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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:28829 |
| Date | 05 June 2015 |
| Creators | Mannschatz, Theresa |
| Contributors | Feger, Karl-Heinz, Dietrich, Peter, Kolditz, Olaf, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0097 seconds