Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes

Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a \u3e50% probability of obtaining weekly clear observations

Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Estimating the Onset and Extent of Dieback Of Phragmites australis Using the Normalized Difference Vegetation Index and Remotely Sensed Land Cover Classifications

Phragmites australis is cosmopolitan plant species with an invasive variety present throughout most of North America. In the Balize Delta, Louisiana, USA, P. australis plays an important role in combatting subsidence, maintaining navigation channels, and protecting interior fish and wildlife habitat from waves and storm surge. In 2016 a dieback of P. australis was reported by wetland managers, coinciding with the appearance of an invasive Asian scale insect (Nipponaclerda biwakoensis), though the specific cause is still unknown. Two previous efforts attempted to identify the onset of dieback conditions met with limited success. Using Landsat images from 1985 to 2019 we classified P. australis. That classification was ground-truthed with information from five helicopter surveys made between 1988 and 2013. P. australis was stable from 2010 until 2014 but then decreased in area in 2015 and decreased in NDVI from 2014 to 2016. Area of total marsh vegetation and P. australis varied in similar patterns from the 1980s until the 2000s; since then, they vary in different ways. I concluded that detectable dieback conditions in the area began as early as 2014 and started recovery post 2016. Spatial patterns of decline is consistent with multiple stressors inducing dieback conditions such as eutrophication, salinity, or water level

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWetland is one of the most valuable ecological systems in nature. Wetland habitat is a set of comprehensive information of wetland distribution, wetland habitat types are essential to wetland management programs. Maps of wetland should provide sufficient detail, retain an appropriate scale and be useful for further mapping and inventory work (Queensland wetland framework). Remotely sensed image classification techniques are useful to detect vegetation patterns and species combination in the inaccessible regions. Automated classification procedures are conducted to save the time of the research. The purpose of the research was to develop a hierarchical classification approach that effectively integrate ancillary information into the classification process and combines ISODATA (iterative self-organizing data analysis techniques algorithm) clustering, Maximum likelihood and rule-based classifier. The main goal was to find out the best possible combination or sequence of classifiers for typically classifying wetland habitat types yields higher accuracy than the existing classified wetland map from Landsat ETM data. Three classification schemes were introduced to delineate the wetland habitat types in the idea of comparison among the methods. The results showed the low accuracy of different classification schemes revealing the fact that image classification is still on the way toward a fine proper procedure to get high accuracy result with limited effort to make the investigation on sites. Even though the motivation of the research was to apply an appropriate procedure with acceptable accuracy of classified map image, the results did not achieve a higher accuracy on knowledge-based classification method as it was expected. The possible reasons are the limitation of the image resolution, the ground truth data requirements, and the difficulties of building the rules based on the spectral characteristics of the objects which contain high mix of spectral similarities

Long-term Prairie Wetlands Extraction and Change Detection with Multi-spatial and Multi-temporal Remote Sensing Data

Prairie wetlands, also called “potholes”, provide both ecological and hydrological functions and have experienced dramatic change over the past century. This research aims to: 1) compare the capacity of Landsat and SPOT in mapping open water and wet areas with advanced classification methods; 2) monitor and quantify the changes in wetlands and drainage channels, between 1948 and 2009, with aerial photography; and 3) evaluate Landsat’s ability to extract historical wetland coverage data across seasons using a variety of methods. Results indicate that Landsat is capable for mapping open water, wet areas and other LULC types in PPR; however only 48.5% of wetland areas are identified as compared with air photos. Historical analysis of air photo generated wetland and drainage channels show that the whole basin’s wetlands rapidly decreased from 1958 to 1990 (24% to 13%) and slowly decreased from 1990 to 2009 (13% to 10%) with the least reduction in sub basin 1. Drainage channels slowly increased from 1958 to 1990 (119 km to 269 km) and dramatically increased from 1990 to 2009 (269 km to 931km). Wetland area is highly correlated with accumulated snowfall in the previous three years in sub basin 2 (r=0.91, p<0.05) due to its memory effect to previous water conditions. For the full basin, however, there were not enough years of data to prove this correlation. Even though the minimum distance algorithm in early spring is optimal for mapping wetlands in the Prairie Pothole Region (PPR), comparing with air photos, SPOT imagery underestimated wetlands smaller than 1200 m2, while Landsat imagery is not able to detect wetlands smaller than 900 m2 and underestimates areas smaller than 1600 m2. Although free-archived Landsat can detect water bodies larger than 900 m2, its ability to detect prairie wetland is limited due to missing numerous small-scale wetlands and misclassification of seasonal wetlands.

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

Spatial structure and dynamics of the plant communities in a pro-grading river delta : Wax Lake Delta, Atchafalaya Bay, Louisiana

River deltas are dynamic depositional environments that are controlled to varying degrees by coastal and fluvial forces. Plant communities in deltas respond to many of the same allogenic forces that shape delta geomorphology. This study examines the factors that influence plant community development, productivity, and species distributions in the Wax Lake delta, a young, actively pro-grading river delta in coastal Louisiana, USA. A species distribution map created using high-resolution 8-band WorldView-2 imagery was found to have an overall accuracy of 75 percent. Classification tree analysis suggested that most of the observed variation in plant species distributions within the delta can be explained by variables related to flooding, riverine and tidal flushing, soil development, ecological succession, and exposure. This full model explained 65 percent of the spatial variability, compared to 54 percent explained by elevation alone, indicating that elevation is the most important driver of species distributions in this deltaic system. Analysis of a time series of NDVI data derived from 94 Landsat images from 1973 to 2011 suggests that both total and mean plant community productivity within the delta has increased over time and that seasonal fluctuations occur that are related to water temperature and discharge. While significant short-term decreases in NDVI were found following five major storm events, in each case, total and mean NDVI recovered to within the 95 percent prediction interval of the long-term trend by the following growing season. Following the historic 2011 Mississippi River flood, the area of the delta increased by nearly 5 km2. Greater increases in delta area occurred at higher water levels, suggesting substantial vertical accretion across much of the subaerial delta. The plant community responded to this vertical accretion by shifting to higher elevation species across nearly 9 km2 of the delta. Overall, these results indicate that the plant community in the Wax Lake delta is largely driven by allogenic factors related to delta geomorphology and is increasing in productivity as the delta continues to accrete over time. The marshes in the delta show great resilience to storm disturbance, and a strong response to allogenic succession driven by extreme flood events

Mapping and Monitoring Wetland Dynamics Using Thermal, Optical, and SAR Remote Sensing Data

Wetlands are transition zone where the flow of water, the cycling of carbon and nutrients, and the energy to form a unique ecosystem are characterized by its hydrology, soils, and vegetation, between dryland and water. Over the years, remote sensing techniques have proven to be a successful tool for monitoring wetlands. Both optical and microwave earth observation sensors can be used for monitoring wetlands. Land surface temperature (LST), as one of the most important variables in physical processes of the Earth, is one of the unexplored parameters for studying wetland dynamics. In this chapter, seasonal LST, SAR data values (dual polarization VV + VH), as well as the seasonal normalized difference water index will be explored, and the relation between them will be analyzed. For this purpose, satellite images from Landsat 8 and Sentinel-1, over a wetland area, were downloaded, preprocessed, and analyzed. As a study case, Seyfe Lake located in the central Anatolian part of Turkey has been selected. The results show Seyfe Lake’s seasonal dynamics and the relation between the investigated parameters. The results helped in understanding the wetland seasonal dynamics which can be used in better managing and monitoring wetlands using remote sensing data