A Fully Automatic Burnt Area Mapping Processor Based on AVHRR Imagery - A TIMELINE Thematic Processor

The German Aerospace Center's (DLR) TIMELINE project ("Time Series Processing of Medium Resolution Earth Observation Data Assessing Long-Term Dynamics in our Natural Environment") aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA) - Advanced Very High-Resolution Radiometer (AVHRR) raw data into Level (L) 1b, L2, and L3 products. This article presents the current status of the fully automated L3 burnt area mapping processor, which is based on multi-temporal datasets. The advantages of the proposed approach are (I) the combined use of different indices to improve the classification result, (II) the provision of a fully automated processor, (III) the generation and usage of an up-to-date cloud-free pre-fire dataset, (IV) classification with adaptive thresholding, and (V) the assignment of five different probability levels to the burnt areas detected. The results of the AVHRR data-based burn scar mapping processor were validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product MCD64 at four different European study sites. In addition, the accuracy of the AVHRR-based classification and that of the MCD64 itself were assessed by means of Landsat imagery

The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment

Wildfires significantly influence ecosystem patterns and processes on a global scale. In many cases, they pose a threat to human lives and property. Through greenhouse gas emissions, wildfires also directly contribute to climate change. The monitoring of such events and the analysis of acquired data is crucial for understanding wildfire and ecosystem interactions. The FireBIRD small satellite mission, operated by the German Aerospace Center (DLR), was specifically designed for the detection of wildfires. It features a higher spatial resolution than available with other Earthobservation systems. In addition to the detection of active fire locations, the system also allows the derivation of fire intensity by means of the Fire Radiative Power (FRP). This indicator can be used as a basis to derive the amount of emitted pollutant, which makes it valuable for climate studies. With the FireBIRD mission facing its end of life in 2021, this study retrospectively evaluates the performance of the system through an inter-comparison with data from two satellite missions of the National Aeronautics and Space Administration (NASA) and discusses the potential of such a system. The comparison is performed regarding both geometrical and radiometric aspects, the latter focusing on the FRP. This study uses and compares two different methods to derive the FRP from FireBIRD data. The data are analyzed regarding six major fire incidents in different regions of the world. The FireBIRD results are in accordance with the reference data, showing a geometrical overlapping rate of 83% and 84% regarding MODIS (Moderate-resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) overpasses in close temporal proximity. Furthermore, the results show a positive bias in FRP of about 11% compared to MODIS

An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time

In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment

Development and analysis of global long-term burned area based on avhrr-ltdr data

La tesis doctoral titulada “Development and analysis of global Long-Term Burned Area based on AVHRR-LTDR data” propone la extensión temporal de la información global de área quemada obtenida a partir de imágenes de satélite. Un nuevo y consistente producto global de área quemada fue desarrollado ofreciendo datos por casi cuarenta años (1982-2018). El producto fue generado, analizado y validado, además de aplicado en el estudio global de tendencias espaciales y temporales. El trabajo fue financiado y desarrollado bajo el proyecto Fire Disturbance (Fire_cci) perteneciente al programa Climate Change initiative (CCI) de la Agencia Espacial Europea (ESA). Un producto global en una escala temporal larga contribuye a rellenar un vacío en la información necesaria para los modelos del clima y el estudio del cambio climático. Para llevar a cabo este objetivo fue preciso utilizar la base de datos de imágenes globales de satélite más extensa disponible, los datos pertenecientes al sensor Advanced Very High Resolution Radiometer (AVHRR) y de los satélites National Oceanic and Atmospheric Administration (NOAA). Por ello, se hizo uso de un algoritmo novedoso que introdujo una visión renovada para afrontar estas limitaciones y detectar área quemada. Modelos mensuales de Random Forest fueron desarrollados. Un innovador índice sintético y la obtención de proporciones de área quemada por cada pixel, hizo de este algoritmo y producto, únicos. Además, una validación y un estudio espacio-temporal fue realizado por primera vez en una serie temporal larga de casi cuarenta años. Los resultados de la inter-comparación con otros productos globales de área quemada, ofreció correlaciones altas, mostrando relaciones mensuales con los productos MCD64A1 (r = 0.89, %MAE = 21%) y FireCCI51 (r = 0.95, %MAE = 10%) durante las series temporales comunes. También se obtuvieron altas correlaciones con los perímetros oficiales que se extendían a la época pre-MODIS, como (Australia: r = 0.89, %MAE = 26%; Canadá: r = 0.81, %MAE = 33%; Alaska: r = 0.96, %MAE = 42%). La degradación de los satélites no influyó a los patrones de área quemada en la serie temporal. La validación fue novedosa al realizar a una serie temporal de casi 30 años, con un buen comportamiento del producto, y el uso de proporciones fue capaz de reducir errores. Los datos del periodo AVHRR2 del producto tienen mayor incertidumbre que AVHRR3 debido a la calidad de los sensores, aunque ambos periodos son consistentes. El producto desarrollado en esta tesis reveló tendencias de disminución de área quemada en África oriental, regiones boreales, Asia central y el sur de Australia, y tendencias de aumento de área quemada en África occidental y central, Sudamérica, USA y el norte de Australia

Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper