Caracterização de uso e cobertura da terra na Amazônia utilizando imagens duais multitemporais do COSMO-SkyMed

The use of radar imagery is an alternative source of information to support the monitoring of the Amazon region, since the optical images have imaging limitations in tropical areas due to the occurrence of clouds. Therefore, the goal of this study is to analyze the radar images in X-band multi-temporal polarized obtained by COSMO-SkyMed satellite (COnstellation of small Satellites for Mediterranean basin Observation), in the intensity mode, isolated and/or combined with textural information, to thematic characterization of land use/land cover in the Humaitá, Amazonas State region. The methodology used includes: analysis of the dual images obtained during two subsequent acquisitions, in order to explore the potential of the dataset as a quad-pol intensity; extraction of textural attributes from the co-occurrence matrix (Gray Level Co-occurrence Matrix) and subsequent contextual classification; statistical assessment of the thematic performance of the intensity and textural images, isolated and in polarized groups. Within the results achieved, the group formed only by the intensity images presented a better performance if compared to those containing the textural attributes. In this discrimination, the classes involved were forest, alluvial forest, reforestation, savannah, pasture and burned areas, yielding 66% overall accuracy and a Kappa value of 0.55. The results showed that X band images, from COSMO-SkyMed, StripMap mode (Ping-Pong), multi-polarized, presents a moderate potential to characterize and monitor the dynamics of land use/land land cover in the Brazilian Amazon.A utilização de imagens de radar é fonte alternativa de informações para subsidiar o monitoramento da região amazônica, visto que as imagens ópticas têm limitações de imageamento em zonas tropicais face a ocorrência de nuvens. Por conseguinte este trabalho teve como objetivo analisar a capacidade das imagens-radar de banda X multitemporais e polarizadas obtidas pelo satélite COSMO-SkyMed (COnstellation of small Satellites for Mediterranean basin Observation), no modo intensidade, isoladamente e agregados às informações texturais, na caracterização temática de uso e cobertura da terra no município de Humaitá/AM. A metodologia empregada consistiu daanálise das imagens duais obtidas em duas aquisições subsequentes, de forma a explorar a potencialidade do conjunto de dados na forma quad-pol intensidade; extração dos atributos texturais a partir da matriz de coocorrência (Gray Level Co-occurrence Matrix) e posterior classificação contextual; avaliação estatística de desempenho temático das imagens intensidade e texturais, isoladas e em grupos polarizados. Dentre os vários resultados alcançados, foi verificado que o grupo formado somente pelas imagens intensidade apresentou o melhor desempenho, comparado àqueles contendo os atributos texturais. Nesta separabilidade, estavam envolvidas as classes de floresta, floresta aluvial, reflorestamento, savana, pasto e queimada, obtendo-se 66% de acurácia total e valor Kappa de 0,55. Os resultados mostraram que as imagens de banda X do COSMO-SkyMed, modo StripMap (Ping-Pong), multipolarizadas, têm potencial moderado para a caracterização e monitoramento da dinâmica de uso e cobertura da terra na Amazônia brasileira

Land use/land cover characterization in Amazonia using COSMO-SkyMed multitemporal images

The use of radar imagery is an alternative source of information to support the monitoring of the Amazon region, since the optical images have imaging limitations in tropical areas due to the occurrence of clouds. Therefore, the goal of this study is to analyze the radar images in X-band multi-temporal polarized obtained by COSMO-SkyMed satellite (COnstellation of small Satellites for Mediterranean basin Observation), in the intensity mode, isolated and/or combined with textural information, to thematic characterization of land use/land cover in the Humaitá, Amazonas State region. The methodology used includes: analysis of the dual images obtained during two subsequent acquisitions, in order to explore the potential of the dataset as a quad-pol intensity; extraction of textural attributes from the co-occurrence matrix (Gray Level Co-occurrence Matrix) and subsequent contextual classification; statistical assessment of the thematic performance of the intensity and textural images, isolated and in polarized groups. Within the results achieved, the group formed only by the intensity images presented a better performance if compared to those containing the textural attributes. In this discrimination, the classes involved were forest, alluvial forest, reforestation, savannah, pasture and burned areas, yielding 66% overall accuracy and a Kappa value of 0.55. The results showed that X band images, from COSMO-SkyMed, StripMap mode (Ping-Pong), multi-polarized, presents a moderate potential to characterize and monitor the dynamics of land use/land land cover in the Brazilian Amazon.A utilização de imagens de radar é fonte alternativa de informações para subsidiar o monitoramento da região amazônica, visto que as imagens ópticas têm limitações de imageamento em zonas tropicais face a ocorrência de nuvens. Por conseguinte este trabalho teve como objetivo analisar a capacidade das imagens-radar de banda X multitemporais e polarizadas obtidas pelo satélite COSMO-SkyMed (COnstellation of small Satellites for Mediterranean basin Observation), no modo intensidade, isoladamente e agregados às informações texturais, na caracterização temática de uso e cobertura da terra no município de Humaitá/AM. A metodologia empregada consistiu daanálise das imagens duais obtidas em duas aquisições subsequentes, de forma a explorar a potencialidade do conjunto de dados na forma quad-pol intensidade; extração dos atributos texturais a partir da matriz de coocorrência (Gray Level Co-occurrence Matrix) e posterior classificação contextual; avaliação estatística de desempenho temático das imagens intensidade e texturais, isoladas e em grupos polarizados. Dentre os vários resultados alcançados, foi verificado que o grupo formado somente pelas imagens intensidade apresentou o melhor desempenho, comparado àqueles contendo os atributos texturais. Nesta separabilidade, estavam envolvidas as classes de floresta, floresta aluvial, reflorestamento, savana, pasto e queimada, obtendo-se 66% de acurácia total e valor Kappa de 0,55. Os resultados mostraram que as imagens de banda X do COSMO-SkyMed, modo StripMap (Ping-Pong), multipolarizadas, têm potencial moderado para a caracterização e monitoramento da dinâmica de uso e cobertura da terra na Amazônia brasileira

Mapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon: Performance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season.

Abstract: The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to dis- criminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwest- ern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algo- rithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an over- all accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on du- al-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests

Optical and radar remotely sensed data for large-area wildlife habitat mapping

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models

Monitoring der Vegetationsdynamik in Ostafrika mit multisensoralen Satellitendaten

Diese Arbeit hat zum Ziel, die Vegetationsbedeckung sowie die Vegetationsdynamik in ihrer saisonalen und interannuellen Ausprägung mit Hilfe multisensoraler Satellitendaten zu erfassen. Auf unterschiedlichen zeitlichen und räumlichen Skalen werden vor allem degradierte Flächen, im Sinne einer verringerten Vegetationsbedeckung, analysiert. Das Untersuchungsgebiet liegt westlich des Mount Kenya in Zentralkenia, in einem semihumiden bis semiariden Gebiet, geprägt von einer hohen raum-zeitlichen Niederschlagsvariabilität, hohem Bevölkerungsdruck und unterschiedlichen Landnutzungssystemen. Die Klassifikation der Vegetationsbedeckung erfolgte mit Hilfe unterschiedlicher Methoden, um das Potential der Kombination der neuen ENVISAT MERIS- und ASAR-Daten zu prüfen. Für die untersuchten 10 Landbedeckungsklassen wurde mit der Maximum Likelihood Klassifikation des Layerstack von MERIS- und ASAR-Daten die höchste Gesamtgenauigkeit mit 64 % erreicht, gefolgt von 62 % bei der Klassifikation mit Neuronalen Netzen. Der Vorteil gegenüber der Klassifikation von MERIS-Daten allein liegt hauptsächlich in der erhöhten räumlichen Auflösung. Die Klassifikation von ASAR-Daten allein oder unter Verwendung zusätzlicher Texturmaße ergab nur geringe Gesamtgenauigkeiten. Die Analyse der saisonalen Dynamik erfolgte zum einen über den annuellen Variationskoeffizienten (Vk) der neuen MERIS Vegetationsindizes, "`MERIS Global Vegetation Index"' (MGVI), "`MERIS Terrestrial Chlorophyll Index"' (MTCI), "`Red Edge Position"' (REP) und der Radarrückstreuung von ASAR (HH-, HV- und VV-Polarisation), zum anderen über phänologische Maße, die mit SPOT VEGETATION NDVI berechnet wurden. Die klassenweise Analyse des Vk über den Verlauf eines Jahres zeigt, dass degradierte Flächen mehrheitlich einen höheren Vk bei einem niedrigeren Mittelwert aufweisen. Die räumlichen Muster von Vegetationsbeginn und -länge geben vor allem die Niederschlagsmuster wieder. Bei geringen Niederschlagsmengen scheinen sich die degradierten Flächen jedoch in ihrer Phänologie zu unterscheiden. Entsprechend ist die Korrelation zwischen geringen Niederschlägen und dem NDVI standortspezifisch. Für die Untersuchung der interannuellen Vegetationsänderungen wurde mit einer hohen räumlichen jedoch geringen zeitlichen Auflösung die NDVI-Differenz von LANDSAT TM, ETM+ und ASTER für den Zeitraum 1987 bis 2005 berechnet. Für die Veränderungsdetektion mit einer hohen zeitlichen jedoch geringen räumlichen Auflösung wurde mittels Change-Vektor-Analyse (CVA) eine SPOT VEGETATION NDVI-Zeitreihe von 1999 bis 2004 analysiert. Während die multitemporale CVA in diesem semiariden Ökosystem vor allem niederschlagsbedingte Änderungen zeigte, konnten mit den LANDSAT- und ASTER-Daten Gebiete höchster und konstant negativer Vegetationsänderung ausgewiesen werden. Sie liegen vor allem im Bereich der kleinbäuerlichen Farmen und deuten auf die Übernutzung der Savannenvegetation hin. Schließlich wurden die verschiedenen Ergebnisse in einem Geographischen Informationssystem zueinander in Beziehung gesetzt, um bereits degradierte und degradationsgefährdete Gebiete, sogenannte "`Hot Spots"' der Vegetationsentwicklung, auszuweisen. Insbesondere für diese Regionen ist ein angepaßtes Ressourcenmanagement dringend notwendig, um eine weitere Degradation zu verhindern und eine nachhaltige Nutzung zu ermöglichen. Die Ergebnisse dieser Studie bezüglich Vegetationsdynamik und Landdegradation können als Grundlage für ein weiteres Monitoring in diesem fragilen Ökosystem dienen, sowie als Basis für ein Entscheidungsunterstützungssystem für Landmanagement.Monitoring of Vegetation Dynamics in East Africa Using Multisensoral Satellite Data In this thesis, vegetation cover as well as vegetation dynamics in its seasonal and interannual variation are analysed using multi-sensoral satellite data. Especially degraded areas in terms of a reduced vegetation cover are investigated on different spatial and temporal scales. The study area is located in central Kenya, west of Mount Kenya, in a semi-humid to semi-arid environment characterised by high rainfall variability, a high population density and different land use systems. The vegetation cover was classified using different methods in order to test the potential of combining ENVISAT MERIS and ASAR data. The highest classification accuracy (65 %) was achieved using the Maximum Likelihood classification of a layer stack of MERIS and ASAR data for 10 land cover classes. Using feed-forward neural networks resulted in a similarly good classification accuracy (62 %). The main advantage in using a combination of MERIS and ASAR data lies in the higher spatial resolution of the resulting classification in comparison with a classification based on the MERIS data alone. The classification of ASAR data alone or in combination with texture measures resulted in rather low classification accuracies. The seasonal dynamics were analysed first by using the coefficient of variation (CV) of several new MERIS vegetation indices, "`MERIS Global Vegetation Index"' (MGVI), "`MERIS Terrestrial Chlorophyll Index"' (MTCI), "`Red Edge Position"' (REP), and the HH-, HV- and VV-polarized radar data from ASAR sensor. Second, phenological metrics were calculated based on SPOT VEGETATION NDVI. The class specific investigation of CV over one year showed that degraded areas are mainly characterised by higher CV and lower mean values. The spatial pattern of start and length of the vegetation periods reflected primarily the main rainfall patterns. However, after sparse precipitation, differences in phenology can be attributed to different land cover types. Accordingly the correlation of time series with low rainfall amounts and NDVI shows site specific differences. To monitor interannual vegetation cover changes at high spatial but low temporal resolution, NDVI differences were calculated for LANDSAT TM, ETM+ and ASTER images between 1987 and 2005. To calculate vegetation changes at low spatial but high temporal resolution Change-Vector-Analysis (CVA) of SPOT VEGETATION NDVI-time series from 1999 to 2004 was applied. While the multitemporal CVA captures mainly changes due to the high rainfall variability in this semi-arid environment, by using LANDSAT- and ASTER-data enabled the identification of areas characterised by a pronounced and constant negative vegetation change. These sites are found mostly within the area of small scale farms and indicate an overuse of savanna vegetation. Finally, the different results were displayed using a Geographic Information System (GIS) to delineate areas either being at risk of degradation or already degraded areas. Areas with uncertain vegetation periods, high rainfall variability, vegetation decrease and a high population pressure may lead to "`hot spots"' of vegetation change. Particularly for these regions an adapted resource management is essential to avoid further land degradation and to ensure more sustainable land use practices. The results on vegetation dynamics and land degradation may provide not only a comprehensive basis for the further monitoring of this fragile ecosystem but could also serve as decision support on land management

Remote sensing based assessment of land cover and soil moisture in the Kilombero floodplain in Tanzania

Wetlands provide important ecological, biological, and social-economic services that are critical for human existence. The increasing demand for food, arable land shortage and changing climate conditions in East Africa have created a paradigm shift from upland cultivation to wetland use due to their year-round soil water availability. However, there is need to control and manage the activities within the wetlands to ensure sustainable use while negating any negative effects caused by these activities. This is implemented through the decisions made by the land managers within the wetlands. Providing the users of the wetlands with scientific knowledge acts as a support tool for policy-making geared towards the sustainable use of the wetlands. The overall research contains two main components: First, the need for timely land cover maps at a reasonable scale, and secondly, the assessment of soil moisture as a major contributor to agricultural production. The objectives of the study were to generate land cover maps from multi-sensor optical datasets and to assess the performance of single-polarized Sentinel-1 Gray Level Co-occurrence Matrix (GLCM) texture and Principal Component Analysis (PCA) features by applying multiple classification algorithms in a floodplain in the Kilombero catchment. Furthermore, soil moisture spatial-temporal patterns over three hydrological zones was assessed, estimation of soil moisture from radar data and generation of soil moisture products from global products was investigated. The correlation of the merged products to Normalized Difference Vegetation Index (NDVI) measures was also investigated. RapidEye, Sentinel-2 and Landsat images were used in determining the areal extents of four major land cover classes namely vegetated, bare, water and built up. The acquisition period of the images ranges from August 2013 to June 2015 for the RapidEye images, December 2015 to August 2016 for the Sentinel-2 images and 2013 to 2016 Landsat-8 images were included in the land cover time series dynamic study. However, the major challenge arising was cloud coverage and hence Sentinel-1 images were tested in the application of Synthetic Aperture Radar (SAR) in wetland mapping. Variograms were used in spatial-temporal assessment of soil moisture data collected from three hydrological zones, riparian, middle and fringe. A roughness parameter was derived from a semi-empirical model. Soil moisture was retrieved from TerraSAR-X and RadarSAT-2 with the retrieved roughness parameter as an input in a linear regression equation. Triple collocation was applied in error assessment of the global soil moisture products prior to development of a merged product. Cross-correlation was applied in relating NDVI to soil moisture. Optical data (RapidEye, Landsat-8, and Sentinel-2) generated land cover maps used in assessing the land cover dynamics over time. The land cover ratios were related to depth to groundwater. As the depth to groundwater reduced in June the bare land coverage was 45-57% while that of vegetation was 34-47%. In December when the depth to groundwater was highest, bare land coverage was 62-69% while that of the vegetated area was 27-25%. This indicates that depth of groundwater and vegetation coverage responds to seasonality. During the dry season, 68-81% of the total vegetation class is within the riparian zone. In the classification of the SAR images, the overall accuracies for the single polarized VV images ranged from 54-76%, 60-81% and 61-80% for Random Forest (RF), Neural Network (NN) and Support Vector Machine (SVM) respectively. GLCM features had overall accuracies of 64-86%, 65-88% and 65-86% for RF, NN, and SVM respectively. PCA derived images had similar overall accuracies of 68-92% for NN, RF, and SVM respectively. The PCA images had the highest overall accuracy for the entire time series indicating that reduction in the number of texture features to layers containing the maximum variance improves the accuracy. The standard deviation of soil moisture was noted to increase with increasing soil moisture. Soil texture plays a key role in soil moisture retention. The riparian fields had a high water content explained by the high clay and organic matter content. A roughness parameter was derived and utilized in the retrieval of soil moisture from SAR resulting to R2 of 0.88- 0.92 between observed and simulated soil moisture values from co-polarized RadarSAT-2 HH and TerraSAR-X HH and VV. Merged soil moisture product from FEWSNET Land Data Assimilation System_NOAH (FLDAS_NOAH), ECMWF Re-Analysis Interim (ERA-Interim) and Soil Moisture and Ocean Salinity (SMOS) and FLDAS_Variable Infiltration Capacity (VIC), ERA-Interim and SMOS had similar patterns attributed to FLDAS_NOAH and FLDAS_VIC forced by the same precipitation product (RFE). Cross-correlation of Moderate-resolution Imaging Spectrometer (MODIS) NDVI and the merged soil moisture products revealed a 2-month lag of NDVI. Hence, the relationship is useful in determining the Start of Season from soil moisture products. In conclusion, the successful land cover mapping of the study area demonstrated the use of satellite imagery for wetland characterization. The vast coverage and frequent acquisitions of optical and microwave remotely sensed data additionally make the approaches transferable to other locations and allow for mapping at larger scales. Soil moisture assessment from point data revealed varied soil moisture patterns whereas global remotely sensed and modeled products rather provide complementary information about growing conditions, and hence a situational assessment tool of potential of physical availability dimension of food security. This study forms a baseline upon which additional monitoring and assessment of the Kilombero wetland ecosystem can be performed with the current results marked as a reference. Moreover, the study serves as a demonstration case of remote sensing based approaches for land cover and soil moisture mapping, whose results are useful to stakeholders to aid in the implementation of adapted production techniques for yield optimization while minimizing the unsustainable use of the natural resources.Feuchtgebiete erbringen wichtige ökologische, biologische und sozial-ökonomische Dienstleistungen, welche entscheidend für das menschliche Dasein sind. Der steigende Bedarf an Nahrung, der Mangel an landwirtschaftlichen Nutzflächen und die Veränderung der klimatischen Bedingungen in Ostafrika haben zu einem Paradigmenwechsel vom Anbau im Hochland hin zur Nutzung von Feuchtgebieten geführt. Allerdings sind Kontrolle und Management der Aktivitäten in Feuchtgebieten notwendig, um die nachhaltige Nutzung zu sichern und negative Effekte dieser Aktivitäten zu vermeiden. Die Implementierung erfolgt durch die Landverwalter in den Feuchtgebieten. Den Nutzern von Feuchtgebieten wissenschaftliche Erkenntnisse bereitzustellen dient als Hilfsmittel zur politischen Entscheidungsfindung für die nachhaltige Feuchtgebietsnutzung. Die Forschung im Rahmen der Dissertation beinhaltet zwei Hauptkomponenten: erstens den Bedarf an aktuellen Landbedeckungskarten auf einer angemessenen Skalenebene und zweitens die Erfassung der Bodenfeuchte als wichtiger Einflussfaktor auf die landwirtschaftliche Produktion. Das Ziel der Untersuchung war, Landbedeckungskarten auf Grundlage von multisensorischen optischen Daten zu erstellen und die Eignung der Textur der einfach polarisierten Sentinel-1 Grauwertmatrix (GLCM) sowie der einer Hauptkomponentenanalyse (PCA) bei Anwendung unterschiedlicher Klassifikationsalgorithmen zu beurteilen. Des Weiteren wurden raum-zeitliche Bodenfeuchtemuster über drei hydrologische Zonen hinweg modelliert, die Bodenfeuchte aus Radardaten abgeleitet sowie die Erstellung von Bodenfeuchteprodukten auf Basis von globalen Produkten untersucht. Die Korrelation der Bodenfeuchteprodukte mit dem Normalisierten Differenzierten Vegetationsindex (NDVI) wurde ebenfalls analysiert. RapidEye, Sentinel-2 und Landsat Bilder wurden genutzt um die räumliche Ausdehnung der vier Hauptklassen (Vegetation, freiliegender Boden, Wasser und Bebauung) der Landbedeckung zu ermitteln. Für die Zeitreihenanalyse der der Landbedeckungsdynamik wurden RapidEye-Daten von August 2013 bis Juni 2015, Sentinel-2-Bilder von Dezember 2015 bis August 2016 und Landsat-8-Bilder von 2013 bis 2016 verwendet. Die größte Herausforderung war jedoch die Wolkenbedeckung, weshalb die Anwendung von Synthetic Aperture Radar (SAR) für die Feuchtgebietskartierung getestet wurde. Die gemessene Bodenfeuchte wurde mittels Variogrammen für die drei hydrologischen Zonen (Uferzone, Mitte und Randgebiete) raum-zeitlich interpoliert. Ein Rauhigkeitsparameter wurde aus einem semi-empirischen Modell hergeleitet. Die Bodenfeuchte wurde aus TerraSAR-X und RadarSAT-2- Bildern unter Verwendung des Rauhigkeitsparameters als Eingangsgröße in einer linearen Regression abgeleitet. Vor der Zusammenführung der Produkte wurde das globale Bodenfeuchteprodukt mithilfe von dreifacher Kollokation auf Fehler überprüft. Die Kreuzkorrelation zwischen NDVI und Bodenfeuchte wurde berechnet. Optische Daten (RapidEye, Landsat-8 und Sentinel-2) wurden genutzt, um die zeitliche Dynamik der Landbedeckung zu bestimmen. Die Landbedeckungsverhältnisse wurde mit der Höhe des Grundwasserspiegels korreliert. Ein hoher Grundwasserstand im Juni resultierte in 45-57% unbedecktem Boden, während der Anteil der Vegetation 34-47% betrug. Im Dezember, als der Grundwasserspiegel seinen Tiefststand hatte, erhöhte sich der Anteil des freiliegenden Bodens auf 62-69% und der Anteil der Vegetation verringerte sich auf 27-25%. Das zeigt, dass Grundwasserspiegel und Vegetation saisonalen Schwankungen unterworfen sind. Während der Trockenzeit liegen 68-81% der gesamten als Vegetation klassifizierten Fläche innerhalb der Uferzone. In der Klassifikation der SAR-Bilder liegt die Gesamtgenauigkeit der einfach polarisierten VV-Bilder im Rahmen von 54-76%, 60-81% und 61-80%, entsprechend für Random Forest (RF), Neuronale Netze (NN) und Support Vector Machine (SVM). Die GLCM ergab eine Gesamtgenauigkeit von 64-86%, 65-88% und 65-86% für RF, NN und SVM. Die über eine PCA abgeleiteten Bilder erreichten eine ähnliche Genauigkeit von 68-92% für NN, RF und SVM. Die PCA-Bilder weisen die höchste Gesamtgenauigkeit der gesamten Zeitreihe auf, was darauf hinweist, dass eine Reduktion von Textureigenschaften auf Layer der maximalen Varianz enthalten, die Genauigkeit erhöht. Die Standardabweichung der Bodenfeuchte stieg mit zunehmender Bodenfeuchte. Die Bodentextur spielt dabei eine Schlüsselrolle für das Wasserhaltevermögen des Bodens. Die Uferzone wies einen hohen Wassergehalt auf, was durch den hohen Anteil von Ton und Humus zu erklären ist. Die beobachteten und simulierten Bodenfeuchtewerte von co-polarisierten RadarSAT-2 HH, TerraSAR-X HH und VV Daten korrelieren mit einem R2 von 0.88 - 0.92. Die zusammengesetzten globalen Bodenfeuchteprodukte von FLDAS_NOAH, ERA-Interim sowie SMOS und FLDAS_VIC, ERA-Interim und SMOS zeigen ähnliche Muster wie FLDAS_NOAH und FLDAS_VIC, was über die Verwendung desselben Niederschlagsproduktes (RFE) zu erklären ist. Die Kreuzkorrelation von MODIS NDVI und den zusammengeführten Bodenfeuchteprodukten ergab eine zeitliche Verzögerung des NDVI von zwei Monaten. Dieser Zusammenhang kann daher bei der Bestimmung des Saisonbeginns aus Bodenfeuchtigkeitsprodukten nützlich sein. Zusammengefasst hat die Studie gezeigt, wie Satellitenbilder zur Charakterisierung von Wetlands genutzt werden können. Die große Abdeckung und häufige Aufnahme der optischen und Mikrowellen-Fernerkundungsdaten ermöglichen darüber hinaus die Übertragung der Ansätze auf weitere Gebiete und Kartierung auf größeren Skalen. Die Punktmessungen zeigen kleinräumige Muster der Bodenfeuchte, während globale Fernerkundungsprodukte und Modelle Informationen über die Wachstumsbedingungen liefern und somit ein Bewertungsinstrument der Ernährungssicherheit darstellen können. Weiterhin bildet die Studie eine Basis, auf der ein weitergehendes Monitoring und eine Bewertung des Feuchtgebietsökosystems durchgeführt werden kann. Sie ist ein Beispiel für fernerkundungsbasierte Ansätze zur Landbedeckungs- und Bodenfeuchtekartierung; ihre Ergebnisse sind nützlich, um Akteuren bei der Implementierung von Produktionstechniken zu unterstützen, welche die Erträge maximieren und gleichzeitig die nicht nachhaltige Nutzung der natürlichen Ressourcen minimieren

Entwicklung einer übertragbaren, synergistischen Methode zur Kartierung von Biotoptypen anhand von hochauflösenden optischen und Radar-basierten Daten

Das übergeordnete Ziel der Arbeit war es zu evaluieren, in welchem Umfang die synergistische Verwendung von modernen Erdbeobachtungsdaten und -methoden zur Kartierung von Biotoptyp- und Landnutzungsinformationen beitragen kann. Anhand einer umfangreichen Literaturrecherche wurden die traditionellen Methoden der Biotoptypenkartierung und der Stand der Forschung im Bereich der Verwendung von Fernerkundungsinformationen für die Biotoptypenkartierung analysiert und Forschungsdefizite aufgezeigt, sowie Ansatzpunkte für eine Weiterentwicklung definiert. Hieraus ergaben sich die folgenden vier übergeordneten Forschungs- beziehungsweise Arbeitsschwerpunkte, welche im Verlauf der Arbeit noch weiter unterteilt wurden: 1. Die Analyse und Extraktion von potenziellen Informationen (Merkmalen) aus den vorliegenden Geoinformationen und die anschließende Reduktion der potenziellen Merkmale auf die relevanten Merkmale für die Kartierung der Biotoptyp- und Landnutzungsinformationen. 2. Die Entwicklung eines Klassifikationsansatzes für die Erfassung der Biotoptypen- und Landnutzungsinformationen anhand eines Entwicklungsdatensatzes. 3. Die Evaluation der Robustheit der Methode mittels Übertragung auf zwei weitere Datensätze. 4. Die Evaluation der Synergie der zugrundliegenden Geoinformationen. Es konnte gezeigt werden, dass das Ziel der Entwicklung einer übertragbaren, synergistischen Methode zur Kartierung von Biotoptypen anhand von hochauflösenden optischen und Radar-basierten Daten erreicht werden konnte. Die entstandenen Karten können als Hilfe für die Entscheidungsfindung im Bereich der Anforderungen der nationalen und internationalen Naturschutzrichtlinien dienen. Die gezeigten Ergebnisse im Bereich der Übertragbarkeit lassen darauf hoffen, dass die entwickelte Methode und die daraus entstehenden Ergebnisse auch in anderen Ökoregionen einsetzbar sind

Spatial and temporal statistics of SAR and InSAR observations for providing indicators of tropical forest structural changes due to forest disturbance

Tropical forests are extremely important ecosystems which play a substantial role in the global carbon budget and are increasingly dominated by anthropogenic disturbance through deforestation and forest degradation, contributing to emissions of greenhouse gases to the atmosphere. There is an urgent need for forest monitoring over extensive and inaccessible tropical forest which can be best accomplished using spaceborne satellite data. Currently, two key processes are extremely challenging to monitor: forest degradation and post-disturbance re-growth. The thesis work focuses on these key processes by considering change indicators derived from radar remote sensing signal that arise from changes in forest structure. The problem is tackled by exploiting spaceborne Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) observations, which can provide forest structural information while simultaneously being able to collect data independently of cloud cover, haze and daylight conditions which is a great advantage over the tropics. The main principle of the work is that a connection can be established between the forest structure distribution in space and signal variation (spatial statistics) within backscatter and Digital Surface Models (DSMs) provided by SAR. In turn, forest structure spatial characteristics and changes are used to map forest condition (intact or degraded) or disturbance. The innovative approach focuses on looking for textural patterns (and their changes) in radar observations, then connecting these patterns to the forest state through supporting evidence from expert knowledge and auxiliary remote sensing observations (e.g. high resolution optical, aerial photography or LiDAR). These patterns are descriptors of the forest structural characteristics in a statistical sense, but are not estimates of physical properties, such as above-ground biomass or canopy height. The thesis tests and develops methods using novel remote sensing technology (e.g. single-pass spaceborne InSAR) and modern image statistical analysis methods (wavelet-based space-scale analysis). The work is developed on an experimental basis and articulated in three test cases, each addressing a particular observational setting, analytical method and thematic context. The first paper deals with textural backscatter patterns (C-band ENVISAT ASAR and L-band ALOS PALSAR) in semi-deciduous closed forest in Cameroon. Analysis concludes that intact forest and degraded forest (arising from selective logging) are significantly different based on canopy structural properties when measured by wavelet based space-scale analysis. In this case, C-band data are more effective than longer wavelength L-band data. Such a result could be explained by the lower wave penetration into the forest volume at shorter wavelength, with the mechanism driving the differences between the two forest states arising from upper canopy heterogeneity. In the second paper, wavelet based space-scale analysis is also used to provide information on upper canopy structure. A DSM derived from TanDEM-X acquired in 2014 was used to discriminate primary lowland Dipterocarp forest, secondary forest, mixed-scrub and grassland in the Sungai Wain Protection Forest (East Kalimantan, Indonesian Borneo) which was affected by the 1997/1998 El Niño Southern Oscillation (ENSO). The Jeffries- Matusita separability of wavelet spectral measures of InSAR DSMs between primary and secondary forest was in some cases comparable to results achieved by high resolution LiDAR data. The third test case introduces a temporal component, with change detection aimed at detecting forest structure changes provided by differencing TanDEM-X DSMs acquired at two dates separated by one year (2012-2013) in the Republic of Congo. The method enables cancelling out the component due to terrain elevation which is constant between the two dates, and therefore the signal related to the forest structure change is provided. Object-based change detection successfully mapped a gradient of forest volume loss (deforestation/forest degradation) and forest volume gain (post-disturbance re-growth). Results indicate that the combination of InSAR observations and wavelet based space-scale analysis is the most promising way to measure differences in forest structure arising from forest fires. Equally, the process of forest degradation due to shifting cultivation and post-disturbance re-growth can be best detected using multiple InSAR observations. From the experiments conducted, single-pass InSAR appears to be the most promising remote sensing technology to detect forest structure changes, as it provides three-dimensional information and with no temporal decorrelation. This type of information is not available in optical remote sensing and only partially available (through a 2D mapping) in SAR backscatter. It is advised that future research or operational endeavours aimed at mapping and monitoring forest degradation/regrowth should take advantage of the only currently available high resolution spaceborne single-pass InSAR mission (TanDEM-X). Moreover, the results contribute to increase knowledge related to the role of SAR and InSAR for monitoring degraded forest and tracking the process of forest degradation which is a priority but still highly challenging to detect. In the future the techniques developed in the thesis work could be used to some extent to support REDD+ initiatives