空載雷射掃描為一可快速獲取地面物體三維空間資訊之技術，而新型發展之全波形（Full-Waveform）系統可完整記錄雷射回波訊號之波形，透過波形偵測與波形擬合等資料前處理，可得到代表地物獨特反射特性的波形參數資料，包括振幅值（Amplitude）、波形寬（Pulse-width）與後續計算之散射截面積係數（Backscatter cross-section coefficient）。 得到各點位之波形資料後，將以波形資料為主進行位於山區之實驗區地物分類，並將使用由實驗區航照影像提供之RGB波段光譜資料計算之綠度指數（Greenness）與計算影像灰階統計值之紋理參數如均質度（Homogeneity）、熵值（Entropy）與R波段平均值（Mean）等參數輔助分類。分類進行之前，透過抽樣實驗區候選地類包括樹林、草地、道路與樹種建物，並以貝氏定理（Bayes Theorem）分析計算不同地物類別在各分類參數區間內的貝氏機率，接著以多項式函數擬合各地類在不同參數之貝氏機率曲線，並以計算反曲點之方式自動化決定該分類參數之門檻值區間。 分類成果顯示，全波形系統提供之波形資料對於受上層植物遮蔽與陰影區之植物點與道路點之分類有顯著之成果，且透過物體對於波形資料之反射特性不同，具備應用於區別不同建築材質類別之潛力。 / Airborne Laser Scanning is a technique capable of acquiring 3D information of land objects. The latest full-waveform system is further improved with the ability of recording complete waveform of reflected laser signal. After the preprocessing procedures such as pulse detection and pulse fitting, the waveform information including amplitude, pulse width and backscatter cross-section were derived. Such information was valuable as they represented unique properties of land objects. In this study, waveform information of all scanned points were utilized to classify land cover in the test area located in mountain area. Additionally, the Greenness value as well as the texture parameters such as Homogeneity, Entropy and Mean of R band calculated from the ortho-image were used for classification. We aimed to classify the point cloud into vegetation, road and building categories. The Bayes Theorem was used to determine the threshold range of each parameters for classification. As a result, the waveform information were useful for classifying road points covered by upper vegetation points and also vegetation and road points located in shadow area. Moreover, through the analysis of reflective properties of different object using waveform parameters, it was of potential to be applied to distinguish material of buildings.

Knowledge of a species habitat-use patterns, as well as an understanding of the distribution and spatial arrangement of preferred habitat, is essential for developing comprehensive management or conservation plans. This information is absent for many species, especially so for those living or breeding in remote areas. Habitat-use models can assist in delineating specific habitat requirements or preferences of a species. When coupled with geographic information system (GIS) technology, such models are now frequently used to identify important habitats and to better define species distributions.<p> Recent and persistent warming, widespread contaminant accumulation, and intensifying land use in the arctic heighten the urgent need for better information about spatial distributions and key habitats for northern wildlife. Here, I used aerial survey and corresponding digital land cover data to investigate breeding-ground distributions and landscape-level habitat associations of greater white-fronted geese (Anser albifrons frontalis), small Canada geese (Branta canadensis hutchinsii), tundra swans (Cygnus columbianus), king eiders (Somateria spectabilis), and long-tailed ducks (Clangula hyemalis) in the Queen Maud Gulf Migratory Bird Sanctuary and the Rasmussen Lowlands, Nunavut, Canada.<p> First, I addressed the sensitivity of inferences about predicting waterfowl presence on the basis of the amounts and configurations of arctic habitat sampled at four scales. Detection and direction of relationships of focal species with land cover covariates often varied when land cover data were analysed at different scales. For instance, patterns of habitat use for a given species at one spatial scale may not necessarily be predicted from patterns arising from measurements taken at other scales. Thus, inference based on species-habitat patterns from some scales may lead to inaccurate depictions of how habitat influences species. Potential variation in species-environment relationships relative to spatial scale needs to be acknowledged by wildlife managers to avoid inappropriate management decisions.<p> Second, I used bird presence determined during aerial surveys and classified satellite imagery to develop species-habitat models for describing breeding-ground distributions and habitat associations of each focal species. Logistic regression models identified lowland land cover types to be particularly important for the species considered. I used the Receiver Operating Characteristic (ROC) technique and the area under the curve (AUC) metric to evaluate the precision of models, where the AUC is equal to the probability that two randomly selected encounter and non-encounter survey segments will be discriminated as such by the model. In the Queen Maud Gulf, AUC values indicated reasonable model discrimination for white-fronted geese, Canada geese, and tundra swans (i.e, AUC > 0.7). Precision of species-habitat models for king eiders and long-tailed ducks was lower than other species considered, but predict encounters and non-encounters significantly better than the null model. For all species, precision of species-habitat models was lower in the Rasmussen Lowlands than in the Queen Maud Gulf, although discrimination ability remained significantly better than the null model for three of five species (king eider and long-tailed duck models performed no better than the null model here).<p> Finally, I simulated anticipated environmental change (i.e., climate warming) in the arctic by applying species-habitat models to manipulated land cover data, and then predicted distributional responses of focal species. All species considered in this research exhibited some association to lowland cover types; white-fronted geese, Canada geese, and tundra swans in particular demonstrated strong affinity toward these habitats. Others authors predict lowland cover types to be most affected by warming. Reductions of wet sedge, hummock, and tussock graminoid cover predicted in this simulation, predominantly along the coast of the Queen Maud Gulf study area and in central areas of the Rasmussen Lowlands, suggest that distributions of species dependant on these lowland habitats will be significantly reduced, if predictions about warming and habitat loss prove to be correct. Research presented here provides evidence that modeling of species distributions using landscape-level habitat data is a tractable method to identify habitat associations, to determine key habitats and regions, and to forecast species responses to environmental changes.

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map. / Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte.

Since the early 1800's, many studies have recognized increased heat in urban areas, known as the urban heat island (UHI) effect, as one of the results of human modification to the natural landscape. UHI is related to differences in land surface temperature (LST) between rural areas and urban areas where factors of the built environment such as the thermodynamic capacities of materials, structural geometry, and heat generating activities cause increased storage and re-radiation of heat to the atmosphere. This thesis examines the correlation between factors of urbanization and differences in land surface temperature (LST) in the subtropical climate of Pinellas County, Florida using remote sensing techniques. It describes the spatial pattern of LST, analyzes its relationship to factors of urbanization relative to NDVI, percentage of impervious surface, and land use land cover in the study area. It also assesses the effectiveness of remote sensing as an efficient method of identifying LST patterns at the local and neighborhood level for mitigation strategies. Landsat TM thermal band imagery for three dates; April 1986, 2001 and 2009 was processed using Qin's mono-window algorithm (MWA) technique to derive LST levels. This data was compared to in-situ readings, then normalized and statistically analyzed for correlation with vegetation ratio (NDVI) and imperviousness percentages derived using linear spectral mixing/unmixing, and also with land use/land cover classification. The resulting LST spatial pattern is a gradient across the peninsular landscape, from cooler water and wetland areas to a generally warmer interior, interspersed with micro-urban heat islands (MUHIs), corresponding to urban structures and "cool-islands" of parkland and lakes. Correspondence between LST pattern and urban structures and land use demonstrates the suitability of medium resolution remote sensing data and techniques for identifying micro-urban heat islands (MUHIs) for possible mitigation. Mitigation could include relatively low-cost measures like replacement of inefficient asphalt roofs with more reflective and emissive "cool roofs," placement of "street trees" to enhance shade, and replacement of impervious pavements by permeable surfaces. The thesis concludes that Landsat TM imagery processed with the MWA provides an efficient, relatively low-cost method for locating MUHIs. Satellite remote sensing, combined with aerial photography can facilitate neighborhood level analysis for the implementation of low-cost mitigation techniques. Previous studies have demonstrated that these are successful ways to mitigate the UHI effect at the micro-scale level; lowering urban heat and saving energy, and also facilitating the reintegration of natural elements into the urban environment.

Abstract Sanitation coverage in the Republic of Panama is 5 to 10 percent below the Millennium Development Goals targets set for the country. Population growth, urbanization, unplanned development and waste mismanagement have resulted in improvised trash sites and waste discharges into river systems that are important components of the biologically diverse natural environment of Panama. The study sought to investigate and estimate the burden of waste from domestic and agricultural sources in three regions of the Chilibre corrigimiento (district). It was hypothesized that the water quality and land cover data would reflect that the most populated region in the study sample (Region 2) would have more water quality violations than the adjacent background and attenuation regions (Region 1 and Region 3) in the study sample. The results supported that Region 2 had the most water quality violations -- particularly at the CHIL 3 monitoring station. Based on the results the most appropriate best management practices (BMPs) were recommended for the household, community, watershed, and regional level waste management in the study region. Future research will look determine the effectiveness of microfinance programs in bolstering sanitation-based entrepreneurship in Chilibre and across Panama.

Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information. An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes. Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA. Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high. Aggregated to the community-level, social survey of household data provides a comprehensive perspective additionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception. At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attribute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used affect the quality of the developed model this was also discussed based on the different sensors examined. To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces. / Da das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten. Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverarbeitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen. Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der Pixel-basierten Konzept verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert. Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post-Klassifizierungs- Änderungserkennung besser geeignet ist als radiometrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen. Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semi-automatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die besten Modelle waren mit einem unabhängigen Satz von kreuz-validierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen. Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln.

The purpose of the study was to examine the temporal trends of three monthly variables: stream runoff, rainfall and air temperature and to find out if any correlation exists between rainfall and stream runoff in the Blue Ridge and Piedmont provinces of the southeast United States. Trend significance was determined using the non-parametric Mann-Kendall test on a monthly and annual basis. GIS analysis was used to find and integrate the urban and non-urban stream gauging, rainfall and temperature stations in the study area. The Mann-Kendall test showed a statistically insignificant temporal trend for all three variables. The correlation of 0.4 was observed for runoff and rainfall, which showed that these two parameters are moderately correlated.

The advance in remote sensing technology helps people more easily assess urban growth. In this study, the utility of multiple endmember spectral mixture analysis (MESMA) is examined in a sub-pixel analysis of Landsat Thematic Mapper (TM) imagery to map urban physical components in Tampa, FL. The three physical components of urban land cover (LC): impervious surface, vegetation and soil, were compared using the proposed MESMA with a traditional spectral mixture analysis (SMA). MESMA decomposes each pixel to address the heterogeneity of urban LC characteristic by allowing the number and types of endmembers to vary on a per pixel basis. This study generated 642 spectral mixture models of 2-, 3-, and 4-endmembers for each pixel to estimate the fractions of impervious surface, vegetation, soil, and shade in the study area with a constraint of lowest root mean square error (RMSE). A comparative analysis of the impervious surface areas (ISA) mapped with MESMA and SMA demonstrated that MESMA produced more accurate results of mapping urban physical components than those by SMA. With the multiyear Landsat TM data, we quantified sub-pixel %ISA and the %ISA changes to assess urban growth in the City of Tampa, Florida during the past twenty years. The experimental results demonstrate that the MESMA approach is effective in mapping and monitoring urban land use/land cover changes using moderate-resolution multispectral imagery at a sub-pixel level.

Agricultural production is becoming increasingly important as the world demand increases. On the other hand, there are several factors threatening that production such as the climate change. Therefore, monitoring and management of different parameters affecting the production are important. The current study is dedicated to two key parameters, namely agricultural land cover and soil-moisture mapping using X- and L-Band Synthetic Aperture Radar (SAR) data. Land-cover mapping plays an essential role in various applications like irrigation management, yield estimation and subsidy control. A model of multi-direction/multi-distance texture analysis on SAR data and its use for agricultural land cover classification was developed. The model is built and implemented in ESRI ArcGIS software and integrated with “R Environment”. Sets of texture measures can be calculated on a plot basis and stored in an attribute table for further classification. The classification module provides various classification approaches such as support vector machine and artificial neural network, in addition to different feature-selection methods. The model has been tested for a typical Mid-European agricultural and horticultural land use pattern south to the town of Pirna (Saxony/Germany), where the high-resolution SAR data, TerraSAR-X and ALOS/PALSAR (HH/HV) imagery, were used for land-cover mapping. The results indicate that an integrated classification using textural information of SAR data has a high potential for land-cover mapping. Moreover, the multi-dimensional SAR data approach improved the overall accuracy. Soil moisture (SM) is important for various applications such as crop-water management and hydrological modelling. The above-mentioned TerraSAR-X data were utilised for soil-moisture mapping verified by synchronous field measurements. Different speckle-reduction techniques were applied and the most representative filtered image was determined. Then the soil moisture was calculated for the mapped area using the obtained linear regression equations for each corresponding land-cover type. The results proved the efficiency of SAR data in soil-moisture mapping for bare soils and at the early growing stage of fieldcrops. / Landwirtschaftliche Produktion erlangt mit weltweit steigender Nahrungsmittelnachfrage zunehmende Bedeutung. Zahlreiche Faktoren bedrohen die landwirtschaftliche Produktion wie beispielsweise die globale Klimaveränderung einschließlich ihrer indirekten Nebenwirkungen. Somit ist das Monitoring der Produktion selbst und der wesentlichen Produktionsparameter eine zweifelsfrei wichtige Aufgabe. Die vorliegende Studie widmet sich in diesem Kontext zwei Schlüsselinformationen, der Aufnahme landwirtschaftlicher Kulturen und den Bodenfeuchteverhältnissen, jeweils unter Nutzung von Satellitenbilddaten von Radarsensoren mit Synthetischer Apertur, die im X- und L-Band operieren. Landnutzungskartierung spielt eine essentielle Rolle für zahlreiche agrarische Anwendungen; genannt seien hier nur Bewässerungsmaßnahmen, Ernteschätzung und Fördermittelkontrolle. In der vorliegenden Arbeit wurde ein Modell entwickelt, welches auf Grundlage einer Texturanalyse der genannten SAR-Daten für variable Richtungen und Distanzen eine Klassifikation landwirtschaftlicher Nutzungsformen ermöglicht. Das Modell wurde als zusätzliche Funktionalität für die ArcGIS-Software implementiert. Es bindet dabei Klassifikationsverfahren ein, die aus dem Funktionsschatz der Sprache „R“ entnommen sind. Zum Konzept: Ein Bündel von Texturparametern wird durch das vorliegende Programm auf Schlagbasis berechnet und in einer Polygonattributtabelle der landwirtschaftlichen Schläge abgelegt. Auf diese Attributtabelle greift das nachfolgend einzusetzende Klassifikationsmodul zu. Die Software erlaubt nun die Suche nach „aussagekräftigen“ Teilmengen innerhalb des umfangreichen Texturmerkmalsraumes. Im Klassifikationsprozess kann aus verschiedenen Ansätzen gewählt werden. Genannt seien „Support Vector Machine“ und künstliche neuronale Netze. Das Modell wurde für einen typischen mitteleuropäischen Untersuchungsraum mit landwirtschaftlicher und gartenbaulicher Nutzung getestet. Er liegt südlich von Pirna im Freistaat Sachsen. Zum Test lagen für den Untersuchungsraum Daten von TerraSAR-X und ALOS/PALSAR (HH/HV) aus identischen Aufnahmetagen vor. Die Untersuchungen beweisen ein hohes Potenzial der Texturinformation aus hoch aufgelösten SAR-Daten für die landwirtschaftliche Nutzungserkennung. Auch die erhöhte Dimensionalität durch die Kombination von zwei Sensoren erbrachte eine Verbesserung der Klassifikationsgüte. Kenntnisse der Bodenfeuchteverteilung sind u.a. bedeutsam für Bewässerungsanwendungen und hydrologische Modellierung. Die oben genannten SAR-Datensätze wurden auch zur Bodenfeuchteermittlung genutzt. Eine Verifikation wurde durch synchrone Feldmessungen ermöglicht. Initial musste der Radar-typische „Speckle“ in den Bildern durch Filterung verringert werden. Verschiedene Filtertechniken wurden getestet und das beste Resultat genutzt. Die Bodenfeuchtebestimmung erfolgte in Abhängigkeit vom Nutzungstyp über Regressionsanalyse. Auch die Resultate für die Bodenfeuchtebestimmung bewiesen das Nutzpotenzial der genutzten SAR-Daten für offene Ackerböden und Stadien, in denen die Kulturpflanzen noch einen geringen Bedeckungsgrad aufweisen.

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