RF model were the Normalized Difference Vegetation Index (NDVI), Pigment Specific Simple Ratio (PSSR), Enhanced Vegetation Index (EVI), Red Edge Normalized Difference Vegetation Index (RENDVI) and Structure Insensitive Pigment Index (SIPI). Most of their relationships with AGB were non-linear. The resultant AGB estimates showed consistent results with a vegetation cover map of the ESAE. Areas of the ESAE with AGB lower than 10 Mg.ha−1 were coincident with the occurrence of grassland physiognomies (savanna grasslands and shrub savannas), while areas with AGB higher than 25 Mg.ha−1 matched the occurrence of dense savanna physiognomies (woodland savanna and dense woodland savanna). Grassland areas showed larger values of coefficient of variation (CV) than areas of dense savannas. These first-hand results set a baseline of models and metrics for AGB modeling of savannas during the future transition from current sampling-type hyperspectral missions (< 10 km of swath) to large-coverage hyperspectral satellites (> 100 km of swath).

Zanthoxylum bungeanum Maxim mapping with multi-temporal Sentinel-2 images: The importance of different features and consistency of results

Zanthoxylum bungeanum Maxim (ZBM) is an important woody species in large parts of Asia, which provides oils and medicinal materials. Timely and accurate mapping of its spatial distribution and planting area is of great significance to local economy and ecology. As a special tree species planted in the Grain for Green Program of China, Linxia Hui Autonomous Prefecture (Linxia) in Gansu Province of China has vigorously developed ZBM cultivation since the launch of the program. However, lacking the accurate ZBM planting information hinders the assessment of the benefits and losses of the program to local people. Therefore, this study investigated the potential of multi-temporal Sentinel-2 Multi-Spectral Instrument (MSI) to accurately map ZBM in the study area in 2019. We first investigated the classification accuracies of four alternative Random Forest (RF) classifications using either alone or in combination, spectral bands, vegetation indices (VIs), and topographical variables. The importance of the three categories of features was examined based on the mean decrease accuracy (MDA) metric. The classification results with the most important features were further assessed for their consistency by considering the voting rates of 800 trees based on testing samples. Results show that the sole use of the spectral bands (40 input features) already achieves a satisfactory classification accuracy of 95.43%. Adding extra VIs and topographical variables further improves the results, but only to a small extent. However, certain VIs and topographic variables are far more effective in classification compared with the original spectral bands, especially the Red Edge Normalized Difference Vegetation Index (NDVI705) and Normalized Difference Yellow Index (NDYI). The classification accuracy achieves nearly 95% when using only the top 15 most important features. The desirable periods for differencing of ZBM and other land cover types are fruit coloring and ripening periods. The final map shows that the ZBM planting in Linxia is mainly distributed along the Yellow River and around the Liujiaxia reservoir. The total mapped acreages of ZBM is 51,601 ha, covering 9.51% of the study area. 99% of ZBM tends to grow between 1500 and 2400 m altitude, and 67% of ZBM are planted in areas with slopes between 5 and 25°. Voting rates show that the percentages of classification results with strong and good consistency are generally over 70% for all land cover types, proving the derived land cover map's high credibility, including ZBM. Altogether, our results demonstrate the high potential of multi-temporal Sentinel-2 images in accurate mapping of ZBM, which can serve as a reference for other specialty crops or tree species.

Innovative spectral library for identification common wild plants using hyperspectral technology in Northwestern Coast, Egypt

Many wild and medicinal plants today face extinction but the information is lacking in detail. For this mapping and identification of natural vegetation are main issues for biodiversity management. It could be concluded that hyperspectral remote sensing techniques is effective valuable in identification and conservation of wild plants in northwest coast of Egypt. This current work aims to establish a spectral library for identification and studying spectral characteristics of common plants in northwestern coast of Egypt. Such library will support characterization of wild plants in study area, in addition to enhancing wild plants observation by remote sensing. A field spectroradiometer was used to measure the spectral reflectance pattern for 27 important plant species along the North West coast of Egypt. The spectral library was created; based on measured spectral reflectance values in order to identify common wild plant species spectrally. The optimal spectral zones and waveband/s to discriminate between the different species were identified. Numbers of vegetation indices have been extracted and calculated from spectrometric analysis to assess the vitality of canopy. The study produced an updated map for the spatial distribution of all investigated wild plants along the study area. The results of Tukey’s analyses showed that NIR and SWIRII spectral zones are the best zones for the discrimination between plants. It was found that Normalized Difference Vegetation Index (NDVI) high correlated with Red Edge Normalized Difference Vegetation Index (RENDVI). Results indicate that studying of spectral characteristics of the different plants in the study area were a necessary step to identify the locations of the important and economically valued plants.

FIRST INVESTIGATION OF MEDITERRANEAN OAK TREE VITALITY WITH HIGH-RESOLUTION WORLDVIEW-3 SATELLITE DATA: COMPARING TEN VEGETATION INDICES AND THREE MACHINE LEARNING CLASSIFIERS

Abstract. Oak trees are the primary component in Mediterranean agro-silvopastoral systems. Since the second half of the 20th century, however, a severe oak decline has been observed. Climate change reinforces this problem, which is consistent with worldwide observable tree dieback. As the trees have significant ecological and socio-economic functions, their observation and assessment of vitality are increasingly researched. Satellite remote sensing is very well suitable for large-scale surveys of the extensive and sometimes hardly accessible areas. This study investigates the usability of high-resolution WorldView-3 data for the classification of tree vitality. The ground truth was collected on an Andalusian dehesa at the end of September 2019, timely corresponding with the satellite data acquisition. After customary post-processing of the WorldView-3 data, 10 vegetation indices (ARVI, CIgreen, CSI, DPI, EVI, GNDVI, NDVI, PSRI, RENDVI, and RGI) were calculated from the multispectral image. Three machine learning classifiers (Maximum Likelihood, Random Forest, and Support Vector Machine) were then used for a supervised image classification with three vitality classes (healthy, sick, and dead). Independent ground truth data were used for the validation. The best results were achieved with the red edge normalized difference vegetation index (RENDVI) and the Support Vector Machine classifier (F1 scores between 0.27 and 0.72). A maximal overall accuracy of around 0.6 is, however, improvable. Further studies should focus on other classification methods, more reliable ground truth, and combined analyses of spectral and structural data.

Assessment of mining activities on tree species and diversity in hilltop mining areas using Hyperion and Landsat data.

The tree species and its diversity are two critical components to be monitored for sustainable management of forest as well as biodiversity conservation. In the present study, we have classified the tree species and estimated its diversity based on hyperspectral remote sensing data at a fine scale level in the Saranda forest. This area is situated near the mining fields and has a dense forest cover around it. The forest surrounding the study area is exhibiting high-stress condition as evidenced by the dying and dry plant material, consequently affecting tree species and its diversity. The preprocessing of 242 Hyperion (hyperspectral) spectral wavebands resulted in 145 corrected spectral wavebands. The 21 spectral wavebands were selected through discrimination analysis (Walk's Lambda test) for tree species analysis. The SVM (support vector machine), SAM (spectral angle mapper), and MD (minimum distance) algorithms were applied for tree species classification based on ground spectral data obtained from the spectroradiometer. We have identified six local tree species in the study area at the spatial level. The result shows that Sal and Teak tree species are located in the upper and lower hilly sides of two mines (Meghahatuburu and Kiriburu). We have also used hyperspectral narrow banded vegetation indices (VIs) for species diversity estimation based on the field-measured Shannon diversity index. The statistical result shows that NDVI705 (red edge normalized difference vegetation index) is having the best R2 (0.76) and lowest RMSE (0.04) for species diversity estimation. That is why we have used NDVI705 for species diversity estimation. The result shows that higher species diversity values are located in the upper and lower hilly sides of two mines. The linear regression between Hyperion and field measured Shannon index shows the R2 (0.72) and RMSE (0.15). This study will aid in effective geoenvironmental planning and management of forest in the hilltop mining areas.

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.

Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

Integration of WorldView-2 and airborne laser scanning data to classify defoliation levels in Quercus ilex L. Dehesas affected by root rot mortality: Management implications

The dehesa ecosystem is severely affected by root rot decline caused by the soilborne pathogenic oomycetes of the genus Phytophthora. Defoliation mapping is particularly challenging in low density ecosystems with high spectral variability due to environmental heterogeneity within relatively short distances. In this study, holm oak defoliation was assessed by classifying two different levels (low and high defoliation trees, with a 30% crown-defoliation threshold) using a combination of multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data in an automated Random Forest (RF) modeling approach. Besides the original remote sensing data, a set of vegetation indices (VI) and ALS-derived structural parameters were derived. Seven predictors of defoliation were selected from the ALS metrics, WV-2 bands, and indices datasets: the height percentile of the tree in which occurs the maximum number of returns, mean values in the tree crown of the Chlorophyll index green, Plant Senescence Reflectance Index, Red Edge Normalized Difference Vegetation Index, Double Peak Index, band 3 of the WV-2 image, and bincentil 70 values. The confusion matrix of the external evaluation achieved the highest overall classification accuracy (86.7%) and the highest Kappa index (0.73). Finally, we produced a predicted tree-defoliation map which was used to establish the relationship between Quercus ilex damage levels and soil variables and management practices. High defoliated trees were more abundant in zones of shallow and compacted soil, with lower silt proportion and Ca concentration. Our results offer to forest managers a tool for the rapid and effective assessment of large areas affected by root rot defoliation in their planning of control and restoration measures aimed at reducing the holm oak mortality of holm oaks in management units.

Selection of a Spectral Index for Detection of Orange Spotting Disease in Oil Palm (Elaeis guineensis Jacq.) Using Red Edge and Neural Network Techniques

Spectral screening can play an important role in successful detection of viroid-infected oil palm seedlings from nursery stage prior to transplanting into the field. Coconut cadang–cadang viroid (CCCVd) is the main causal agent of orange spotting (OS) disease. OS disease is an emerging disease in Malaysian plantation. In this study, a glasshouse experiment was conducted with fifteen CCCVd-inoculated and five healthy oil palm seedlings in the growing season of 2015. Spectral screening was performed using a hyperspectral spectroradiometer, Analytic Spectral Device HandHeld 2 (325–1075 nm). The red edge, a steep gradient in reflectance between red and near-infrared bands (680–780 nm), was used for selection of red edge bands. A maximum point (i.e., 700 nm) and minimum point (i.e., 768 nm) of red edge were selected from healthy and inoculated spectra. Shifts of red edge inflection point from healthy to inoculated spectra were also studied. Four well-known spectral indices, namely simple ratio, red edge normalized difference vegetation index, two-band enhanced vegetation index 2 (EVI2), and chlorophyll index red edge, were evaluated using selected red edge bands. The multilayer perceptron neural network model was used to establish a nonlinear relationship between selected spectral bands and each spectral index. EVI2 was selected as a best spectral index which resulted in zero errors at the training, testing, and validation datasets. The highest coefficient of correlation (r = 1) was recorded between spectral bands (input values) and EVI2 (target values).