Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

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

Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Forest attributes mapping with SAR data in the romanian South-Eastern Carpathians requirements and outcomes

Esta tesis doctoral se centra en la estimación de variables forestales en la zona Sureste de los Cárpatos Rumanos a partir de imágenes de radar de apertura sintética. La investigación abarca parte del preprocesado de las imágenes, métodos de generación de mosaicos y la extracción de la cobertura de bosque, sus subtipos o su biomasa. La tesis se desarrolló en el Instituto Nacional de Investigación y Desarrollo Forestal Marín Dracea (INCDS) y la Universidad de Alcalá (UAH) gracias a varios proyectos: el proyecto EO-ROFORMON del INCDS (Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forests), y el proyecto EMAFOR de la UAH (Synthetic Aperture Radar (SAR) enabled Analysis Ready Data (ARD) cubes for efficient monitoring of agricultural and forested landscapes). El proyecto EO-ROFORMON fue financiado por la Autoridad Nacional para la Investigación Científica de Rumania y el Fondo Europeo de Desarrollo Regional. El proyecto EMAFOR fue financiado por la Comunidad Autónoma de Madrid (España). El objetivo de esta tesis es el desarrollo de algoritmos para la extracción de variables forestales de uso general como la cobertura, el tipo o la biomasa del bosque a partir de imagen de radar de apertura sintética. Para alcanzar dicho propósito se analizaron posibles fuentes de sesgo sistemático que podrían aparecer en zonas de montaña (ej., normalización topográfica, generación de mosaicos), y se aplicaron técnicas de aprendizaje de máquina para tareas de clasificación y regresión. La tesis contiene ocho secciones: una introducción, cinco publicaciones en revistas o actas de congresos indexados, una pendiente de publicación (quinto capítulo) y las conclusiones. La introducción contextualiza la importancia del bosque, cómo se recoge la información sobre su estado (ej., inventario forestal) y las iniciativas o marcos legislativos que requieren dicha información. A continuación, se describe cómo la teledetección puede complementar la información de inventario forestal, detallando el contexto histórico de las distintas tecnologías, su funcionamiento, y cómo pueden ser aplicadas para la extracción de información forestal. Por último, se describe la problemática y el monitoreo del bosque en Rumanía, detallando el objetivo de la tesis y su estructura. El primer capítulo analiza la influencia del modelo digital de elevaciones (MDE) en la calidad de la normalización topográfica, analizando tres MDE globales (SRTM, AW3D y TanDEM-X DEM) y uno nacional (PNOA-LiDAR). Los experimentos se basan en la comparación entre órbitas, con un MDE de referencia, y la variación del acierto en la clasificación dependiendo del MDE empleado para la normalización. Los resultados muestran una menor diferencia ente órbitas al utilizar un MDE con una mejor resolución (ej. TanDEM-X, PNOA-LIDAR), especialmente en el caso de zonas con fuertes pendientes o formas del terreno complejas, como pueden ser los valles. En zonas de alta montaña las imágenes de radar de apertura sintética (SAR) sufren frecuentes distorsiones. Estas distorsiones dependen de la geometría de adquisición, por lo que es posible combinar imágenes adquiridas desde varias órbitas para que la cobertura sea lo más completa posible. El segundo capítulo evalúa dos metodologías para la clasificación de usos del suelo utilizando datos de Sentinel-1 adquiridos desde varias órbitas. El primer método crea clasificaciones por órbita y las combina, mientras que el segundo genera un mosaico con datos de múltiples órbitas y lo clasifica. El acierto obtenido mediante combinación de clasificaciones es ligeramente mayor, mientras que la clasificación de mosaicos tiene importantes omisiones de las zonas boscosas debido a problemas en la normalización topográfica y a los efectos direccionales. El tercer capítulo se enfoca en separar la cobertura forestal de otras coberturas del suelo (urbano, vegetación baja, agua) analizando la utilidad de las variables basadas en la coherencia interferométrica. En él se realizan tres clasificaciones de máquina vector-soporte basadas en un conjunto concreto de variables. El primer conjunto contiene las estadísticas anuales de la retrodispersión (media y desviación típica anual), el segundo añade la coherencia a largo plazo (separación temporal mayor a un año), el tercero incluye las estadísticas de la coherencia a corto plazo (mínima separación temporal). Utilizar variables basadas en la coherencia aumenta el acierto de la clasificación hasta un 5% y reduce los errores de omisión de la cobertura forestal. El cuarto capítulo evalúa la posibilidad de detectar talas selectivas utilizando datos de Sentinel-1 y Sentinel-2. Sus resultados muestran que la detección resulta muy difícil debido a la saturación de los sensores y la confusión introducida por el efecto de la fenología. El quinto capítulo se centra en la clasificación de tipos de bosque basado en una serie temporal de datos Sentinel-1. Se basa en la creación de un conjunto de modelos que describen la relación entre la retrodispersión y el ángulo local de incidencia para un determinado tipo de bosque y fecha concreta. Para cada píxel se calcula el residuo respecto al modelo de cada uno de los tipos de bosque, acumulando dichos residuos a lo largo de la serie temporal. Hecho esto, cada píxel es asignado al tipo de bosque que acumula un menor residuo. Los resultados son prometedores, mostrando que frondosas y coníferas tienen un comportamiento distintivo, y que es posible separar ambos tipos de bosque con un alto grado de acierto. El sexto capítulo está dedicado a la estimación de biomasa utilizando datos Sentinel-1, ALOS PALSAR y regresión Random Forest. Se obtiene un error similar para ambos sensores a pesar de utilizar una banda diferente (band-C vs. -L), con poca reducción en el error cuando ambas bandas se utilizan conjuntamente. Sin embargo, el ajuste de un estimador adaptado a las condiciones locales de Rumanía sí ofreció una reducción de del error al ser comparado con las estimaciones globales de biomasa

A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds

Assessing forest cover change is a key issue for any national forest inventory. This was tested in two study areas in Switzerland on the basis of stereo airborne digital sensor (ADS) images and advanced digital surface model (DSM) generation techniques based on image point clouds. In the present study, an adaptive multi-scale approach to detect forest cover change with high spatial and temporal resolution was applied to two study areas in Switzerland. The challenge of this approach is to minimize DSM height uncertainties that may affect the accuracy of the forest cover change results. The approach consisted of two steps. In the first step, a ‘change index' parameter indicated the overall change status at a coarser scale. The tendency towards change was indicated by derivative analysis of the normalized histograms of the difference between the two canopy height models (DCHMs) in different years. In the second step, detection of forest cover change at a refined scale was based on an automatic threshold and a moving window technique. Promising results were obtained and reveal that real forest cover changes can be distinguished from non-changes with a high degree of accuracy in managed mixed forests. Results had a lower accuracy for forests located on steep alpine terrain. A major benefit of the proposed method is that the magnitude of forest cover change of any specific region can be made available within a short time as often required by forest managers or policy-makers, especially after unexpected natural disturbance