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

Thesis (MscFor)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: The purpose of the study was to investigate the effect of mid rotation fertilizer application on leaf area index (LAI), basal area and volume increment in thinned Pinus radiata stands on the most common soils of the Boland region in the Western Cape. The study was conducted on a range of sites in the Boland region of MTO Forestry Company, chosen to reflect the two most common soil types and a water availability gradient in each soil type. A factorial combination of fertilizer treatments with three levels each for nitrogen (N) at 0, 100 and 200 kg ha-1 and phosphorus (P) at 0, 50 and 100 kg ha- 1 was used. This design was replicated four times across a gradient of water availability for each of the two common soil groups, forming a complete trial series. All replications were laid out in P. radiata stands that had received their mid-rotation thinning prior to treatment implementation. LAI, diameter at breast height and height measurements as well as foliar analysis were determined before the implementation of the study in 2008 and then subsequently at predetermined intervals in 2009 and 2010. Leaf area index and stem volume increment were measured in order to evaluate the influence on growth efficiency. LAI was estimated using the gap fraction method with the use of a ceptometer. Volume increment was calculated using diameter and height measurements and basal area was calculated by means of diameter measurements. The abovementioned growth responses were then used to determine the effect of increased nutrient availability on stand growth. There were no significant interactions detected between any of the factors, N, P and water availability class in their effect on LAI, basal area, volume increment and growth efficiency. LAI increment responded significantly to N and P in the first year but only to P in the second year after treatment. Significant basal area responses to N and P were recorded in the second but not the first year. This might have been due to the fact that trees had to re-build their canopies after thinning before a basal area response could be obtained. For the variables where an analysis of total growth response over the two year period was done, basal area increment and volume increment significantly responded to the application of nitrogen but not to phosphorus. Growth efficiency was not significantly influenced by either nitrogen or phosphorus over the full two year monitoring period. Water availability class consistently and significantly influenced basal area increment, volume increment and growth efficiency over the two year period as well as during year one and year two. The best responses generally occurred as a result of the additive effects of N and P. The growth response did not remain the same across the water availability classes. The wetter sites tended to have greater responses than the drier sites. Although these are still early results, the growth responses could be attributed to an increase in LAI. Nutrient analysis through vector analysis indicated that the additional N and P from fertilizer application were taken up by the trees thereby resulting in greater LAI and increased stem wood production. / AFRIKAANSE OPSOMMING: Die studie het ten doel gestel om die effek van mid-rotasie bemesting op blaar oppervlak indeks (BOI), basale oppervlakte- en volume aanwas te ondersoek in gedunde opstande van Pinus radiata op die mees algemene grondtipes van die Bolandstreek, Wes-Kaapland. Eksperimente is uitgelê oor 'n reeks van groeiplekke in die Bolandstreek wat gekies is om 'n water beskibaarheidsgradient te verteenwoordig oor elk van die twee mees algemene grondtipes. 'n Faktoriaal-kombinasie van kunsmisbehandelings met drie vlakke elk van stikstof [(N) teen 0, 100 en 200 kg ha-1] en fosfor [(P) teen 0, 50 en 100 kg ha-1] is toegedien. Hierdie ontwerp is vier maal herhaal oor 'n gradient van grondwater beskikbaarheid, oor elk van die twee mees algemene grondtipes, om sodoende 'n volledige eksperimentele reeks te vorm. Elke herhaling is uitgelê in 'n P. radiata opstand wat reeds 'n mid-rotasie dunning ondergaan het voor implementering van die kunsmis behandelings. Metings van BOI, deursnee op borshoogte, boomhoogte asook blaarmonsters is geneem voor implementering in 2008 en daarna met vooraf bepaalde tussenposes in 2009 en 2010. Die BOI en stam volume aanwas is bepaal om die effek van behandelings op groeieffektiwiteit te evalueer. Die gaping fraksie tegniek is gebruik om BOI te skat met behulp van 'n sonvlek septometer. Volume aanwas is bereken vanaf deursnee en hoogtemetings en basale oppervlak aanwas vanaf deursnee-metings. Metings van al bogenoemde groeireaksies is gebruik om die effek van verhoogde voedingstof beskikbaarheid op opstandsgroei te evalueer. Daar was geen betekenisvolle interaksies tussen enige van die faktore N, P of water beskikbaarheidsklas met betrekking tot reaksies op BOI, basale oppervlak- en volume aanwas of groei-effektiwiteit nie. Die BOI het betekenisvol gereageer op N en P in die eerste jaar, maar slegs op P in die tweede jaar na behandeling. Basale oppervlakte aanwas is betekenisvol verbeter deur N en P in die tweede jaar maar nie in die eerste jaar nie. Dit is waarskynlik as gevolg van die feit dat opstande eers hul kroondak moes herstel (na dunnings) voordat 'n reaksie in basale oppervlak verkry kon word. Vir die veranderlikes waar 'n analise van die groeireaksie oor die volle twee jaar moniteringsperiode gedoen is, het basale oppervlak- en volume aanwas betekenisvol gereageer op stikstof maar nie op fosfor nie. Groei-effektiwiteit is nie betekenisvol geaffekteer deur N of P oor die volle twee jaar moniteringsperiode nie. Water beskikbaarheidsklas het basale oppervlak en volume aanwas asook groei-effektiwiteit betekenisvol en voortdurend beïnvloed in die eerste en tweede jaar, asook gedurende die volle twee jaar moniteringsperiode. Die beste groeireaksie is oor die algemeen verkry waar N en P gesamentlik toegedien is en waar dus aanvullende reaksies verkry is. Groeireaksies het betekenisvol verskil na gelang van water beskikbaarheidsklas, met die grootste reaksie op die natste groeiplekke. Hoewel hierdie vroeë resultate is, kan ons die meganisme van die reaksie primêr toeskryf aan 'n toename in BOI. Vektor analise van blaar voedingstof vlakke het aangedui dat addisionele N en P na kunsmis toediening opgeneem is, wat die weg gebaan het vir 'n toename in BOI en verhoogde volume aanwas.

<p> </p> <p>Leaf Area Index (LAI) is commonly defined as the total area of a leaf per unit area of the ground. LAI is an important variable for characterizing plant canopy related to the interception of solar radiation. Direct measurement of LAI by destructive sampling is tedious, time-consuming, and labor-intensive. With the advance of remote sensing, studies have explored multispectral and hyperspectral remote sensing image data and LiDAR point clouds as individual sources to estimate LAI indirectly. This study investigates the estimation of LAI for maize row crops over the growing season based on features derived from high resolution LiDAR and hyperspectral data acquired simultaneously from a UAV platform. Support Vector Regression (SVR) models are developed using cross validation and evaluated relative to the contribution of the multi-modality remote sensing data. The study is based on data acquired for experiments in plant breeding and evaluation of nitrogen management practice trials conducted at the Agronomy Center for Research and Education (ACRE) in 2021 and 2022, respectively. Reference data for the models were collected using a LI-COR® LAI-2200-C Plant Canopy Analyzer. Including both LiDAR and hyperspectral data sources in the SVR model improved the 𝑅_ref^2 (relative to 1:1 comparison line), RMSE and Relative RMSE (rRMSE) values for both the plant breeding and nitrogen management practice experiments, although incremental gains were small overall. More importantly, it was observed that the contributions of the LiDAR vs hyperspectral inputs to the models also varied throughout the growing season. </p>

Budongo Forest Reserve (BFR) is a flagship reserve for primate conservation due to its abundant chimpanzee (Pan troglodytes) population, and its current management policy for multiple economic, conservation and environmental benefits. The identification and better understanding of the structure and dynamics of the forest/plant community types, patterns of species distribution and quantitative properties of their diversity is important to the conservation and sustainable management of tropical rainforests. This study seeks to contribute to a better understanding of the BFR forest community types, species diversity patterns and environmental correlates, as well as natural regeneration processes (i.e. seedling establishment and sprouting). Data on vegetation and environmental variables were collected using rectangular 50 x 100m (0.5 ha) plots, sub-divided into five equal contiguous (20 x 50 m) 0.1 ha sub-plots. Data on land-use/cover changes, and relevant associated socio-economic parameters were collected through the analysis of multi-temporal satellite imagery and field observations, as well as interviews of local households and key informants. The study revealed significant land-use/cover changes, with the area under sugarcane cultivation increasing over 17-fold, from 690 ha in 1988 to 12729 ha in 2002, with a concomitant loss of about 4680 ha (8.2% loss) of forest/woodland, mainly in the southern part of BFR. These changes are attributed to agricultural expansion, a rapidly increasing human population, exacerbated by large influxes of refugees, lack of alternative sources of income, conflicts of interest and political interference in the management of BFR, and an unclear land tenure system. The need for more land for agricultural expansion and the loss of woodlands (a source of building materials and fuelwood for the local communities) is leading to the invasion of and encroachment on BFR, which threatens plant and wild animal conservation. The study revealed that the vegetation of BFR is formed by a mosaic of plant communities, with the major forest types being; Pseudospondias microcarpa Swamp Forest, Funtumia elastica - Pouteria altissima, Lasiodiscus mildbraedi - Khaya anthotheca and Cynometra alexandri - Rinorea ilicifolia forest communities. Canonical correspondence analysis (CCA) indicated that soil nutrients (Si, Ca, N, Fe and Li) and anthropogenic disturbances are the main factors controlling forest community patterns. The variances explained as a proportion of total inertia were relatively high (0.53 and 0.56 for basal area and abundance, respectively), showing how well the measured variables explained species composition. These plant communities differed significantly in terms of woody species diversity and richness; being highest in the Pseudospondias microcarpa swamp and lowest in the Cynometra alexandri-Rinorea ilicifolia forest. However, about 48 species were shared between the forest community types. A total of 269 species representing 171 genera and 51 families was recorded. Fisher’s alpha-diversity ranged 4.45-30.59 and 3.07-29.7 for stem diameters ≥2.0 cm and ≥10 cm, respectively, being significantly higher for stem diameters ≥2.0 cm. The use of stem diameters ≥2.0 cm unveiled 53 more species (19.7%), with only 216 species recorded for the standard ≥10 cm dbh minimum size usually applied in tropical forests. A SHE analysis also showed greater richness (ln(S)) and H diversity for the >2.0 cm than the ≥10cm stem diameters. Hence, the study reaffirmed that the use of 10 cm as a minimum dbh in woody plant diversity studies in forests, where many tree species rarely exceed 10 cm stem diameter, is highly likely to underestimate diversity and richness, potentially biasing the understanding of diversity patterns. The most speciose families were Euphorbiaceae, Fabaceae, Rubiaceae, Moraceae, Meliaceae, Rutaceae, Annonaceae, and Flacourtiaceae, accounting for 147 species. Families with the highest Familial Importance values (FIV) were; Fabaceae (17.5), followed by Euphorbiaceae (16.3), and Ulmaceae (8.35). The BFR exhibits characteristics intermediate between log-normal and log-series species-abundance distributions, indicating a community with a small number of abundant species and a relatively large proportion of rare species. Both Whittaker’s (βw) and the Morisita-Horn Index measures of β-diversity consistently showed higher β-diversity for logged and arboricide treated areas, followed by logged only, and then nature reserve historical management practice types. β-diversity was relatively high at the total forest community scale, but lower for stem diameter ≥2.0 cm than ≥10.0 cm data. Environmental variables significantly explained 66.5% and 61.9% of the variance in species composition for stem diameter ≥2.0 cm and ≥10.0 cm data, respectively. Hence, the variation in species composition of BFR is characterised by significant spatial patterns, and the patterns in β-diversity are to a great extent associated with environmental heterogeneity (i.e. soil nutrients, topographic and light gradients) and anthropogenic disturbances. Investigation of natural regeneration showed that sprouting is generally common among the woody species, with both canopy and sub-canopy trees sprouting prolifically. Of the 122 species affected by ii harvesting, and tree and branch fall disturbances, 199 (97.5%) from 31 families sprouted from the cut stumps, with only Caloncoba crepiniana (De Wild. & Th.Dur.) Gilg exhibiting both stem and root sprouting. Stump basal diameter, height, bark-thickness, and height of stump above the ground at which the first sprout emerged, were significant predictors of sprouting ability among individuals. Number of sprouts/stump differed significantly among families, species, and stump size-classes. Of the 241 seedling species, representing 46 families, about 30.3% were rare (only 2-10 individuals); while 12% were very rare (only 1 individual each). Cynometra alexandri C.H. Wright and Lasiodiscus mildbraedii Engl. were the most abundant seedlings and also among the most widely distributed species in the forest. Analysis of similarity (ANOSIM) revealed significant differences in seedling composition between transects, but not between topographic positions or historical management practice types. Canonical Correspondence Analysis (CCA) showed that the measured environmental variables significantly explained 59.4% of the variance in seedling species distributions, with the three most important variables being organic matter, titanium and leaf area index (LAI; an indicator of light availability below the canopy). Hence, the important mechanisms influencing regeneration via seedlings in BFR operate through the soil system, and the ground and canopy vegetation characteristics. Nine of the 15 intensively studied multiple-use species, namely L. mildbraedii, Celtis Mildbraedii Engl., Pouteria altissima (A. Chiev.) Aubrev. & Pellegr., Chrysophyllum albidum G. Don., C. alexandri, Diospyros abyssinica (Hiern) F. White, Funtumia elastica (Preuss) Stapf., Chrysophyllum perpulchrum Hutch. & Dalz, and Antiaris toxicaria (Pers.) Lesch. had highly negative size-class distribution (SCD) slopes and substantial seedling regeneration. While Alstonia boonei De Wild. and Cordia millenii Bak. had weakly negative SCD slopes and pulsed or sporadic regeneration patterns. The wide distribution of seedlings for a variety of species, and with most of the intensively studied species having population structures showing healthy regeneration patterns, suggests that BFR is currently experiencing a continuous regeneration phase. In conclusion, the gradients in the vegetation of BFR are a reflection not only of site conditions as shown by the edaphic and abiotic factors, but also the history of human interventions.

Forests affect climate by absorbing CO₂ but also by altering albedo, latent heat flux, and sensible heat flux. In this study we used the Community Earth System Model to assess the biophysical effect of North American temperate forests on climate and how this effect changes with location, tree type, and forest management. We calculated the change in annual temperature and energy balance associated with afforestation with either needle leaf evergreen trees (NET) or broadleaf deciduous trees (BDT) and between forests with high and low leaf-area indices (LAI). Afforestation from crops to forests resulted in lower albedo and higher sensible heat flux but no consistent difference in latent heat flux. Forests were consistently warmer than crops at high latitudes and colder at lower latitudes. In North America, the temperature response from afforestation shifted from warming to cooling between 34° N and 40° N for ground temperature and between 21° N and 25° N for near surface air temperature. NET tended to have lower albedo, higher sensible heat flux and warmer temperatures than BDT. The effect of tree PFT was larger than the effect of afforestation in the south and in the mid-Atlantic. Increasing LAI, a proxy for increased management intensity, caused a cooling effect in both tree types, but NET responded more strongly and albedo decreased while albedo increased for BDT. Our results show that forests' location, tree type, and management intensity can have nearly equal biophysical effects on temperature. A forest will have maximum biophysical cooling effect if it is in the south, composed of broadleaf PFT, and is managed to maximize leaf area index. / Master of Science

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Grasslands are one of the most important ecosystems on Earth, covering approximately one-third of the Earth’s surface. Grassland biodiversity is important as many services provided by such ecosystems are crucial for the human economy and well-being. Given the importance of grasslands ecosystems, in recent years research has been carried out on the potential to monitor them with novel remote sensing techniques. Improved detectors technology and novel sensors providing fine-scale hyperspectral imagery have been enabling new methods to monitor plant traits (PTs) and biodiversity. The aims of the work were to study different approaches to monitor key grassland PTs such as Leaf Area Index (LAI) and biodiversity-related traits. The thesis consists of 3 parts: 1) Evaluating the performance of remote sensing methods to estimate LAI in grassland ecosystems, 2) Estimating plant biodiversity by using the optical diversity approach in grassland ecosystems, and 3) Investigating the relationship between PTs variability with alpha and beta diversity for the applicability of the optical diversity approach in a subalpine grassland of the Italian Alps To evaluate the performance of remote sensing methods to estimate LAI, temporal and spatial observations of hyperspectral reflectance and LAI were analyzed at a grassland site in Monte Bondone, Italy (IT-MBo). In 2018, ground temporal observations of hyperspectral reflectance and LAI were carried out at a grassland site in Neustift, Austria (AT-NEU). To estimate biodiversity, in 2018 and 2019 a floristics survey was conducted to determine species composition and hyperspectral data were acquired at two grassland sites: IT-MBo and University of Padova’s Experimental Farm, Legnaro, Padua, Italy (IT-PD) respectively. Furthermore, in 2018, biochemistry analysis of the biomass samples collected from the grassland site IT-MBo was carried out to determine the foliar biochemical PTs variability. The results of the thesis demonstrated that the grassland spectral response across different spectral regions (Visible: VIS, red-edge: RE, Near-infrared: NIR) showed to be both site-specific and scale-dependent. In the first part of the thesis, the performance of spectral vegetation indices (SVIs) based on visible, red-edge (RE), and NIR bands alongside SVIs solely based or NIR-shoulder bands (wavelengths 750 - 900 nm) was evaluated. A strong correlation (R2 &gt; 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), it was demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. In the second part, the applicability of the spectral variability hypothesis (SVH) was questioned and highlighted the challenges to use high-resolution hyperspectral images to estimate biodiversity in complex grassland ecosystems. It was reported that the relationship between biodiversity (Shannon, Richness, Simpson, and Evenness) and optical diversity metrics (Coefficient of variation (CV) and Standard deviation (SD)) is not consistent across plant communities. The results of the second part suggested that biodiversity in terms of species richness could be estimated by optical diversity metrics with an R2 = 0.4 at the IT-PD site where the grassland plots were artificially established and are showing a lower structure and complexity from the natural grassland plant communities. On the other hand, in the natural ecosystems at IT-MBo, it was more difficult to estimate biodiversity indices, probably due to structural and biochemical PTs co-variation. The effects of canopy non-vegetative elements (flowers and dead material), shadow pixels, and overexposed pixels on the relationship between optical diversity metrics and biodiversity indices were highlighted. In the third part, we examined the relationship between PTs variability (at both local and community scales, measured by standard deviation and by the Euclidean distances of the biochemical and biophysical PTs respectively) and taxonomic diversity (both α-diversity and β-diversity, measured by Shannon’s index and by Jaccard dissimilarity index of the species, families, and functional groups percent cover respectively) in Monte Bondone, Trentino province, Italy. The results of the study showed that the PTs variability metrics at alpha scale were not correlated with α-diversity. However, the results at the community scale (β-diversity) showed that some of the investigated biochemical and biophysical PTs variations metrics were associated with β-diversity. The SVH approach was also tested to estimate β-diversity and we found that spectral diversity calculated by spectral angular mapper (SAM) showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity failed to estimate alpha diversity, this leading to the conclusion that the link between functional and species diversity may be an indicator of the applicability of optical sampling methods to estimate biodiversity. The findings of the thesis highlighted that grassland structural heterogeneity strongly affects the ability to retrieve both LAI and biodiversity, with high uncertainties due to structural and biochemical PTs co-variation at complex grassland ecosystems. In this context, the uncertainties of satellite-based products (e.g., LAI) in monitoring grassland canopies characterized by either spatially or temporally varying structure need to be carefully taken into account. The results of the study highlighted that the poor performance of optical diversity proxies in estimating biodiversity in structurally heterogeneous grasslands might be due to the complex relationships between functional diversity and biodiversity, rather than the impossibility to detect functional diversity with spectral proxies.

Extensive deforestations, particularly in the (sub)tropics, have led to intense soil degradation and erosion with concomitant reduction in soil fertility. Reforestations or plantations on those degraded sites may provide effective measures to mitigate further soil degradation and erosion, and can lead to improved soil quality. However, a change in land use from, e.g., grassland to forest may have a crucial impact on water balance. This may affect water availability even under humid tropical climate conditions where water is normally not a limiting factor. In this context, it should also be considered that according to climate change projections rainfall may decrease in some of these regions. To mitigate climate change related problems (e.g. increases in erosion and drought), reforestations are often carried out. Unfortunately, those measures are seldom completely successful, because the environmental conditions and the plant specific requirements are not appropriately taken into account. This is often due to data-scarcity and limited financial resources in tropical regions. For this reason, innovative approaches are required that are able to measure environmental conditions quasi-continuously in a cost-effective manner. Simultaneously, reforestation measures should be accompanied by monitoring in order to evaluate reforestation success and to mitigate, or at least to reduce, potential problems associated with reforestation (e.g. water scarcity). To avoid reforestation failure and negative implications on ecosystem services, it is crucial to get insights into the water balance of the actual ecosystem, and potential changes resulting from reforestation. The identification and prediction of water balance changes as a result of reforestation under climate change requires the consideration of the complex feedback system of processes in the soil-vegetation-atmosphere continuum. Models that account for those feedback system are Soil-Vegetation-Atmosphere-Transfer (SVAT) models. For the before-mentioned reasons, this study targeted two main objectives: (i) to develop and test a method combination for site evaluation under data scarcity (i.e. study requirements) (Part I) and (ii) to investigate the consequences of prediction uncertainty of the SVAT model input parameters, which were derived using geophysical methods, on SVAT modeling (Part II). A water balance modeling approach was set at the center of the site evaluation approach. This study used the one-dimensional CoupModel, which is a SVAT model. CoupModel requires detailed spatial soil information for (i) model parameterization, (ii) upscaling of model results and accounting for local to regional-scale soil heterogeneity, and (iii) monitoring of changes in soil properties and plant characteristics over time. Since traditional approaches to soil and vegetation sampling and monitoring are time consuming and expensive (and therefore often limited to point information), geophysical methods were used to overcome this spatial limitation. For this reason, vis-NIR spectroscopy (visible to near-infrared wavelength range) was applied for the measurement of soil properties (physical and chemical), and remote sensing to derive vegetation characteristics (i.e. leaf area index (LAI)). Since the estimated soil properties (mainly texture) could be used to parameterize a SVAT model, this study investigated the whole processing chain and related prediction uncertainty of soil texture and LAI, and their impact on CoupModel water balance prediction uncertainty. A greenhouse experiment with bamboo plants was carried out to determine plant-physiological characteristics needed for CoupModel parameterization. Geoelectrics was used to investigate soil layering, with the intent of determining site-representative soil profiles for model parameterization. Soil structure was investigated using image analysis techniques that allow the quantitative assessment and comparability of structural features. In order to meet the requirements of the selected study approach, the developed methodology was applied and tested for a site in NE-Brazil (which has low data availability) with a bamboo plantation as the test site and a secondary forest as the reference (reference site). Nevertheless, the objective of the thesis was not the concrete modeling of the case study site, but rather the evaluation of the suitability of the selected methods to evaluate sites for reforestations and to monitor their influence on the water balance as well as soil properties. The results (Part III) highlight that one needs to be aware of the measurement uncertainty related to SVAT model input parameters, so for instance the uncertainty of model input parameters such as soil texture and leaf area index influences meaningfully the simulated model water balance output. Furthermore, this work indicates that vis-NIR spectroscopy is a fast and cost-efficient method for soil measurement, mapping, and monitoring of soil physical (texture) and chemical (N, TOC, TIC, TC) properties, where the quality of soil prediction depends on the instrument (e.g. sensor resolution), the sample properties (i.e. chemistry), and the site characteristics (i.e. climate). Additionally, also the sensitivity of the CoupModel with respect to texture prediction uncertainty with respect to surface runoff, transpiration, evaporation, evapotranspiration, and soil water content depends on site conditions (i.e. climate and soil type). For this reason, it is recommended that SVAT model sensitivity analysis be carried out prior to field spectroscopic measurements to account for site specific climate and soil conditions. Nevertheless, mapping of the soil properties estimated via spectroscopy using kriging resulted in poor interpolation (i.e. weak variograms) results as a consequence of a summation of uncertainty arising from the method of field measurement to mapping (i.e. spectroscopic soil prediction, kriging error) and site-specific ‘small-scale’ heterogeneity. The selected soil evaluation method (vis-NIR spectroscopy, structure comparison using image analysis, traditional laboratory analysis) showed that there are significant differences between the bamboo soil and the adjacent secondary forest soil established on the same soil type (Vertisol). Reflecting on the major study results, it can be stated that the selected method combination is a way forward to a more detailed and efficient way to evaluate the suitability of a specific site for reforestation. The results of this study provide insights into where and when during soil and vegetation measurements a high measurement accuracy is required to minimize uncertainties in SVAT modeling.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Umfangreiche Abholzungen, besonders in den (Sub-)Tropen, habe zu intensiver Bodendegradierung und Erosion mit einhergehendem Verlust der Bodenfruchtbarkeit geführt. Eine wirksame Maßnahme zur Vermeidung fortschreitender Bodendegradierung und Erosion sind Aufforstungen auf diesen Flächen, die bisweilen zu einer verbesserten Bodenqualität führen können. Eine Umwandlung von Grünland zu Wald kann jedoch einen entscheidenden Einfluss auf den Wasserhaushalt haben. Selbst unter humid-tropischen Klimabedingungen, wo Wasser in der Regel kein begrenzender Faktor ist, können sich Aufforstungen negativ auf die Wasserverfügbarkeit auswirken. In diesem Zusammenhang muss auch berücksichtigt werden, dass Klimamodelle eine Abnahme der Niederschläge in einigen dieser Regionen prognostizieren. Um die Probleme, die mit dem Klimawandel in Verbindung stehen zu mildern (z.B. Zunahme von Erosion und Dürreperioden), wurden und werden bereits umfangreiche Aufforstungsmaßnahmen durchgeführt. Viele dieser Maßnahmen waren nicht immer umfassend erfolgreich, weil die Umgebungsbedingungen sowie die pflanzenspezifischen Anforderungen nicht angemessen berücksichtigt wurden. Dies liegt häufig an der schlechten Datengrundlage sowie an den in vielen Entwicklungs- und Schwellenländern begrenzter verfügbarer finanzieller Mittel. Aus diesem Grund werden innovative Ansätze benötigt, die in der Lage sind quasi-kontinuierlich und kostengünstig die Standortbedingungen zu erfassen und zu bewerten. Gleichzeitig sollte eine Überwachung der Wiederaufforstungsmaßnahme erfolgen, um deren Erfolg zu bewerten und potentielle negative Effekte (z.B. Wasserknappheit) zu erkennen und diesen entgegenzuwirken bzw. reduzieren zu können. Um zu vermeiden, dass Wiederaufforstungen fehlschlagen oder negative Auswirkungen auf die Ökosystemdienstleistungen haben, ist es entscheidend, Kenntnisse vom tatsächlichen Wasserhaushalt des Ökosystems zu erhalten und Änderungen des Wasserhaushalts durch Wiederaufforstungen vorhersagen zu können. Die Ermittlung und Vorhersage von Wasserhaushaltsänderungen infolge einer Aufforstung unter Berücksichtigung des Klimawandels erfordert die Berücksichtigung komplex-verzahnter Rückkopplungsprozesse im Boden-Vegetations-Atmosphären Kontinuum. Hydrologische Modelle, die explizit den Einfluss der Vegetation auf den Wasserhaushalt untersuchen sind Soil-Vegetation-Atmosphere-Transfer (SVAT) Modelle. Die vorliegende Studie verfolgte zwei Hauptziele: (i) die Entwicklung und Erprobung einer Methodenkombination zur Standortbewertung unter Datenknappheit (d.h. Grundanforderung des Ansatzes) (Teil I) und (ii) die Untersuchung des Einflusses der mit geophysikalischen Methoden vorhergesagten SVAT-Modeleingangsparameter (d.h. Vorhersageunsicherheiten) auf die Modellierung (Teil II). Eine Wasserhaushaltsmodellierung wurde in den Mittelpunkt der Methodenkombination gesetzt. In dieser Studie wurde das 1D SVAT Model CoupModel verwendet. CoupModel benötigen detaillierte räumliche Bodeninformationen (i) zur Modellparametrisierung, (ii) zum Hochskalierung von Modellergebnissen unter Berücksichtigung lokaler und regionaler Bodenheterogenität, und (iii) zur Beobachtung (Monitoring) der zeitlichen Veränderungen des Bodens und der Vegetation. Traditionelle Ansätze zur Messung von Boden- und Vegetationseigenschaften und deren Monitoring sind jedoch zeitaufwendig, teuer und beschränken sich daher oft auf Punktinformationen. Ein vielversprechender Ansatz zur Überwindung der räumlichen Einschränkung sind die Nutzung geophysikalischer Methoden. Aus diesem Grund wurden vis-NIR Spektroskopie (sichtbarer bis nah-infraroter Wellenlängenbereich) zur quasi-kontinuierlichen Messung von physikalischer und chemischer Bodeneigenschaften und Satelliten-basierte Fernerkundung zur Ableitung von Vegetationscharakteristika (d.h. Blattflächenindex (BFI)) eingesetzt. Da die mit geophysikalisch hergeleiteten Bodenparameter (hier Bodenart) und Pflanzenparameter zur Parametrisierung eines SVAT Models verwendet werden können, wurde die gesamte Prozessierungskette und die damit verbundenen Unsicherheiten und deren potentiellen Auswirkungen auf die Wasserhaushaltsmodellierung mit CoupModel untersucht. Ein Gewächshausexperiment mit Bambuspflanzen wurde durchgeführt, um die zur CoupModel Parametrisierung notwendigen pflanzenphysio- logischen Parameter zu bestimmen. Geoelektrik wurde eingesetzt, um die Bodenschichtung der Untersuchungsfläche zu untersuchen und ein repräsentatives Bodenprofil zur Modellierung zu definieren. Die Bodenstruktur wurde unter Verwendung einer Bildanalysetechnik ausgewertet, die die qualitativen Bewertung und Vergleichbarkeit struktureller Merkmale ermöglicht. Um den Anforderungen des gewählten Standortbewertungsansatzes gerecht zu werden, wurde die Methodik auf einem Standort mit einer Bambusplantage und einem Sekundärregenwald (als Referenzfläche) in NO-Brasilien (d.h. geringe Datenverfügbarkeit) entwickelt und getestet. Das Ziel dieser Arbeit war jedoch nicht die Modellierung dieses konkreten Standortes, sondern die Bewertung der Eignung des gewählten Methodenansatzes zur Standortbewertung für Aufforstungen und deren zeitliche Beobachtung, als auch die Bewertung des Einfluss von Aufforstungen auf den Wasserhaushalt und die Bodenqualität. Die Ergebnisse (Teil III) verdeutlichen, dass es notwendig ist, sich den potentiellen Einfluss der Messunsicherheiten der SVAT Modelleingangsparameter auf die Modellierung bewusst zu sein. Beispielsweise zeigte sich, dass die Vorhersageunsicherheiten der Bodentextur und des BFI einen bedeutenden Einfluss auf die Wasserhaushaltsmodellierung mit CoupModel hatte. Die Arbeit zeigt weiterhin, dass vis-NIR Spektroskopie zur schnellen und kostengünstigen Messung, Kartierung und Überwachung boden-physikalischer (Bodenart) und -chemischer (N, TOC, TIC, TC) Eigenschaften geeignet ist. Die Qualität der Bodenvorhersage hängt vom Instrument (z.B. Sensorauflösung), den Probeneigenschaften (z.B. chemische Zusammensetzung) und den Standortmerkmalen (z.B. Klima) ab. Die Sensitivitätsanalyse mit CoupModel zeigte, dass der Einfluss der spektralen Bodenartvorhersageunsicherheiten auf den mit CoupModel simulierten Oberflächenabfluss, Evaporation, Transpiration und Evapotranspiration ebenfalls von den Standortbedingungen (z.B. Klima, Bodentyp) abhängt. Aus diesem Grund wird empfohlen eine SVAT Model Sensitivitätsanalyse vor der spektroskopischen Feldmessung von Bodenparametern durchzuführen, um die Standort-spezifischen Boden- und Klimabedingungen angemessen zu berücksichtigen. Die Anfertigung einer Bodenkarte unter Verwendung von Kriging führte zu schlechten Interpolationsergebnissen in Folge der Aufsummierung von Mess- und Schätzunsicherheiten (d.h. bei spektroskopischer Feldmessung, Kriging-Fehler) und der kleinskaligen Bodenheterogenität. Anhand des gewählten Bodenbewertungsansatzes (vis-NIR Spektroskopie, Strukturvergleich mit Bildanalysetechnik, traditionelle Laboranalysen) konnte gezeigt werden, dass es bei gleichem Bodentyp (Vertisol) signifikante Unterschiede zwischen den Böden unter Bambus und Sekundärwald gibt. Anhand der wichtigsten Ergebnisse kann festgehalten werden, dass die gewählte Methodenkombination zur detailreicheren und effizienteren Standortuntersuchung und -bewertung für Aufforstungen beitragen kann. Die Ergebnisse dieser Studie geben einen Einblick darauf, wo und wann bei Boden- und Vegetationsmessungen eine besonders hohe Messgenauigkeit erforderlich ist, um Unsicherheiten bei der SVAT Modellierung zu minimieren.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Extensos desmatamentos que estão sendo feitos especialmente nos trópicos e sub-trópicos resultam em uma intensa degradação do solo e num aumento da erosão gerando assim uma redução na sua fertilidade. Reflorestamentos ou plantações nestas áreas degradadas podem ser medidas eficazes para atenuar esses problemas e levar a uma melhoria da qualidade do mesmo. No entanto, uma mudança no uso da terra, por exemplo de pastagem para floresta pode ter um impacto crucial no balanço hídrico e isso pode afetar a disponibilidade de água, mesmo sob condições de clima tropical úmido, onde a água normalmente não é um fator limitante. Devemos levar também em consideração que de acordo com projeções de mudanças climáticas, as precipitações em algumas dessas regiões também diminuirão agravando assim, ainda mais o quadro apresentado. Para mitigar esses problemas relacionados com as alterações climáticas, reflorestamentos são frequentemente realizados mas raramente são bem-sucedidos, pois condições ambientais como os requisitos específicos de cada espécie de planta, não são devidamente levados em consideração. Isso é muitas vezes devido, não só pela falta de dados, como também por recursos financeiros limitados, que são problemas comuns em regiões tropicais. Por esses motivos, são necessárias abordagens inovadoras que devam ser capazes de medir as condições ambientais quase continuamente e de maneira rentável. Simultaneamente com o reflorestamento, deve ser feita uma monitoração a fim de avaliar o sucesso da atividade e para prevenir, ou pelo menos, reduzir os problemas potenciais associados com o mesmo (por exemplo, a escassez de água). Para se evitar falhas e reduzir implicações negativas sobre os ecossistemas, é crucial obter percepções sobre o real balanço hídrico e as mudanças que seriam geradas por esse reflorestamento. Por este motivo, esta tese teve como objetivo desenvolver e testar uma combinação de métodos para avaliação de áreas adequadas para reflorestamento. Com esse intuito, foi colocada no centro da abordagem de avaliação a modelagem do balanço hídrico local, que permite a identificação e estimação de possíveis alterações causadas pelo reflorestamento sob mudança climática considerando o sistema complexo de realimentação e a interação de processos do continuum solo-vegetação-atmosfera. Esses modelos hidrológicos que investigam explicitamente a influência da vegetação no equilíbrio da água são conhecidos como modelos Solo-Vegetação-Atmosfera (SVAT). Esta pesquisa focou em dois objetivos principais: (i) desenvolvimento e teste de uma combinação de métodos para avaliação de áreas que sofrem com a escassez de dados (pré-requisito do estudo) (Parte I), e (ii) a investigação das consequências da incerteza nos parâmetros de entrada do modelo SVAT, provenientes de dados geofísicos, para modelagem hídrica (Parte II). A fim de satisfazer esses objetivos, o estudo foi feito no nordeste brasileiro,por representar uma área de grande escassez de dados, utilizando como base uma plantação de bambu e uma área de floresta secundária. Uma modelagem do balanço hídrico foi disposta no centro da metodologia para a avaliação de áreas. Este estudo utilizou o CoupModel que é um modelo SVAT unidimensional e que requer informações espaciais detalhadas do solo para (i) a parametrização do modelo, (ii) aumento da escala dos resultados da modelagem, considerando a heterogeneidade do solo de escala local para regional e (iii) o monitoramento de mudanças nas propriedades do solo e características da vegetação ao longo do tempo. Entretanto, as abordagens tradicionais para amostragem de solo e de vegetação e o monitoramento são demorados e caros e portanto muitas vezes limitadas a informações pontuais. Por esta razão, métodos geofísicos como a espectroscopia visível e infravermelho próximo (vis-NIR) e sensoriamento remoto foram utilizados respectivamente para a medição de propriedades físicas e químicas do solo e para derivar as características da vegetação baseado no índice da área foliar (IAF). Como as propriedades estimadas de solo (principalmente a textura) poderiam ser usadas para parametrizar um modelo SVAT, este estudo investigou toda a cadeia de processamento e as incertezas de previsão relacionadas à textura de solo e ao IAF. Além disso explorou o impacto destas incertezas criadas sobre a previsão do balanço hídrico simulado por CoupModel. O método geoelétrico foi aplicado para investigar a estratificação do solo visando a determinação de um perfil representante. Já a sua estrutura foi explorada usando uma técnica de análise de imagens que permitiu a avaliação quantitativa e a comparabilidade dos aspectos estruturais. Um experimento realizado em uma estufa com plantas de bambu (Bambusa vulgaris) foi criado a fim de determinar as caraterísticas fisiológicas desta espécie que posteriormente seriam utilizadas como parâmetros para o CoupModel. Os resultados do estudo (Parte III) destacam que é preciso estar consciente das incertezas relacionadas à medição de parâmetros de entrada do modelo SVAT. A incerteza presente em alguns parâmetros de entrada como por exemplo, textura de solo e o IAF influencia significantemente a modelagem do balanço hídrico. Mesmo assim, esta pesquisa indica que vis-NIR espectroscopia é um método rápido e economicamente viável para medir, mapear e monitorar as propriedades físicas (textura) e químicas (N, TOC, TIC, TC) do solo. A precisão da previsão dessas propriedades depende do tipo de instrumento (por exemplo da resolução do sensor), da propriedade da amostra (a composição química por exemplo) e das características das condições climáticas da área. Os resultados apontam também que a sensitividade do CoupModel à incerteza da previsão da textura de solo em respeito ao escoamento superficial, transpiração, evaporação, evapotranspiração e ao conteúdo de água no solo depende das condições gerais da área (por exemplo condições climáticas e tipo de solo). Por isso, é recomendado realizar uma análise de sensitividade do modelo SVAT prior a medição espectral do solo no campo, para poder considerar adequadamente as condições especificas do área em relação ao clima e ao solo. Além disso, o mapeamento de propriedades de solo previstas pela espectroscopia usando o kriging, resultou em interpolações de baixa qualidade (variogramas fracos) como consequência da acumulação de incertezas surgidas desde a medição no campo até o seu mapeamento (ou seja, previsão do solo via espectroscopia, erro do kriging) e heterogeneidade especifica de uma pequena escala. Osmétodos selecionados para avaliação das áreas (vis-NIR espectroscopia, comparação da estrutura de solo por meio de análise de imagens, análise de laboratório tradicionais) revelou a existência de diferenças significativas entre o solo sob bambu e o sob floresta secundária, apesar de ambas terem sido estabelecidas no mesmo tipo de solo (vertissolo). Refletindo sobre os principais resultados do estudo, pode-se afirmar que a combinação dos métodos escolhidos e aplicados representam uma forma mais detalhada e eficaz de avaliar se uma determinada área é adequada para ser reflorestada. Os resultados apresentados fornecem percepções sobre onde e quando, durante a medição do solo e da vegetação, é necessário se ter uma precisão mais alta a fim de minimizar incertezas potenciais na modelagem com o modelo SVAT.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185