# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 1 Introduction  
Wildland fires are periodic disturbance events that dramatically affect the structure and distribution of global forest ecosystems, altering soil erosion, causing loss of biodiversity, habitat, production and productivity, endangering human life, and disrupting livelihoods [1]. Over the past two decades, 7.20 billion ha of land was burned globally by wildland fires at an average of 400 million ha annually [2]. According to an analysis by the Food and Agricultural Organization, between 2001 and 2019, forest (tree-covered) areas accounted for about 29% of the total area burnt by wildfires [3]. The largest burnt tree-covered area in the total wildland fire area was in Central America (47%), while the smallest was in the Oceania region (2%) [3].  
Furthermore, wildland fires have a strong influence on climate change due to the rapid release of carbon sequestered in forest biomass into the atmosphere [4, 5]. In recent decades, the rapidly changing climate has increased the frequency and severity of wildland fires [6, 7, 8, 9, 10], and it is projected that further warming effects will likely cause more dramatic consequences to the forests, and an increase of burnt area (BA) [11, 12, 13, 14, 15]. Growing concerns about the impact of climate change has increased the need for the development ofsustainable forest management policies aiming to mitigate greenhouse gas emissions and increase the efficiency of fire management [16; 17; 18]. Broad scale severity levels at which fire affects a forest cover is a key factor for the successful assessment of post-fire impact and the strategy that promotes natural regeneration, as well as for understanding the effects of climate change on ecosystems [19; 20]. Thus, comprehensive burnt area, burn severity (BS), and post-fire recovery monitoring and assessments are a critical tool for researchers, foresters, and fire managers.  
Over the last few decades, the use of space-borne sensor data has provided unique advances for the professional community to detect forest ecosystem disturbances, especially for estimations of spatio-temporal fire dynamics and BA mapping [21; 22]. Remote sensing (RS) techniques become an unprecedented alternative option to expensive and time-consuming field measures in the monitoring of forest ecosystems over large and remote geographic areas [23; 24; 25; 26]. Multi-temporal satellite images of broad spatial resolution have been extensively used in measurements of forest areas prior to and post-fire acquisitions to map BA at regional and global scales [27; 28; 29; 30], and to estimate burn severity [31; 32; 33; 34] and vegetation recovery [35; 36; 37; 38].  
Given its importance for monitoring forest ecosystems and environmental management policies, the main objective of this paper is a review of state-of-the-art remotely sensed methods and data for estimation of forest BA, BS, and forest recovery patterns at the global level that have been applied in the research papers published in English in peer-reviewed scientific journals within the period between 2000 and 2020. The paper analyses current research trends and summarizes the application of remote sensing techniques for the burnt forest area, burned severity, and post-fire recovery. Finally, we recommend future research for the use of the new generation of RS systems, classification, and cloud performing techniques, and emerging process platforms for the regional and large-scale applications in BA studies.