High-resolution SAR images for fire susceptibility estimation in urban forestry

We present an adaptive system for the automatic assessment of both physical and anthropic fire impact factors on periurban forestries. The aim is to provide an integrated methodology exploiting a complex data structure built upon a multi resolution grid gathering historical land exploitation and meteorological data, records of human habits together with suitably segmented and interpreted high resolution X-SAR images, and several other information sources. The contribution of the model and its novelty rely mainly on the definition of a learning schema lifting different factors and aspects of fire causes, including physical, social and behavioural ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications

SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories.

Movement data sets collected using today's advanced tracking devices consist of complex trajectories in terms of length, shape, and number of recorded positions. Multiple additional attributes characterizing the movement and its environment are often also included making the level of complexity even higher. Simplification of trajectories can improve the visibility of relevant information by reducing less relevant details while maintaining important movement patterns. We propose a systematic stepwise methodology for simplifying and thematically enhancing trajectories in order to support their visual analysis. The methodology is applied iteratively and is composed of: (a) a simplification step applied to reduce the morphological complexity of the trajectories, (b) a thematic enhancement step which aims at accentuating patterns of movement, and (c) the representation and interactive exploration of the results in order to make interpretations of the findings and further refinement to the simplification and enhancement process. We illustrate our methodology through an analysis example of two different types of tracks, aircraft and pedestrian movement

The application of remote sensing to identify and measure sealed soil and vegetated surfaces in urban environments

Soil is an important non-renewable source. Its protection and allocation is critical to sustainable development goals. Urban development presents an important drive of soil loss due to sealing over by buildings, pavements and transport infrastructure. Monitoring sealed soil surfaces in urban environments is gaining increasing interest not only for scientific research studies but also for local planning and national authorities. The aim of this research was to investigate the extent to which automated classification methods can detect soil sealing in UK urban environments, by remote sensing. The objectives include development of object-based classification methods, using two types of earth observation data, and evaluation by comparison with manual aerial photo interpretation techniques. Four sample areas within the city of Cambridge were used for the development of an object-based classification model. The acquired data was a true-colour aerial photography (0.125 m resolution) and a QuickBird satellite imagery (2.8 multi-spectral resolution). The classification scheme included the following land cover classes: sealed surfaces, vegetated surfaces, trees, bare soil and rail tracks. Shadowed areas were also identified as an initial class and attempts were made to reclassify them into the actual land cover type. The accuracy of the thematic maps was determined by comparison with polygons derived from manual air-photo interpretation; the average overall accuracy was 84%. The creation of simple binary maps of sealed vs. vegetated surfaces resulted in a statistically significant accuracy increase to 92%. The integration of ancillary data (OS MasterMap) into the object-based model did not improve the performance of the model (overall accuracy of 91%). The use of satellite data in the object-based model gave an overall accuracy of 80%, a 7% decrease compared to the aerial photography. Future investigation will explore whether the integration of elevation data will aid to discriminate features such as trees from other vegetation types. The use of colour infrared aerial photography should also be tested. Finally, the application of the object- based classification model into a different study area would test its transferability

Trends and concerns in digital cartography

CISRG discussion paper ;

Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach

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

Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach

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