Academic literature on the topic 'Leaf spectral reflectance'

As a prelude to remote sensing of rhizomania, hyper-spectral leaf reflectance and multi-spectral canopy reflectance were used to study the physiological differences between healthy sugar beets and beets infested with Beet necrotic yellow vein virus. This study was conducted over time in the presence of declining nitrogen levels. Total leaf nitrogen was significantly lower in symptomatic beets than in healthy beets. Chlorophyll and carotenoid levels were reduced in symptomatic beets. Vegetative indices calculated from leaf spectra showed reductions in chlorophyll and carotenoids in symptomatic beets. Betacyanin levels estimated from leaf spectra were decreased at the end of the 2000 season and not in 2001. The ratio of betacyanins to chlorophyll, estimated from canopy spectra, was increased in symptomatic beets at four of seven sampling dates. Differences in betacyanin and carotenoid levels appeared to be related to disease and not nitrogen content. Vegetative indices calculated from multi-spectral canopy spectra supported results from leaf spectra. Logistic regression models that incorporate vegetative indices and reflectance correctly predicted 88.8% of the observations from leaf spectra and 87.9% of the observations for canopy reflectance into healthy or symptomatic classes. Classification was best in August with a gradual decrease in accuracy until harvest. These results indicate that remote sensing technologies can facilitate detection of rhizomania.

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

Ground-based remotely sensed reflectance spectra of hyperspectral resolution were monitored during the growing period of rice under various nitrogen application rates. It was found that reflectance spectrum of rice canopy changed in both wavelength and reflectance as the plants developed. Fifteen characteristic wavebands were identified from the apparent peaks and valleys of spectral reflectance curves, in accordance with the results of the first-order differentiation, measured over the growing season of rice. The bandwidths and center wavelengths of these characteristic wavebands were different among nitrogen treatments. The simplified features by connecting these 15 characteristic wavelengths may be considered as spectral signatures of rice canopy, but spectral signatures varied with developmental age and nitrogen application rates. Among these characteristic wavebands, the changes of the wavelength in band 11 showed a positive linear relationship with application rates of nitrogen fertilizer, while it was a negative linear relationship in band 5. Mean reflectance of wavelengths in bands 1, 2, 3, 5, 11, and 15 was significantly correlated with application rates. Reflectance of these six wavelengths changed nonlinearly after transplanting and could be used in combination to distinguish rice plants subjected to different nitrogen application rates. From the correlation analyses, there are a variety of correlation coefficients for spectral reflectance to leaf nitrogen content in the range of 350-2400 nm. Reflectance of most wavelengths exhibited an inverse correlation with leaf nitrogen content, with the largest negative value (r = �0.581) located at about 1376 nm. Changes in reflectance at 1376 nm to leaf nitrogen content during the growing period were closely related and were best fitted to a nonlinear function. This relationship may be used to estimate and to monitor nitrogen content of rice leaves during rice growth. Reflectance of red light minimum and near-infrared peak and leaf nitrogen content were correlated nonlinearly.

Accurate estimation of the canopy chlorophyll content (CCC) plays a key role in quantitative remote sensing. Maize (Zea mays L.) is a high-stalk crop with a large leaf area and deep canopy. It has a non-uniform vertical distribution of the leaf chlorophyll content (LCC), which limits remote sensing of CCC. Therefore, it is crucial to understand the vertical heterogeneity of LCC and leaf reflectance spectra to improve the accuracy of CCC monitoring. In this study, CCC, LCC, and leaf spectral reflectance were measured during two consecutive field growing seasons under five nitrogen treatments. The vertical LCC profile showed an asymmetric ‘bell-shaped’ curve structure and was affected by nitrogen application. The leaf reflectance also varied greatly between spatio–temporal conditions, which could indicate the influence of vertical heterogeneity. In the early growth stage, the spectral differences between leaf positions were mainly concentrated in the red-edge (RE) and near-infrared (NIR) regions, whereas differences were concentrated in the visible region during the mid-late filling stage. LCC had a strong linear correlation with vegetation indices (VIs), such as the modified red-edge ratio (mRER, R2 = 0.87), but the VI–chlorophyll models showed significant inversion errors throughout the growth season, especially at the early vegetative growth stage and the late filling stage (rRMSE values ranged from 36% to 87.4%). The vertical distribution of LCC had a strong correlation with the total chlorophyll in canopy, and sensitive leaf positions were identified with a multiple stepwise regression (MSR) model. The LCC of leaf positions L6 in the vegetative stage (R2-adj = 0.9) and L11 + L14 in the reproductive stage (R2-adj = 0.93) could be used to evaluate the canopy chlorophyll status (L12 represents the ear leaf). With a strong relationship between leaf spectral reflectance and LCC, CCC can be estimated directly by leaf spectral reflectance (mRER, rRMSE = 8.97%). Therefore, the spatio–temporal variations of LCC and leaf spectral reflectance were analyzed, and a higher accuracy CCC estimation approach that can avoid the effects of the leaf area was proposed.

This article has studied the correlation relationship between the spectral features of polluted leaf surface of Ficus microcarpa and air dustfall in Guangzhou City. The results show that the spectral reflectance of leaves in the industrial area and large traffic area is 3-5.5% higher than that of leaves in cleaning area in the visible band, but is 10-15% lower in the near infrared band. Compared to the spectral reflectance of the cleaned leaf, the spectral reflectance of leaf on nature dirty is 6.6％ higher in the visible band and 25.6% lower in the infrared band. The spectral reflectance difference between dirty leaf and cleaned leaf in the infrared band has a strong correlation with air dustfall in Guangzhou city. The correlation coefficient is 0.821. It is simple and convenient, fast, economic method to monitor the air dustfall using the spectral characteristic of Ficus microcarpa’s leaf.

Spectral reflectance, or indexes that characterize spectral reflectance at concrete wavelength, is commonly used as an indicator of plant stress, or its photosynthetic apparatus status. In this paper, new leaf optical model is presented. Within this paper, experimental determination of surface and internal reflectance of Spring barley leaves and mathematical-physical modelling of internal reflectance were performed. It was proven that a new proposed theoretical model and the experimental spectra of internal reflectance are strongly correlated. It can be concluded that the total reflectance is not a function of epidermis condition, but it testifies about overall functional condition of Spring barley leaves.

Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (|r| > 0.85), between LNA and canopy reflectance from 760 to 1100 nm (|r| > 0.79), and from 460 to 710 nm wavelengths (|r| > 0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.

Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.

The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.

The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.

The site-specific management of alfalfa has not been well-evaluated, despite the economic importance of this crop. The objectives of this work were to i) characterize the effects of soil moisture deficits on alfalfa and alfalfa yield components and ii) evaluate the use of canopy reflectance patterns in measuring treatment-induced differences in alfalfa yield. A randomized complete block design with five replicates of subsurface drip irrigation (SDI) and rainfed treatments of alfalfa was established at the University of Kentucky Animal Research Center in 2003. Potassium, as KCl, was broadcast on split-plots on 1 October 2004 at 0, 112, 336, and 448 kg K2O ha-1. In the drought year of 2005, five harvests (H1 - H5) were taken from each split-plot and from four locations within each SDI and rainfed plot. One day prior to each harvest, canopy reflectance was recorded in each plot. Alfalfa yield, yield components, and leaf area index (LAI) were determined. In 2005, dry matter yields in two harvests and for the seasonal total were increased (Pandlt;0.05) by SDI, but SDI did not affect crown density. Herbage yield was strongly associated with yield components but yields were most accurately estimated from LAI. Canopy reflectance within blue (450 nm), red (660 nm) and NIR bands were related to LAI, yield components, and yield of alfalfa and exhibited low variance (cv andlt; 15%) within narrow ( 0.125 Mg ha-1) yield ranges. Red-based Normalized Difference Vegetation Indices (NDVIs) and Wide Dynamic Range Vegetation Indices (WDRVIs) were better than blue-based VIs for the estimation of LAI, yield components, and yield. Decreasing the influence of NIR reflectance in VIs by use of a scalar (0.1, 0.05, or 0.01) expanded the range of WDRVI-alfalfa yield functions. These results indicate that VIs may be used to estimate LAI and dry matter yield of alfalfa within VI-specific boundaries.

Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xx, 245 p.; also includes graphics. Includes bibliographical references (p. 206-216).

博士
國立屏東科技大學
熱帶農業暨國際合作研究所
95
From the 1970s, the explosion of population, the over-exploitation of natural resources and the fast development of the economy have led to the pollution of environment, the destruction of forests and soil-erosion, which constitute a big threat to the earth’s ecological system. From the beginning of human history up to now, the unsolicited exploitation of natural resources, from cutting trees to mining fossil fuels, has eventually resulted in the deterioration of the ecological system and the annihilation of some partial ecological systems. Because of this, in recent years the studies on ecological system are mostly concerned with sustained development, the diversity of life-forms, the construction method of ecological systems, the anti-disease methods, and some other human precautions, which are meant to further explore into the natural ecological system. Therefore, this thesis focuses on the spectral reflectance and the biochemical analysis of pigments of single leaves through remote sensing, which are based on Nanjenshan Nature Reserve, from which a group of plants are tested for the analysis of the relationship between spectral reflectance and chlorophyll content. Traditionally, the estimation of leaf chlorophyll content can be carried out using two methods; a destructive biochemical analysis and nondestructive method with portable chlorophyll meter (SPAD-502, Minolta Camera Co. Ltd., Japan). The device used to nondestructively measure the quantity of chlorophyll in leaves was fieldscout CM1000 chlorophyll meter which can sense light at wavelengths of 700 nm and 840 nm. The objective of this study was to use and compare CM1000 chlorophyll meter and acetone extraction (destructive method) methods for determining chlorophyll content in 4 species of leaves from hardwood Michelia formosana, Psychotria rubra, Daphniphyllum glaucescens and Neolitsea hiiranensis. The calculated CM1000 readings positively correlated with total chlorophyll concentration (R2 >0.79, Michelia formosana; R2>0.84, Psychotria rubra; R2>0.9, Daphniphyllum glaucescens; R2>0.78, Neolitsea hiiranensis). The results of this study can be used to estimate the actual leaf chlorophyll content using low costing, less laborious and time-saving procedures. This study was conducted to investigate variations of leaf chlorophyll content and surface spectral reflectance of different tree species across contrasting terrain in the Nanjenshan Nature Reserve of Kenting National Park, southern Taiwan. Tree species composition and forest types vary because of intense northeast monsoons that are frequent this area. In this study, we used several remote sensing techniques indices; normalized difference vegetation index (NDVI), modified normalized difference vegetation index (mNDVI), simple ratio (SR) and modified simple ratio (mSR) to analyze the spectral reflectance data which was collected from portable spectroradiometers - GER 1500, and CM1000 chlorophyll meters to estimate leaf chlorophyll content. The results showed that there were significant differences (P<0.01) only among the modified indices mSR705 and mNDVI705. The index mNDVI705 seemed more sensitive to detect chlorophyll content in a wide range of tree species across a terrain. Among the indices calculated, the mNDVI consistently deviated from the general relationship between chlorophyll content and spectral reflection in different vegetation. The findings indicated that the modified indices were more sensitive to studying different tree species than normalized indices across terrain. This study mainly explores into the relationship between the hyperspectral dates including vegetation index and red edge optical parameter and the chlorophyll contents of forest plants. Four species are selected from the Nanjenshan Nature Reserve, including Daphniphyllum glaucescens, Michelia formosana, Illicium dunnianum, and Machilus kusanoi. Based on different topographic conditions, the hyperspectral reflectance and the chlorophyll and carotenoid contents are determined, GER1500 is used to explore the relationship between the feature of the hyperspectral REP (Red Edge Inflection Point) and pigment contents and correlation analyzing method based on statistics is adopted to investigate some differences and correlations. The study shows that the relation between REP and chlorophyll content are: Daphniphyllum glaucescen (R2=0.508, P<0.000), Michelia formosana(R2=0.667, P<0.000), Illicium dunnianum (R2=0.503, P<0.000) and Machilus kusanoi (R2=0.774, P<0.000), respectively, which showed the obvious correlation between them, proving that hyperspectral data can be used to estimate the fixed quantity of chlorophyll content of leaves. The correlation between the vegetation index and chlorophyll content were as follows: SR705 (R2=0.236, P<0.000)，mSR705 (R2=0.5283, P<0.000)，NDVI705 (R2=0.265, P<0.000)，mNDVI705 (R2=0.573, P<0.000). From the results we know that, on the basis of correlation coefficient for the same species, mSR and mNDVI are higher than SR and NDVI, and there is a huge difference between those two sets of indices. The result also proves that, compared with traditional broad band remote sensing data, hyperspectral remote sensing is feasible for measuring the chlorophyll content of plants, and it also decides the sensitive wavelength range for chlorophyll contents. In the future, if more hyperspectral materials collected from satellite can be applied, they would be a very important help for assessing vegetations’ chlorophyll contents and their health conditions rapidly, easily, and without damage.

碩士
國立嘉義大學
森林暨自然資源研究所
94
Abstract This project uses the hand held spectroradiometer to detect the spectral signatures of vegetation samples under different background. A controlled experiment was designed to measure the reflected radiance of those objects. These measurements were used for examine how different background will influence the spectral characteristics of the objects. Using LI300A and LI3100 instruments, we can also get tree samples’ biomass and leaf area index (LAI). A multivariate regression analysis was conducted to extract a suitable model for representing the relationship between the spectral signatures and biomass, canopy density, and canopy LAI; the spectral behavior model of the tested tree samples were defined by the biomass experimental results. Results showed that the spectral characteristics of the objects under different background mix up seriously. The spectral characteristics of the objects in the visible light zone are highly homogeneous and it is very hard for us to distinguish them. Only in the near-infrared and the middle-infrared zone, the spectral characteristics of objects are able to be distinguished. Canopy biomass, density, and LAI is linearly correlated to the canopy reflectance. Using the signatures of some bands whose wavelength centered at some blue, green, red, and near infrared region, there is approximately about 0.80 of determination coefficient could be achieved for precisely estimating the vegetative biomass. Finally, a suitable model could be also well defined to depicting the spectral behavior of the vegetative biomass. Some important findings could be applied for further application in forest monitoring with remote sensing techniques. Briefly, they are plants reflectance in green and near infrared bands are exactly positive proportional to the biomass, while is negatively proportional in the mid infrared, i.e. near λ=2050nm.

碩士
國立東華大學
自然資源與環境學系
102
Chamaecyparis obtusa var. formosana is distributed over mid-elevation cloud forests of Taiwan. There are some preliminary studies of Chamaecyparis obtusa var. formosana photosynthetic characteristics at the Chi-Lan Mountain site of northeastern Taiwan. This study explored photosynthetic characteristics of Chamaecyparis obtusa var. formosana through the rapid, convenient and non-destructive leaf reflectance spectra method. The first purpose of this study was to establish the relationship between optical indices and ecophysiological parameters of Chamaecyparis obtusa var. formosana. The optical indices included PRI, NDVI, CI and mND705. The second purpose was to study the temporal variation of leaf reflectance spectral characteristics of Chamaecyparis obtusa var. formosana. The third purpose was to study the spatial heterogeneity variation of leaf reflectance spectral characteristics of Chamaecyparis obtusa var. formosana. The study found that PRI had a good relationship with light use efficiency(LUE) and photosystem II quantum yield(ΦPSII), the R2 value was 0.71 and 0.81, respectively. But the relationship was weak when PRI decreased to below zero. Leaf chlorophyll content had the highest correlation with mND705(R2=0.64), followed by CI(R2=0.57), and NDVI was not correlation with chlorophyll content. Temporal variations showed that PRI had significantly low value in winter than other seasons. The seasonal variability of PRI was majorly affected by mND705, and there was a significant positive correlation(R2=0.69) between them. Air temperature had a weaker correlation with PRI. Spatial variations showed that the mature leaf of low level had highest mND705, this represent the leaf had high chlorophyll content at the low canopy. We also found that the mND705 of young leaf had no difference at different canopy height. The study revealed that leaf reflectance spectral analysis provide s a rapid, non-destructive method to monitor light use efficiency and pigment content of Chamaecyparis obtusa var. formosana. However, the PRI had a weaker relationship with light use efficiency when PRI decrease to below zero. We must pay attention to the problem when we select PRI for estimating light use efficiency of Chamaecyparis obtusa var. formosana in the future.

In the wildfire community fuel moisture content (FMC) is the quantity of choice when it comes to assess vegetation water status in relation to fire risk and fire behaviour. Field measurements of FMC are both expensive and time consuming and, in addition, sampling is often spatially inadequate. Remote sensing could represent an almost ideal solution both in terms of spatial and temporal coverage, if a consistent relationship between FMC and spectral reflectance could be established. A review of the literature suggests that it is difficult to retrieve FMC for dense forest canopies with remote sensing platforms. This study took a step back and explored the relationship between spectral reflectance and vegetation water content at the leaf level, where several confounding factors present at the canopy level are eliminated or controlled for. It also considered a conifer species, because relatively little research has been produced on this topic for this type of vegetation. The main goal was to establish if FMC can be derived directly from spectral reflectance in the solar spectrum using two well known approaches, such as spectral indices and continuum removal. It is also aimed at exploring if an alternative, indirect way to measure FMC as ratio of Equivalent Water Thickness (EWT) and Specific Leaf Weight (SLW) is feasible and accurate. The results derived from Douglas-fir (Pseudotsuga menziesii (Mirb) Franco) needles used in this study suggested that FMC was not directly retrievable from spectral reflectance but vegetation water content could be assessed with sufficient accuracy in terms of EWT. Also the retrieval of SLW from reflectance of fresh foliage proved to be challenging. Finally, the study also highlighted several aspects in the relationships among foliar water content, dry matter content and reflectance that require additional research.
Graduate

Ph.D., Faculty of Science, University of the Witwatersrand, 2011
Deep-level gold mining in the Witwatersrand Basin Goldfields (WBG) of central South Africa is characterised by the production of extensive unlined tailings storage facilities (TSFs) comprising large quantities of pulverised rock and water contaminated with salts and a wide range of other inorganic pollutants (Weiersbye et al., 2006). There are more than 200 such TSFs covering a total area of more than 400 km2 (Rosner et al., 2001), and significant contaminated “footprint” areas occur after removal and reprocessing of the original TSFs (Chevrel et al., 2003). It is estimated that the Witwatersrand Basin contains six billion tons of gold and uranium tailings (Chevrel et al., 2003), 430 000 tons of uranium (Council of Geoscience, 1998; Winde, 2004a; b; c) and approximately 30 million tons of sulphur (Witkowski and Weiersbye, 1998a). An estimated 105 million tons of waste per annum is generated by the gold mining industry within the WBG (Department of Tourism, Economic and Environmental Affairs, 2002; Chamber of Mines of South Africa, 2004). A major environmental problem resulting from deep level mining in the WBG is the contaminated water that seeps from TSFs into adjacent lands and groundwater. Van As (1992) reported on the significant environmental hazards resulting from the storage of highly pulverised pyrite rock waste in TSFs (Straker et al., 2007). Adjacent lands become polluted through near-surface seepage, and this is enhanced by the movement of polluted groundwater in shallow aquifers that are commonly 1-30 m below ground (Funke, 1990; Hodgson et al., 2001; Rosner et al., 2001; Naicker et al., 2003). The impact of the mines and the TSFs extends far beyond their localities (Cogho et al., 1990). The Vaal River catchment receives a large proportion of the pollutants from WBG mining activities, with consequent acidification and salinisation of surface and ground waters. Salt discharges to the Vaal River were estimated to be 170 000 t/annum (Best, 1985), whereas discharges from the Free State gold mines south of the Vaal catchment were estimated at 350 000 t/annum of salts (Cogho et al., 1990). Concern also exists over the spread of dangerous contaminants such as uranium, chromium and mercury (Coetzee et al., 2006; Winde, 2009). Engineering solutions to these problems are hindered by the large sizes and great extent of TSFs, the high and indefinite costs involved, and the typically low hydraulic conductivity in affected aquifers, which makes the “pump and treat” option impractical. An alternative phytoremediation strategy is to establish belts or blocks of trees in strategic areas surrounding the TSFs in order to reduce the seepage of contaminated water into adjacent lands and groundwater bodies. The major reasons why trees are likely to have a greater impact on seepage water than the existing grasslands that characterise the area around most TSFs in the WBG, are that some tree species have the potential to develop very deep root systems and to continue transpiring water throughout the year. This is in contrast to seasonally dormant grasslands. In addition, some tree species are known to be tolerant to salts and other pollutants. Trees are thus potentially able to reach deep water tables, take up large quantities of water, and remove some of the pollutants in this water. It is crucial for a successful implementation of this strategy to know on what sites trees are able to access mine seepage water, and consequently maintain a high year-round rate of water use. If this access is limited, then growth and water use will be curtailed during the long winter dry season, and control of seepage will be considerably below potential. A primary aim of this study was to develop methodologies to discriminate between water-stressed and non-water-stressed trees currently growing in three gold mining districts (Welkom, Vaal River, West Wits) within the WBG. This information was required to assess what site types are likely to support adequate tree growth and permit high rates of water use and seepage control. The tree species selected were those most widely occurring in these areas, and include the non-native species Eucalyptus sideroxylon A. Cunningham ex Woolls and Eucalyptus camaldulensis Dehnhardt, as well as the indigenous species Searsia lancea L.f. Various remote sensing technologies including leaf-level spectroscopy, satellite and airborne remote sensing images were evaluated for their usefulness in detecting levels of winter-time water stress. Four commonly used ground-truthing techniques (predawn leaf water potential, leaf chlorophyll fluorescence, leaf chlorophyll and carotenoid pigment content, and leaf water content) were used for localised measurements of plant water stress and for ground-truthing of remotely sensed data on 75 sample sites and 15 sample sites. This study provided a unique opportunity to test and compare the use of stress reflectance models derived from different remote sensing data acquired at different spatial and spectral resolutions (i.e. multispectral and hyperspectral) for the same geographical location. The use of remote sensing to examine the spectral responses of vegetation to plant stress has been widely described in the scientific literature. A collation of published spectral reflectance indices provided the basis for investigating the use of hand-held remote sensing technology to detect plant water stress, and was used as a stepping stone to further develop spectral plant water stress relationships for specific tree species in this study. Seventy seven spectral reflectance indices and specific individual spectral wavelengths useful for detecting plant water stress, plant pigment content, the presence of stress related pigments in vegetation, and changes in leaf cellular structure, were investigated using hand-held spectroscopy. Ground-based measurements of plant water stress were taken on 75 sample trees. In this study, the measurement of predawn leaf water potential has been identified as a key methodology for linking remotely sensed assessments of plant water stress to actual plant water stress; a reading of -0.8 MPa was used to separate stressed trees from unstressed trees in the landscape (Cleary and Zaerr, 1984). The results of the predawn leaf water potential measurements ranged from -0.56 to -0.68 MPa at unstressed sites, and from -0.93 to -1.78 MPa at stressed sites. A novel approach of using spectral reflectance indices derived from previous studies was used to identify specific indices which are applicable to South Africa and to the three species investigated in the WGB. Maximal multiple linear regression models were derived for all possible combinations of plant water stress measurements and the 77 spectral reflectance indices extracted from leaf-level spectral reflectance data, and included the interactions of district and species. The results of the multiple linear regression models indicated that the (695/690) index, DATT index (850-710)/(850-680), near infra-red index (710/760) and the water band (900/970) index performed well and accounted for more than 50% of the variance in the data. The stepwise regression model derived between chlorophyll b content and the DATT index was selected as the “best” model, having the highest adjusted R2 of 69.3%. This was shown to be the most robust model in this application, which could be used at different locations for different species to predict chlorophyll content at the leaf-level. Satellite earth observation data were acquired from two data sources for this investigation; the Hyperion hyperspectral sensor (United States Geological Survey Earth Resources Observation Systems) and the Proba Chris pseudo-hyperspectral sensor (European Space Agency). The Hyperion sensor was selected to obtain high spatial and spectral resolution data, whereas the Proba Chris sensor provided high spatial and medium spectral resolution earth observation data. Twelve vegetation indices designed to capture changes in canopy water status, plant pigment content and changes in plant cellular structure, were selected and derived from the satellite remote sensing imagery. Ground-based measurements of plant water stress undertaken during late July 2004 were used for ground-truthing the Hyperion image, while measurements undertaken during July 2005 and August 2005 were used for ground-truthing the Proba Chris images. Predawn leaf water potential measurements undertaken for the three species, ranged from -0.42 to -0.78 MPa at unstressed sites, and -0.95 to -4.66 MPa at stressed sites. Predawn leaf water potentials measured for E. camaldulensis trees sampled in species trials in Vaal River were significantly different between stressed and non stressed trees (t = 3.39, 8df, P = 0.009). In contrast, E. camaldulensis trees sampled near a pan within the Welkom mining district, which had greater access to water but were exposed to higher concentrations of salts and inorganic contaminants, displayed differences in total chlorophyll content (t = -2.20, 8df, P = 0.059), carotenoid content (t = -5.68, 8df, P < 0.001) and predawn leaf water potential (t = 4.25, 8df, P = 0.011) when compared to trees sampled on farmland. E. sideroxylon trees sampled close to a farm dam in the West Wits mining district displayed differences in predawn leaf water potential (t = 69.32, 8df, P < 0.001) and carotenoid content (t = -2.13, 8df, P = 0.066) when compared to stressed trees further upslope away from the water source. Multiple linear regressions revealed that the predawn leaf water potential greenness normalised difference vegetation index model, and the predawn leaf water potential water band index model were the “best” surrogate measures of plant water stress when using broad band multispectral satellite and narrow-band hyperspectral satellite data respectively. It was concluded from these investigations that vegetation indices designed to capture changes in plant water content/plant water status and spectral changes in the red edge region of the spectrum, performed well when applied to high spectral resolution remote sensing data. The greenness normalised difference vegetation index was considered to be a fairly robust index, which was highly correlated to chlorophyll fluorescence and predawn leaf water potential. It is recommended that this index has the potential to be used to map spatial patterns of winter-time plant stress for different genera/species and in different geographical locations. Airborne remote sensing surveys were conducted to investigate the application of high spatial resolution remote sensing data to detect plant water stress. Multispectral airborne imagery was acquired by Land Resource International (PTY) Ltd, South Africa. Ground-based measurements of plant water stress were carried out during July and August 2005.Four individual spectral bands and two vegetation spectral reflectance indices, which are sensitive to changes in plant pigment content, were derived from the processed multispectral images viz. red, green, blue and near-infrared spectral bands and the normalised difference vegetation index (NDVI) and greenness normalised difference vegetation index (GNDVI).The results of the multispectral airborne study revealed that carotenoid content together with the green spectral waveband resulted in the “best” surrogate measure of plant water stress when using broad-band multispectral airborne data. Airborne remote sensing surveys were conducted by Bar-Kal Systems Engineering Ltd, Israel, to investigate the application of hyperspectral airborne imagery to detect plant water stress. Six vegetation spectral reflectance indices designed to capture changes in plant pigment and plant water status/content, were derived from the processed hyperspectral images. When using airborne hyperspectral data, predawn leaf water potential with the normalized difference water index was selected as the most appropriate model. It was concluded, upon evaluation of the multiple linear regression models, that the airborne hyperspectral data produced several more regression models with higher adjusted R2 values (Ra2 range 6.2 - 76.2%) when compared to the airborne multispectral data (Ra2 range 6 - 50.1). Exploration of relationships between vegetation indices derived from leaf-level, satellite and airborne spectral reflectance data and ground-based measurements used as “surrogate” measures of plant water stress, revealed that several prominent and recurring spectral reflectance indices could be applied to identify species-specific plant water stress within the Welkom, Vaal River and West Wits mining districts. The models recommended for mapping and detecting spatial patterns of plant water stress when using different sources of remote sensing data are as follows: the chlorophyll b DATT spectral reflectance model when derived from leaf-level spectral reflectance data, can be applied across all three mining districts the predawn leaf water potential GNDVI spectral reflectance model and predawn leaf water potential water band index spectral reflectance model when utilising satellite multispectral and hyperspectral remote sensing data carotenoid content green band spectral reflectance model can be used for airborne multispectral resolution data predawn leaf water potential NDVI spectral reflectance model is best suited for airborne high spatial and hyperspectral resolution data. These results indicate that measurements of predawn leaf water potential and plant pigment content have been identified as key methodologies for ground-truthing of remotely sensed data and can be used as surrogate measures of plant water stress. Some preliminary research was undertaken to evaluate if wood anatomy characteristics could be used as a non-destructive and rapid low-cost survey approach for identifying trees which are experiencing long-term plant stress. Seventy two wood core samples were extracted and analysed. Predawn leaf water potential measurements were used to classify stressed and unstressed trees. Relative differences in radial vessel diameter, vessel frequency and wood density were examined. Comparison of the radial vessel diameter and vessel frequency measurements revealed significant differences in three of the five comparative sampling sites (p <0.05). The results of the density analyses were significantly different for all five comparative sampling sites (p < 0.01). In general, trees experiencing higher plant water stress displayed smaller vessel diameters, compared to less stressed or healthy trees. Sites which were influenced by high levels of contaminated water also displayed smaller vessel diameters, indicating that the uptake of contaminants could affect the wood anatomy of plants. Trees considered to be experiencing higher plant water stress displayed higher vessel frequency. This preliminary study showed that plant stress does influence the wood anatomical characteristics (radial vessel diameter, vessel frequency and wood density) in E. camaldulensis, E. sideroxylon and S. lancea in the three mining districts. Spatial patterns of trees, mapped in the three gold mining districts, Welkom (27º57´S, 26º34´E) in the Free State Province, Vaal River (26º55´S, 26º40´E) located in the North West Province, and West Wits (26º25´S, 27º21´E) located in Gauteng, which were not experiencing winter-time water stress were correlated to site characteristics such as average soil depth, percent clay in the topsoil, groundwater chloride and sulphate concentrations, total dissolved solids, electrical conductivity and groundwater water level. The spectral reflectance model derived between predawn leaf water potential and the green normalised difference vegetation index using broad-band multispectral Proba Chris satellite data was used to map spatial patterns of unstressed trees across the three mining districts. Very high resolution (75 cm) multispectral airborne images acquired by LRI in 2005 were used to demarcate and classify vegetation using the maximum likelihood supervised classification technique. Interpolated surfaces of groundwater chloride and sulphate concentrations, total dissolved solids, electrical conductivity, pH and groundwater table levels were created using the kriging geostatistical interpolation technique for each mining district. Random sample analyses between stressed and unstressed trees were extracted in order to determine whether site characteristics were significantly different (using t-tests). Site characteristic surfaces which were significantly different from stressed areas were spatially linked to trees which were not experiencing winter-time plant water stress for each tree species investigated in each mining district. This spatial correlation was used to make recommendations and prioritise sites for the establishment of future block plantings. Analysis of the site characteristic data and the geophysical surveys undertaken in the three mining districts which provided detailed information on groundwater saturation and an indication of the salinity conditions, confirmed the presence of relatively shallow and saline groundwater sources. This would imply that tree roots could access the relatively shallow groundwater even during the dry winter season and assist in containing contaminated groundwater seeping into surrounding lands. Keywords : airborne imagery, ground-based measurements of plant water stress, hyperspectral, leaf-level spectroscopy, multispectral, satellite imagery, spatial patterns of unstressed trees, spectral reflectance indices

碩士
國立中興大學
生命科學系
92
In order to understand the chlorophyll fluorescence and leaf reflectance spectral characteristics among species, 12 species with different elevation distribution and temperature adaptation were used. The experiments were made in the campus of National Chung Hsing University (78 m), Hui-Sun Forest Station (800 m), and Tatachia area (2600 m). The results indicated that the PSII efficiency estimated from chlorophyll fluorescence parameters of Pinus taiwanensis (conifer) was lesser influenced by the low temperature and high illumination than those of 2 Miscanthus (C4) species when they were measured in Tatachia. Among 2 Miscanthus species, low elevation origin M. floridulus was more influenced by low temperature than that of high elevation origin M. transomrrisonensis. In Tatachia, transplanted M. floridulus showed lower photochemical reflectance index (PRI) calculated from leaf reflectance spectra in the winter, indicating it required higher xanthophyll cycle to dissipate more excess absorbed energy due to PSII efficiency were more inhibited by low temperature. It also found that no difference of potential of PSII efficiency (Fv/Fm) between flatland and crest line grew P. taiwanensis in Hui-Sun Forest Station. However the PRI of crest line grew P. taiwanensis was lower than that of flatland grown in dry season, probably due to the difference of water condition between 2 habitats. It showed positive correlation between photosynthesis capacity (Pn) and electron transport rate (ETR) for C4 species. This regression coefficient was higher in the species with higher photosynthetic capacity, and no significant correlation could be found in Miscanthus, which showed the lowest Pn among 5 tested C4 species. When merged together of 5 C4 species to statistic analysis, the leaf with higher photosynthetic capacity showed higher portion of absorbed light energy for photochemical (P), and low portion for non-photochemical (D) dissipations. The slope between Pn and P, as well as Pn and D were decreasing with PAR increased. However, the portion of excess energy was not influenced by PAR. From November to December, which daily minimum temperature ranging from 11.6oC to 22.4oC, predawn Fv/Fm of mango (Mangifera indica, cv. Aiwen) and Podocarpus nagi decrease with low temperature, and mango was more influenced than P. nagi. On the contrary, predawn Fv/Fm of Taiwan alder (Alnus formosana) was lesser influenced by temperature. Nevertheless, predawn Fv/Fm showed a strong significant correlation with predawn PRI (PRIp) for statistical analysis when merged together of 3 species. Therefore PRIp could be used as an indicator to estimate the seasonal variation of the potential photochemical efficiency of PSII. Both Fv/Fm and Ф (actual PSII efficiency) showed significant curvilinear correlation with PRI (PRIn) when 3 species were merged together for statistical analysis which data measured at noon. However, more strong correlation between Fv/Fm and ΔPRI (PRIp - PRIn) as well as between Ф and ΔPRI were found. In addition, non photochemical quenching (NPQ) did not correlated with PRIn, but significant correlated with ΔPRI. Thus ΔPRI is suit to indicate the actual dissipation of the excess energy as well as PSII efficiency during illumination. As a conclusion, both chlorophyll fluorescence parameters and leaf reflectance spectra indexes are powerful tools for ecophysiological study.

AbstractEvolutionary trees recount the history of how biological diversity came to be and how evolution gave rise to the incredible variation in plant form and function that can be captured by spectral reflectance. Understanding plant spectra in light of evolution is thus important for assessing biodiversity and critical for explaining how spectral diversity is generated. Here, we focus on leaf spectra and how they are linked to the plant tree of life. We review what evolutionary trees (phylogenies) are and how to interpret them. We then describe how to model the evolution of quantitative traits, discuss which evolutionary processes are involved, and explain specific concepts and metrics, such as phylogenetic signal and evolutionary rates, and how they can be applied to reflectance spectra. Next, we describe a framework that links phylogenies and leaf spectra by coupling models of evolution and radiative transfer models. In doing so, we review some of the challenges of subjecting spectra to evolutionary analyses. We then discuss how spectra can help us to understand leaf evolution and to differentiate plant taxa at different phylogenetic scales from populations to lineages, advancing our potential to remotely detect biodiversity.

AbstractLeaves absorb, scatter, and transmit sunlight at all wavelengths across the visible, near-infrared, and shortwave-infrared spectrum. The optical properties of a leaf are determined by its biochemical and biophysical characteristics, including its 3-D cellular organization. The absorption and scattering properties of leaves together create the shape of their reflectance spectra. Terrestrial seed plant species share similar physiological and metabolic processes for fluxes of gases (CO2, O2, H2O), nutrients, and energy, while differences are primarily consequences of how these properties are distributed and their physical structures. Related species generally share biochemical and biophysical traits, and their optical properties are also similar, providing a mechanism for identification. However, it is often the minor differences in spectral properties throughout the wavelengths of the solar spectrum that define a species or groups of related species. This chapter provides a review and summary of the most common interactions between leaf properties and light and the physical processes that regulate the outcomes of these interactions.

Precise estimation of leaf chlorophyll content (LCC) and leaf water content (LWC) of soybean, using remote sensing technology, provides a new avenue for the nondestructive evaluation of inoculation effects of arbuscular mycorrhizal fungi (AMF) and Bradyrhizobium japonicum (BJ) on soybean growth condition. In this study, a series of pot experiments were conducted in the greenhouse, soybean inoculated with Glomus intraradices (G.i, one of AMF species), G.i and BJ, and non-inoculation were planted under drought stress (DS) and normal irrigation (NI) conditions. Leaf spectra and LCC and LWC were measured on the 28th and 56th days after inoculation. Two new simple ratio (SR) indices, derived from the first derivative spectral reflectance at λ1 nm (Dλ1) and the raw spectral reflectance at λ2 nm (Rλ2), were developed to estimate LCC and LWC. The results indicate that under DS, plants inoculated with G.i had higher LCC and LWC than the non-inoculated plants, followed by the counterparts co-inoculated with G.i and BJ. Linear estimation models, established by the D650/Rred edge and D1680/R680, achieved great improved accuracy for quantifying LCC and LWC of soybean under inoculation and drought stress treatments, with determination of coefficient of 0.63 and 0.76, respectively.

Irrigated agriculture is an important strategic sector for Morocco, contributing to food security and employment. Nowadays, irrigation scheme managers shall ensure that water is optimally used. The main objective was to support the irrigation monitoring and management of wheat in the irrigated perimeter using optical remote sensing and crop modeling. The potential of spectral indices derived from SPOT-5 images was explored for quantifying and mapping surface water content changes at large scale. Indices were computed using the reflectance in red, near infrared, and shortwave infrared bands. A field crop model (AquaCrop) was adjusted and tested to simulate the grain yield and the temporal evolution of soil moisture status. This research aimed at providing a scientific and technical approach to assist policymakers and stakeholders to improve monitoring irrigation and mitigating wheat water stress at field and irrigation perimeter levels in semi-arid areas. The approach could lead to operational management tools for an efficient irrigation at field and regional levels.

Optical time domain reflectometers (OTDR) are widely used in testing, installation and maintenance of optical communication networks. An OTDR launches a series of high speed pulses into a fibre network and measures the amplitude and the delay time of reflected signals to locate events or faults along a fibre link. Precise measurement capabilities of an OTDR on distance, loss/attenuation and reflectance are required to locate and evaluate the severity of faults accurately. The Standards and Calibration Laboratory (SCL) has developed a calibration system for calibrating single mode OTDRs fitted with FC connectors at wavelength 1310 nm and 1550 nm in accordance with the international standards IEC 61746-1:2009. The characteristic parameters calibrated include the distance deviation, attenuation deviation and reflectance deviation. The principle of the calibration is to apply a set of reference standards, namely distance calibration artifact, attenuation calibration artifact and reflectance calibration artifacts, to the OTDR, and to compare against the displayed values for a given OTDR setting. The distance calibration artifact consists of a recirculating delay line and of a lead-in fiber which generates a series of reflection peaks separated by calculable distances. The linear regression of the measured data is processed in Microsoft Excel to estimate the distance scale deviation and zero location offset. The attenuation artefact consists of a G.652 fibre spool with known spectral attenuation and attenuation uniform. The reflectance artifact is composed of a set of known reflectances ranging from -10 dB to -50 dB. The best measurement uncertainties for the distance, attenuation and reflectance deviation calibration are 2m, 0.06 dB and 1.7 dB respectively.

Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.

Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribution of the forest floor to the total BRF was up to 69 % in the red wavelength range and up to 54 % in the NIR wavelength range. Values of SR and NDVI for the forest and the canopy differed within 10 % and 30 % in red and within 1 % and 10 % in the NIR wavelength range. The relative variation of the BRF with the azimuth and view zenith angles was not very sensitive to the forest floor vegetation. Hence, linear correlation of the modelled total BRF and the Ross-thick kernel was strong for dense forests (R2 > 0.9). The agreement between modelled BRF and satellite-based reflectance values was good when measured LAI, clumping index and leaf single scattering albedo values for a boreal forest were used as input to the model.