Special Issue on Global Land Product Validation

Overview of the Special Issue on Global Land Product Validation: In parallel with the recent bloom of sensors providing frequent medium-resolution observations (Fig. 1), global land products have been increasingly developed and released within the community. The raw data acquired by these sensors are transformed into higher level products that can be more easily exploited by the user community. In many cases, multiple products are developed from each sensor and similar products derived from different sensors. With this, users need access to quantitative information on product uncertainties to help them assess the most suitable product, or combination of products for their specific needs. As remote sensing observations are generally merged with other sources of information or assimilated within process models, evaluation of product accuracy is required. Making quantified accuracy information available to the user can ultimately provide developers the necessary feedback for improving the products, and can possibly provide methods for their fusion to construct a consistent long-term series of surface status.This work was supported in part by the National Aeronautics and Space Administration under Grants EOS/03-0408-0637 and NNG04GL85G

Meeting Report: Long Term Monitoring of Global Vegetation using Moderate Resolution Satellites

The international community has long recognized the need to coordinate observations of Earth from space. In 1984, this situation provided the impetus for creating the Committee on Earth Observation Satellites (CEOS), an international coordinating mechanism charged with coordinating international civil spaceborne missions designed to observe and study planet Earth. Within CEOS, its Working Group on Calibration and Validation (WGCV) is tasked with coordinating satellite-based global observations of vegetation. Currently, several international organizations are focusing on the requirements for Earth observation from space to address key science questions and societal benefits related to our terrestrial environment. The Global Vegetation Workshop, sponsored by the WGCV and held in Missoula, Montana, 7-10 August, 2006, was organized to establish a framework to understand the inter-relationships among multiple, global vegetation products and identify opportunities for: 1) Increasing knowledge through combined products, 2) Realizing efficiency by avoiding redundancy, and 3) Developing near- and long-term plans to avoid gaps in our understanding of critical global vegetation information. The Global Vegetation Workshop brought together 135 researchers from 25 states and 14 countries to advance these themes and formulate recommendations for CEOS members and the Global Earth Observation System of Systems (GEOSS). The eighteen oral presentations and most of the 74 posters presented at the meeting can be downloaded from the meeting website (www.ntsg.umt.edu/VEGMTG/). Meeting attendees were given a copy of the July 2006 IEEE Transactions on Geoscience and Remote Sensing Special Issue on Global Land Product Validation, coordinated by the CEOS Working Group on Calibration and Validation (WGCV). This issue contains 29 articles focusing on validation products from several of the sensors discussed during the workshop

Validation and application of the MERIS Terrestrial Chlorophyll Index.

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity

Modelling, Monitoring and Validation of Plant Phenology Products

Phänologie, die Lehre der periodisch wiederkehrenden Entwicklungserscheinungen in der Natur, hat sich in den letzten Jahrzehnten zu einem wichtigen Teilgebiet der Klimaforschung entwickelt. Einer der Haupteffekte der globalen Erwärmung ist die Veränderung der Wachstumsmuster und Fortpflanzungsgewohnheiten von Pflanzen, und somit veränderte Phänologie. Um die Auswirkungen der Klimaveränderung auf wildwachsende sowie Kulturpflanzen vorherzusagen, werden phänologische Modelle angewendet, verbessert und validiert. Dabei ist Wissen über den aktuellen Stand der Vegetation notwendig, welches aus Beobachtungen und fernerkundliche Messungen gewonnen wird. Die hier präsentierte Arbeit befasst sich mit dem Verständnis der Zusammenhänge zwischen fernerkundlichen Messungen und phänologischen Stadien und somit den Herausforderungen der modernen phänologischen Forschung: Der Vorhersage der Phänologie durch Modellierungsansätze, der Beobachtung der Phänologie mit optischen boden- und satellitengestützten Sensoren und der Validierung phänologischer Produkte.Phenology, the study of recurring life cycle events of plants and animals has emerged as an important part of climate change research within the last decades. One of the main effects of global warming on vegetation is altered phenology, since plants have to modify their growth patterns and reproduction habits as reaction to changing environmental conditions. Forecasting phenology, thus phenological modelling, is a timely challenge given the necessity to predict the impact of global warming on wild-growing species and agricultural crops. However, assessing the present state of vegetation, thus phenological monitoring, is essential to update and validate model results. An improved comprehension of the relationships between plant phenology and remotely sensed products is crucial to interpret these results. Consequently, the presented thesis deals with the main challenges faced in modern phenology research, covering phenological forecasting with a modelling approach, satellite-based phenology extraction, and near-surface long-term monitoring of phenology

Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors

La tesis doctoral titulada “Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors” propone el desarrollo de un algoritmo de detección de área quemada global para sensores ópticos de resolución espacial moderada. El trabajo ha sido financiado y desarrollado bajo los proyectos Fire Disturbance (FireCCI) del programa Climate Change Initiative (CCI) de la European Space Agency (ESA) y el Copernicus Climate Change Service (C3S) de la European Commission (EC). El autor de este trabajo también ha recibido financiación del Ministerio de Ciencia, Innovación y Universidades, a través de una beca FPU. Cuando se propuso esta tesis solo había un único producto global de área quemada que ofrecía una serie temporal larga y consistente. Se trataba del producto MCD64A1 de la National Aeronautics and Space Administration (NASA) que se generaba operacionalmente y que proveía información de área quemada a nivel global a 500 m desde noviembre del 2000. Por la parte europea solo había dos productos, el FireCCI41 y el GIO_GL1_BA, pero se trataba de productos que o bien ofrecían una serie temporal demasiado reducida (FireCCI41) o bien una serie con baja fiabilidad. En cualquier caso, los tres productos, incluido el MCD64A1, presentaban limitaciones que les hacían estar lejos de cumplir los requerimientos establecidos por los usuarios en términos de errores de comisión y omisión. Es en este contexto donde se plantea esta tesis que pretende avanzar en el conocimiento de los algoritmos de área quemada globales y la generación de productos globales que cumplan o se acerquen de forma más significativa a las expectativas de los usuarios. Para este propósito, se ha utilizado información proveniente de sensores que no se habían utilizado hasta el momento para generar productos de área quemada globales. Esta información incluye las bandas de alta resolución a 250 m del Moderate Resolution Imaging Spectroradiometer (MODIS), las bandas del Ocean and Land Colour Instrument (OLCI) y del SYNERGY, así como fuegos activos de MODIS y del Visible Infrared Imaging Radiometer Suite (VIIRS). En este último caso, ha sido la primera vez que se utilizan globalmente para generar este tipo de productos. Así, se han desarrollado cuatro algoritmos y se han generado sus respectivos productos de área quemada a escala global. Cada uno de ellos ha jugado un papel complementario al resto, ya sea a modo de versión mejorada o como adaptación de un mismo algoritmo a distintos sensores. Todos los productos derivados han sido validados globalmente y se han llevado a cabo comparaciones exhaustivas con otros productos existentes. Además, para confirmar la estabilidad de los patrones espacio temporales, los productos se han aplicado para dar respuesta a distintas preguntas científicas relacionadas con las anomalías en las tendencias del área quemada en distintas partes del mundo. Para explicar todo este proceso la tesis se ha estructurado en ocho capítulos: introducción, seis publicaciones en revistas internacionales y unas conclusiones

Validation and application of the MERIS Terrestrial Chlorophyll Index

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Validation of the moderate-resolution satellite burned area products across different biomes in South Africa

Biomass burning in southern Africa has brought significant challenges to the research society as a fundamental driver of climate and land cover changes. Burned area mapping approaches have been developed that generate large-scale low and moderate resolution products made with different satellite data. This consequently afford the remote sensing community a unique opportunity to support their potential applications in e.g., examining the impact of fire on natural resources, estimating the quantities of burned biomass and gas emissions. Generally, the satellite-derived burned area products produced with dissimilar algorithms provide mapped burned areas at different levels of accuracy, as the environmental and remote sensing factors vary both spatially and temporally. This study focused on the inter-comparison and accuracy evaluation of the 500-meter Moderate Resolution Imaging Spetroradiomter (MODIS) burned area product (MCD45A1) and the Backup MODIS burned area product (hereafter BMBAP) across the main-fire prone South African biomes using reference data independently-derived from multi-temporal 30-meter Landsat 5 Thematic Mapper (TM) imagery distributed over six validation sites. The accuracy of the products was quantified using confusion matrices, linear regression and subpixel burned area measures. The results revealed that the highest burned area mapping accuracies were reported in the fynbos and grassland biomes by the MCD45A1 product, following the BMBAP product across the pine forest and savanna biomes, respectively. Further, the MCD45A1 product presented higher subpixel detection probabilities for the burned area fractions 50% of a MODIS pixel. Finally the results demonstrated that the probability of identifying a burned area within a MODIS pixel is directly related to the proportion of the MODIS pixel burned and thus, highlights the relevance of fractional burned area during classification accuracy assessment of lower resolution remotely-sensed products using data with higher spatial resolution.Dissertation (MSc)--University of Pretoria, 2011.Geography, Geoinformatics and Meteorologyunrestricte

Validating the TET-1 satellite sensing system in detecting and characterizing active fire 'hotspots'

Wildfires, or bushfires as they are known in Australia, are a natural occurrence in nearly every country over the globe, which take place during the hotter months of the year. Wildfires can be triggered through natural events, such as lightning strikes, which account for half of all wildfires in Australia, or through human induced methods, for example deliberately lit or through failure of infrastructure or equipment. In Australia, fires are a major natural hazard affecting over 25,000 km2 of land annually. Historically, fire detection has been performed by fire spotters, usually in towers or spotter aircraft, but in countries such as Australia, with a large extent of land that needs to be monitored, leads remote sensing techniques to be the obvious choice in providing resources in gathering this information when compared to other methods. Remote sensing technologies provide efficient and economical means of acquiring fire and fire-related information over large areas at regional to global scale on a routine basis, allowing for the early detection and monitoring of active fire fronts, which is essential for emergency services in responding timely to outbreaks of wildfires. The objective of this study is to investigate the hotspot (fires and other thermal anomalies) detection and characterization product from the TET-1 satellite sensing system from the German Aerospace Centre (DLR). The satellite is envisioned, as part of a constellation of satellites, to provide detection and characterization of fires at a higher spatial resolution when compared to the current standard global coverage from the MODIS fire products. This study aims to validate the output from the detection and characterization algorithm, to provide a guide for the sensitivity of the system, especially for low power (small area and low temperature) fires. This consisted of conducting a simulation study into the limits of detection for the system, as well as performing a case study. A simulation study was conducted in order to determine the sensitivity of the TET-1 satellite sensing system in detecting hotspots, for the purpose of determining limits of operation and as an aid in developing tests to assess the accuracy of the algorithm in detecting and characterizing fires. Determining the sensitivity involved ascertaining the minimum area and temperatures (in combination the total energy emitted by a fire) of a fire that would be able to be detected by the algorithm. The study found that under ideal conditions, the TET-1 detection and characterization algorithm is theoretically able to detect a fire of only 1m², albeit for temperatures of 1000K (approx. 727°C) and over. As the area of the fire increases, the required temperature decreases rapidly, for instance a 9m² fire is detectable from 650K (377°C). Once a fire becomes significantly large, for example 100m², the detectable temperatures falls to 500K (227°C), which is considered a smouldering temperature. The characterization portion of the algorithm was found to accurately estimate the fire characteristics with low systematic errors (area ±12% and temperature ±3%). Adjusting the background temperature was found to not significantly influence either the detection or the estimation of the fire characteristics. A case study was performed to validate the results from the simulation study, which was conducted near the town of Kangaroo Ground on 31st July 2015. This was an example of a low power fire with an effective fire area of 15.1m² and an average fire temperature at satellite overpass of 63°C (336K). Upon investigating the output from the camera system, although the fire could be seen in the MIR image in two adjoining pixels, the fire did not possess enough power to trigger the automatic detection threshold of the algorithm, and as such was not classified as a legitimate fire. Although not detected, a comparison was made of the energy emitted by the fire (measured in radiance to directly compare with the camera) to the amount detected by the satellite. The energy from the fire was determined to be; = 0.302 W/sr.m²µm and = 7.612 W/sr.m².µm, while the radiances captured by the sensors was; for pixel 1 = 0.3102 W/sr.m².µm and = 6.835 W/sr.m².µm, and for pixel 2 = 0.3102 W/sr.m².µm and = 6.817 W/sr.m².µm. These results show that the MIR radiances were comparable, but that the TIR radiances were not, although no definitive reason for this discrepancy could be determined. Other errors with the output from the satellite camera system were found, most serious being the geo-location of the pixels. The reported position of the test site by the camera system differed by over 12km from the actual location of the test site