Buildings are one of the major consumers of energy and make up a considerable portion in the generation of greenhouse gases. Green roofs are regarded as an appropriate strategy to reduce the heating and cooling loads in buildings. However, their energy performance is influenced by different design parameters which should be optimized based on the corresponding climate zone. Previous investigations mainly analyzed various design parameters in a single climate zone. However, the interaction of parameters in different climate zones was not considered. Also, the studies have been conducted mostly for commercial or residential buildings. Among different building types, schools with large roof surface are one of the major consumers of energy in North America. However, the literature review shows the lack of study on the effect of green roof on the thermal and energy performance of this type of building. This study performs a comprehensive parametric analysis to evaluate the influence of the green roof design parameters on the thermal or energy performance of a secondary school building in four climate zones in North America (i.e. Toronto, ON; Vancouver, BC; Las Vegas, NV and Miami, FL). Soil moisture content, soil thermal properties, leaf area index, plant height, leaf albedo, thermal insulation thickness and soil thickness were used as variables. Optimal parameters of green roofs were found to be closely related to meteorological conditions in each city. In terms of energy savings, the results show that the light substrate has better thermal performance for the uninsulated green roof. Also, the recommended soil thickness and leaf area index in the four cities are 0.15 m and 5, respectively. The optimal plant height for the cooling dominated climates is 0.3 m and for the heating dominated cities are 0.1 m. The plant albedo had the least impact on the energy consumption while it is effective in mitigation effect of heat island effect. Finally, unlike the cooling load which is largely influenced by the substrate and vegetation, the heating load is considerably affected by the thermal insulation instead of green roof design parameters. / Graduate

This study examined leaf area index (LAI) and relationships with corresponding tree growth, climate and soil characteristics across New Zealand forest plantations. The aim of this study was to determine if quick measures of projected leaf area across environmental gradients of New Zealand were an accurate indicator of site quality. Projected leaf areas of Pinus radiata D Don and Cupressus lusitanica Mills seedlings were measured using a Li-Cor LAI-2000 plant canopy analyser at 22 locations representing the soil and climatic diversity across New Zealand plantation forests. Seedlings planted at 40 000 stems per hectare were used to test treatment effects of fertiliser, site disturbance and species over a 4 year period. It was hypothesised that collected climate and soil information would explain differences in LAI development patterns across sites as the canopies approached site and seasonal maxima. Averaged across sites Cupressus lusitanica 7.28 (± 2.59 Std.) m2 m-2 had significantly (p = 0.0094) greater projected LAIs than Pinus radiata 6.47 (± 2.29) m2m-2. Maximum site LAI (LAImax) varied from 2.9 to 11.8 m2 m-2 for Pinus radiata and from 3.1 to 12.6 m2 m-2 for Cupressus lusitanica. LAImax of both species was significantly and positively correlated with vapour pressure deficit, soil carbon, nitrogen, phosphorous and CEC, but negatively with solar radiation, temperature and soil bulk density. A seasonal model of LAI across sites illustrated an 8.5% fluctuation in LAI of established canopies over the course of a year. Despite considerable variation in climate and soil characteristics across sites the combined effects of LAI at harvest and temperature were significantly correlated with site productivity (r2 = 0.84 and 0.76 for Pinus radiata and Cupressus lusitanica respectively). A national model of LAImax (r2 = 0.96) was proposed for Pinus radiata across climate and soil environments and the significance of LAImax as a component of site quality monitoring tools is discussed.

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers. This thesis focused on estimating LAI from: i) height and density metrics derived from Light Detection and Ranging (LiDAR); ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and iii) a combination of these two remote sensing technologies. In situ measurements of LAI were calculated from digital hemispherical photographs (DHPs) and remotely sensed variables were derived from low density LiDAR and high resolution WorldView-2 data. Multiple Linear Regression (MLR) models were created using these variables, allowing forest-wide prediction surfaces to be created. Results from these analyses demonstrated: i) moderate explanatory power (i.e., R2 = 0.54) for LiDAR models incorporating metrics that have proven to be related to canopy structure; ii) no relationship when using SVIs; and iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with low ranges of LAI for model calibration. On the other hand, it was anticipated that traditional SVI relationships to LAI would be present with WorldView-2 data, a result that is not easily explained. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at a high spatial resolution for decision makers in the forestry community. / Thesis (Master, Geography) -- Queen's University, 2012-09-24 16:18:09.96

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Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 160-172). Also available on the World Wide Web.

The increasing interest of modeling global carbon cycling during the past two decades has driven this research to map leaf area index (LAI) at multiple spatial resolutions by combining LAI field observations with various sensor images at local, regional, and global scale. This is due to its important role in process based models that are used to predict carbon sequestration of terrestrial ecosystems. Although a substantial research has been conducted, there are still many challenges in this area. One of the challenges is that various images with spatial resolutions varying from few meters to several hundred meters and even to 1 km have been used. However, a method that can be used to collect LAI field measurements and further conduct multiple spatial resolution mapping and accuracy assessment of LAI is not available. In this study, a pilot study in a complex landscape located in the Southern Illinois was carried out to map LAI by combining field observations and remotely sensed images. Multi-scale mapping and accuracy assessment of LAI using aerial photo, Landsat TM and MODIS images were explored by developing a multi-scale sampling design. The results showed that the sampling design could be used to collect LAI observations to create LAI products at various spatial resolutions and further conduct accuracy assessment. It was also found that the TM derived LAI maps at the original and aggregated spatial resolutions successfully characterized the heterogeneous landscape and captured the spatial variability of LAI and were more accurate than those from the aerial photo and MODIS. The aerial photo derived models led to not only over- and under-estimation, but also pixilated maps of LAI. The MODIS derived LAI maps had an acceptable accuracy at various spatial resolutions and are applicable to mapping LAI at regional and global scale. Thus, this study overcame some of the significant gaps in this field.

Remote sensing provides temporal, spectral and spatial information covering a wide area. Therefore, it has great potential in offering a detailed quantitative determination of the leaf area index (LAI) and other crop parameters in precision agriculture. The spatially differentiated assessment of LAI is of utmost importance for enabling an adapted field management, with the aim of increasing yields and reducing costs at the same time. The scientific focus of this work was the investigation of the potential of hyperspectral remote sensing data of different spectral resolutions, which were acquired at different spatial scales, for a precise assessment of wheat LAI. For this reason, three research experiments were conducted: 1) a comparison of different empirical-statistical regression techniques and their capabilities for a robust LAI prediction; 2) a determination of the required spectral resolution and important spectral regions/bands for precise LAI assessment; and 3) an investigation of the influence of the ground sampling distance of remote sensing images on the quality of spatial LAI predictions. The first part of this thesis compared three empirical-statistical regression techniques – namely, partial least-squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR) – and their achieved model qualities for the assessment of wheat LAI from field reflectance measurements. In this context, the two different validation techniques – leave-one-out cross-validation (cv) and independent validation (iv) – were applied for verifying the accuracy of the different empirical-statistical regression models. The results clearly showed that model performance markedly depends on the validation technique used to assess model accuracy. In the case of leave-one-out cross-validation, SVR provided the best results, while PLSR proved to be superior to SVR and RFR when independent validation was applied. In the second part of this thesis, the spectral characteristics of the hyperspectral airborne sensor aisaDUAL (98 spectral bands) and the upcoming hyperspectral satellite mission EnMAP (204 spectral bands) were investigated to show their capability regarding the precise determination of wheat LAI. Moreover, the feature selection algorithm RReliefF, combined with a randomized sampling approach, was applied to identify the spectral bands that were most sensitive to changes in LAI. The results demonstrated that only three spectral bands of aisaDUAL, as well as EnMAP, at specific locations within the investigated spectral range (400–2,500 nm) were necessary for an accurate LAI prediction. The third part of this thesis dealt with the influence of the spatial resolution of aisaDUAL (3 m) and simulated EnMAP (30 m) image data on the assessment of wheat LAI. While the ground sampling distance (GSD) of aisaDUAL allowed a robust regression model calibration and validation, LAI predictions based on simulated EnMAP image data led to poor results because of the distinct difference in size between the EnMAP pixels (900 m2) and the sampled field plots (0.25 m2) for which the LAI was measured. In order to enable a more precise determination of wheat LAI from EnMAP image data, the two different approaches of image aggregation and image fusion were examined. In this context, the fusion approach has proven to be the more suitable method, which allowed a more accurate LAI prediction compared to the results based on the EnMAP data with a GSD of 30 m. In summary, the findings of the research reported in this thesis demonstrated that the accuracy of spatial LAI predictions from remote sensing data depends on several factors. Besides the applied empirical-statistical retrieval- and validation method, the spatial and spectral characteristics of the used image data sets played an important role. With the forthcoming hyperspectral satellite missions (e.g., EnMAP, HyspIRI), the area-wide assessment of LAI and other crop parameters (e.g., biomass, chlorophyll content) will be strongly supported. The moderate spatial resolutions of these satellites systems, however, require a combined use with spatial higher resolution multi- or superspectral satellite data (e.g., RapidEye, Sentinel-2). This multisensoral approach offers great potential for the prompt identification of spatial variations in crop conditions on sub-field scale, which is a mandatory prerequisite for precision agricultural applications.

Commercially available leaf area index (LAI) meters are tools that can be used in making insect management decisions. However, proper technique must be determined for LAI estimation, and accuracy must be validated for the meters. Full-season soybean require LAI values of at least 3.5 to 4.0 by early to mid-reproductive developmental stages to achieve maximum yield potential, but the relationship between double-crop soybean LAI and yield is unknown. This research (1) evaluated minimum plot size requirements for mechanically defoliated soybean experiments using the LAI-2000 Plant Canopy Analyzer, (2) compared LAI estimates among LAI-2000 detector types which respond to different wavelengths of light, (3) compared LAI-2000 estimates with directly determined LAI values for 0, 33, 66, and 100% mechanical defoliation levels, (4) used linear and non-linear models to describe the response of full-season and double-crop soybean yields to reductions in LAI through mechanical defoliation, and (5) evaluated the response of double-crop soybean yields to reductions in LAI through insect defoliation. The minimum plot size for obtaining accurate LAI estimates of defoliated canopies in soybean with 91 cm row centers is four rows by 2 m, with an additional 1 m at the ends of the two middle rows also defoliated. The wide-blue detector, which is found in newer LAI-2000 units and responds to wavelengths of light from 360 to 460 nm, gave higher LAI estimates than the narrow-blue detector, which responds to light from 400 to 490 nm. The unit with the narrow-blue detector gave estimates equal to directly determined LAI in two of three years for 0, 33, and 66% defoliation levels, while the units with the wide-blue detectors gave estimates higher than directly determined LAI in the two years that they were studied, except for a few accurate 33% defoliation estimates. Therefore, the LAI-2000 usually provides reasonable estimates of LAI. Yield decreased linearly with LAI when LAI values were below 3.5 to 4.0 by developmental stages R4 to R5 in both full-season and double-crop soybean. Usually, there was no relationship between yield and LAI at LAI values greater than 4.0. There was an average yield reduction of 820 ± 262 kg ha⁻¹ for each unit decrease in LAI below the critical 3.5 to 4.0 level; maximum yields ranged from 1909 to 3797 kg ha⁻¹. Insect defoliators did not defoliate double-crop soybean plots to LAI levels less than 4.0, and there was no yield difference between insect-defoliated and control plots. Therefore, double-crop soybean that maintains LAI values above the 3.5 to 4.0 critical level during mid-reproductive developmental stages is capable of tolerating defoliating pest / Ph. D.

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.

An energy-based model accounting for snow accumulation and snowmelt in a coniferous forest and in an open area An energy balance approach was used to simulate snow water equivalent (SWE) evolution in an open area, forest clearing and coniferous forest during winter seasons 2011/12 and 2012/13 in the Bystřice River basin (Krušné Mountains). The aim was to describe the impact of vegetation on snow accumulation and snowmelt under different forest canopy structure and density of trees. Hemispherical photographs were used to describe the forest canopy structure. Energy balance model of snow accumulation and melt was set up. For forest sites the snow model was altered for accounting the effects of the forest canopy on the driving meteorological variables of the snow model. Leaf area index derived from 32 hemispherical photographs of the vegetation and sky was used for forest influence implementation in the snow model. The model was evaluated using snow depth and SWE field data measured at 16 localities in winter seasons from 2011 to 2013. The model was able to reproduce the SWE evolution in both winter seasons beneath the forest canopy, forest clearing and open area with correlations to observations ranging from 0.16 to 0.99. The SWE maximum in forest sites is by 18% lower than in open areas and forest...