Detection Of Vegetation Stress Research Articles

Toward Precision Agriculture in Outdoor Vertical Greenery Systems (VGS): Monitoring and Early Detection of Stress Events

Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management.

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

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 is validated using 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 performance 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 and around the mines whilelow-stress regions are positioned farther from the mines. The findings additionally demonstrated that the VIs-based stress result (AUC-0.69) fits the data more accurately 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. Therefore, effective vegetation health monitoring and planning in coal mining sites might benefit from these results .

Evaluation of SIF retrievals from narrow-band and sub-nanometer airborne hyperspectral imagers flown in tandem: Modelling and validation in the context of plant phenotyping

Solar-induced chlorophyll fluorescence (SIF) can be used as an indicator of crop photosynthetic activity and a proxy for vegetation stress in plant phenotyping and precision agriculture applications. SIF quantification is sensitive to the spectral resolution (SR), and its accurate retrieval requires sensors with sub-nanometer resolutions. However, for accurate SIF quantification from imaging sensors onboard airborne platforms, sub-nanometer imagers are costly and more difficult to operate than the commonly available narrow-band imagers (i.e., 4- to 6-nm bandwidths), which can also be installed on drones and lightweight aircraft. Although a few theoretical and experimental studies have evaluated narrow-band spectra for SIF quantification, there is a lack of research focused on comparing the effects of the SR on SIF from airborne hyperspectral imagers in practical applications. This study investigates the effects of SR and sensor altitude on SIF accuracy, comparing SIF quantified at the 760-nm O2-A band (SIF760) from two hyperspectral imagers with different spectral configurations (full width at half-maximum resolutions of 0.1–0.2 nm and 5.8 nm) flown in tandem on board an aircraft. SIF760 retrievals were compared from two different wheat and maize phenotyping trials grown under different nitrogen fertilizer application rates over the 2019–2021 growing seasons. SIF760 from the two sensors were correlated (R2 = 0.77–0.9, p < 0.01), with the narrow-band imager producing larger SIF760 estimates than the sub-nanometer imager (root mean square error (RMSE) 3.28–4.69 mW/m2/nm/sr). Ground-level SIF760 showed strong relationships with both sub-nanometer (R2 = 0.90, p < 0.001, RMSE = 0.07 mW/m2/nm/sr) and narrow-band (R2 = 0.88, p < 0.001, RMSE = 3.26 mW/m2/nm/sr) airborne retrievals. Simulation-based assessments of SIF760 for SRs ranging from 1 to 5.8 nm using the SCOPE model were consistent with experimental results showing significant relationships among SIF760 quantified at different SRs. Predictive algorithms of leaf nitrogen concentration using SIF760 from either the narrow-band or sub-nanometer sensor yielded similar performance, supporting the use of narrow-band resolution imagery for assessing the spatial variability of SIF in plant phenotyping, vegetation stress detection and precision agriculture contexts.

Assessing impacts of canopy 3D structure on chlorophyll fluorescence radiance and radiative budget of deciduous forest stands using DART

Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (ε1D−3D and ε0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in ε1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (ε up to 17%) and their “blocking” effect (ε ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.

Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

Unoccupied Aircraft Systems (UAS) for Marine Ecosystem Restoration

Assessing, implementing and monitoring ecosystem restoration can be a labor intensive process, often short term (< 3 years), and potentially destructive to the habitat. Advances in remote sensing technology are generating rapid, non-destructive methods for siting, executing and monitoring restoration efforts, particularly in fragile marine environments. Unoccupied aircraft systems (UAS), or drones, are a highly flexible method for accessing and remote sensing ecosystems with on-demand capabilities, greater resolution than sensors from satellites and occupied aircraft, and the ability to cover large areas quickly. With the variety of platforms and payloads available, UASs are providing a suite of tools for conservation practitioners to properly plan marine ecosystem restoration projects and evaluate their success. Both conventional and specialized sensors coupled with image processing techniques can be used to gauge impact to and recovery of entire ecological communities. For example, high-resolution, multispectral imaging allows for discernment of population changes across trophic levels, concurrent with the discrimination of species (including rare) across a landscape, and detection of vegetation stress. Structure from Motion photogrammetric processing provides centimeter-scale three-dimensional models of habitat structure to measure ecologically significant aspects like rugosity and assess their change through time. Water quality around a broad impacted area can be remotely monitored via a number of payloads before and after restoration. Additionally, specially designed payloads can be used to manually disperse seeds or materials for restoration applications without disturbing the habitat. UASs have increasing potential to reduce the costs (both time and money) associated with restoration efforts, making site assessment and long-term, broad-scale monitoring more achievable. Here we present a review of the applications of UASs in marine ecosystem restoration with an overview of the special considerations of using this technology in the marine environment.

Thermal and Optical Indices for Wetland Habitats, are They Showing the Same Thing?

This study is focused on detecting vegetation stress in natural and seminatural riparian wetland ecosystems. Wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services as long as they are in good condition. The global climate change influences a local environment, in which wetlands health become an indicator of drought conditions. Persisting adverse conditions might lead to irreversible changes in these precious ecosystems. Hence, modern methods for the monitoring of these ecosystems are required to be developed. In the scope of this study, based on basic meteorological elements (air temperature and humidity) and canopy temperature from two different platforms (meteorological tower and Landsat-8 satellite), crop water stress index (CWSI) was proposed for riparian wetland habitats and wet meadows monitoring. The study was conducted in the Upper Biebrza Basin (NE, Poland). As a first step, CWSI was calculated based on meteorological measurements and compared with CWSI calculated for surrounding Landsat-8 pixels covered by the same land-use type as one being in the tower sensor field of view. CWSI from both sensors were in significant agreement. In the second step of the analysis, CWSI and selected optical indices were calculated for a longer period based on satellite data. Those indices were compared with test if they indicate the same conditions, or if their indications are inconsistent. Results show that CWSI and optical indices indicate different plant stress factors. Hence, for proper wetlands condition assessment, still more research is required to properly assess their conditions and its causes.

THE POTENTIAL OF DUAL-WAVELENGTH TERRESTRIAL LASER SCANNING IN 3D CANOPY FUEL MOISTURE CONTENT MAPPING

Abstract. Terrestrial laser scanning (TLS) instruments have been widely utilized in measuring vegetation canopy structural parameters, being capable of providing high density point clouds. However, less attention has been paid to using TLS intensity data in estimating vegetation biochemical attributes, and calculating water status metrics, that can help in early detection of vegetation stress and risk of wildfire. Water status metrics, such as the leaf Equivalent Water Thickness (EWT) and the Fuel Moisture Content (FMC), are being commonly estimated from optical remote sensing data. However, such estimates mainly reflect the water status of canopy top and ignore the vertical heterogeneity of water content distribution within the canopy. The estimates are also affected by canopy structure and understory reflectance. Such limitations can potentially be addressed using TLS intensity data, as observations are performed in three dimensions (3D). This study therefore investigated the potential of using dual-wavelength TLS intensity data to estimate FMC in 3D. The calculated Normalized Difference Index (NDI) of 808 nm near infrared and 1550 nm shortwave infrared wavelengths was found to be correlated to FMC at leaf level for four different tree species. The correlation was moderate, and the relationships were not consistent between species. NDI was subsequently used to estimate FMC at canopy level in seven trees in a small tree plot with an average error &lt; 5 %. The 3D estimates of FMC revealed vertical heterogeneity in all trees measured, which varied between species and also between trees from the same species.

Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques

Soil contamination is a worldwide crisis, which diminishes food and agricultural production. Alterations in the soil environment due to soil contamination cause biophysical and biochemical changes in vegetation. Due to dynamic nature of these changes, early monitoring can permit for preventive interferences before intense and sometimes inevitable vegetation and soil problems occur. As plants are rooted in soil substrate, vegetation changes can be used as bio-indicators of soil conditions. Traditionally, vegetation changes have been usually determined by visual analysis or detected after major destructive sampling during the growth period. As the characteristics of vegetation influence its spectral properties, effective remote and non-contact detection methods offer an alternative and near real-time way for detecting plant changes, even prior to visual symptoms and negative effects appearance. The aim of the current study is to review the potential of optical proximal and remote sensing techniques at different platforms for indirect assessment of plant–soil interactions via monitoring vegetation anomalies related to soil contamination. It is strongly felt that this rapidly progressing technological direction will permit extending the use of the techniques to geology, soil science and precision agriculture and an overall broad range of applications.

UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

Agreement analysis and spatial sensitivity of multispectral and hyperspectral sensors in detecting vegetation stress at management scales

Remotely sensed imagery is commonly used to map and monitor large land areas based on the ability to detect vegetation stress. Many sensors are available, including both hyper- and multispectral, but have varying costs, convenience, and characteristics. There were two objectives in this study: (1) to compare a hyperspectral sensor to two multispectral sensors with regards to each sensor’s ability to detect vegetation stress indicators in the visible, red edge, near-infrared, and shortwave infrared portions of the spectrum and (2) to determine the ability of coarser-resolution sensors to detect stress indicators in areas, where a finer resolution sensor detected stress indicators. Pairwise agreements between the sensors were ∼80% in each case, but much of this agreement was a function of agreement where stress indicators were absent. Spatial sensitivity analysis supported a conclusion that coarser-resolution sensors were consistently able to detect stress indicators in areas much smaller than their pixel size.

Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest

Biophysical and biochemical plant foliage parameters play a key role in assessing vegetation health. Those plant parameters determine the spectral reflectance and transmittance properties of vegetation; therefore, hyperspectral remote sensing, particularly imaging spectroscopy, can provide estimates of leaf and canopy chemical properties. Based on the relationship between spectral response and biochemical/biophysical properties of the leaves and canopies, the PROSPECT radiative transfer model simulates the interaction of light with leaves. In this study, more than 1100 leaf samples from the Amazon forest of Ecuador were collected at several study sites, some of which are affected by petroleum pollution, and across the vertical profile of the forest. For every sample, field spectroscopy at leaf level was conducted with a spectroradiometer. The goal of this study was to assess leaf optical properties of polluted and unpolluted rainforest canopies across the vertical profile and identify vegetation stress expressed in changes of biophysical and biochemical properties of vegetation. An ANOVA followed by Holme’s multiple comparisons of means and a principal component analysis showed that photosynthetic pigments, chlorophyll and carotenoids have significantly lower levels across the vertical profile of the forest, particularly in sites affected by petroleum pollution. On the other hand, foliar water content showed significantly higher levels in the polluted site. Those findings are symptoms of vegetation stress caused by reduced photosynthetic activity and consequently decreased transpiration and water-use efficiency of the plants. Cross-comparison between SPAD-502 chlorophyll content meter index and chlorophyll content showed strong positive correlation coefficients (r = 0.71 and r2 = 0.51) which suggests that using the SPAD-502 chlorophyll index itself is sensitive enough to detect vegetation stress in a multispecies tropical forest. Therefore, the SPAD-502 can be used to assess chlorophyll content of vegetation across polluted and non-polluted sites at different canopy layers. The results presented in this paper contribute to the very limited literature on field spectroscopy and radiative transfer models applied to the vertical profile of the Amazon forest.

Remote Laser Fluorimeter to Detect Stress of Plants

One of the promising trends in laser monitoring of natural formations is a vegetation control. A variety of reasons such as plant diseases, deficit of moisture or nutrients in the soil, pests of one sort or another, etc. may cause plant stress (conditions under which normal development of plants is impossible). Often (especially at the early stages) to detect these plant stress conditions by appearance is difficult. Therefore, development of remote methods to detect vegetation stress conditions is of relevance. To date, the method based on detection of plant chlorophyll fluorescence is the only method that allows detection of changes in the photosystem of 2 living plants. The overwhelming majority of papers, as a characteristic of the vegetation fluorescence, use a spectrum shape of the fluorescence irradiance, and the stress conditions of plants are defined by the shape distortion of the fluorescence spectra (as compared with vegetation in the normal condition). The paper presents new experimental data on the spectra of laser-induced fluorescence of plants in normal and stressful conditions (due to various reasons). Their analysis allows us to define a technical configuration of the remote laser fluorimeter to detect the vegetation stress conditions. The papers’ analysis shows that to excite laser-induced fluorescence of plants the second harmonic of a pulsed solid-state YAG-Nd laser with a wavelength of 0.532 μm is the most appropriate in terms of hardware implementation. In this case, the excitation wavelength of 0.532 μm is characterized by high (close to maximum) fluorescence excitation efficiency. The results of experimental studies allow us to find the best options of the laser-induced fluorescence irradiance windows for detection of the plant stress conditions. In the most simple implementation, a laser fluorimeter with 0.532 μm excitation wavelength of fluorescence can detect the laser-induced fluorescence irradiance of plants only in two spectral bands with the central wavelengths 0.685 μm and 0.740 μm.

A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area

Abstract This study aimed at developing a geobotanical remote sensing method to explore mineral deposits in areas covered by thick vegetation. For this, a new vegetation index (VI) is proposed using reflectance data from five bands in the visible green to shortwave infrared region. This index is called VIGS (Vegetation Index considering Greenness and Shortwave infrared), developed so that the VI can accurately detect vegetation stress caused by metal contamination of soils. A set of laboratory experiments was conducted to demonstrate the capability of VIGS, which investigates change in reflectance spectra based on the concentration of four selected metals (Cu, Pb, Zn, and Cd) in soils. The results show that VIGS values are more sensitive to vegetation stress than the Normalized Difference Vegetation Index and can amplify the stress difference, depending on soil metal contents. The VIGS is further examined for a mineralized area containing hydrothermal copper deposits in Jambi, central Sumatra, Indonesia, for which a set of geochemical data of the top layer composed of weathered rocks and soils were systematically obtained. Through kriging of point content data, the spatial distributions of Cu, Pb, and Zn in soil are found to be strongly correlated with the geology and controlled by faults. Using one Landsat ETM + scene image after atmospheric correction, VIGS values are calculated by a combination of reflectances in bands 2, 3, 4, 5, and 7. The effectiveness of VIGS is proven by this case study, because VIGS anomalies appeared in high-content zones common to the three metals. This concordance probably originated from the fact that plant formations (mainly primary forest) in the high metal zones are closely related to the geological units.

Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.62, p <;0.001 vs. R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.21, p > 0.1). Maximal R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.

Using multi-date high spectral resolution data to assess the physiological status of macroscopically undamaged foliage on a regional scale

Forests play an important role in regulation of the global climate; moreover, they provide human beings with a whole range of ecosystem services. Forest health and ecosystem functioning have been influenced by anthropogenic activities and their consequences, such as air pollution, surface mining, heavy metal contamination, and other biotic and abiotic stress factors, which had an especially serious effect on central Europe. Many aspects of the physiological state of trees are more or less related to the concentrations of two main groups of leaf photosynthetic pigments: chlorophylls and carotenoids. Therefore, their contents can be used as non-specific indicators of the actual tree physiological status, stress and the pre-visible tree damage. Variations in leaf biochemical composition affect foliar optical properties and can be assessed remotely using high spectral resolution data (hyperspectral data). These data were successfully used in earlier studies to detect vegetation stress and damage. However, only a few approaches have dealt with the use of hyperspectral remote sensing to assess vegetation physiological status on a regional scale. Moreover, little or no research has been done on assessing vegetation health while utilizing multi-date hyperspectral images.In this study, the method for assessing forest health conditions using optical indices retrieved from hyperspectral data was applied to the two temporal HyMap date sets acquired in 07/2009 and 08/2010 to detect stress for the Norway spruce forests in Sokolov, NW Bohemia, a region affected by long-term extensive mining. The classification results were validated by ground truth data (total chlorophyll – Cab, carotenoids – Car and carotenoid to chlorophyll ratio – Car/Cab) and were associated with the geochemical conditions of the forest stands. Both biochemical analysis of the sampled foliage and classification of 2009 and 2010 hyperspectral image identified the same sites affected by vegetation stress. In addition to higher Car/Cab, which enabled detection of the stressed trees using hyperspectral image data, these sites showed critically low pH and lower values for the macronutrient parameters in both organic horizons and, in addition, both sites exhibit critically low base cation to aluminum ratios (Bc/Al) for lower organic and top mineral (0–20cm) soil horizons.The results of this study demonstrate (i) the potential application of hyperspectral remote sensing as a rapid method of identifying tree stress prior to symptom expression, and (ii) the added value of multi-temporal approaches for hyperspectral data and its further potential for monitoring forest ecosystems.

Early detection of oil-induced stress in crops using spectral and thermal responses

Oil pollution is a major source of environmental degradation, and requires accurate monitoring and timely detection for an effective control of its occurrence. This paper examines the potential of a remote sensing approach using the spectral and thermal responses of crops for the early detection of stress caused by oil pollution. In a glasshouse, pot-grown maize was treated with oil at sublethal and lethal applications. Thereafter, leaf thermal, spectral and physiological measurements were taken every two to three days to monitor the development of stress responses. Our results indicate that absolute leaf temperature was a poor indicator of developing stress. However, a derived thermal index (IG) responded consistently in the early stages of physiological damage. Various spectral reflectance features were highly sensitive to oil-induced stress. A narrow-band index using wavelengths in the near-infrared and red-edge region, (R755−R716)/(R755+R716), was optimal for previsual detection of oil-induced stress. This index had a strong linear relationship with photosynthetic rate. This indicates that by detecting vegetation stress, thermal and hyperspectral remote sensing has considerable potential for the timely detection of oil pollution in the environment.

First observations of global and seasonal terrestrial chlorophyll fluorescence from space

Abstract. Remote sensing of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global coverage of the functional status of vegetation. For example, fluorescence observations may provide a means to detect vegetation stress before chlorophyll reductions take place. Although there have been many measurements of fluorescence from ground- and airborne-based instruments, there has been scant information available from satellites. In this work, we use high-spectral resolution data from the Thermal And Near-infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) that is in a sun-synchronous orbit with an equator crossing time near 13:00 LT. We use filling-in of the potassium (K) I solar Fraunhofer line near 770 nm to derive chlorophyll fluorescence and related parameters such as the fluorescence yield at that wavelength. We map these parameters globally for two months (July and December 2009) and show a full seasonal cycle for several different locations, including two in the Amazonia region. We also compare the derived fluorescence information with that provided by the MODIS Enhanced Vegetation Index (EVI). These comparisons show that for several areas these two indices exhibit different seasonality and/or relative intensity variations, and that changes in fluorescence frequently lead those seen in the EVI for those regions. The derived fluorescence therefore provides information that is related to, but independent of the reflectance.

Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection

Progress in assessing the feasibility for imaging fluorescence using the O 2-A band with 1 nm full-width half-maximum (FWHM) bands centered at 757.5 and 760.5 nm is reported in this paper. Multispectral airborne data was acquired at 150 m above ground level in the thermal, visible and near infrared regions yielding imagery at 15 cm spatial resolution. Simultaneous field experiments conducted in olive, peach, and orange orchards (water stress trials), and an olive orchard (variety trial) enabled the detected variability in fluorescence emission to be examined as function of stress status. In a parallel modelling activity the coupled leaf–canopy reflectance–fluorescence model, FluorMOD, was used to assess fluorescence retrieval capability by the in-filling method, as well as by fluorescence indices from the published literature. Fluorescence retrievals using the in-filling method, the derivative index D702/D680 and reflectance indices R690/R630, R761–R757, and R761/R757 yielded the best results in the simulation study, while demonstrating insensitivity to leaf area index (LAI) variation. The fluorescence in-filling method, derivative index D702/D680, and R761–R757 were the indices least affected by chlorophyll a + b (Cab) variation. On the other hand, other published indices for fluorescence detection at leaf and canopy levels exhibited high sensitivity to variations in Cab and LAI, and therefore were considered less suitable for in-field fluorescence detection. The fluorescence signal extraction from airborne imagery using the in-filling method was validated through comparisons with field-measured steady-state fluorescence ( Fs) using the PAM-2100 and GFS-3000 instruments, confirming simulation predictions. The water stress experiments conducted on olive and peach orchards demonstrated the feasibility of chlorophyll fluorescence ( F) extraction at the tree level from the airborne imagery, yielding determination coefficients r 2 = 0.57 (olive), and r 2 = 0.54 (peach). Consistent results were obtained between airborne F and ground truth assimilation ( A) measured in the olive variety field experiment under no water stress levels, yielding r 2 = 0.71.

Hyperspectral detection of chemical vegetation stress: evaluation for the Canadian HERO satellite mission

The potential for direct detection of chemicals on vegetation and for detecting vegetation stress and damage from exposure was reviewed within the context of the proposed Hyperspectral Environment and Resource Observer (HERO) satellite mission. The direct detection of 10 selected toxic chemicals was deemed unlikely. Detecting vegetation stress resulting from exposure to these chemicals may be feasible, based on several image analysis approaches considered for two levels of damage: (i) severe defoliation, mortality, and effects at the full crop or forest stand scale; and (ii) more subtle stress and damage such as discolouration, spotting, bleaching, and light defoliation that occur at subpixel scales. Image classification, vegetation indices, spectral mixture analysis (SMA), and change detection are expected to be suitable for the former, whereas only SMA and advanced modelling approaches are expected to be suitable for the latter because of the subtle nature of reflectance change at subpixel scales. A key recommendation is the need for a spectral library of toxic substances and the stress and damage caused to various types of vegetation at different growth stages. Current information on this is limited and represents a constraint to effective analysis and evaluation. Based on this application, HERO mission parameters were considered, with recommendations that the current spectral resolution and ground sampling distance (GSD) be improved, with a sensitivity analysis required to determine proper values to provide optimal detection of toxic hazards and vegetation stress. Other sensor and mission parameters such as spectral range, signal-to-noise ratio (SNR), swath width, and orbital repeat period with off-nadir sensing and temporal relook frequency were deemed acceptable, but improvements would be desirable. Extensive testing involving laboratory experiments and airborne hyperspectral imagery for a variety of toxic substances over different areas and vegetation types is required for assessment using different image analysis algorithms to confirm this and other recommendations for HERO prelaunch.