Abstract

We present an analysis of the spatio-temporal trends derived from long-term burned area (BA) data series. Two global BA products were included in our analysis, the FireCCI51 (2001–2019) and the FireCCILT11 (1982–2018) datasets. The former was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m reflectance data, guided by 1 km active fires. The FireCCILT11 dataset was generated from Land Long-Term Data Record data (0.05°), which provides a consistent time series for Advanced Very High Resolution Radiometer images, acquired from the NOAA satellite series. FireCCILT11 is the longest time series of a BA product currently available, making it possible to carry out temporal analysis of long-term trends. Both products were developed under the FireCCI project of the European Space Agency. The two datasets were pre-processed to correct for temporal autocorrelation. Unburnable areas were removed and the lack of the FireCCILT11 data in 1994 was examined to evaluate the impact of this gap on the BA trends. An analysis and comparison between the two BA products was performed using a contextual approach. Results of the contextual Mann-Kendall analysis identified significant trends in both datasets, with very different regional values. The long-term series presented larger clusters than the short-term ones. Africa displayed significant decreasing trends in the short-term, and increasing trends in the long-term data series, except in the east. In the long-term series, Eastern Africa, boreal regions, Central Asia and South Australia showed large BA decrease clusters, and Western and Central Africa, South America, USA and North Australia presented BA increase clusters.

Highlights

  • Fire is a global phenomenon that affects ecosystems and the atmosphere [1,2,3,4]

  • contextual Mann-Kendall (CMK) was calculated for each resolution of each product, as shown in Figure 2 in the case of FireCCILT11

  • The maximum dataset obtained 29% less trends, the minimum dataset 6% more and the mean dataset 3% less than the control dataset. These results showed that CMK and the Theil-Sen estimator are very robust to the presence of outliers, since they can tolerate a substantial fraction of bad observations without losing accuracy

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Summary

Introduction

Fire is a global phenomenon that affects ecosystems and the atmosphere [1,2,3,4]. It is a critical component of the Climate System and it has been identified as one of the Essential Climate Variables (ECVs) by the Global Climate Observing System [1].Fire Disturbance was selected as one of the projects of the Climate ChangeInitiative (CCI) Programme of the European Space Agency (ESA). Fire is a global phenomenon that affects ecosystems and the atmosphere [1,2,3,4] It is a critical component of the Climate System and it has been identified as one of the Essential Climate Variables (ECVs) by the Global Climate Observing System [1]. FireCCI aims to produce consistent, long-term and global Burned Area (BA) datasets, mostly oriented towards climate modellers [5]. This BA information is critical to assess the environmental impacts of biomass burning, as well as to analyse fire regime characteristics and temporal changes. The most reliable ones are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, including the MCD64A1

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