Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains

Identifying cadmium and lead co-accumulation from living rice blade spectrum

Neither cadmium (Cd) nor lead (Pb) is necessary for crop growth, but they both can accumulate in soil and crop tissues, resulting in land degradation and crop reduction. Few researchers have explored how to detect Cd-Pb co-accumulation in leaves using proximal sensing techniques, especially by low-cost, easy-to-use leaf clips that capture hyperspectral reflections at suitable foliar positions. In this study, a hyperspectral imager was employed to collect images of the rice canopy from a designed greenhouse experiment that included 16 pretreatments of Cd-Pb co-accumulation, followed by spectral extractions from 3 foliar positions: the blade root, the middle of the leaf, and the leaf apex. A support vector machine with leave-one-out cross-validation was performed to diagnose the contaminative levels based on the feature wavelengths selected by an improved successive projection algorithm. Partial least squares regression was used to predict Cd-Pb concentrations in rice blades. The results indicated that diagnostic accuracies were varied using spectra of different foliar positions. The blade root and leaf apex of rice blades were the optimal foliar position for detecting Cd and Pb contamination, respectively. At the optimal foliar positions, diagnostic accuracies exceeded 0.80 for distinguishing whether the rice is subject to Cd-Pb contamination. The Cd prediction performed 'very good' with a residual prediction deviation (RPD) of 2.21, a R2 of 0.79, and a root mean square error (RMSE)of 6.14, while that of Pb was 1.62, 0.61, and 186.54. Important wavelengths were identified at 659-694 nm and 667-694 nm to detect Cd and Pb contamination. In summary, our results verified the feasibility and clarified the optimal foliar positions of rice blades to detect Cd-Pb contamination. The wavelengths selecting have the great potential in the design of future leaf clips, and the optimal foliar position can provide suggestions to improve diagnostic performances in field applications.

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

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.

Hg and As pollution in the soil-plant system evaluated by combining multispectral UAV-RS, geochemical survey and machine learning

The combination of a low-density geochemical survey, multispectral data obtained with Unmanned Aerial Vehicle-Remote Sensing (UAV-RS), and a machine learning technique was tested in the search for a statistically robust prediction of contaminant distribution in soil and vegetation, for zones with a highly variable pollutant load. To this end, a novel methodology was devised by means of a limited geochemical study of topsoil and vegetation combined with multispectral data obtained by UAV-RS. The methodology was verified in an area affected by Hg and As contamination that typifies abandoned mining-metallurgy sites in recent decades. A broad selection of spectral indices were calculated to evaluate soil-plant system response, and four machine learning techniques (Multiple Linear Regression, Random Forest, Generalized Boosted Models, and Multivariate Adaptive Regression Spline) were tested to obtain robust statistical models. Random Forest (RF) provided the best non-biased models for As and Hg concentration in soil and vegetation, with R2 and rRMSE (%) ranging from 0.501 to 0.630 and from 180.72 to 46.31, respectively, and with acceptable values for RPD and RPIQ statistics. The prediction and mapping of contaminant content and distribution in the study area were well enough adjusted to the geochemical data and revealed superior accuracy for As than Hg, and for vegetation than topsoil. The results were more precise than those obtained in comparable studies that applied satellite or spectrometry data. In conclusion, the methodology presented emerges as a powerful tool for studies addressing soil and vegetation pollution and an alternative approach to classical geochemical studies, which are time-consuming and expensive.

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

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.

Oil-Contaminated Soil Modeling and Remediation Monitoring in Arid Areas Using Remote Sensing

Oil contamination is a major source of pollution in the environment. It may take decades for oil-contaminated soils to be remedied. This study models oil-contaminated soils using one of the world’s greatest environmental disasters, the onshore oil spill in the desert of Kuwait in 1991. This work uses state-of-art remote sensing technologies and machine learning to investigate the oil spills during the first Gulf War. We were able to identify oil-contaminated and clear locations in Kuwait using unsupervised classification over pre- and post-oil spill data. The research area’s pre-war and post-war circumstances, in terms of oil spills, were discovered by developing spectral signatures with different wavelengths and several spectral indices utilized for oil-contamination detection. Following that, we use this data for sampling and training to model various oil-contaminated soil levels. In addition, we analyze two separate datasets and used three modeling methodologies, Random Tree (RT), Support Vector Machine (SVM) and Random Forest (RF). The results show that the suggested approach is effective in detecting oil-contaminated soil. As a result, the location and degree of contamination may be established. The results of this analysis can be a valid support to the studies of an appropriate remediation.

Assessing Vegetation Decline Due to Pollution from Solid Waste Management by a Multitemporal Remote Sensing Approach

Nowadays, the huge production of Municipal Solid Waste (MSW) is one of the most strongly felt environmental issues. Consequently, the European Union (EU) delivers laws and regulations for better waste management, identifying the essential requirements for waste disposal operations and the characteristics that make waste hazardous to human health and the environment. In Italy, environmental regulations define, among other things, the characteristics of sites to be classified as “potentially contaminated”. From this perspective, the Basilicata region is currently one of the Italian regions with the highest number of potentially polluted sites in proportion to the number of inhabitants. This research aimed to identify the possible effects of potentially toxic element (PTE) pollution due to waste disposal activities in three “potentially contaminated” sites in southern Italy. The area was affected by a release of inorganic pollutants with values over the thresholds ruled by national/European legislation. Potential physiological efficiency variations of vegetation were analyzed through the multitemporal processing of satellite images. Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the trend in the Normalized Difference Vegetation Index (NDVI) over the years. The multitemporal trends were analyzed using the median of the non-parametric Theil–Sen estimator. Finally, the Mann–Kendall test was applied to evaluate trend significance featuring areas according to the contamination effects on investigated vegetation. The applied procedure led to the exclusion of significant effects on vegetation due to PTEs. Thus, waste disposal activities during previous years do not seem to have significantly affected vegetation around targeted sites.

Monitoring the soil copper pollution degree based on the reflectance spectrum of an arid desert plant

Visible and near-infrared reflectance spectroscopy offers a rapid, inexpensive, and environmentally friendly method for monitoring copper pollution in the soil. However, the application of this approach in vegetation-covered areas is still a challenge due to interference from plants, making it difficult to acquire soil reflectance spectra. To address this problem, this study assesses whether the reflectance spectrum of a widely distributed arid desert plant (Seriphidium terrae-albae) can be used to rapidly evaluate copper pollution in the soil. A pot experiment was conducted for five months from April to September 2019. The reflectance spectra of the plants were measured in June, July, and August 2019 using an ASD Fieldspec3 spectrometer. Each month, the five vegetation indexes with the highest correlation with the evaluation value of the copper pollution degree were input into an extreme learning machine (ELM) to build a model to monitor the degree of copper pollution in the soil. The results showed that the model could quickly evaluate the degree of copper pollution, but the accuracy varied widely among the calculated vegetation indexes depending on the month when the spectral data were extracted. The model constructed by selecting ten vegetation indexes composed of plant spectra collected in June and July provides high recognition accuracy, reaching 89.02%. Only seven bands were needed due to the model's low complexity, which means that it has great potential to be applied to remote sensing images to establish a real-time monitoring system to detect copper pollution in the soil. This study proposed a simple and rapid method for monitoring copper pollution in soil using plant spectra, and this method could provide extremely valuable for soil protection and management in arid desert areas.

Assessment of risk of non-cancer disease in contaminated plant (Ocimum basilicum L.) and soil

This study tried to conduct an investigation into the rate of contamination by heavy metals (HMs) in both the soil used in the plantation of the basil (Ocimum basilicum L.) as well as the plant itself. The proposed methodology works by assessing the concentrations of 4 heavy metals, inclusive of Pb, Zn, Ni, and Cd. The target hazard quotient (THQ) and the bioconcentration factor (BCF) were deployed for assessing the rate of contamination by HMs within the plant. The plant samples were also analyzed at different stages of growth (DSG) through inspection of their reaction to electromagnetic waves (EW). The results indicated that the THQ was substantially high for Pb and Zn, indicative of the high contamination of the study samples by the metals thereof. The hazard index (HI) for non-carcinogenic hazards was also measured for the entire HMs at 46.64, denoting a high level of contamination in the basil. BCF results also indicated Cd as the most absorbed contaminant (BCF = 1.88) by the target plant. The optimal vegetation index for assessment of HM contamination in the target plant, on the report of the findings, was identified as PD312. Therefore, utilizing EW, the reaction of contaminated plants in DSG is forecastable.

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Spatio-temporal analysis of oil lake and oil-polluted surfaces from remote sensing data in one of the Libyan oil fields

The study area, which is part of the Sirt sedimentary basin in the north-central part of Libya, is characterized by natural resources of important environmental value that need special attention as they are threatened by many human activities. The focus of this study was mainly on the production of high-resolution maps of oil-contaminated surfaces, and the series time maps of events resulting from oil pollution using multi temporal satellite data and validation of the results. Digital image processing techniques were used on satellite-based sensing, whether optical or radar data, which proved to be a cost-effective way to collect information on the volume of lake water, and to assess the depth and concentration of pollution in the study area rich in lakes taken from different periods (1972 to 2006). The area of the oil-contaminated lake, called produced water, was calculated from the 1972 Landsat MSS digital satellite imagery data and was about 1.8 km² and then increased to 10.7 km², during 2006 from Landsat digital image TM data. The size change in this area was due to the increase of the quantities of water production that continued to increase as the oil and gas fields reached maturity. The 2019 Landsat satellite imagery reveals a drastic shrinkage in the area of the lake attributed to the suspension of the produced water pumping as well as the cycle of evaporation that resulted to the water led to a limited volume of water remaining in the lake.

Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

Rapid Identification and Prediction of Cadmium-Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy

Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd–Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment.

Experimental and Numerical Investigation of Dustfall Effect on Remote Sensing Retrieval Accuracy of Chlorophyll Content

Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately estimating chlorophyll content of vegetation using remote sensing for assessing the vegetation growth status in dusty areas is vital. However, dust deposited on the leaf may affect the chlorophyll content retrieval accuracy. Thus, quantitatively studying the dustfall effect is essential. Using selected vegetation indices (VIs), the medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI), and the double difference index (DD), we studied the retrieval accuracy of chlorophyll content at the leaf scale under dusty environments based on a laboratory experiment and spectra simulation. First, the retrieval accuracy under different dustfall amounts was studied based on a laboratory experiment. Then, the relationship between dustfall amount and fractional dustfall cover (FDC) was experimentally analyzed for spectra simulation of dusty leaves. Based on spectral data simulated using a PROSPECT-based mixture model, the sensitivity of VIs to dust under different chlorophyll contents was analyzed comprehensively, and the MTCI was modified to reduce its sensitivity to dust. The results showed that (1) according to experimental investigation, the DD model provides low retrieval accuracy, the MTCI model is highly accurate when the dustfall amount is less than 80 g/m², and the retrieval accuracy decreases significantly when the dustfall amount is more than 80 g/m²; (2) a logarithmic relationship exists between FDC and dustfall amount, and the PROSPECT-based mixture model can simulate the leaf spectra under different dustfall amounts and different chlorophyll contents with a root mean square error of 0.015; and (3) according to numerical investigation, MTCI's sensitivity to dust in the chlorophyll content range of 25 to 60 μg/cm² is lower than in other chlorophyll content ranges; DD's sensitivity to dust was generally high throughout the whole chlorophyll content range. These findings may contribute to quantitatively understanding the dustfall effect on the retrieval of chlorophyll content and would help to accurately retrieve chlorophyll content in dusty areas using remote sensing.

Developing a New Spectral Index for Detecting Cadmium-Induced Stress in Rice on a Regional Scale

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution; (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95; (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.

Selection of the Optimal Spectral Resolution for the Cadmium-Lead Cross Contamination Diagnosing Based on the Hyperspectral Reflectance of Rice Canopy

This paper proposed an optimal spectral resolution for diagnosing cadmium-lead (Cd-Pb) cross contamination with different pollution levels based on the hyperspectral reflectance of rice canopy. Feature bands were sequentially selected by two-way analysis of variance (ANOVA2) and random forests from the high-dimensional hyperspectral data after preprocessing. Then Support Vector Machine (SVM) was applied to diagnose the pollution levels using different feature bands combination with different spectral resolutions and cross validation was conducted to evaluate the distinguishing accuracies. Finally, the optimal spectral resolution could be determined by comparing the diagnosing accuracies of the optimal feature bands combination in each spectral resolution. In the experiments, the hyperspectral reflectance data of rice canopy with ten different spectral resolutions was captured, covering 16 pretreatments of Cd and Pb pollution. The experimental results showed the optimal spectral resolution was 9 nm with the highest average accuracy of 0.71 and relatively standard deviation of 0.07 for diagnosing the categories and levels of Cd-Pb cross contamination. The useful exploration provided an evidence for optimal spectral resolution selection to reduce the cost of heavy metal pollution diagnose.

Heavy metal pollution at mine sites estimated from reflectance spectroscopy following correction for skewed data

The heavy metal concentration of soil samples often exhibits a skewed distribution, especially for soil samples from mining areas with an extremely high concentration of heavy metals. In this study, to model soil contamination in mining areas using reflectance spectroscopy, the skewed distribution was corrected and heavy metal concentration estimated. In total, 46 soil samples from a mining area, along with corresponding field soil spectra, were collected. Laboratory spectra of the soil samples and the field spectra were used to estimate copper (Cu) concentration in the mining area. A logarithmic transformation was used to correct the skewed distribution, and based on the sorption of Cu on spectrally active soil constituents, the spectral bands associated with iron oxides were extracted from the visible and near-infrared (VNIR) region and used in the estimation. A genetic algorithm was adopted for band selection, and partial least squares regression was used to calibrate the estimation model. After transforming the distribution of Cu concentration, the accuracies (R²) of the estimation of Cu concentration using laboratory and field spectra separately were 0.94 and 0.96. The results indicate that Cu concentration in the mining area can be estimated using reflectance spectroscopy following correction of skewed distribution.

Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets

This paper highlights the importance of optimized shape index for agricultural management system analysis that utilizes the contiguous bands of hyperspectral data to define the gradient of the spectral curve and improve image classification accuracy. Currently, a number of machine learning methods would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three new optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of optimized shape indices, specifically the Moment Distance Ratio Right/Left (MDRRL), to discriminate between types of tillage (corn-min and corn-notill) and between grass/pasture and grass/trees, tree and grass under object-based random forest approach.

Estimation of Soil Organic Matter, Total Nitrogen and Total Carbon in Sustainable Coastal Wetlands

Soil plays an important role in coastal wetland ecosystems. The estimation of soil organic matter (SOM), total nitrogen (TN), and total carbon (TC) was investigated at the topsoil (0–20 cm) in the coastal wetlands of Dafeng Elk National Nature Reserve in Yancheng, Jiangsu province (China) using hyperspectral remote sensing data. The sensitive bands corresponding to SOM, TN, and TC content were retrieved based on the correlation coefficient after Savitzky–Golay (S–G) filtering and four differential transformations of the first derivative (R′), first derivative of reciprocal (1/R)′, second derivative of reciprocal (1/R)″, and first derivative of logarithm (lgR)′ by spectral reflectance (R) as R′, (1/R)′, (1/R)″, (lgR)′ of soil samples. The estimation models of SOM, TN, and TC by support vector machine (SVM) and back propagation (BP) neural network were applied. The results indicated that the effective bands can be identified by S–G filtering, differential transformation, and the correlation coefficient methods based on the original spectra of soil samples. The estimation accuracy of SVM is better than that of the BP neural network for SOM, TN, and TC in the Yancheng coastal wetland. The estimation model of SOM by SVM based on (1/R)′ spectra had the highest accuracy, with the determination coefficients (R2) and root mean square error (RMSE) of 0.93 and 0.23, respectively. However, the estimation models of TN and TC by using the (1/R)″ differential transformations of spectra were also high, with determination coefficients R2 of 0.88 and 0.85, RMSE of 0.17 and 0.26, respectively. The results also show that it is possible to estimate the nutrient contents of topsoil from hyperspectral data in sustainable coastal wetlands.

Vegetation reflectance spectroscopy for biomonitoring of heavy metal pollution in urban soils

Heavy metals in urban soils may impose a threat to public health and may negatively affect urban tree viability. Vegetation spectroscopy techniques applied to bio-indicators bring new opportunities to characterize heavy metal contamination, without being constrained by laborious soil sampling and lab-based sample processing. Here we used Tilia tomentosa trees, sampled across three European cities, as bio-indicators i) to investigate the impacts of elevated concentrations of cadmium (Cd) and lead (Pb) on leaf mass per area (LMA), total chlorophyll content (Chl), chlorophyll a to b ratio (Chla:Chlb) and the maximal PSII photochemical efficiency (Fv/Fm); and ii) to evaluate the feasibility of detecting Cd and Pb contamination using leaf reflectance spectra. For the latter, we used a partial-least-squares discriminant analysis (PLS-DA) to train spectral-based models for the classification of Cd and/or Pb contamination. We show that elevated soil Pb concentrations induced a significant decrease in the LMA and Chla:Chlb, with no decrease in Chl. We did not observe pronounced reductions of Fv/Fm due to Cd and Pb contamination. Elevated Cd and Pb concentrations induced contrasting spectral changes in the red-edge (690-740 nm) region, which might be associated with the proportional changes in leaf pigments. PLS-DA models allowed for the classifications of Cd and Pb contamination, with a classification accuracy of 86% (Kappa = 0.48) and 83% (Kappa = 0.66), respectively. PLS-DA models also allowed for the detection of a collective elevation of soil Cd and Pb, with an accuracy of 66% (Kappa = 0.49). This study demonstrates the potential of using reflectance spectroscopy for biomonitoring of heavy metal contamination in urban soils.

Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images

Regional-level information on heavy metal pollution in agro-ecosystems is essential for food security because excessive levels of heavy metals in crops may pose risks to humans. However, collecting this information over large areas is inherently costly. This paper investigates the possibility of applying multi-temporal Sentinel-2 satellite images to detect heavy metal-induced stress (i.e., Cd stress) in rice crops in four study areas in Zhuzhou City, Hunan Province, China. For this purpose, we compared seven Sentinel-2 images acquired in 2016 and 2017 with in situ measured hyper-spectral data, chlorophyll content, rice leaf area index, and heavy metal concentrations in soil collected from 2014 to 2017. Vegetation indices (VIs) related to red edge bands were referred to as the sensitive indicators for screening stressed rice from unstressed rice. The coefficients of spatio-temporal variation (CSTV) derived from the VIs allowed us to discriminate crops exposed to pollution from heavy metals as well as environmental stressors. The results indicate that (i) the red edge chlorophyll index, the red edge position index, and the normalized difference red edge 2 index derived from multi-temporal Sentinel-2 images were good indicators for screening stressed rice from unstressed rice; (ii) Rice under Cd stress remained stable with lower CSTV values of VIs overall growth stages in the experimental region, whereas rice under other stressors (i.e., pests and disease) showed abrupt changes at some growth stages and presented "hot spots" with greater CSTV values; and (iii) the proposed spatio-temporal anomaly detection method was successful at detecting rice under Cd stress; and CSTVs of rice VIs stabilized regardless of whether they were applied to consecutive growth stages or to two different crop years. This study suggests that regional heavy metal stress may be accurately detected using multi-temporal Sentinel-2 images, using VIs sensitive to the spatio-temporal characteristics of crops.

Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride

This study considers whether a relationship exists between response to lithium (Li) exposure and select vegetation indices (VI) determined from reflectance spectra in each of four plant species: Arabidopsis thaliana, Helianthus annuus (sunflower), Brassica napus (rape), and Zea mays (corn). Reflectance spectra were collected every week for three weeks using an ASD FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view probe (FOV) for plants treated twice weekly in a laboratory setting with 0 mM (control) or 15 mM of lithium chloride (LiCl) solution. Plants were harvested each week after spectra collection for determination of relevant physical endpoints such as relative water content and chlorophyll content. Mixed effects analyses were conducted on selected endpoints and vegetation indices (VI) to determine the significance of the effects of treatment level and length of treatment as well as to determine which VI would be appropriate predictors of treatment-dependent endpoints. Of the species considered, A. thaliana exhibited the most significant effects and corresponding shifts in reflectance spectra. Depending on the species and endpoint, the most relevant VIs in this study were NDVI, PSND, YI, R₁₆₇₆/R₁₉₃₃, R₇₅₀/R₅₅₀, and R₉₅₀/R₇₅₀.

Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China

This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

Assessing Soil Contamination Due to Oil and Gas Production Using Vegetation Hyperspectral Reflectance

The remote assessment of soil contamination remains difficult in vegetated areas. Recent advances in hyperspectral spectroscopy suggest making use of plant reflectance to monitor oil and gas leakage from industrial facilities. However, knowledge about plant response to oil contamination is still limited, so only very few imaging applications are possible at this stage. We therefore conducted a greenhouse experiment on three species long-term exposed to either oil-contaminated or water-deficient soils. Reflectance measurements were regularly performed at leaf and plant scale over 61 days of exposure. Results showed an increase of reflectance in the visible (VIS), the red-edge and the short-wave infrared (SWIR) under both oil and water-deficit stress exposure. A contrasted response in the near-infrared (NIR) was also observed among species. Spectra underwent transformations to discriminate species' responses to the different treatments using linear discriminant analysis (LDA) with a stepwise procedure. Original and transformed spectra enabled to discriminate the plants' responses to the different treatments without confusion after 61 days. The discriminating wavelengths were consistent with the spectral differences observed. These results suggest differential changes in plant pigments, structure and water content as a response to various stressors, and open up promising perspectives for airborne and satellite applications.

Extraction of Rice Phenological Differences under Heavy Metal Stress Using EVI Time-Series from HJ-1A/B Data

An effective method to monitor heavy metal stress in crops is of critical importance to assure agricultural production and food security. Phenology, as a sensitive indicator of environmental change, can respond to heavy metal stress in crops and remote sensing is an effective method to detect plant phenological changes. This study focused on identifying the rice phenological differences under varied heavy metal stress using EVI (enhanced vegetation index) time-series, which was obtained from HJ-1A/B CCD images and fitted with asymmetric Gaussian model functions. We extracted three phenological periods using first derivative analysis: the tillering period, heading period, and maturation period; and constructed two kinds of metrics with phenological characteristics: date-intervals and time-integrated EVI, to explore the rice phenological differences under mild and severe stress levels. Results indicated that under severe stress the values of the metrics for presenting rice phenological differences in the experimental areas of heavy metal stress were smaller than the ones under mild stress. This finding represents a new method for monitoring heavy metal contamination through rice phenology.

Use of radiometric indices to evaluate Zn and Pb stress in two grass species (Festuca rubra L. and Vulpia myuros L.)

Vegetation indices obtained from radiometric measurements have been used to estimate the stress response of plants grown in contaminated sites. The phytotoxicity of Pb and Zn in Festuca rubra L. and Vulpia myuros L. plants grown under hydroponic conditions was evaluated using vegetation indices obtained from radiometric measurements. The plants were supplied with 3 mM Zn (+Zn), 500 μM Pb (+Pb) and 500 μM Pb with EDTA (+PbEDTA) for 3 months. Significantly higher Zn concentrations in F. rubra shoots compared with V. myuros shoots were detected for Zn and Pb treatments. EDTA increased Pb transport to the shoots for both grasses, while Pb-treated plants retained Pb primarily in the roots. All vegetation indices tested showed the highest differences in F. rubra under +PbEDTA treatment and minor effects under +Zn, whereas the major variations for V. myuros corresponded to +Zn treatment, followed by +PbEDTA. Red edge normalized difference vegetation index, yellowness index and anthocyanin concentration index were the most sensitive indices to report Zn and Pb phytotoxicity in these grasses. According to the results obtained, both metal concentrations and radiometric indices suggested that Pb is more phytotoxic to F. rubra, which tolerates high Zn levels, whereas V. myuros was strongly affected by high Zn levels and markedly tolerant to Pb, even when applied in a mobile form (PbEDTA). Both species could be used in the phytostabilization of Zn- and Pb-contaminated soils. The abilities of F. rubra to accumulate Zn and V. myuros to accumulate Pb in the roots would facilitate a more efficient phytoremediation strategy when used in combination.

Seed priming with polyethylene glycol induces antioxidative defense and metabolic regulation of rice under nano-ZnO stress

The present study was carried out to investigate the beneficial impact of seed priming with polyethylene glycol (PEG) under different concentrations of zinc oxide nanoparticles (nano-ZnO), i.e., 0, 250, 500, and 750 mg L(-1) in two cultivars of Oryza sativa (Zhu Liang You 06 and Qian You No. 1). Physiological parameters were improved by priming with 30 % PEG in both cultivars under stress treatments. Seed priming with 30 % PEG improved α-amylase activities and total soluble sugar contents of both cultivars under nano-ZnO stress. In addition, glutathione reductase (GR) activity, reactive oxygen species (ROS) accumulation, and proline contents decreased after the priming treatment in both cultivars under different nano-ZnO concentrations. Expression of GR1, GR2, Amy2A, and Amy3A genes in shoots and roots of both cultivars increased and had higher transcription levels under the nano-ZnO stress condition. Fourier transform infrared spectroscopy (FTIR) analysis did not show any significant effects of the priming treatment on the band observed at 3400, 900, 1600, and 1000 cm(-1) corresponding to alkenyl stretch (C = C), carboxyl acid (O-H), nitrile (C = N), and aromatic (C-H), respectively, in both cultivars under nano-ZnO stress.

Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice

This study systematically analyzed the performance of multivariate hyperspectral vegetation indices of rice (Oryza sativa L.) in estimating the arsenic content in agricultural soils. Field canopy reflectance spectra was obtained in the jointing-booting growth stage of rice. Newly developed and published multivariate vegetation indices were initially calculated to estimate soil arsenic content. The well-performing vegetation indices were then selected using successive projections algorithm (SPA), and the SPA selected vegetation indices were adopted to calibrate a multiple linear regression model for estimating soil arsenic content. Results showed that a three-band vegetation index (R716-R568)/(R552-R568) performed best in the newly developed vegetation indices in estimating soil arsenic content. The photochemical reflectance index (PRI) and red edge position (REP) performed well in the published vegetation indices. Moreover, the linear combination of two vegetation indices ((R716-R568)/(R552-R568) and REP) selected using SPA improved the estimation of soil arsenic content. These results indicated that the newly developed three-band vegetation index (R716-R568)/(R552-R568) might be recommended as an indicator for estimating soil arsenic content in the study area. PRI and REP could be used as universal vegetation indices for monitoring soil arsenic contamination.

Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance

To investigate the mechanisms and potential for the remote sensing of phenanthrene-induced vegetation stress, we measured field canopy spectra, and associated plant and soil parameters in the field controlled experiment in the Yellow River Delta of China. Two widely distributed plant communities, separately dominated by reed (Phragmites australis) and glaucous seepweed (Suaeda salsa), were treated with different doses of phenanthrene. The canopy spectral changes of plant community resulted from the decreases of biomass and foliar projective coverage, while leaf photosynthetic pigment concentrations showed no significance difference among treatments. The spectral response to phenanthrene included a flattened red edge, with decreased first derivative of reflectance. The red edge slope and area consistently responded to phenanthrene, showing a strong relationship with aboveground biomass, coverage and canopy pigments density. These results suggest the potential of remote sensing and the importance of field validation to correctly interpret the causes of the spectral changes.

Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants

The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP)=14.7 mg kg(-1); r=0.64; residual predictive deviation (RPD)=1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP=13.7 mg kg(-1); r=0.71; RPD=1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r=0.68, RMSEP=13.7 mg kg(-1), and RPD=1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.

Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals

Soil contamination by heavy metals is an increasingly important problem worldwide. Quick and reliable access to heavy metal concentration data is crucial for soil monitoring and remediation. Visible and near-infrared reflectance spectroscopy, which is known as a noninvasive, cost-effective, and environmentally friendly technique, has potential for the simultaneous estimation of the various heavy metal concentrations in soil. Moreover, it provides a valid alternative method for the estimation of heavy metal concentrations over large areas and long periods of time. This paper reviews the state of the art and presents the mechanisms, data, and methods for the estimation of heavy metal concentrations by the use of visible and near-infrared reflectance spectroscopy. The challenges facing the application of hyperspectral images in mapping soil contamination over large areas are also discussed.

[Band depth analysis and partial least square regression based winter wheat biomass estimation using hyperspectral measurements]

The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550-750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2 = 0.792, RMSE = 0.164 kg x m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.

Soil TPH concentration estimation using vegetation indices in an oil polluted area of eastern China

Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m⁻² to 5.3 g m⁻² with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg⁻¹ to 652 mg kg⁻¹. The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R² = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg⁻¹). For other vegetation indices and red edge parameters, the R² and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg⁻¹ to 106.8 mg kg⁻¹ respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R² = 0.70 and RMSE = 110.1 mg kg⁻¹) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable.

Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils

Assessment of soil contamination and its long-term monitoring are necessary to evaluate the effectiveness of phytoremediation systems. Spectral sensing-based monitoring methods promise obvious benefits compared to field-based methods: lower cost, faster data acquisition and better spatio-temporal monitoring. This paper reviews the theoretical basis whereby proximal spectral sensing of soil and vegetation could be used to monitor phytoremediation of metal-contaminated soils, and the eventual upscaling to imaging sensing. Both laboratory and field spectroscopy have been applied to sense heavy metals in soils indirectly via their intercorrelations with soil constituents, and also through metal-induced vegetation stress. In soil, most predictions are based on intercorrelations of metals with spectrally-active soil constituents viz., Fe-oxides, organic carbon, and clays. Spectral variations in metal-stressed plants is particularly associated with changes in chlorophyll, other pigments, and cell structure, all of which can be investigated by vegetation indices and red edge position shifts. Key shortcomings in obtaining satisfactory calibration for monitoring the metals in soils or metal-related plant stress include: reduced prediction accuracy compared to chemical methods, complexity of spectra, no unique spectral features associated with metal-related plant stresses, and transfer of calibrations from laboratory to field to regional scale. Nonetheless, spectral sensing promises to be a time saving, non-destructive and cost-effective option for long-term monitoring especially over large phytoremediation areas, and it is well-suited to phytoremediation networks where monitoring is an integral part.

Soil metal concentrations and productivity of Betula populifolia (gray birch) as measured by field spectrometry and incremental annual growth in an abandoned urban Brownfield in New Jersey

A forested brownfield within Liberty State Park, Jersey City, New Jersey, USA, has soils with arsenic, chromium, lead, zinc and vanadium at concentrations above those considered ambient for the area. Using both satellite imagery and field spectral measurements, this study examines plant productivity at the assemblage and individual specimen level. Longer term growth trends (basal area increase in tree cores) were also studied. Leaf chlorophyll content within the hardwood assemblage showed a threshold model for metal tolerance, decreasing significantly beyond a soil total metal load (TML) of 3.0. Biomass production (calculated with RG-Red/Green Ratio Index) in Betula populifolia (gray birch), the co-dominant tree species, had an inverse relationship with the Zn concentration in leaf tissue during the growing season. Growth of B. populifolia exhibited a significant relationship with TML. Assemblage level NDVI and individual tree NDVI also had significant decreases with increasing TML. Ecosystem function measured as plant production is impaired at a critical soil metal load.

Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.)

Mustard spinach plants were grown in mercury-spiked and contaminated soils collected in the field under controlled laboratory conditions over a full growth cycle to test if vegetation grown in these soils has discernible characteristics in visible/near-infrared (VNIR) spectra. Foliar Hg concentrations (0.174-3.993ppm) of the Mustard spinach plants were positively correlated with Hg concentration of soils and varied throughout the growing season. Equations relating foliar Hg concentration to spectral reflectance, its first derivative, and selected vegetation indices were generated using stepwise multiple linear regression. Significant correlations are found for limited wavelengths for specific treatments and dates. Ratio Vegetation Index (RVI) and Red Edge Position (REP) values of plants in Hg-spiked and field-contaminated soils are significantly lower relative to control plants during the early and middle portions of the growth cycle which may be related to lower chlorophyll abundance or functioning in Hg-contaminated plants.