Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Flaring and pollution detection in the Niger Delta using Remote Sensing

Merged with duplicate record 10026.1/6553 on 28.02.2017 by CS (TIS)Abstract Through the Global Gas Flaring Reduction (GGFR) initiative a substantial amount of effort and international attention has been focused on the reduction of gas flaring since 2002 (Elvidge et al., 2009). Nigeria is rated as the second country in the world for gas flaring, after Russia. In an attempt to reduce and eliminate gas flaring the federal government of Nigeria has implemented a number of gas flaring reduction projects, but poor governmental regulatory policies have been mostly unsuccessful in phasing it out. This study examines the effects of pollution from gas flaring using multiple satellite based sensors (Landsat 5 TM and Landsat 7 ETM+) with a focus on vegetation health in the Niger Delta. Over 131 flaring sites in all 9 states (Abia, Akwa Ibom, Bayelsa, Cross Rivers, Delta, Edo, Imo, Ondo and Rivers) of the Niger Delta region have been identified, out of which 11 sites in Rivers State were examined using a case study approach. Land Surface Temperature data were derived using a novel procedure drawing in visible band information to mask out clouds and identify appropriate emissivity values for different land cover types. In 2503 out of 3001 Landsat subscenes analysed, Land Surface Temperature was elevated by at least 1 ℃ within 450 m of the flare. The results from fieldwork, carried out at the Eleme Refinery II Petroleum Company and Onne Flow Station, are compared to the Landsat 5 TM and Landsat 7 ETM+ data. Results indicate that Landsat data can detect gas flares and their associated pollution on vegetation health with acceptable accuracy for both Land Surface Temperature (range: 0.120 to 1.907 K) and Normalized Differential Vegetation Index (sd ± 0.004). Available environmental factors such as size of facility, height of stack, and time were considered. Finally, the assessment of the impact of pollution on a time series analysis (1984 to 2013) of vegetation health shows a decrease in NDVI annually within 120 m from the flare and that the spatio-temporal variability of NDVI for each site is influenced by local factors. This research demonstrated that only 5 % of the variability in δLST and only 12 % of the variability in δNDVI, with distance from the flare stack, could be accounted for by the available variables considered in this study. This suggests that other missing factors (the gas flaring volume and vegetation speciation) play a significant role in the variability in δLST and δNDVI respectively

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa