Article

Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks

August 2004
Remote Sensing of Environment 92(2):207-217

DOI:10.1016/j.rse.2004.06.002

Authors:

K. L. Smith

University of Nottingham

M. D. Steven

University of Nottingham

Hyperspectral features in the red-edge region were tested as an index of plant stress responses to soil–oxygen depletion. The aim was to provide the basis for a warning system to identify natural gas leakage by the spectral responses of plants growing in the affected soil.Elevated concentrations of natural gas in the soil atmosphere were used to deplete oxygen concentrations around the roots of grass, wheat (Hordeum vulgare cv Claire) and bean (Vicia faba cv Clipper) growing in a field facility. Visible symptoms due to the natural gas included reduced growth of the plants and chlorosis of the leaves.Spectral responses included increased reflectance in the visible wavelengths and decreased reflectance in the near infra-red. Derivative analysis identified features within the red-edge at 720–730 and 702 nm. Ratios of the magnitude of the derivative at 725 to that at 702 nm were less in areas where gas was present. This ratio enabled identification of stress due to gas leakage up to 7 days before visible symptoms were observed and also at the edges of gassed plots where visible symptoms were not expressed. The technique was able to identify stress responses to long-term leaks in all the crops tested but to short-term leaks only in grass. This study therefore suggests that under appropriate conditions remote sensing could be used to detect pipeline gas leaks from decreases in the ratio of peaks within the red-edge.

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks

Article

Jun 2024
ENVIRON SCI TECHNOL

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Article

Full-text available

Mar 2024

Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural stresses by analyzing the hyperspectral reflectance of vegetation. The effectiveness of this discrimination is assessed across three distinct spectral ranges (VNIR, SWIR, and Full spectra). Greenhouse experiments subjected three plant species to controlled environmental stressors, including gas leakage, salinity impact, heavy-metal contamination, and drought exposure. Spectral curves obtained from the experiments underwent preprocessing techniques such as standard normal variate, first-order derivative, and second-order derivative. Principal component analysis was then employed to reduce dimensionality in the spectral feature space, facilitating input for linear/quadratic discriminant analysis (LDA/QDA) to identify and discriminate gas leaks. Results demonstrate an average accuracy of 80% in identifying gas-stressed plants from unstressed ones using LDA. Gas leakage can be discriminated from scenarios involving a single distracting stressor with an accuracy ranging from 76.4% to 84.6%, with drought treatment proving the most successful. Notably, first-order derivative processing of VNIR spectra yields the highest accuracy in gas leakage detection.

Underground Natural Gas Microleakage Detection With Hyperspectral Imagery Based on Temporal Features and Ensemble Learning

Article

Full-text available

Jan 2023

Micro-leakage in underground natural gas storage have serious impacts on the environment or public safety. Recent studies have shown that hyperspectral imagery can detect natural gas micro-leakage by spectral or spatial features of vegetation indirectly. However, the identification of natural gas micro-leakage based on hyperspectral imagery still suffers from the following problems: (1) the spectral and spatial features of vegetation change in a complex way with increasing stress time; (2) the effectiveness of ensemble classifiers in recognizing natural gas stressed vegetation in hyperspectral imagery is unclear; (3) there is also a lack of studies on the spatial and temporal changes of vegetation stress in natural gas micro-leakage. Therefore, hyperspectral images of wheat, bean and grass in different periods were collected. Firstly, the spectral features were filtered using Relief-F algorithm. The spatial texture features were extracted using the grayscale co-occurrence matrix. The temporal features were extracted using the Bi-Temporal Band Ratio (BTBR). Then, an ensemble classification model fusing spectral, spatial and temporal features was established. Finally, the natural gas micro-leakage information was extracted based on the minimum external circle, and the spatial-temporal changes of vegetation stress were analyzed. The results showed that the average stress radius of wheat, bean, and grass were 1.07m, 0.83m, and 0.86m, respectively. The mean absolute localization error (MALE) of natural gas micro-leakage points were less than 0.4 m. This study provides theoretical basis and technical support for the future use of satellite hyperspectral detection of micro-leakage in underground gas storage reservoirs.

A method for detecting underground natural gas pipeline micro-leakage in vegetated areas using high spatial resolution hyperspectral imagery

Article

Feb 2023

Natural gas is an important clean energy source that is mainly transported through buried pipelines. However, slight leakage during transportation is inevitable and needs to be identified in time to reduce the risk of related accidents. For this purpose, remote sensing can be used as an indirect and non-destructive technique to detect natural gas leakage through vegetation. Abundant spectral and detailed spatial information enables the high spatial resolution hyperspectral imagery to capture subtle stress responses from vegetation. In this study, we designed a simulation experiment of natural gas leakage in vegetated areas, and collected the high spatial resolution hyperspectral images of three plant species. Considering the shadow problem contained in high spatial resolution images, a new vegetation pixel extraction method to eliminate the influence of background shadows was proposed. Then, two spectral transformation methods, continuum removal (CR) and variational mode decomposition (VMD), were integrated to analyze the vegetation spectra, and a spectral index which could reflect the degree of vegetation stress was designed based on the spectral characteristics, named CVI (Continuum removal - Variational mode decomposition Index). Finally, the threshold segmentation methods and the cumulative minimum circumscribed circle method were used to extract the range and location of natural gas leakage. The study demonstrated the potential of using high spatial hyperspectral imagery to map and monitor natural gas leakage through its stress on vegetation. Results of this research can be a reference for the fine identification of stressed plants and are expected to be applied to provide support for related departments and stakeholders.

Leaf Spectra Changes of Plants Grown in Soils Pre- and Post-Contaminated with Petroleum Hydrocarbons

Article

Full-text available

Jul 2022

Leaks from accidents or damage to pipelines that transport liquid petroleum hydrocarbons (PHC) such as gasoline and diesel are harmful to the environment as well as to human health, and may be hard to detect by inspection mechanisms alone when they occur in small volumes or persistently. In the present study, we aim to identify spectral anomalies in two plant species (Brachiaria brizantha and Neonotonia wightii) linked to contamination effects at different developmental phases of these plants. To do so, we used spectroscopy and remote sensing approaches to detect small gasoline and diesel leaks by observing the damage caused to the vegetation that covers simulated pipelines. We performed a contamination test before and after planting using gasoline and diesel volumes that varied between 2 and 16 L/m3 soil, in two experimental designs: (i) single contamination before planting, and (ii) periodic contaminations after planting and during plant growth. We collected the reflectance spectra from 35 to approximately 100 days after planting. We then compared the absorption features positioned from the visible spectral range to the shortwave infrared and the spectral parameters in the red edge range of the contaminated plants to the healthy plants, thus confirming the visual and biochemical changes verified in the contaminated plants. Despite the complexity in the indirect identification of soil contamination by PHCs, since it involves different stages of plant development, the results were promising and can be used as a reference for methods of indirect detection from UAVs (Unmanned Aerial Vehicles), airplanes, and satellites equipped with hyperspectral sensors.

Assessing wheat responses to soil acidification and liming using hyperspectral imaging

Article

Full-text available

Nov 2024
PLANT SOIL

Background and aims Soil acidification can negatively affect agricultural production by reducing uptake of essential nutrients and increasing aluminium toxicity in crops. This study investigated whether hyperspectral imaging could accurately measure wheat response to soil acidification and subsequent remediation via liming. Methods A high-throughput, automated greenhouse and hyperspectral imaging facility was used to evaluate differences between hyperspectral data of wheat plants in either acidic soil or soil that had been limed. Using RGB imaging and growth rate prediction, plant growth was measured to assess if it increased with lime application. The study also used partial least squares regression analysis (PLSR) to assess whether hyperspectral imaging could predict plant tissue nutrient concentration and indicate nutrient deficiencies and toxicities associated with soil acidity. Results Spectral differences were observed between plants in acidic and non-acidic soil around the end of tillering and beginning of stem elongation. The red edge spectral region contributed significantly to this difference and, when used in vegetation indices, confirmed increases in plant growth following lime application, observed via high throughput phenotypic analysis. PLSR analysis was ineffective in predicting nutrient concentration of plant tissue in this context, likely due to low concentrations of nutrients associated with acidification, limited sample size, and small variation in nutrient concentrations. Conclusions Findings suggest that hyperspectral imaging could prove useful in the detection of soil acidification effects on wheat crops under contained controlled environmental conditions, and may, with further in-field testing, enable improved spatial mapping of paddocks to help remediate this significant agricultural issue.

Reflectance Signatures Developed from Multi-Spectral Imaging of Peanut (Arachis hypogaea L.) Expressing Visible Herbicide Injury

Article

Jun 2022

David L Jordan

Multi-spectral imaging can be used to define a number of abiotic and biotic stresses associated with crop production and management. Research was conducted during 2004 and 2005 to develop spectral signatures of peanut leaves expressing visual symptoms of herbicide injury. Single leaf measurements using an artificial light source were used to develop spectra ranging from 350 to 2500 nm grouped into specific categories. Reflectance was determined for plants treated with acifluorfen, bentazon, clethodim, imazapic, paraquat, or 2,4-DB at 3 and 24 h and 3 and 6 d after application. Reflectance following herbicide applications was significant at bands 470-500 nm, 500-590 nm, 800 nm, and UMIR, at 3 and 24h after treatment and for bands 470-500 nm, 500-590 nm, 680-700 nm, 800 nm, and UMIR at 3 and 6d after treatment. Differences in reflectance were observed when comparing herbicides with different sites of action, especially when measurements were taken 3 and 6 d after treatment. These results indicate that there is potential for multi-spectral imaging to be used to discriminate among herbicides with different sites of action.

Disentangling the effect of heritability and plasticity on Populus fremontii leaf reflectance across a temperature gradient

Preprint

Full-text available

Oct 2024

We quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance using clonal replicates propagated from 16 populations and grown across three common gardens spanning a mean annual temperature gradient of 10.7-22.3°C. We used variance partitioning to decompose phenotypic variation expressed in the leaf spectra into genotypic versus environmental components and estimate broad-sense heritability and found that heritability was most strongly expressed in the red-edge (~680-750nm) and SWIR (~1400-3000nm). Support vector machine models predicted P. fremontii source population and garden location at 78% and 93% accuracy, respectively, demonstrating that genotypic and environmental variation can be differentiated from the same leaf spectra. However, model accuracy declined by ~49% when using leaf reflectance from any two common gardens to predict the source population at the third site. Prediction accuracies were lowest for the hottest site, which was linked to leaf stress responses in the visible and red-edge wavelengths (400-750nm). We conclude that leaf spectra display heritability and plasticity across different parts of the spectrum. When mapping in regions/seasons with extreme climates, spectral plasticity linked to heat stress can decrease spectral heritability but may offer opportunities to understand phenological responses to extreme temperatures at large scales.

Optimizing wheat crop performance: Genomic and phenomic insights into yield and days to maturity prediction using multi‐temporal UAV imagery

Article

Full-text available

Oct 2024

This study assesses strategies for utilizing multispectral imaging data (from flowering to maturity) to predict late‐season traits in the Norwegian wheat breeding program, comparing them with genomic prediction (GP). In the phenomic prediction (PP) approach, spectral bands, their multispectral relationship matrix (M‐matrix), and vegetation indices (VIs) were considered. GP involved the genomic relationship matrix (G), extended to multi‐kernel predictors by incorporating environmental and genotype–environment interaction effects, complemented with multispectral reflectance data. Two different models including PLSR (partial least square regression) and Bayesian genomic best linear unbiased prediction regressor were applied. The phenological stage of spectral data collection impacted the trait prediction accuracy correlating with the relationship between multispectral data and measured traits. Higher correlations resulted in higher PP prediction accuracy. The results revealed that spectral bands and M‐matrix outperformed VIs by 10%–40% across different timepoints and all timepoints together for grain yield (GY) prediction. The single‐kernel GP model (G) outperformed PP by 28% (using Bayesian) and 29% (using PLSR). The integration of multi‐kernel GP models with spectral data improved GY prediction by up to 4%. In terms of days to maturity (DM) prediction, phenomic methods excelled, surpassing the single‐kernel GP (G: r = 0.63) model by 11% (Bayesian). In conclusion, this study underscores the effectiveness of phenomics prediction for traits like DM and its potential to enhance predictions for complex traits such as GY while highlighting the importance of correlation between measured traits and spectral data, kernel combinations, and model selection for prediction accuracy.

Rapid Detection of Grapevine Viral Disease with High-Resolution Hyperspectral Remote Sensing Technology

Conference Paper

Jul 2024

Imaging spectroscopy for bark beetle detection in Norway spruce and the relevance of the red-edge spectral range

Article

Full-text available

Aug 2024
INT J APPL EARTH OBS

In this study, hyperspectral data of two geographically distant study areas in Germany were analyzed to find the most effective spectral wavelengths for red-edge-based vegetation indices to differentiate bark beetle-infested Norway spruce into three infestation classes, including green-attack. Considering that the relationship between red-edge position (REP) and reflection shoulder position (RESP) is crucial for the relative quantification of chlorophyll concentration and LAI and their changes due to bark beetle infestation, we tested normalized difference red-edge indices (NDRE) formed from these parameters. Using combinations of 75 spectral bands in the range 685 nm to 850 nm, we analyzed 1187 different NDREs, and two additional vegetation indices, and show that the sorted, normalized Kruskal-Wallis H-value, followed by a frequency analysis of the RESP and REP bands of the 10 % of the highest ranked NDREs is an effective way to identify the global optima of these parameters. The optimal REP and RESP estimated using this approach were determined at 714 nm and 758 nm, respectively. The derived normalized difference red-edge index NDRE758_714 could be successfully applied to both test sites. Moreover, we found that aggregating hyperspectral bands into broader bands – 703 nm to 729 nm for a REP band and 741 nm to 767 nm for a RESP band – did not degrade model accuracy when used in a multispectral NDRE, suggesting that a hyperspectral camera is not required to perform the task. Multinomial logistic regression (MNLR) and random forest (RF) classification models were successfully transferred between the two geographically distant study sites. The predictive accuracies of the models for the test data indicated MNLR to be more robust than the RF. With the MLNR models applied, overall accuracies 77.4 % were achieved in the first study area imaged during the green-attack core period, and 88.2 % in the second study area imaged at the final green-attack stage. Our results contribute to forest health monitoring with imaging spectroscopy data and provide practical recommendations for sensor design with broader bands.

Leveraging remote sensing to distinguish closely related beech species in assisted gene flow scenarios

Preprint

Full-text available

Aug 2024

European beech (Fagus sylvatica L.) forests are suffering under increasingly severe and frequent drought. Three closely related, hybridizing beech species, ranging from Bulgaria through Asia Minor and the Caucasus to Iran, offer potential resources for assisted gene flow (AGF) with the aim of increasing the adaptive capacity of European beech forests. However, due to similar morphology and leaf color, as well as hybridization, it is challenging to track the fate of introduced beech genotypes from these related species. Traditional identification methods relying on detailed morphological characterization and genetic testing are labor-intensive and costly, making them impractical for large-scale applications. Using multispectral data from PlanetScope SuperDove, we developed a classification approach that captures phenological differences between the European beech F. sylvatica and co-planted Caucasian beech (Fagus hohenackeriana Palibin). The approach focuses on key temporal windows and spectral features to optimize classification performance. We evaluated various machine learning algorithms with stratified spatial and temporal cross-validation on data from more than 200 genetically classified individuals in two well-studied sites in France and Switzerland, where Caucasian beech was introduced over a century ago. Our approach was then tested on three different study areas in Germany, where Caucasian beech was also planted, but without specific tree coordinates. Our results reveal consistent temporal and spectral differences during spring and autumn, aligning with budbreak and senescence periods. Most algorithms achieved classification accuracies of 90% and above. The algorithms effectively identified candidate zones for Caucasian beech within or near areas indicated by local foresters. This study demonstrates the potential of high-resolution multispectral satellite imagery and machine learning for scalable classification of closely related and hybridizing species, thereby facilitating forest management in the face of global change.

Broad Scope of Site-Specific Crop Management and Specific Role of Remote Sensing Technologies Within It—A Review

Article

Full-text available

Jul 2024
J AGRON CROP SCI

Precision agriculture (PA) has great potential to increase agricultural productivity and profitability while reducing input costs and environmental impacts. Within PA, site-specific crop management (SSCM) is considered the main premise, in which tillage operations and precise crop inputs (such as seed, fertiliser, water, pesticide and agrochemical) are applied according to field variability. The main aim of this review was to highlight the methods and tools used for spatial crop monitoring, soil and weather data influencing crop productivity and to support the adoption of SSCM technology. To achieve this goal: we discussed the main five components of SSCM, methods for monitoring crop and soil data, delineating field management zones (FMZs) and variable rate technologies (VRT) such as precision planting and digital smart sensors used for SSCM application. The review summarised that recent advances in plant and soil sensing systems, artificial intelligence (AI) and machine learning should be used in retrieving and analysing GIS big data for optimised crop inputs supply. Within VRT, light-bar systems, automatic controllers and sensors are user-friendly technologies that should be employed in SSCM solution. The authors highlight that adoption of PA can be increased through proper training and education of the farmers, and developing simple, affordable and efficient PA technologies. The review suggests five criteria that should be strictly adopted to get maximum benefits from SSCM: (i) all factors influencing crop yields can be identified; (ii) their effects on crop yields can be determined by using appropriate digital tools and crop modelling; (iii) variable rate crop inputs (VRCIs) should be calculated based on accurate information obtained from plant, soil and environment; (iv) targeted crop inputs should be exercised through global positioning system (GPS) enabled automatic controllers or wireless sensors network (WSN); and (v) right doses of crop inputs (e.g., nitrogen and irrigation) must be applied at the right time and place.

Imaging Hyperspectral Feature Fusion for Estimation of Rapeseed Pod’s Water Content and Recognition of Pod’s Maturity Level

Article

Full-text available

May 2024

Imaging hyperspectral technology is becoming popular in agriculture to provide detailed information on crop growth. In this work, we propose an estimation of rapeseed pod’s water content model and identification of maturity levels (green, yellow, and full) model by using this technology. Four types of hyperspectral features are extracted—color, texture, spectral three-edge parameters, and spectral indices. By integrating these features, satisfactory results are achieved: the optimal feature combination is from spectral indices and three-edge parameters, with low RRMSE and RE for yellow maturity. Incorporating spectral indices significantly improved the pod’s water content estimation, reducing RRMSE by up to 43.30% and 30.11% in the green and full maturity stages. Random forest and support vector machine with kernel method (SVM-KM) algorithms outperformed other statistical models, with SVM-KM achieving up to 96.90% accuracy in identifying maturity levels. These findings provide valuable insights for managing rapeseed production during the pod stage.

Evaluating the potential of high-resolution hyperspectral UAV imagery for grapevine viral disease detection in Australian vineyards

Article

Full-text available

May 2024

Spectral Analysis of Images of Plants Under Stress Using a Close-Range Camera

Article

Full-text available

Oct 2023

Plants signal their health in a broader spectrum than we can see with our eyes. We compared sunlight reflectance on plants at spectral wavelengths ranging from 430 nm to 870 nm in our study. These are based on multispectral images captured at a distance of 2 m. Indoor plants were observed over a period of 18 days and stressed due to a lack of sunlight or water. Wild sedge photographed on the forest floor at close range and with a difficult capture setup produced results comparable to published multispectral signatures derived from aerial imagery. Changes of leaf reflectance were noticed in spectral signatures and in vegetation indices. When calculating vegetation indices, our results show that comparing red and red edge reflectance values is superior to comparing red and NIR reflectance values.

Hyperspectral Radiometry for Detection and Estimation of Infestation Caused by Major Sucking Pests in Brinjal

Article

Sep 2016

Studies were carried out on detection and estimation of infestation caused by sucking pests of brinjal using hyperspectral radiometry in potted plants grown in green house during 2013 - 14 at Department of Remote Sensing and GIS, Tamil Nadu Agricultural University (TNAU), Coimbatore. The results revealed that the spectral reflectance curve of brinjal plants infested by redspider mites, mealybugs and aphids were different from that of the healthy plants. The Red Edge Position (REP) of mites infested plants with medium (25 - 50%), high (>50 %) level of damage (10 - 25 %) and plants with high infestation of mealybug, shifted from 720.26 to 707.64 nm, 701.33 nm and 718.68 nm, respectively. Whereas, REP of aphid infested plants were same as that of healthy plants (720.26 nm). There was a significant negative correlation between damages caused by sucking pests and vegetation indices (VIs) namely, normalized difference vegetation index (NDVI), simple ratio (SR), green red vegetation index (GRVI) and soil adjusted vegetation index (SAVI) values. Linear regression equations were developed for estimating pest damages based on NDVI, SR and GRVI values. SR, NDVI and SAVI were more sensitive to sucking pest infestation in brinjal. The respective per cent sensitivity of SR, NDVI, GRVI and SAVI were - 60.1, -35.4, -5.3 and -38.6, for mite infestation; – 48.6, -39.2, -8.8 and -24.1, for mealybug infestation and - 43.3, -31.7, -8.9 and -12.3, for aphid infestation indicating the usefulness of SR and NDVI, which were the highest in magnitude irrespective of the sign. Thus, it was found that detection, estimation and discrimination of infestation caused by sucking pests of brinjal could be done using hyperspectral radiometry.

Spectral characteristics of the correlation between elemental arsenic and vegetation stress in the Yueliangbao gold mining

Article

Full-text available

Aug 2023
ENVIRON GEOCHEM HLTH

Some soils in the Yueliangbao gold mining area have been contaminated by heavy metals, resulting in variations in vegetation. Hyperspectral remote sensing provides a new perspective for heavy metal inversion in vegetation. In this paper, we collected ground truth spectral data of three dominant vegetation species, Miscanthus floridulus, Equisetum ramosissimum and Eremochloa ciliaris, from contaminated and healthy non-mining areas of the Yueliangbao gold mining region, and determined their heavy metal contents. Firstly, we compared the spectral characteristics of vegetation in the mining and non-mining areas by removing the envelope and derivative transformation. Secondly, we extracted their characteristic identification bands using the Mahalanobis distance and PLS-DA method. Finally, we constructed the inverse model by selecting the vegetation index (such as the PRI, DCNI, MTCI, etc.) related to the characteristic band combined with the heavy metal content. Compared to previous studies, we found that the pollution level in the Yueliangbao gold mining area had greatly reduced, but arsenic metal pollution remained a serious issue. Miscanthus floridulus and Eremochloa ciliaris in the mining area exhibited obvious arsenic stress, with a large “red-edge blue shift” (9 and 6 nm). The extracted characteristic wavebands were around 550 and 680–740 nm wavelengths, and correlation analysis showed significant correlations between vegetation index and arsenic, allowing us to construct a prediction model for arsenic and realize the calculation of heavy metal content using vegetation spectra. This provides a methodological basis for monitoring vegetation pollution in other gold mining areas.

Detection of rice leaf folder, Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Crambidae) infestation using ground-based hyperspectral radiometry

Article

Jan 2023
CURR SCI INDIA

Hyperspectral remote sensing is a useful technique for detecting spatio-temporal changes in crop morphological and physiological health. In order to identify the pest-sensitive bands for rice leaf folder (RLF), the ground-based hyperspectral data were recorded at varying damage levels. The first-and second-order derivatives were correlated with correlation coefficient r and per cent leaf damage. The common region identified were recognized as sensitive regions (508-529, 671-680, 721-759, 779-786 and 804-820 nm). The absorption dips were also found using Sensitivity and Continuum Removal Analysis. Combining all, a total of nine spectral bands (518, 549, 661, 674, 678, 731, 789, 816 and 898 nm) were identified as pest-sensitive bands for RLF. The feature-selection method was employed using RELIEFF algorithm to find out the band combinations and bands 518, 661 and 731 nm yielded maximum accuracy of 81.67%.

Advances in Space Research Detection and mapping of vegetation stress using AVIRIS-NG hyperspectral imagery in coal mining sites

Article

Mar 2023
ADV SPACE RES

Heavy metal pollution caused by mining leads to vegetative and environmental stress conditions. This paper primarily focuses on identifying and mapping vegetation stress caused by the impact of coal mining using airborne hyperspectral image (AVIRIS-NG) at a fine scale level and validated spectroscopic field data. In this work, we have calculated and tested vegetation stress-affected narrow banded vegetation indices (VIs) based on Separability (S) and Coefficient of discrimination (R 2) statistical tests for the identification of suitable vegetation stress indices. We have considered the highest S and R2 values of stress indices for weighted and vegetation combined pixels' analysis. The final weighted combination index has been used for vegetation stress detection and mapping in coal mining sites. The outcome has been validated using field-based healthy and stress-affected plant spectral data and compared to ENVI software's agriculture stress tool (AST) based vegetation stress result. Some indices exhibited higher performances for vegetation stress analysis (NDVI, NSI-2, SR-1, SWST-3, SRWI, NWI-2, CSVI, HMSSI, and ARI-1), according to statistical results (S and R2) of VIs. Based on the vegetation stress index, high-stress zones are located in mines and nearby sites. Also, low-stress regions are positioned at high distances from mines. The findings additionally demonstrated that the VIs-based stress result (AUC-0.69) fit the data more closely than the ENVI's AST result (AUC-0.61). The VI's-based stress index had a clearly negative correlation with the distance of vegetation from the mines, which could be verified using the on-field NIR spectral measurements. However, effective vegetation health monitoring and planning in coal mining sites might benefit from these results at a very fine scale.

Detection and mapping of vegetation stress using AVIRIS-NG hyperspectral imagery in coal mining sites

Article

Mar 2023
ADV SPACE RES

Heavy metal pollution caused by mining leads to vegetative and environmental stress conditions. This paper primarily focuses on identifying and mapping vegetation stress caused by the impact of coal mining using airborne hyperspectral image (AVIRIS-NG) at a fine-scale level and validated spectroscopic field data. In this work, we have calculated and tested vegetation stress-affected narrow-banded vegetation indices (VIs) based on Separability (S) and Coefficient of discrimination (R2) statistical tests for the identification of suitable vegetation stress indices. We have considered the highest S and R2 values of stress indices for weighted and vegetation combined pixels’ analysis. The final weighted combination index has been used for vegetation stress detection and mapping in coal mining sites. The outcome has been validated using field-based healthy and stress-affected plant spectral data and compared to ENVI software's agriculture stress tool (AST) based vegetation stress result. Some indices exhibited higher performances for vegetation stress analysis (NDVI, NSI-2, SR-1, SWST-3, SRWI, NWI-2, CSVI, HMSSI, and ARI-1), according to statistical results (S and R2) of VIs. Based on the vegetation stress index, high-stress zones are located in mines and nearby sites. Also, low-stress regions are positioned at high distances from mines. The findings additionally demonstrated that the VIs-based stress result (AUC-0.69) fit the data more closely than the ENVI's AST result (AUC-0.61). The VI’s-based stress index had a clearly negative correlation with the distance of vegetation from the mines, which could be verified using the on-field NIR spectral measurements. However, effective vegetation health monitoring and planning in coal mining sites might benefit from these results at a very fine scale.

Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing

Article

Full-text available

Mar 2023
SENSORS-BASEL

Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for the non-destructive and rapid detection of plant diseases. The present study used proximal hyperspectral sensing to detect virus infection in Pinot Noir (red-berried winegrape cultivar) and Chardonnay (white-berried winegrape cultivar) grapevines. Spectral data were collected throughout the grape growing season at six timepoints per cultivar. Partial least squares-discriminant analysis (PLS-DA) was used to build a predictive model of the presence or absence of GLD. The temporal change of canopy spectral reflectance showed that the harvest timepoint had the best prediction result. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. Our results provide valuable information on the optimal time for GLD detection. This hyperspectral method can also be deployed on mobile platforms including ground-based vehicles and unmanned aerial vehicles (UAV) for large-scale disease surveillance in vineyards.

Information Technology Drivers in Smart Farming Management Systems

Chapter

Full-text available

Sep 2022

The chapter describes the possibilities of collecting digital data on crop and livestock production and their use in "smart farming" systems. Earth drone and spectral mobile mapping technologies can provide plant production-related measures with high temporal and spatial resolution. Remote sensing helps better understand farming patterns and crop management. Improving understanding of the link between remotely sensed data and risk assessment and management in "smart farming" is very important. Controlled-environment agriculture takes advantage of light recipes, related to spectral light-emitting diode (LEDs) and sensors. In livestock farming, analyzing a database of digital data on the environment and livestock individuals can help farmers make decisions better. The heterogeneous digital data from plant and livestock production are collected into a Data Lake. Then the data are processed to transform the data into the proper format for data analytics. Data Warehouse should be integrated into an ERP system that is dedicated to the agricultural environment.

Hyperspectral Remote Sensing and Field Spectroscopy: Applications in Agroecology and Organic Farming

Chapter

Full-text available

Aug 2022

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

Article

Full-text available

Jun 2022

Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of continuous climate change and the reduced availability of arable land, it is urgent to breed new maize varieties and screen for the desired traits, e.g., high yield and strong stress tolerance. Traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of uncrewed aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are valuable for estimating plant traits. In this study, we collected UAV hyperspectral imagery over a maize breeding field biweekly across the growing season, resulting in 11 data collections in total. Multiple machine learning models were developed to estimate the grain yield and flowering time of the maize breeding lines using the hyperspectral imagery. The performance of the machine learning models and the efficacy of different hyperspectral features were evaluated. The results showed that the models with the multi-temporal imagery outperformed those with imagery from single data collections, and the ridge regression using the full band reflectance achieved the best estimation accuracies, with the correlation coefficients (r) between the estimates and ground truth of 0.54 for grain yield, 0.91 for days to silking, and 0.92 for days to anthesis. In addition, we assessed the estimation performance with data acquired at different growth stages to identify the good periods for the UAV survey. The best estimation results were achieved using the data collected around the tasseling stage (VT) for the grain yield estimation and around the reproductive stages (R1 or R4) for the flowering time estimation. Our results showed that the robust phenotyping framework proposed in this study has great potential to help breeders efficiently estimate key agronomic traits at early growth stages.

Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images

Article

Full-text available

Jul 2022
SENSORS-BASEL

The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.

Estimation of heavy metal concentrations (Cd and Pb) in plant leaves using optimal spectral indicators and artificial neural networks

Article

Full-text available

Jun 2022
ENVIRON SCI POLLUT R

The necessity of continuously monitoring the agricultural products in terms of their health has enforced the development of rapid, low-cost, and non-destructive monitoring solutions. Heavy metal contamination of the plants is known as a source of health threats that are made by their proximities with pollutant soil, water, and air. In this paper, a method was proposed to measure lead (Pb) and cadmium (Cd) contamination of plant leaves through field spectrometry as a low-cost solution for continuous monitoring. The study area was Mahneshan county of Zanjan province in Iran with rich heavy metal mines that have more potential for plant contamination. At first, we collected different plant samples throughout the study area and measured the Pb and Cd concentrations using ICP-AES, in which we observed that the concentrations of Pb and Cd are in the range of 1.4 ~ 282.6 and 0.3 ~ 66.7 μgg⁻¹, respectively, and then we tried to find the optimum estimator model through a multi-objective version of genetic algorithm (GA) optimization that finds simultaneously the structure of an artificial neural network and its input features. The features extracted from the raw spectrums have been collimated to be compatible with the Sentinel-2 multispectral bands for the possibility of further developments. The results demonstrate the efficiency of the optimum estimator model in estimation of the leaves’ Pb and Cd contamination, irrespective of the plant type, which has reached the R² of 0.99 and 0.85 for Pb and Cd, respectively. Additionally, the results suggested that the 783-, 842-, and 865-nm spectral bands, which are similar to the 7, 8, and 8a sentinel-2 spectral bands, are more efficient for this purpose.

Improving water status prediction of winter wheat using multi-source data with machine learning

Article

Sep 2022
EUR J AGRON

Water and nitrogen (N) are the most important factors limiting crop productivity. Effective monitoring of the water status of winter wheat under different N treatments is imperative for precision irrigation in improved crop management. Hyperspectral remote sensing is widely used for monitoring the crop water status. However, changes in canopy architecture during ontogeny lead to poor inversion of crop properties and limit the use of spectral indices. This study aimed to improve the water status prediction of winter wheat using multi-source data with machine learning (ML). Two multi-irrigation levels (0, 120, 240, 360 mm) and N rates (75, 225 kg N ha⁻¹) experiments were conducted during the 2019–2021 wheat growing seasons under field conditions using a rainout shelter. Hyperspectral, soil, plant, and climate data were evaluated with two feature selection methods. Selected results were chosen as input variables for prediction models by using three ML algorithms. By constructing the normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI), the DSI(2015, 2375), NDSI(2175, 2245), and RSI(720, 1200) showed the strongest correlation with canopy water content (CWC), plant water content (PWC), and canopy equivalent water thickness (CEWT), respectively. The best feature selection method and data were delivered by the Pearson correlation coefficient together with the soil, plant, and climate data. The best performing ML algorithm for CWC and PWC prediction was RF, while SVM was the best ML algorithm for CEWT prediction. The R² of the optimal models ranged from 0.86 to 0.97. These models with multi-source data significantly improved the prediction accuracy of the water status and can thus assist in precision irrigation management of winter wheat.

Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article)

Article

Full-text available

May 2022

Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. Remote sensing has numerous returns in the area of crop monitoring and yield prediction which are closely related to differences in soil, climate, and any biophysical and biochemical changes. Different remote techniques could be used for crop monitoring and yield prediction including multi and hyper spectral data, radar and lidar imagery. This study reviews the potentialities, advantages and disadvantages of each technique and the applicability of these techniques under different agricultural conditions. It also shows the different methods in which these techniques could be used efficiently. In addition, the study expects future scenarios of remote sensing applications in vegetation monitoring and the ways to overcome any obstacles that may face this work. It was found that using satellite data with high spthermaatial resolution are still the most powerful method to be used for crop monitoring and to monitor crop parameters. Assessment of crop spectroscopic parameters through field or laboratory devices could be used to identify and quantify many crop biochemical and biophysical parameters. They could be also used as early indicators of plant infections; however, these techniques are not efficient for crop monitoring over large areas

Predicting Copper and Lead Concentration in Crops Using Reflectance Spectroscopy Associated With Intrinsic Wavelength-Scale Decomposition Spectral Transformation

Article

Full-text available

Jan 2022

Hyperspectral remote sensing is a reliable solution for monitoring heavy metal pollution in crops. However, there are few studies on the spectral transformation and estimation of heavy metal content in crops using time-frequency analysis. In this study, intrinsic wavelength-scale decomposition (IWD) was proposed to decompose hyperspectral data to fully exploit the sensitive information implied in them and to investigate the feasibility of the detection of copper (Cu) and lead (Pb) in maize leaves. Leaf spectra and Cu ²⁺ and Pb ²⁺ contents were obtained from potted maize plants under Cu and Pb stress in the laboratory. After the spectral data were processed using IWD to obtain the proper rotation components (PRC

{i}

), the characteristic bands were extracted, a Hankel matrix was constructed, and singular value decomposition (SVD) was performed. Finally, singular entropy information was obtained to characterize the heavy metal content. Singular entropy, with a higher correlation with Cu ²⁺ and Pb ²⁺ contents, was selected to establish the univariate and multivariate partial least squares regression (PLSR) models. The results showed the following: (1) the

R^{2}

of the univariate model for the prediction of copper and lead content was 0.68~0.81, and the RMSE was 0.99~7.03. (2) The

R^{2}

of the multivariate PLSR model was as high as 0.83, and the RMSE was as high as 0.83. This study showed that the characteristic bands can be effectively extracted by IWD spectral transformation, which provides a promising method for estimating heavy metal pollution in vegetation.

A convolutional block multi-attentive fusion network for underground natural gas micro-leakage detection of hyperspectral and thermal data

Article

Feb 2025
ENERGY

Combining vegetation index with mineral identification for detection of high-geothermal-potential zones using hyperspectral satellite data

Article

Nov 2024
GEOTHERMICS

Geothermal energy represents a large-output, high-capacity, and sustainable energy source for electric power generation, with critical implications for the transition toward a low-carbon society; hence, it is crucial to accurately explore and assess geothermal resources. Many areas rich in geothermal resources are located in non-arid, densely vegetated regions. Therefore, the purpose of this study was to develop a method, applicable at the first stage of regional resource exploration, using hyperspectral remotely-sensed images to detect surface geothermal manifestations with high reliability in densely vegetated areas. The Patuha geothermal field in West Java, Indonesia, was selected as the study area given the availability of accumulated survey data to validate our proposed method. A single satellite image acquired by the Hyperion sensor was used for the case study. Two vegetation indices were defined to detect spectral features of stressed vegetation: a blue shift of the red edge and an increase in shortwave-infrared reflectance. These indices were suitable to detect vegetation stress under soil acidification conditions caused by ascending geothermal water and gases. After normalization to a zero mean and unit standard deviation, these indices were combined into a single vegetation index considering blue shift and shortwave-infrared reflectance (VIBS). The advantage of the VIBS over the normalized difference vegetation index was demonstrated by better correspondence with geothermal manifestations and better consistency along major faults. By further combining the VIBS values (in vegetated areas) with mineral weights calculated by linear spectral unmixing for kaolinite (in non-vegetated areas), we proposed a new index, the geothermal manifestation potential (GMP). General matching between high-GMP zones and geothermal manifestations or fault traces demonstrated the usefulness of this index; this was confirmed by field survey measurements of reflectance spectral features characterizing vegetation under geothermal stress. Additionally, the highest-GMP zones were located near surface water possessing high sulfate concentrations and above a deep vapor-dominated underground reservoir.

Detection of underground natural gas pipeline micro-leakage based on UAV hyperspectral remote sensing and GIS

Article

Oct 2024

Detecting natural gas storage microleakage based on K-means clustering under constraint of Jeffries-Matusita distance criterion using mobile LiDAR data

Article

Sep 2024
J ENVIRON MANAGE

Exploring the Potential of Reconstructed Multispectral Images for Urban Tree Segmentation in Street View Images

Article

Full-text available

Jan 2024

Deep learning has gained popularity in recent years for reconstructing hyperspectral and multispectral images, offering cost-effective solutions and promising results. Research on hyperspectral image reconstruction feeds deep learning models with images at specific wavelengths and outputs images in other spectral bands. Although encouraging results of previous works, it should be determined to what extent the reconstructed information can lead to an advantage over the captured images. In this context, the present work inspects whether or not reconstructed spectral images add relevant information to segmentation networks for improving urban tree identification. Specifically, we generate red-edge (ReD) and near-infrared (NIR) images from RGB images using a conditional Generative Adversarial Network (cGAN). The training and validation are carried out with 5770 multispectral images obtained after a custom data augmentation process using an urban hyperspectral dataset. The testing outcomes reveal that ReD and NIR can be generated with an average Structural Similarity Index Measure of 0.93 and 0.88, respectively. Next, the cGAN generates ReD and NIR information of two RGB-based urban tree datasets (i.e., Jekyll, 3949 samples, and Arbocensus, 317 samples). Subsequently, DeepLabV3 and SegFormer segmentation networks are trained, validated, and tested using RGB, RGB+ReD, and RGB+NIR images from Jekyll and Arbocensus datasets. The experiments show that reconstructed multispectral images might not add information to segmentation networks that enhance their performance. Specifically, the p-values from a T-test show no significant difference between the performance of segmentation networks.

REMOTE SENSING INSIGHTS INTO EUROPEAN AND ORIENTAL BEECH SUBSPECIES

Conference Paper

Full-text available

Jun 2024

Differentiating European beech (F. sylvatica ssp. sylvatica) from Oriental beech (F. sylvatica ssp. orientalis) is crucial for ecological management and assisted gene flow. Current methods rely on molecular markers, impractical for widespread monitoring. Our study uses advanced remote sensing to differentiate these subspecies. Employing diverse spectroscopy data and a novel open-source classification system, we identify distinct spectral patterns. Our approach integrates genetic testing, confirming the accuracy of subspecies differentiation via spectroscopy. Phenological differences prove crucial for subspecies identification, supported by in situ observations. Our method, employing spectral indices and machine learning, enhances differentiation accuracy, detectable through satellite imagery across the growing season, validated by field observations. Consistent spectral differences over four years highlight satellite-based sensing's potential for accurate subspecies differentiation. This study contributes to refining remote sensing for subspecies discrimination, offering insights into ecosystem dynamics, hybridization patterns, and assisted gene flow monitoring in European and Oriental beech forests.

A study on an accurate modeling for distinguishing nitrogen, phosphorous and potassium status in summer maize using in situ canopy hyperspectral data

Article

Jun 2024
COMPUT ELECTRON AGR

A knowledge guided deep learning framework for underground natural gas micro-leaks detection from hyperspectral imagery

Article

May 2024
ENERGY

Precision opto-imaging techniques for seed quality assessment: prospects and scope of recent advances

Chapter

Dec 2023

Bhaswati Sarmah

An interval band selection method based on class saliency map to identify vegetation under natural gas microleakage stress

Article

Nov 2023
MICROCHEM J

Identificating vegetation stress under natural gas micro-leakage based on leaf scale temporal hyperspectrum

Article

Nov 2023

Estimation of leaf photosynthetic capacity parameters using spectral indices developed from fractional-order derivatives

Article

Sep 2023
COMPUT ELECTRON AGR

Multimodal remote sensing application for weed competition time series analysis in maize farmland ecosystems

Article

Jun 2023
J ENVIRON MANAGE

Although weeds cause serious harm to crops through competition for resources, they also have ecological functions. We need to study the change law of competition between crops and weeds, and achieve scientific farmland weed management under the premise of protecting weed biodiversity. In the research, we perform a competitive experiment in Harbin, China, in 2021, with five periods of maize as the study subjects. Comprehensive competition indices (CCI-A) based on maize phenotypes were used to describe the dynamic processes and results of weeds competition. The relation between in structural and biochemical information of maize and weed competitive intensity (Levels 1-5) at different periods and the effects on yield parameters were analyzed. The results showed that the differences of maize plant height, stalk thickness, and N and P elements among different competition levels (Levels 1-5) changed significantly with increasing competition time. This directly resulted in 10%, 31%, 35% and 53% decrease in maize yield; and 3%, 7%, 9% and 15% decrease in hundred grain weight. Compared to the conventional competition indices, CCI-A had better dispersion in the last four periods and was more suitable for quantifying the time-series response of competition. Then, multi-source remote sensing technologies are applied to reveal the temporal response of spectral and lidar information to community competition. The first-order derivatives of the spectra indicate that the red edge (RE) of competition stressed plots biased in short-wave direction in each period. With increasing competition time, RE of Levels 1-5 shifted towards the long wave direction as a whole. The coefficients of variation of canopy height model (CHM) indicate that weed competition had a significant effect on CHM. Finally, the deep learning model with multimodal data (Mul-3DCNN) is created to achieve a large range of CCI-A predictions for different periods, and achieves a prediction accuracy of R2 = 0.85 and RMSE = 0.095. Overall, this study use of CCI-A indices combined with multimodal temporal remote sensing imagery and DL to achieve large scale prediction of weed competitiveness in different periods of maize.

Weed resistance assessment through airborne multimodal data fusion and deep learning: A novel approach towards sustainable agriculture

Article

Jun 2023

Herbicide-resistant weeds represent a significant challenge to modern agriculture. The need for innovative and sustainable weed management strategies has become increasingly pressing as the threat of herbicide-resistant weeds continues to escalate. Unmanned aerial vehicles (UAVs) and various sensors have become indispensable tools in plant phenotyping studies. In this study, a comprehensive resistance score (CRS) was proposed to effectively quantify weed resistance in the field. Multimodal data fusion and deep learning were utilized to perform regression of CRS, three different fusion methods for 3D-CNN and 2D-CNN to extract and fuse multimodal information collected by UAVs including spectral, structural, and texture information for weed resistance. Our findings demonstrate that (1) discernible differences in spectral response exist between susceptible and resistant weeds, with the optimal band for the Successive Projections Algorithm (SPA) selection coinciding with the optimal band for resistance expression band; (2) resistance assessment accuracy is enhanced through multimodal data fusion, with the late deep fusion network exhibiting the best accuracy, R2 of 0.777 and RMSE of 0.547; (3) the multimodal fusion network model displays robust adaptability in resistance assessment across varying densities and effectively generates weed resistance map. Overall, this research demonstrates the effectiveness of using multimodal data fusion and CRS, combined with deep learning for achieving accurate and reliable weed resistance assessment in agricultural fields.

Wheat growth, photosynthesis and physiological characteristics under different soil Zn levels

Article

Jul 2022

In recent years, heavy metal hazards in the soil have seriously affected agricultural production. This study aims to examine the effects of different levels of heavy metal Zn on the growth, photosynthesis and physiological characteristics of wheat, and provide a theoretical basis for the diagnosis and control of heavy metal pollution in agricultural production. The field test method was used to explore the changes of wheat agronomic traits, photosynthetic capacity, chlorophyll fluorescence parameters, spectral characteristic curve, active oxygen metabolism system, cell ultrastructure, and yield, under different Zn levels (0, 250, 500, 750, and 1 000 mg kg–1). The results show that, low-level Zn treatments can effectively promote the synthesis of wheat chlorophyll, improve photosynthetic capacity, and increase yield. The yield of ZnL1 (250 mg kg–1) was the highest in the two-year test, which increased by 20.4% in 2018 and 13.9% in 2019 compared with CK (0 mg kg–1). However, a high Zn level had a significant stress effect on the photosystem of wheat. PIabs (reaction center performance index) and Fv/Fm (maximum photochemical efficiency) were significantly reduced, the active oxygen metabolism system was damaged, and the photosynthetic capacity was reduced, which in turn led to reduced yield. Among them, the yield of ZnL4 (1 000 mg kg–1) was the lowest in the two-year test, which was 28.1 and 16.4% lower than CK in 2018 and 2019, respectively. The green peak position of ZnL3 and ZnL4 had “red shift” to the long wave direction, while the red valley position of ZnL4 had “blue shift” to the short wave direction. Under ZnL4, some wheat leaf organelles began to decompose, vacuoles increased, cytoplasm decreased, cell walls thickened, chloroplast basal lamellae were disordered, and mitochondrial membranes disintegrated. Stepwise regression and Path analysis showed that Pn (net photosynthetic rate) played a leading role in the formation of yield. Redundancy (RDA) analysis showed that the optimal Zn level for wheat growth was about 250 mg kg–1, and wheat would be stressed when the soil Zn level exceeded 500 mg kg–1 in the test condition of this study. Findings of this study provide a theoretical basis for the diagnosis and prevention of heavy metal (Zn) pollution in the soil.

Autumnal senescence in a poplar plantation in response to elevated carbon dioxide, from cell to canopy

Thesis

Jan 2007

Matthew James Tallis

p>Global data sets derived from remote sensing of terrestrial vegetation and phonological observations of bud set, leaf colour change and leaf drop have provided evidence for the recent extension of the growing season. Atmospheric carbon dioxide concentration has risen by ~39% since pre-industrial times and is considered a strong driver for a mean global temperature rise. This increased temperature is generally thought to be the cause of the extension in the growing seasons. In this thesis the influence increased atmospheric carbon dioxide concentration may have on the autumnal phenology of a poplar plantation was examined. Following up to six years growth in an atmosphere enriched with carbon dioxide (CO₂) using free air CO₂ enrichment technology, the autumnal phenology of two poplar genotypes was examined. Using remote sensing technology, at spatial and spectral resolutions varying from leaf level to airborne sensors, the changes in canopy spectral reflectance during senescence were monitored. These changes were associated with a delayed autumnal decline in canopy leaf area and leaf level chlorophyll concentration for the trees exposed to elevated CO₂. Associated with this was a decrease in both specific leaf area (leaf area per unit mass) and leaf nitrogen content (on a leaf mass basis). The extension of autumnal senescence in this plantation resulting from atmospheric CO₂ enrichment was estimated to contribute approximately 2% to the annual gross primary production., The change in gene expression associated with this delay was studied using microarray technology. Delayed senescence in elevated CO₂ was also evident at the level of gene expression, confirming the remotely-sensed observations. For the first time, an up-regulation of genes encoding enzymes within the pathways of phenylpropanoid metabolism were identified during autumnal senescence in elevated CO₂ inferring increased stress tolerance.</p

Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Article

Jul 2022
SCI TOTAL ENVIRON

Weed competition causes serious economic losses to maize production. Timely and accurate assessment of pressure from competition is crucial for ecological weed management. In this work, we apply hyperspectral remote sensing (HRS) technology to conduct a competitive experiment in Harbin, China, in 2021, with 5-leaf maize as the study target. A weed competition assessment method that combines comprehensive competition indices (CCI) and deep learning is proposed. For the comprehensive competition assessment, the relationship between different weed competitive pressures (Levels 1–5) and changes in the structural and physiological information of maize was analyzed. The accumulative/transient competition indices CCI-A and CCI-T were designed for accurate quantification. The results showed that parameters such as plant height, stalk thickness and nutrient elements of maize decreased with increasing competition level. Parameters, such as stomatal conductance and transpiration rate, showed a fluctuating change of increasing and then decreasing with increasing competition level. Compared with the traditional relative competitive intensity (RCI), the standard deviation of CCI is 0.303 and 0.499. The dispersion effect of CCI is better and more suitable for quantifying the competition response. HRS images combined with 3D-CNN model were then applied to reveal the spectral response to different weed competition pressures (Levels 1–5) and to make early predictions of weed competition. The first-order derivative showed that the spectral reflectance exhibited significant differences at 520–525 nm peak, 570–655 nm trough, and near 700 nm red edge. For hyperspectral spatial-spectral features, the 3D-CNN model is proposed for prediction of competing indices CCI. In addition, the VIP method is used to select the characteristic wavelengths. The 3D-CNN model achieves a prediction accuracy of RMSE = 0.106 and 0.152 using 13 feature bands, which can accurately quantify the subtle changes in competition indices. Overall, this study shows that the combination of CCI and deep learning can provide a multivariate and comprehensive assessment of weed competition pressure.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Article

Full-text available

Jun 2022

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Identification of vegetation under natural gas leakage by spectral index based on feature selection

Article

Apr 2022

The leakage of natural gas storage has a significant impact on economy, personal safety and natural environment. When the leakage is slight, the effect of direct detection is not ideal. Hyperspectral remote sensing can detect it indirectly through the spectral changes of surface vegetation. In this study, wheat, bean and grass were used as surface experimental objects to analyse the variation characteristics of canopy spectrum and physiological and biochemical parameters of vegetation under natural gas leakage. The results showed that with the increase of natural gas concentration in the soil, the spectral reflectance of vegetation increased significantly in the visible region, and decreased significantly in the near infrared region. The natural gas identification index (NGII) (R622−R532)/(R622+R532) was constructed according the optimal weight index screened by Relief-F algorithm. The quantitative test by Jeffries-Matusita (JM) distance showed that NGII can identify (JM > 1.8) stressed vegetation under natural gas leakage in a short time. This study can provide technical reference for detecting leakage of underground natural gas storage.

Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination

Article

Full-text available

May 2022

Maize is one of the most important crops in China, and it is under a serious, ever-increasing threat from southern corn rust (SCR). The identification of wheat rust based on hyperspectral data has been proved effective, but little research on detecting maize rust has been reported. In this study, full-range hyperspectral data (350~2500 nm) were collected under solar illumination, and spectra collected under solar illumination (SCUSI) were separated into several groups according to the disease severity, measuring height and leaf curvature (the smoothness of the leaf surface). Ten indices were selected as candidate indicators for SCR classification, and their sensitivities to the disease severity, measuring height and leaf curvature, were subjected to analysis of variance (ANOVA). The better-performing indices according to the ANOVA test were applied to a random forest classifier, and the classification results were evaluated by using a confusion matrix. The results indicate that the PRI was the optimal index for SCR classification based on the SCUSI, with an overall accuracy of 81.30% for mixed samples. The results lay the foundation for SCR detection in the incubation period and reveal potential for SCR detection based on UAV and satellite imageries, which may provide a rapid, timely and cost-effective detection method for SCR monitoring.

Vegetation Stress: an Introduction to the Stress Concept in Plants

Article

Full-text available

Dec 1996
J PLANT PHYSIOL

Hartmut Lichtenthaler

This is a presentation of the essentials of the present stress concept in plants, which has been well developed in the past 60 years. Any unfavorable condition or substance that affects or blocks a plants metabolism, growth or development, is to be regarded as stress. Plant and vegetation stress can be induced by various natural and anthropogenic stress factors. One has to differentiate between short-term and long-term stress effects as well as between low stress events, which can be partially compensated for by acclimation, adaptation and repair mechanisms, and strong stress or chronic stress events causing considerable damage that may eventually lead to cell and plant death. The different stress syndrome responses of plants are summarized in a scheme. The major abiotic, biotic and anthropogenic stressors are listed. Some stress tolerance mechanisms are mentioned. Stress conditions and stress-induced damage in plants can be detected using the classical ecophysiological methods. In recent years various non-invasive methods sensing different parameters of the chlorophyll fluorescence have been developed to biomonitor stress constraints in plants and damage to their photosynthetic apparatus. These fluorescence methods can be applied repeatedly to the same leaf and plant, e.g. before and after stress events or during recovery. A new dimension in early stress detection in plants has been achieved by the novel high resolution fluorescence imaging analysis of plants, which not only senses the chlorophyll fluorescence, but also the bluegreen fluorescence emanating from epidermis cell walls which can change under stress induced strain. This powerful new technique opens new possibilities for stress detection in plants.

Spectral responses of pot-grown plants to displacement of soil oxygen

Article

Full-text available

Oct 2004

Detection of leaking gas pipelines is important for safety, economic and environmental reasons, and remote sensing of vegetation offers the potential to identify gas leakage through spectral responses in the plants growing above. Pot-scale investigations were carried out to determine the effects of soil-oxygen displacement using natural gas, argon, nitrogen and waterlogging on the overlying vegetation and to determine whether changes in spectral characteristics were specific to natural gas or were a generic response to soil-oxygen displacement. Leaves responded to soil-oxygen displacement by increased reflectance in the visible wavelengths and changes in the position and shape of the red edge. The red edge of control plants shifted towards longer wavelengths as they matured, while the red edge of treated plants remained stationary, indicating an inhibition of maturing. Bean and barley exhibited different shapes in the peaks at the red edge. Argon and waterlogging produced greater responses than natural gas, which was administered non-continuously. These results suggest that the response to natural gas is generic to soil-oxygen deficiency rather than specific to this agent. Hence, although it might be possible to detect leaking gas by remote sensing of vegetation, ancillary information such as pipeline maps would be required to discriminate natural gas responses from those due to other stresses.

Quantitative characterization of the vegetation red edge reflectance 1. An inverted-Gaussian reflectance model

Article

Full-text available

Oct 1990

An inverted-Gaussian model for the vegetation red edge reflectance is evaluated with respect to its applicability as a simple four-parameter descriptor of vegetation reflectance in the 670 to 800 nm spectral region under a wide range of environmental/measurement conditions. The model has been fitted to laboratory spectral reflectance measurements of single leaves, leaf stacks and needle clump stacks for a number of species. For all of these data the model has been found to provide an effective quantitative representation of the shape and position of the vegetation red edge reflectance in terms of four parameters of physical significance: 1R shoulder reflectance Rs, chlorophyll-well minimum reflectance R0, red edge inflection point wavelength λp and reflectance minimum wavelength λ0. Provided that an appropriate strategy has been adopted to select the initial guess model parameters and the spectral range of reflectance data to be fitted, the values of derived model parameters can be used for a quantitative description of the temporal and species-dependent behaviour of the characteristics of the red edge. Sequential measurements of the same leaf stack in which the first leaf is changed and as well as remeasurement of the same sample indicate that appropriate confidence limits to the derived model parameters are: R0 (±0·5 per cent), Rs ( ± 4 per cent), λ0 and λp (±2·0nm). It is also suggested that in the analysis of high spectral resolution vegetation reflectance data, inverted-Gaussian model estimates of the spectral position of the red edge obtained with discrete filter-passband radiometers (or multispectral imagers) can be usefully compared directly to that obtained with more sophisticated field or laboratory spectrometers.

Seasonal patterns in leaf reflectance red-edge characteristics

Article

Full-text available

Jul 1991

Leaves from ten tree species, including three conifers from a woodlot in southern Ontario were sampled at weekly intervals for a period of 150 days spanning the phenological events in deciduous trees of leaf development and expansion (flushing), leaf maturity and leaf senescence. The highly diverse seasonal red-edge reflection patterns were studied collectively and individually both from the perspective of long-term trends and relatively short-term or oscillating trends. The phenological events characteristic of deciduous trees were most effectively described using the red-edge position (λpg) and the chlorophyll-well position (λog ) derived from the inverted Gaussian model. Moreover, these parameters appeared consistent with what is known about the seasonal turnover of leaf chlorophyll and with other parameters R550 or Rog which more specifically quantify leaf chlorophyll. By these terms of reference chlorophyll declines considerably earlier in the season than the onset of other physiological or structural changes normally associated with senescence. Both parameters λPR and λog and the real red-edge position (λpr) conjointly provide specific information about each species and differentiate clearly deciduous from coniferous growth forms. Individual species variation in these and other variables was more ‘short term’ than ‘long term’, providing support for the perception that the long-term trends as visualized by λpg and λog are species-specific and can probably be correlated with ecological parameters such as tolerance.Short-term or oscillating variations were observed in all species most specifically in the shoulder reflectance (Rgr), the red-edge parameter (λpr ) and, to a lesser extent, in the inverted Gaussian model parameters, the wavelength at minimum reflectance (λog) and the average red-edge position (λpg). The generally synchronous behaviour of Rsr between all species, and of reflectance and spectral parameters within specific species, suggest the oscillations were not attributable to sampling error. Instead they appeared to be associated with rainfall and temperature events. Furthermore, systematic uncoupling of short-term variations between the spectral and the reflectance parameters observed in other species suggest different casual factors for spectral and reflectance parameters of the red edge.

Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations

Article

Full-text available

Sep 2002

Chlorophyll red-edge descriptors have been used to estimate leaf nitro-gen concentration in ryegrass (L olium spp.) pasture. Two-layer model calculations have been used to predict the inn uence of chlorophyll content and Leaf Area Index (LAI) on the shape and location of the peaks observed in the derivative spectra of a ryegrass canopy. The complex structure of the resulting derivative spectra pre-cluded extracting red-edge wavelengths by tting inverted Gaussian curves to ree ectance proo les. Fitting a combination of three sigmoid curves to the calculated ree ectance spectra provided a better representation of subsequent derivative spectra. The derivative spectra in the vicinity of the chlorophyll red-edge is predicted to contain two peaks (~705 and ~725 nm), which on increasing the canopy LAI is generally found to shift to longer wavelengths. However, for a canopy containing leaves of low chlorophyll content and LAI>5, the wavelength of the rst peak becomes insensitive to changes in LAI. The same phenomenon is predicted for high-chlorophyll leaves of LAI>10. The role of multiple scattering, primarily due to increased leaf transmittance at higher wavelengths, has also been verii ed. In subsequent experiments, the predicted shape of the derivative spectra was observed and the use of three sigmoid curves to better represent this shape veri ed. Changes in the descriptors used to describe the chlorophyll red-edge were observed to explain 60% and 65% of the variance of leaf nitrogen concentration and total leaf nitrogen content, respectively. The resulting regression equation was found to predict leaf nitrogen concentration, in the range of 2–5.5%, with a standard error of prediction (SEP) of 0.4%. The confounding inn uence of canopy biomass on the red-edge determination of leaf nitrogen concentration was found to be signii cantly less at higher canopy biomass, conn rming both theoretical predictions and the potential of using the chlorophyll red-edge as a biomass-independent means of estimating leaf chlorophyll, and hence nitrogen, concentration in high-LAI ryegrass pastures.

Characterization of wetland plant stress using leaf spectral reflectance: Implications for wetland remote sensing

Article

Full-text available

Dec 1996

Spectral measurements were recorded for leaves from two monospecific stands ofAcer rubrum in an attempt to characterize leaf reflectance at different stages of flooding. The stands occupied two different soil types possessing different soil moisture regimes. Leaves were excised from different parts of the trees, and their reflectance properties were measured with a hand-held spectroradiometer recording from 400 to 900 nanometers in 3-nm increments. Soil redox potentials were recorded at the sites in an attempt to characterize stress as a function of the soil reducing conditions. Spectral curves, reflectance peaks, soil moisture observations, and redox potentials were plotted and analyzed to document the conditions of the trees during a two-and-a-half month period in the early local growing season. Compared to non-flooded trees, spectral measurements for flooded trees showed elevated reflectance in both the green spectral region at 550 nm as well as the near infrared region at 770 nm. In addition, the reflectance measurements were strongly related (r >- 0.80) to redox potentials recorded during the same period. The results indicated that spectrally detectable changes in visible and near infrared leaf reflectance may be more influenced by prolonged flooding than saturation. This suggests that where remote sensing is used for wetland mapping, there may be optimal times to spectrally separate stands of forested wetlands during the growing season.

Root growth and water uptake in winter wheat under deficit irrigation

Article

Full-text available

Nov 2003

Root growth is critical for crops to use soil water under water-limited conditions. A field study was conducted to investigate the effect of available soil water on root and shoot growth, and root water uptake in winter wheat (Triticum aestivum L.) under deficit irrigation in a semi-arid environment. Treatments consisted of rainfed, deficit irrigation at different developmental stages, and adequate irrigation. The rainfed plots had the lowest shoot dry weight because available soil water decreased rapidly from booting to late grain filling. For the deficit-irrigation treatments, crops that received irrigation at jointing and booting had higher shoot dry weight than those that received irrigation at anthesis and middle grain filling. Rapid root growth occurred in both rainfed and irrigated crops from floral initiation to anthesis, and maximum rooting depth occurred by booting. Root length density and dry weight decreased after anthesis. From floral initiation to booting, root length density and growth rate were higher in rainfed than in irrigated crops. However, root length density and growth rate were lower in rainfed than in irrigated crops from booting to anthesis. As a result, the difference in root length density between rainfed and irrigated treatments was small during grain filling. The root growth and water use below 1.4 m were limited by a caliche (45% CaCO3) layer at about 1.4 m profile. The mean water uptake rate decreased as available soil water decreased. During grain filling, root water uptake was higher from the irrigated crops than from the rainfed. Irrigation from jointing to anthesis increased seasonal evapotranspiration, grain yield, harvest index and water-use efficiency based on yield (WUE), but did not affect water-use efficiency based on aboveground biomass. There was no significant difference in WUE among irrigation treatments except one-irrigation at middle grain filling. Due to a relatively deep root system in rainfed crops, the higher grain yield and WUE in irrigated crops compared to rainfed crops was not a result of rooting depth or root length density, but increased harvest index, and higher water uptake rate during grain filling.

The red edge of leaf reflectance

Article

Full-text available

Jul 1983

A detailed study of the red edge spectral feature of green vegetation based on laboratory reflectance spectrophotometry is presented. A parameter lambda is defined as the wavelength is defined as the wavelength of maximum slope and found to be dependent on chlorophyll concentration. Species, development stage, leaf layering, and leaf water content of vegetation also influences lambda. The maximum slope parameter is found to be independent of simulated ground area coverage. The results are interpreted in terms of Beer's Law and Kubelka-Munk theory. The chlorophyll concentration dependence of lambda seems to be explained in terms of a pure absorption effect, and it is suggested that the existence of two lambda components arises from leaf scattering properties. The results indicate that red edge measurements will be valuable for assessment of vegetative chlorophyll status and leaf area index independently of ground cover variations, and will be particularly suitable for early stress detection.

Methanotrophic Bacteria

Article

Full-text available

Jul 1996
Microbiol Rev

Methane-utilizing bacteria (methanotrophs) are a diverse group of gram-negative bacteria that are related to other members of the Proteobacteria. These bacteria are classified into three groups based on the pathways used for assimilation of formaldehyde, the major source of cell carbon, and other physiological and morphological features. The type I and type X methanotrophs are found within the gamma subdivision of the Proteobacteria and employ the ribulose monophosphate pathway for formaldehyde assimilation, whereas type II methanotrophs, which employ the serine pathway for formaldehyde assimilation, form a coherent cluster within the beta subdivision of the Proteobacteria. Methanotrophic bacteria are ubiquitous. The growth of type II bacteria appears to be favored in environments that contain relatively high levels of methane, low levels of dissolved oxygen, and limiting concentrations of combined nitrogen and/or copper. Type I methanotrophs appear to be dominant in environments in which methane is limiting and combined nitrogen and copper levels are relatively high. These bacteria serve as biofilters for the oxidation of methane produced in anaerobic environments, and when oxygen is present in soils, atmospheric methane is oxidized. Their activities in nature are greatly influenced by agricultural practices and other human activities. Recent evidence indicates that naturally occurring, uncultured methanotrophs represent new genera. Methanotrophs that are capable of oxidizing methane at atmospheric levels exhibit methane oxidation kinetics different from those of methanotrophs available in pure cultures. A limited number of methanotrophs have the genetic capacity to synthesize a soluble methane monooxygenase which catalyzes the rapid oxidation of environmental pollutants including trichloroethylene.

Vegetation Stress Detection through Chlorophyll + Estimation and Fluorescence Effects on Hyperspectral Imagery

Article

Full-text available

Sep 2002

Physical principles applied to remote sensing data are key to successfully quantifying vegetation physiological condition from the study of the light interaction with the canopy under observation. We used the fluorescence-reflectance-transmittance (FRT) and PROSPECT leaf models to simulate reflectance as a function of leaf biochemical and fluorescence variables. A series of laboratory measurements of spectral reflectance at leaf and canopy levels and a modeling study were conducted, demonstrating that effects of chlorophyll fluorescence (CF) can be detected by remote sensing. The coupled FRT and PROSPECT model enabled CF and chlorophyll a + b (Ca + b) content to be estimated by inversion. Laboratory measurements of leaf reflectance (r) and transmittance (t) from leaves with constant Ca + b allowed the study of CF effects on specific fluorescence-sensitive indices calculated in the Photosystem I (PS-I) and Photosystem II (PS-II) optical region, such as the curvature index [CUR; (R675.R690)/R2(683)]. Dark-adapted and steady-state fluorescence measurements, such as the ratio of variable to maximal fluorescence (Fv/Fm), steady state maximal fluorescence (F'm), steady state fluorescence (Ft), and the effective quantum yield (delta F/F'm) are accurately estimated by inverting the FRT-PROSPECT model. A double peak in the derivative reflectance (DR) was related to increased CF and Ca + b concentration. These results were consistent with imagery collected with a compact airborne spectrographic imager (CASI) sensor from sites of sugar maple (Acer saccharum Marshall) of high and low stress conditions, showing a double peak on canopy derivative reflectance in the red-edge spectral region. We developed a derivative chlorophyll index (DCI; calculated as D705/D722), a function of the combined effects of CF and Ca + b content, and used it to detect vegetation stress.

The quantitative analysis of chlorophylls a and b in plant extracts

Article

J. Bruinsma

Remote sensing of leaf responses to leaking underground natural gas

Article

Jul 2002

Karon Lesley Smith

Detection of leaking gas pipelines is important for safety, economic and environmental reasons. Remote sensing of vegetation offers the potential to identify gas leakage. The research aim was to determine the effects of elevated soil concentrations of natural gas on overlying vegetation. Pot-scale investigations were carried out to determine whether changes in spectral characteristics were specific to natural gas or were a generic response to soil-oxygen displacement. Natural gas, argon, nitrogen and waterlogging were used to displace soil-oxygen. Leaf response to soil oxygen displacement was increased reflectance in the visible wavelengths and changes in the position and shape of the red-edge, which shifted towards longer wavelengths as the control plant matured, while the red-edge of the treated plant remained stationary indicating an inhibition of maturing. The shape of the red-edge differed in bean and barley with bean exhibiting a single peak in the first derivative that moved with plant maturity; barley exhibited a peak at 704 nm with a shoulder at 722 nm that shifted to shorter wavelengths during plant stress. Argon and waterlogging exhibited a greater response than natural gas, which had been administered noncontinuously. These experiments suggest the response to natural gas was generic to soil-oxygen deficiency. Field studies were conducted to determine whether spectral changes in leaves identified in pot trials were observable in crop canopies under field conditions. Reflectance of barley growing above a leaking gas pipeline was increased in the visible wavelengths and the red-edge was at a shorter wavelength. When the majority of the crop was fully developed, the barley above the gas leak was greener, suggesting that development was inhibited by soil-oxygen displacement. It might be possible to detect leaking gas by remote sensing of vegetation in conjunction with pipeline maps, but limitations in the spatial resolution of current satellite sensors and the infrequency of cloud free skies in the UK suggest that further work is needed before an operational system could be available.

Narrow-band Reflectance Imagery Compared with ThermalImagery for Early Detection of Plant Stress

Article

Dec 1996
J PLANT PHYSIOL

A field experiment compared plant stress detection by narrow-band reflectance and ratio images withthermal infrared images. Stress was induced in a mixed stand of 5 year old loblolly pine (Pinus taeda L.) and slash pine {Pinus elliottii Engelm.) by a soil application of diuron (DCMU) on 22 August followed by bromacil on 19 September, 1994. Herbicide-induced stress was first indicated on 24 and 26 September by significant (p⪯0.05) decreases in photosynthesis and the ratio of variable to maximum fluorescence (Fv/Fm), respectively. Stress was first detected remotely on 5 October by 694 ± 3 nm reflectance imagery and its ratio with reflectance at 760 ± 5 nm (p⪯0.05). This reflectance increase was detected at least 16 days prior to the first visible signs of damage, as quantified by the CIE color coordinate u', that occurred between 21 and 26 October. Reflectance images at 670 + 5 nm, 700 ± 5 nm and 760 ± 5 nm first detected stress on 21 October, 12 October and 20 December, respectively. Canopy temperature as indicated by imagery in the 8 to 12 μm band never differed significantly between herbicide-treated and control plots. This resulted from the close coupling of leaf temperatures with air temperature, and the tendency of wind and environmental moisture to equalize temperatures among treatments. The high sensitivity to stress of reflectance imagery at 694 ± 3 nm supports similar conclusions of earlier work, and indicates that imagery in the 690 to 700 nm band is far superior to thermal imagery for the early and pre-visual detection of stress in pine.

Response of tomato plants to simulated landfill gas mixtures

Article

Dec 1985

The roots of tomato plants were fumigated with simulated refuse-generated gas mixtures at levels of methane (CH/sub 4/), carbon dioxide (CO/sub 2/), and oxygen (O/sub 2/) previously measured in the atmospheres of landfill cover soils associated with poor growth or death of plants. A concentration of 18% CO/sub 2/ or greater, exceeded in almost 30% of thirty-two landfills examined throughout the US, caused reduced growth and visible symptoms on tomato after 1 wk, regardless of O/sub 2/ level. Doubling the CO/sub 2/ level to that encountered in a typical local site (Edgeboro Landfill) resulted in more severe symptom development and the subsequent death of plants. Methane, in concentrations of 20% and above, found in more than 25% of the landfills visited, while not observed to be toxic per se; was associated with drastic O/sub 2/ depletion in the soil atmosphere, which activity was believed to be the cause of the plant decline.

Some Physical and Chemical Changes in the Soil Brought about by Saturation With Natural Gas1

Article

Jan 1960

Severe chemical and physical changes may occur in soils saturated with natural gas. Plant growth may be retarded or completely eliminated. A study was made of the profile of four gas‐saturated field sites and the adjacent normal soils. Determinations were made for total carbon, available P, pH, exchange‐able Mn, exchangeable ferric iron, and exchangeable ferrous iron. At two sites pH titration curves were determined. A sharp inflection in the curve at pH 2 was observed with gas‐saturated soils. Substantial increases of total carbon, exchangeable Mn, and exchangeable ferric iron occurred in the gas‐saturated soil. Exchangeable ferrous iron increased moderately. Determinations of soil pH and available P gave varied results, but in general showed increases in the gas‐impregnated soils. Physical determinations at the two most severely affected sites revealed some increase in water retention and total porosity with a corresponding decrease in bulk density in the gassaturated soils. The disturbed Fe‐Mn relationships may be one of the major factors accounting for the frequently observed detrimental influence of gas‐saturated soils on vegetative growth. The change in water retention was not considered injurious to the plants.

Technical Note Grass chlorophyll and the reflectance red edge

Article

Jan 1996

The point of maximum slope in the reflectance spectra of leaves occurs around 690-740 nm and is termed the ‘red edge’. Strong correlations have been found between the red edge of leaves and the chlorophyll concentration of those leaves. Imaging spectrometers can record the red edge of canopies and strong correlations have been observed between canopy red edge and the chlorophyll concentration of leaves in those canopies; providing the canopies were optically thick and/or had spatially invariant biomass.This study investigated relations between (i) canopy red edge and the chlorophyll concentration of leaves, and (ii) canopy red edge and the chlorophyll content of the canopy for a grass canopy with a spatially variable biomass. The canopy red edge as recorded by an airborne imaging spectrometer was correlated more strongly with the chlorophyll content of the canopy (r = 0·93) than the chlorophyll concentration of the leaves (r = 0·86).These results suggested that in the absence of other information the most appropriate measure of chlorophyll to relate to the red edge of a grass canopy is content rather than concentration.

Shape of the red edge as vitality indicator for plants

Article

Oct 1990

The shift of the red edge in the reflection spectra of vegetation targets is a known phenomenon documenting changes in the biological status of plants. In our study we analysed the variability of red edge reflection in dependency of differently managed field plots, The results indicate that the red edge is not fully described by the shift of the main inflection point, but has to be considered as a collection of several different and possibly independent features, each of them influenced by biological parameters of the plants. Thus, taking all features, the red edge as derived from high resolution spectra may provide enough information to detect small differences in the chemical and morphological status of plants.

Red edge structure of canopy reflectance spectra of triticale

Article

Nov 1993
REMOTE SENS ENVIRON

Reflectance spectra at the boundary between the visible and near-infrared reflectance in the domain of the socalled “red edge” (660–770 nm) were examined over triticale canopy in a two-factor field experiment. The objective w was to study changes in the red edge position and shape as a function of developmental stage, cultivar, and plant density. The experimental design involved six cultivars and three plant densities with three replications. Eight spectrometric measurements with 10-nm steps were taken from the moment of breaking winter dormancy until soft dough stage at approximately 2-week intervals. Numerical differentiation of spectra (preapproximated by a cubic spline function) was used to locate the red edge. Three components corresponding to maxima in the rate of increase in reflectance have been resolved in the curve of the first derivative on the average at 7700 nm, 715 nm and 745 nm. The features of the components showed no dependence on the cultivar and sowing rate but varied with plant maturation. At the vegetative stage, the first two components moved to longer wavelengths (a shift of about 5 nm), at the reproductive stage to shorter ones. The peak at 745 nm showed the opposite direction of movement: from 746–747 nm in early season to 743 nm at heading and towards the longer wavelengths by the late season. Throughout the growing season this peak continued to be the highest one. The results have confirmed that the red edge of the vegetation reflectance spectrum is a complex contour composed of a number of components the peakedness and relative positions of which vary according to the plant growth stage.

Effect of Leaking Natural Gas on Soil and Vegetation in Urban Areas

Article

Jan 1972
SOIL SCI

J. Hoeks

Leakage of natural gas from the gas distribution system affects the physical, chemical and biological processes in the soil. Particularly the microbial oxidation of methane is then of predominant importance for the composition of the soil gas phase. The rate of methane oxidation was measured under varying conditions of gas phase composition, temperature and nutrient supply. Computation models were evolved with which it is possible to calculate the effect of these and other factors on the distribution of methane, oxygen and carbon dioxide around a leak. Experiments with actual and artificial leaks as well as the calculations showed that the extent of the gas zone largely depends on the leakage rate, the depth of the groundwater table, the soil moisture content and the extent of the pavement. The soil temperature also proved to have a significant influence by its effect on the microbial methane oxidation. At low temperatures this microbial process is limited and consequently the anaerobic zone, which is invariably present in summer, may then disappear completely, thus making the probability of injury to vegetation negligible in winter. After repair of the leak the poor aeration conditions in the soil may persist for quite a long time. This is caused by the high consumption rate of oxygen required for the oxidation of organic substances and reduced anorganic compounds accumulated in the soil during gas leakage. The oxygen overdemand and the oxidation rate were determined for various gassed soils. Measures can be taken to accellerate soil recovery processes and to improve conditions for regeneration of injured trees and before planting new trees. Both experiments and calculations with computation models proved that installation of open ventilation channels is very effective, even if the leak cannot be immediately repaired. So ventilation channels can also be installed as preventive measure.

Veranderungen der Vegetation durch experimentelle Erdgasbehandlung

Article

Jun 1989

Zusammenfassung Die Veränderungen der Vegetation, die durch experimentelle Erdgasbehandlung hervorgerufen wurden, sind in den Jahren 1978–84 in Mittelböhmen untersucht worden. Das verwendete Erdgas enthielt 93,8% Methan, 3,6%Äthan, 0,8% Propan, 0,25% Butan, 0,3% Kohlendioxid. Die getesteten Kulturpflanzen wurden auf Parzellen von 60·5 m angebaut, bei denen das Erdgas durch unterirdische Sonden in den Boden eingebracht wurde. Darüber hinaus wurden die Reaktionen der Unkrautarten untersucht. Die Wirkungen auf der Populationsebene bestanden in Wachstumshemmung und Verminderung der Individuenzahl bei den meisten Arten. Bei Medicago sativa, Brassica oleracea var. acephala, Secale cereale, Lolium multiflorum, Beta vulgaris und Zea mays wurden Farbveränderungen beobachtet. Auffällige phänologische Veränderungen wurden bei Solanum tuberasum, Helianthus annuus and Zea mays festgestellt (Verzögerung des Austreibens und des Blühbeginns), Für einen typischen Effekt des Erdgases kann eingeschränkte oder sogar fehlende Reprodutionsfähigkeit angesehen werden, was besonders für Allium cepa, Triticum aestirum, Hordeum distichon, Trifolium sp. div., Medicago sativa und die meisten Unkrautarten gilt. Oftentstanden verschiedene Organdeformationen ( Solanum tuberosum, Beta vulgaris ). Einzelpflanzen von Chenopodium album und Medicago sativa , die in der Umgebung der Erdgas‐ausströmung wuchsen, hatten eine erhöhte Stomatazahl. Es wurden auch Veränderungen im Verlauf der Reflektionskurven bei Trifolium pratense subsp. sativum beobachtet. Am Ort der stärksten Erdgaswirkung kam es im Vergleich mit der Kontrolle zu auffälligem Abfall der Reflektion im infraroten Bereich des Spektrums. Der Unterschied entstand und verstärkte sich allmählich im Verlauf der Vegetationsperiode mit steigendem Einfluss des Gases. Die Deckungsgrad‐ und Dichtereduktion und Veränderungen in der Artenzusammensetzung (Rückgang der empfindlichen Arten) können für cine allgemeine Folge der Erdgaswirkung auf Gesellschaftsebene gehalten werden. Die ersten Wirkungen wurden etwa 15–30 Tage nach Versuchsbeginn beobachtet, was etwa einer Menge von 80–150 m ³ Gas pro Parzelle entspricht.

The Red Edge Position and Shape as Indicators of Plant Chlorophyll Content, Biomass and Hydric Status

Article

May 1994
INT J REMOTE SENS

Some red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were studied to evaluate plant chlorophyll content, biomassand RelativeWater Content (RWC).Plants of Capsicum annuum and Phaseolus vulgaris under different nitrogen and water availabilities, and plants of Gerbera jamesonii with different hydric status were studied. A high correlation was found between chlorophyll content and the wavelength of the red edge peak (λre ), and between LAI (leaf area index)and the amplitude of the red edge peak (drr e ), but the area of the red edge peak (σ680–780 nm) was the best estimator of LAI. Thus, red edge was found valuable for assessment of plant chlorophyll concentration and LAI, and therefore nutritional status. Water stress also affected drre, but only when the stress was well developed.

Influence of soil gas contamination on tree root growth

Article

Feb 1982

Rooted-cuttings and saplings of green ash (Fraxinus lanceolata) and hybrid poplar (Populus spp) were planted on a former municipal refuse landfill and on a nearby nonlandfill control plot. The root systems of four trees of each species and size were excavated on the landfill plot-two growing in an area where the concentrations of anaerobic landfill gases were relatively high and two in a relatively low-gas area. Two trees of each species and size were also excavated on the control. The root systems of both species were significantly shallower on the landfill plot than on the control. Green ash appeared to avoid the adverse gas environment of the deeper soil layers on the landfill by producing adventitious roots. Hybrid poplar became adapted in a different manner, by redirecting root growth from the deepter soil layers to the soil surface.

Early detection of plant stress by digital imaging within stress-sensitive wavebands

Article

Dec 1994
REMOTE SENS ENVIRON

Digital images of soybean canopies [Glycine max (L.) Merrill] were obtained within selected narrow wavebands (6–10 nm bandwidths) to determine their capability for early detection of plant stress. Images and physiological measurements of stress were acquired 2 days, 4 days, and 7 days following application of control, drought, and herbicide [(3,4-dichlorophenyl)-1, 1-dimethylurea, or DCMU] treatments. As a result of frequent rainfall, drought stress never occurred. However, exposure to herbicide rapidly induced plant stress. By day 4, the ratio of variable to maximum leaf fluorescence (Fv/Fm) decreased and leaf water potentials (ψw) increased in the herbicide treated soybean, indicating damage to the photosynthetic apparatus and stomatal closure. Also, Munsell leaf color had increased from approximately 5GY 4.6/5.7 to a lighter green-yellow value. Canopy reflectances at 670 nm, 694 nm, and in the 410–740 nm band (Rvis), as well as reflectance at 694 nm divided by reflectance at 760 nm (R694/R760), detected stress simultaneously with the physiological measurements and increased consistently with stress through day 7. Reflectances at 420 nm and 600 nm, together with R600/R760 and Rvis/R760, did not increase until leaves were yellow or brown and wilted and canopies had begun to collapse on day 7. None of the reflectance or reflectance ratio images detected stress prior to visible color changes. This was attributed primarily to the rapid inducement of chlorosis by the herbicide. Reflectance in narrow wavebands within the 690–700 nm region and its ratio with near-infrared reflectance should provide earlier detection of stress-induced chlorosis compared with broad band systems or narrow bands located at lesser wavelengths.

Arsenic and selenium-induced changes in spectral reflectance and morphology of soybean plants

Article

Dec 1989
REMOTE SENS ENVIRON

Soybean (Glycine max) plants were grown in hydroponic solutions treated with high concentrations of either arsenic or selenium. Spectral reflectance changes in arsenic-dosed plants included a shift to shorter wavelengths in the long-wavelength edge of the chlorophyll absorption band centered at 680 nm (the red edge) and higher reflectance in the 550–650 nm region. These results are consistent with vegetation reflectance anomalies observed in previous greenhouse experiments and in airborne radiometer studies. The selenium-dosed plants contrast, exhibited a shift to longer wavelengths of the red edge and lower reflectance between 550 nm and 650 wh when compared with control plants. Morphological effects of arsenic uptake included lower overall biomass, stunted and discolored roots, and smaller leaves oriented more vertically than leaves of control plants. Selenium-dosed plants also displayed morphological changes, but root and leaf biomass were less affected than were those of arsenic-dosed plants when compared to control plants.

Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects

Article

Feb 2003
REMOTE SENS ENVIRON

A series of experiments carried out in a controlled environment facility to induce steady-state chlorophyll a fluorescence variation demonstrate that natural fluorescence emission is observable on the derivative reflectance spectra as a double-peak feature in the 690–710 nm spectral region. This work describes that the unexplained double-peak feature previously seen on canopy derivative reflectance is due entirely to chlorophyll fluorescence (CF) effects, demonstrating the importance of derivative methods for fluorescence detection in vegetation. Measurements were made in a controlled environmental chamber where temperature and humidity were varied through the time course of the experiments in both short- and long-term trials using Acer negundo ssp. californium canopies. Continuous canopy reflectance measurements were made with a spectrometer on healthy and stressed vegetation, along with leaf-level steady-state fluorescence measurements with the PAM-2000 Fluorometer during both temperature–stress induction and recovery stages. In 9-h trials, temperatures were ramped from 10 to 35 jC and relative humidity adjusted from 92% to 42% during stress induction, returning gradually to initial conditions during the recovery stage. Canopy reflectance difference calculations and derivative analysis of reflectance spectra demonstrate that a double-peak feature created between 688, 697 and 710 nm on the derivative reflectance is a function of natural steady-state fluorescence emission, which gradually diminished with induction of maximum stress. Derivative reflectance indices based on this doublepeak feature are demonstrated to track natural steady-state fluorescence emission as quantified by two indices, the double-peak index (DPi) and the area of the double peak (Adp). Results obtained employing these double-peak indices from canopy derivative reflectance suggest a potential for natural steady-state fluorescence detection with hyperspectral data. Short- and long-term stress effects on the observed doublepeak derivative indices due to pigment degradation and canopy structure changes were studied, showing that both indices are capable of tracking steady-state fluorescence changes from canopy remote sensing reflectance. California Space Institute and NASA Space Grant Program Peer reviewed

Responses of Leaf Spectral Reflectance to Plant Stress

Article

Feb 1993

Gregory A. Carter

Leaf spectral reflectances were measured to determine whether leaf reflectance responses to plant stress may differ according to the agent of stress and species. As a result of decreased absorption by pigments, reflectance at visible wavelengths increased consistently in stressed leaves for eight stress agents and among six vascular plant species. Visible reflectance was most sensitive to stress in the 535-640 nm and 685-700 nm wavelength ranges. A sensitivity minimum occurred consistently near 670 nm. Infrared reflectance was comparatively unresponsive to stress, but increased at 1,400-2,500 nm with severe leaf dehydration and the accompanying decreased absorption by water. Thus, visible rather than infrared reflectance was the most reliable indicator of plant stress. Visible reflectance responses to stress were spectrally similar among agents of stress and species.

Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline

Article

Mar 1988
REMOTE SENS ENVIRON

Visible IR Intelligent Spectrometer (VIRIS) reflectance data have been found to have similar features that are related to air-pollution-induced forest decline and visible damage in both the red spruce of Vermont and the Norway spruce of Baden-Wuerttemberg; the similarity suggests a common source of damage. Spectra of both species include a 5-nm blueshifting of the red-edge inflection point, while pigment data for both species indicate a loss of total chlorophylls. The blue shift of the chlorophyll absorption maximum, as well as the increased red radiance and decreased near-IR radiance of the damaged spruce, may be used to delineate and map damage areas.

Increase of the chlorophyll fluorescence ratio F690/F735 during the autumnal chlorophyll breakdown

Article

Feb 1992

The chlorophyll content and the fluorescence induction kinetics at two wavelengths (690 nm and 735 nm) have been measured in leaves of nine common broadleaf tree species during the autumnal chlorophyll breakdown. The ratio of the chlorophyll fluorescence maxima F690/F735 was determined at fluorescence maximum (fm) and at steady-state conditions (fs) by the laser-induced fluorescence emission using the two-wavelength fluorometer. The ratio F690/F735 increases with the leaf discolouring during the autumnal chlorophyll breakdown. The relationship between the chlorophyll content and the ratio F690/F735 can be expressed by a power function (curvilinear relationship) which is valid for all the species examined. In most cases the ratio F690/F735 measured in the upper leaf side is lower than that in the lower leaf side, but the trend is the same along the decreasing chlorophyll content. The ratio F690/F735 is always higher at maximum fluorescence than at steady-state fluorescence in the upper as well as lower leaf side and these values are well fitted in a linear correlation. This study confirms the usefulness of the ratio F690/F735 as a suitable non-destructive indicator of the in-vivo chlorophyll content, especially at medium and low chlorophyll content.

Microbial activities in soil near natural gas leaks

Article

Feb 1971

The Stress Concept in Plants: An Introduction

Article

Jul 1998

Hartmut Lichtenthaler

The current concept of stress in plants has been well developed over the past 60 years. Any unfavorable condition or substance that affects or blocks a plant's metabolism, growth, or development is regarded as stress. Vegetation stress can be induced by various natural and anthropogenic stress factors. One has to differentiate between short-term and long-term stress effects as well as between low-stress events that can be partially compensated for by acclimation, adaptation, and repair mechanisms, on the one hand, and strong stress or chronic stress events causing considerable damage that may eventually lead to cell and plant death, on the other hand. Some essential stress syndrome responses of plants are summarized in a unifying stress concept. The major abiotic, biotic, and anthropogenic stressors are listed. Some stress tolerance mechanisms are mentioned. Stress conditions and stress-induced damage in plants have so far been detected using the classical ecophysiological field methods as well as point data measurements of particular chlorophyll fluorescence parameters and of reflectance spectra. The novel laser-induced high-resolution fluorescence imaging technique, which integrates chlorophyll and blue-green fluorescence, marks a new standard in the detection of stress in plants.

Reflectance and Transmittance of Light by Leaves

Article

Jun 1971
PLANT PHYSIOL

Joseph T. Woolley

Spectrophotometric transmittance and reflectance curves were recorded for wavelengths from 0.45 (in some cases 0.34) to 2.7 micrometers for faces and backs of leaves and for stacked leaves of several plant species. Measurements were made at different angles of illumination. Leaf spectrophotometric curves were compared with curves for leaf extracts, potato tuber tissue, glass beads in water, and frozen leaves to demonstrate the physical bases for the leaf curves. Leaves were infiltrated with liquids of different refractive indices for further comparison of spectrophotometric curves. Goniophotometric reflectance curves were recorded, giving visible reflectance and degree of polarization as functions of viewing angle for two different angles of illumination. No retroreflection was observed, and no phenomena were observed which could be attributed to interference because of similarity between leaf structural sizes and wavelengths used.

Remote sensing of leaf responses to leaking under-ground natural gas. Unpublished PhD, University of Nottingham, Not-tingham Spectral responses of pot-grown plants to displacement of soil oxygen

Jan 2002

K L Smith

Smith, K. L. (2002). Remote sensing of leaf responses to leaking under-ground natural gas. Unpublished PhD, University of Nottingham, Not-tingham, UK. Smith, K. L., Steven, M. D., & Colls, J. J. (2004). Spectral responses of pot-grown plants to displacement of soil oxygen. International Journal of Remote Sensing (in press).

Changes in vegetation caused by experimental leakage of natural gas

Jan 1989

Pysek

Pysek, P., & Pysek, A. (1989). Changes in vegetation caused by experi-mental leakage of natural gas. Weed Research, 29(193 – 204).

Water quality for agriculture. Fao Irrigation and Drainage Paper 29 (Rev. 1), Food and Agriculture Orga-nization (FAO) of the United Nations Shape of the red-edge as vitality indicator for plants

Jan 1985
11-1741

R S Ayers
D W Westcot
W Kuhbauch

Ayers, R. S., & Westcot, D. W. (1985). Water quality for agriculture. Fao Irrigation and Drainage Paper 29 (Rev. 1), Food and Agriculture Orga-nization (FAO) of the United Nations, Rome, Italy. Boochs, F., Kupfer, G., Dockter, K., & Kuhbauch, W. (1990). Shape of the red-edge as vitality indicator for plants. International Journal of Remote Sensing, 11(10), 1741 – 1753.

Estimating the chlorophyll concentration of a grassland canopy for chemical monitoring using remotely sensed data. Paper presented at the Remote Sensing and Industry Conference

R A Jago
P J Curran

Jago, R. A., & Curran, P. J. (1996). Estimating the chlorophyll concentra-tion of a grassland canopy for chemical monitoring using remotely sensed data. Paper presented at the Remote Sensing and Industry Con-ference, Remote Sensing Society, University of Nottingham.

Effect of leaking natural gas on soil and vegetation in urban areas Agricultural Research Reports The red edge of plant leaf reflectance

Jan 1972
273-288

J Hoeks
D N H Horler
M Dockray
J Barber

Hoeks, J. (1972). Effect of leaking natural gas on soil and vegetation in urban areas. Agricultural Research Reports, 778. Horler, D. N. H., Dockray, M., & Barber, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273 – 288.

Response of soils and plants to natural gas migration from two wells in the Lloydminster area (No. E-2510-3-E-90)

Jan 1990

R Godwin
Z Abouguendia
J Thorpe

Godwin, R., Abouguendia, Z., & Thorpe, J. (1990). Response of soils and plants to natural gas migration from two wells in the Lloydminster area (No. E-2510-3-E-90). Saskatchewan Research Council.

Methanotrophic bacteria. Microbiolo-gical Reviews

Jan 1996
439-471

R Hanson
T Hanson

Hanson, R., & Hanson, T. (1996). Methanotrophic bacteria. Microbiolo-gical Reviews, 60(2), 439 – 471.

Soils I Derbyshire II, Soil survey record

M J Reeve

Reeve, M. J. (1975). Soils I Derbyshire II, Soil survey record, No 27: Adlard and Son, Bartholomew Press.

Understanding the grassland red-edge using a combined leaf and canopy model. Paper presented at the 25th Annual Conference of The Remote sensing Society 25th: From data to information

G M Llewellyn
P J Curran

Llewellyn, G. M., & Curran, P. J. (1999). Understanding the grassland red-edge using a combined leaf and canopy model. Paper presented at the 25th Annual Conference of The Remote sensing Society 25th: From data to information, University of Cardiff.

Water quality for agriculture. Fao Irrigation and Drainage Paper 29 (Rev. 1), Food and Agriculture Organization (FAO) of the United Nations

R S Ayers
D W Westcot

Effect of leaking natural gas upon the soil

Jan 1930
260

Scholenberger

Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks

Abstract

No full-text available

Recommended publications

Remote Sensing Techniques for Monitoring Plant Stress Responses to Gas Leaks