Estimating foliar and wood lignin concentrations, and leaf area index (LAI) of Eucalyptus clones in Zululand using hyperspectral imagery.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.To produce high quality paper, lignin should be removed from the pulp. Quantification of lignin concentrations using standard wet chemistry is accurate but time consuming and costly, thus not appropriate for a large number of samples. The ability of hyperspectral remote sensing to predict foliar lignin concentrations could be utilized to estimate wood lignin concentrations if meaningful relationships between wood and foliar chemistry are established. LAI (leaf area index) is a useful parameter that is incorporated into physiological models in forest assessment. Measuring LAI over vast areas is labour intensive and expensive; therefore, LAI has been correlated to vegetation indices using remote sensing. Broadband indices use average spectral information over broad bandwidths; therefore details on the characteristics of the forest canopy are compromised and averaged. Moreover, the broadband indices are known to be highly affected by soil background at low vegetation cover. The aim of this study is to determine foliar and wood lignin concentrations of Eucalyptus clones using hyperspectral lignin indices, and to estimate LAI of Eucalyptus clones from narrowband vegetation indices in Zululand, South Africa Twelve Eucalyptus compartments of ages between 6 and 9 years were selected and 5 trees were randomly sampled from each compartment. A Hyperion image was acquired within ten days of field sampling, SI and LAI measurements. Leaf samples were analyzed in the laboratory using the Klason method as per Tappi standards (Tappi, 1996-1997). Wood samples were analyzed for lignin concentrations using a NIRS (Near Infrared Spectroscopy) instrument. The results showed that there is no general model for predicting wood lignin concentrations from foliar lignin concentrations in Eucalyptus clones of ages between 6 and 9 years. Regression analysis performed for individual compartments and on compartments grouped according to age and SI showed that the relationship between wood and foliar lignin concentration is site and age specific. A Hyperion image was georeferenced and atmospherically corrected using ENVI FLAASH 4.2. The equation to calculate lignin indices for this study was: L1R= ~n5il: A'''''y . 1750 AI680 The relationship between the lignin index and laboratory-measured foliar lignin was significant with R2 = 0.79. This relationship was used to calculate imagepredicted foliar lignin concentrations. Firstly, the compartment specific equations were used to calculate predicted wood lignin concentrations from predicted foliar lignin concentrations. The relationship between the laboratorymeasured wood lignin concentrations and predicted wood lignin concentrations was significant with R2 = 0.91. Secondly, the age and site-specific equations were used to convert foliar lignin concentration to wood lignin concentrations. The wood lignin concentrations predicted from these equations were then compared to the laboratory-measured wood lignin concentrations using linear regression and the R2 was 0.79 with a p-value lower than 0.001. Two bands were used to calculate nine vegetation indices; one band from the near infrared (NIR) region and the other from the short wave infrared (SWIR). Correlations between the Vis and the LAI measurements were generated and . then evaluated to determine the most effective VI for estimating LAI of Eucalyptus plantations. All the results obtained were significant but the NU and MNU showed possible problems of saturation. The MNDVI*SR and SAVI*SR produced the most significant relationships with LAI with R2 values of 0.899 and 0.897 respectively. The standard error for both correlations was very low, at 0.080, and the p-value of 0.001. It was concluded that the Eucalyptus wood lignin concentrations can be predicted using hyperspectral remote sensing, hence wood and foliar lignin concentrations can be fairly accurately mapped across compartments. LAI significantly correlated to eight of the nine selected vegetation indices. Seven Vis are more suitable for LAI estimations in the Eucalyptus plantations in Zululand. The NU and MNU can only be used for LAI estimations in arid or semi-arid areas

Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model

Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.This research was supported by the Fundamental Research Funds for the Central Universities under Grant No. 531107051063 and Guangxi Natural Science Foundation under Grant No. 2016JJD110017. We would like to thank Dr. Rautiainen Miina and Mottus Matti for sharing the field data and the USGS for making the EO-1 Hyperion hyperspectral data publically available. (531107051063 - Fundamental Research Funds for the Central Universities; 2016JJD110017 - Guangxi Natural Science Foundation)Accepted manuscrip

Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: theoretical basis

This paper presents the theoretical basis of the algorithm designed for the generation of leaf area index and diurnal course of its sunlit portion from NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR). The Look-up-Table (LUT) approach implemented in the MODIS operational LAI/FPAR algorithm is adopted. The LUT, which is the heart of the approach, has been significantly modified. First, its parameterization incorporates the canopy hot spot phenomenon and recent advances in the theory of canopy spectral invariants. This allows more accurate decoupling of the structural and radiometric components of the measured Bidirectional Reflectance Factor (BRF), improves scaling properties of the LUT and consequently simplifies adjustments of the algorithm for data spatial resolution and spectral band compositions. Second, the stochastic radiative transfer equations are used to generate the LUT for all biome types. The equations naturally account for radiative effects of the three-dimensional canopy structure on the BRF and allow for an accurate discrimination between sunlit and shaded leaf areas. Third, the LUT entries are measurable, i.e., they can be independently derived from both below canopy measurements of the transmitted and above canopy measurements of reflected radiation fields. This feature makes possible direct validation of the LUT, facilitates identification of its deficiencies and development of refinements. Analyses of field data on canopy structure and leaf optics collected at 18 sites in the Hyytiälä forest in southern boreal zone in Finland and hyperspectral images acquired by the EO-1 Hyperion sensor support the theoretical basis.Shared Services Center NAS

The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

Tracking the Seasonal Dynamics of Boreal Forest Photosynthesis Using EO-1 Hyperion Reflectance: Sensitivity to Structural and Illumination Effects

During the growing season, the photosynthesis and growth of boreal forests are regulated by physiological responses to environmental factors. Physiological variations affect the spectral properties of leaves. Linking canopy-level spectral reflectance to leaf-level processes for monitoring forest seasonal physiology using satellite images is hindered by view and illumination effects and variations in canopy structure. To better understand the connection between the two structural levels, we used nine narrow-band vegetation indices (VIs) derived from Hyperion imagery to track the seasonal dynamics of boreal forest stands: the photochemical reflectance indices (PRI and PRI 515 ) related to the xanthophyll cycle, the red edge (RE) index, the Maccioni (Macc) and the green normalized difference vegetation index related to chlorophyll concentration (Ca + b), the carotenoid simple ratio and Gitelson carotenoid concentration index related to carotenoid concentration (Cx + c), the normalized difference vegetation index related to fractional cover, and the plant senescence reflectance index related to the Cx + c/Ca + b ratio. As ground truth, we used measurements of exposed pine shoot light use efficiency (LUE) and photosynthesis. Over the study period (May to August), LUE and photosynthesis were best correlated with the chlorophyll VIs Macc and RE. Both indices also exhibited the lowest coefficient of variation in association with forest structure. PRI, on the other hand, was affected by canopy structure and observation geometry and was uncoupled from LUE during the growing season. Our findings demonstrate that the photosynthesis and productivity of boreal forests in the growing season are best tracked using VIs related to total pigment concentration (i.e., chlorophyll)

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)

Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images

The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI705) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest

Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor.

Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields

Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas

Fire risk assessment is one of the most important components in the management of fire that offers the framework for monitoring fire risk conditions. Whilst monitoring fire risk conditions commonly revolved around field data, Remote Sensing (RS) plays key role in quantifying and monitoring fire risk indicators. This study presents a review of remote sensing data and techniques for fire risk monitoring and assessment with a particular emphasis on its implications for wildfire risk mapping in protected areas. Firstly, we concentrate on RS derived variables employed to monitor fire risk conditions for fire risk assessment. Thereafter, an evaluation of the prominent RS platforms such as Broadband, Hyperspectral and Active sensors that have been utilized for wildfire risk assessment. Furthermore, we demonstrate the effectiveness in obtaining information that has operational use or immediate potentials for operational application in protected areas (PAs). RS techniques that involve extraction of landscape information from imagery were summarised. The review concludes that in practice, fire risk assessment that consider all variables/indicators that influence fire risk is impossible to establish, however it is imperative to incorporate indicators or variables of very high heterogeneous and “multi-sensoral or multivariate fire risk index approach for fire risk assessment in PA.Keywords: Protected Areas, Fire Risk conditions; Remote Sensing, Wildfire risk assessmen