The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Monitoring, Reporting and Verification systems for Carbon in Soils and Vegetation in African, Caribbean and Pacific countries

Improved terrestrial carbon management offers tremendous potential for climate change mitigation and, in many cases, there are associated co-benefits such as increased productivity, resilience, and biodiversity. It is expected that governments will agree to incentives for improved management of some forms of terrestrial carbon in developing countries, including maintaining existing carbon and creating new carbon in agriculture, forestry and other land-uses. Across a wide range of geographic scales and land classes, there is a need for a coherent, integrated information base for effective land management practices that produce real increases in sequestration together with real reductions in greenhouse gas (GHG) emissions from terrestrial sources, and transparent, consistent, and comparable quantification of changes in carbon stocks. In January 2011, the Institute for Environment and Sustainability of the Joint Research Centre organized an inter-service meeting on “Monitoring, Reporting and Verification systems for carbon in soils and vegetation in African, Caribbean and Pacific countries” with the objective of refining its long-term research agenda in that domain. This was achieved in the light of the needs of the Directorates-General involved in the development and environment policies namely DEVCO, ENV, CLIMA, ENTR, RTD and AGRI as well as of the recent evolution of the UNFCCC negotiations. This report encompasses the proceedings of the meeting together with the conclusions and recommendations to JRC work program stated by the invited experts and policy-makers from the different relevant DGs. The presentations given during the meeting and the final report are also available on the ACP Observatory website (http://acpobservatory.jrc.ec.europa.eu/redd.php).JRC.H.7-Land management and natural hazard

Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Mapping intra- and inter-annual dynamics in wetlands with multispectral, thermal and SAR time series

Kartierung der intra- und interannuellen Dynamik von Feuchtgebieten mit multispektralen, thermischen und SAR-Zeitreihen Die Analyse der aktuellen räumlichen Verbreitung und der zeitlichen Entwicklung von Feuchtgebieten stellt eine äußerst komplexe Aufgabe dar, welche durch die Saisonalität, die schwierige Zugänglichkeit und die besonderen Eigenschaften als Ökoton bedingt ist. Erdbeobachtungssysteme sind somit das am besten geeignete Werkzeug, um zeitliche und räumliche Muster von Feuchtgebieten auf globaler Ebene zu beobachten (saisonale Veränderungen und Langzeit-Trends) und um den Einfluss der menschlichen Aktivitäten auf ihre physischen und biologischen Eigenschaften zu untersuchen. Zur Kartierung von raum-zeitlichen Mustern wurden Zeitreihen von Radar- (Sentinel-1), Multispektral- (Sentinel-2) und Thermal-Satellitendaten (MODIS) in fünf Untersuchungsgebieten, mit für Feuchtgebiete unterschiedlichen typischen Charakteristika, untersucht. In Kapitel 1 werden die Problematik in Bezug auf die Definition von Feuchtgebieten erläutert und allgemeine Degradations-Trends beschrieben. Die Kapitel 2 und 3 behandeln einen Algorithmus, der Veränderungen mithilfe von SAR-Zeitreihen feststellt, sowie die Vorteile des Cloud-Computings für das operationelle Monitoring saisonaler Muster und die Erkennung kurzfristig auftretender Veränderungen. In den Kapiteln 4 und 5 werden die zwei Hauptursachen für den Verlust von Feuchtgebieten betrachtet: der Staudammbau und die Ausdehnung landwirtschaftlicher Flächen. In Kapitel 4 werden dichte Zeitreihen multispektraler (Sentinel-2) und SAR-Daten (Sentinel-1) verwendet, um die Feuchtgebiete Albaniens – eines Landes in dem konträre Pläne zum Ausbau seines Wasserkraftpotentials und dem Schutz intakter Flussökosysteme zu Spannungen führen – landesweit zu kartieren. Die synergetischen Vorteile, die sich durch die Fusionierung von multispektralen und SAR-Daten für die Klassifikation ergeben, werden dabei herausgestellt. Kapitel 5 veranschaulicht, dass die Kilombero-Überschwemmungsebene in Tansania ein großes und bedeutendes Feuchtgebiet ist, das in den vergangenen Jahren infolge der weitgehend unkontrollierten Ausbreitung landwirtschaftlicher Flächen in seiner Ausdehnung und seiner Ökologie stark beeinträchtigt wurde. Um die Auswirkungen der Landnutzungsänderungen des Feuchtgebietes während der vergangenen 18 Jahre zu analysieren, wurden eine Zeitreihe (2000 bis 2017) thermaler Daten (MODIS) analysiert. Die drei für die Zeitreihenanalyse angewandten Modelle zeigen, wie landwirtschaftliche Praktiken die Landoberflächentemperatur in den landwirtschaftlich genutzten Gebieten sowie in den angrenzenden natürlichen Feuchtgebieten erhöht haben.Due to wetlands’ seasonality, their difficult access and ecotone character, determining their actual extension and trends over time is a complex task. Earth Observation systems are the most appropriate tool to monitor their spatio-temporal patterns (seasonal changes and long term trends) at global scales, and to study the effects that human activities have in their physical and biological properties. In this work I use time series of radar (Sentinel-1), multispectral (Sentinel-2) and thermal (MODIS) imagery to map the spatio-temporal patterns in 5 wetlands of different characteristics. First, I introduce in chapter 1 the problematic of wetlands’ definitions and their degradation trends. I continue with a brief introduction on remote sensing, time series analysis, and their applications on wetlands’ research and management. In chapters 2 and 3 I implement an algorithm for change detection of time series of Sentinel-1 images and demonstrate the advantages of cloud computation for operational monitoring. In chapters 4 and 5 I address two of the main causes of wetland degradation: dam building and agricultural expansion. In chapter 4 I use dense time series of Sentinel-1 and Sentinel-2 images map all the wetlands of Albania; a country struggling between developing its large hydropower potential or preserving its intact and valuable river ecosystems. I evaluate the synergic advantages of fusing multispectral and radar imagery in combination with knowledge-based rules to produce classification of higher thematic and spatial resolutions. In chapter 5 I present how the Kilombero Floodplain, in Tanzania, has been degraded during the last years due to uncontrolled farmland expansion. I use a time series of thermal imagery (MODIS) from 2000 until 2017 to analyze the effect of land use changes on the wetland. I compare three models for time series analysis and reveal how farming practices have increased the surface temperature of the farmed area, as well as in adjacent natural wetlands.Mapeo de las dinámicas inter- e intra-anuales en humedales con series temporales de imágenes multiespectrales, termales y de radar Debido a la estacionalidad de los humedales, su difícil acceso y sus características de ecotono, determinar su actual extensión y sus tendencias a lo largo del tiempo es una tarea compleja. Los sistemas de observación terrestres son la herramienta más apropiada para monitorear sus patrones espacio-temporales (estacionalidad y tendencias a largo plazo) a escalas globales, y para estudiar los efectos que las actividades humanas causan en sus propiedades físicas y biológicas. En esta tesis uso series temporales de imágenes radar (Sentinel-1), multiespectrales (Sentinel-2) y termales (MODIS) para mapear los patrones espacio-temporales de 5 humedales de diferentes características. En el capítulo 1 describo los retos que derivan de las diferentes definiciones que existen de los humedales. También presento las tendencias globales de degradación que la mayoría de los humedales continúan experimentando en los últimos años. Continúo con una breve introducción de los sistemas de teledetección remota, análisis de series temporales, y sus aplicaciones a la investigación y gestión de los humedales. En los capítulos 2 y 3 implemento un algoritmo de detección de cambios para series temporales de imágenes radar, y muestro las ventajas de usar sistemas de computación en la nube para monitorear cambios en la cobertura del suelo a corto plazo. En los capítulos 4 y 5 trato con dos de las causas más comunes de degradación de humedales: la construcción de presas y la expansión de la agricultura. En el capítulo 4 uso series temporales de imágenes multiespectrales (Sentinel-2) y radar (Sentinel-1) para mapear todos los humedales Albania; un país que se debate entre desarrollar su potencial hidroenergético o preservar sus valiosos e intactos ecosistemas de rivera. Mediante la fusión de imágenes radar y multiespectrales y el uso de reglas de decisión genero un mapa de suficiente resolución espacial y temática para que pueda ser usado por sectores interesados y gestores. En el capítulo 5 presento como las llanuras inundables de Kilombero, en Tanzania, han sido degradadas durante los últimos años debido a la expansión incontrolada de la agricultura. Usando series temporales de imágenes termales (MODIS) desde 2000 hasta 2017 y mapas de cambios de usos del suelo, determino los efectos que estos cambios han tenido en el humedal. Comparo 3 modelos diferentes de análisis de series temporales y muestro cómo la expansión de la agricultura ha incrementado la temperatura superficial terrestre, no solo de la zona cultivada, sino también de zonas adyacentes aún naturales