Vegetation based assessment and monitoring tools for landfill leachate treatment and fugitive plumes

Solid waste and leachate generation from solid waste landfills has a legacy of detrimental and toxic impacts on the environment. Disposal practices are expensive, failure prone and have not been able to keep up with the pace of disposal of toxic compounds. In general, a landfill acts as a bathtub with infiltration of water through the landfill cover into the landfill, reacting with the waste and transferring toxic components into the leachate. Irrigating the evapotranspiration (ET) covers with leachate collected from the landfill has been developed and applied. Such methods can keep the leached pollutants in a loop, which reduces the risk of leachate contamination of nearby aquifers. Utilizing trees and grasses on ET covers as a means of phytoremediation and stabilization of pollutants, while controlling erosion, is a step towards an efficient and sustainable remediation of landfill systems. Assessment of plant health and stress is critical for optimizing these systems and to avoid mortality of plants and total failure of phytotechnologies and phytoremediation systems. Leachate application rates should provide better treatment efficiency, but not cause toxicity. Hyperspectral measurements for monitoring plant health and stress were included in this study. Hyperspectral results revealed that plant stress can be sensed remotely, which correlates with destructive testing methods such as biomass measurements. This study provides multiple findings of importance in assessing plant stress while maintaining effective treatment, with low labor costs and the ability to cover large areas rapidly. This study also suggests that remote sensing can be applied to detect plant stress caused by fugitive leachate plumes, thereby mitigating the potential threat to human health and ecological damages from these plumes that would often go unnoticed --Abstract, page iii

REMOTE SENSING APPLIED IN MINING SECTOR

We will discuss in this paper about how remote sensing methods are used in mining domains, doing reference at minerals, geology, soil, topography, land cover and vegetation, water and groundwater and mine contamination. There are a lot of methods used for exploration are airborne and ground based geophysical methods, such as magnetic, electromagnetic, gravity, radiometric and seismic investigations to map the subsurface geology; multispectral and hyperspectral airborne and satellite remote sensing can provide valuable information about the surface mineralogy and geology. Detailed knowledge of the surface topography or the change in surface topography is important in several aspects of the mining sector. Mapping and monitoring of vegetation around mine sites is important in all phases of mining, from mine planning to mine closure and rehabilitation. Vegetation maps are often required. Knowledge of surface and ground water pathways is also important in and around mining areas. Ground water can to some extent be mapped with optical remote sensing techniques. A common type of contamination caused by mining is acid rock drainag

Coastal Wetland Vegetation in Response to Global Warming and Climate Change

Under the background of global warming, rising sea level, extreme weather and other global climate changes, vegetation has played a targeted and irreplaceable role. The characteristics of individual plant, community landscape and vegetation succession in response to the major driving factor (mainly includes habitat relative elevation, net loss of coastal habitat, salinity, etc.) were analyzed. An obvious development of vegetation landscape fragmentation has results from the competitive advantages of salt-tolerant species or invasive species, which eventually results in the regressive succession and unreasonable secondary succession of vegetation. Compared with the botanical community statistics method, the method of combined of GIS-mapping and remote sensing data provide a more effective way to extract the individual plant stress information, vegetation community structure and dynamic change of vegetation landscape pattern, which can reflect the spatial differentiation of the vegetation at a macro-scale. In addition, in view of the high-efficiency carbon sequestration capability of coastal wetland vegetation, the spatial distribution, temporal dynamic and extraction method of vegetation and soil sequestration were discussed. Synthesize above analysis result, further studies in vegetation response to global climate change were proposed, which need to be improved or expanded

Estimate of Heavy Metals in Soil Using Combined Geochemistry and Field Spectroscopy in Miyi Mining Area

Heavy metal-contaminated soil and water is a major environmental issue in the mining areas. However, as the heavy metals migrate frequently, the traditional method of estimating the soil’s heavy metal content by field sampling and laboratory chemical analysis followed by interpolation is time-consuming and expensive. This chapter intends to use field hyperspectra to estimate the heavy metals in the soil in Bai-ma, De-sheng and YuanBaoshan mining areas, Miyi County, Sichuan Province. By analyzing the spectra of soil, the spectral features derived from the spectra of the soils can be found to build the models between these features and the contents of Mn and Co in the soil by using the linear regression method. The spectral features of Mn are 2142 and 2296 nm. The spectral features of Co are 1918, 1922 and 2205 nm. With these feature spectra, the best models to estimate the heavy metals in the study area can be built according to the maximal determination coefficients (R2). The determination coefficients (R2) of the models of retrieving Mn and Co in the soil are 0.645 and 0.8, respectively. The model significant indexes of Mn and Co are 2.04507E-05 and 7.73E-06. These results show that it is feasible to predict contaminated heavy metals in the soils during mining activities for soil remediation and ecological restoration by using the rapid and cost-effective field spectroscopy

An approach for heavy metal pollution detected from spatio-temporal stability of stress in rice using satellite images

Stable stressors on crops (e.g., salts, heavy metals), which are characterized by stable spatial patterns over time, are harmful to agricultural production and food security. Satellite data provide temporally and spatially continuous synoptic observations of stable stress on crops. This study presents a method for identifying rice under stable stress (i.e., Cd stress) and exploring its spatio-temporal characteristics indicators. The study area is a major rice growing region located in Hunan Province, China. Moderate-resolution imaging spectroradiometer (MODIS) and Landsat images from 2008–2017 as well as in situ measurements were collected. The coupling of a leaf canopy radiative transfer model with the World Food Study Model (WOFOST) via a wavelet transform isolated the effects of Cd stress from other abrupt stressors. An area wavelet transform stress signal (AWTS), based on a time-series Enhanced Vegetation Index (EVI), was used to detect rice under Cd stress, and its spatio-temporal variation metrics explored. The results indicate that spatial variation coefficients (SVC) of AWTS in the range of 0–1 ha d a coverage area greater than 70% in each experimental region, regardless of the year. Over ten years, the temporal variation coefficients (TVC) of AWTS in the range of 0–1 occurred frequently (more than 60% of the time). In addition, the Pearson correlation coefficient of AWTS over two consecutive years was usually greater than 0.5. We conclude that a combination of multi-year satellite-derived vegetation index data with a physical model simulation is an effective and novel method for detecting crops under environmental stress. A wavelet transform proved promising in differentiating between the effects of stable stress and abrupt stress on rice and may offer a way forward for diagnosing crop stress at continental and global scales

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.info:eu-repo/semantics/acceptedVersio