An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

Abstract

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%.

Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

“…Despite these facilities, related both to data availability and processing, up to now, a few studies have been conducted using the GEE platform or similar tools for Monitoring Wildfires for mapping burned areas in (i) the Northeastern Peruvian Amazon using Landsat-8 and Sentinel-2 Imagery [ 123 ], (ii) in Italy using S2 [ 124 ], and (iii) on the global scale using Landsat Images [ 125 , 126 , 127 , 128 ].…”

“…Medium spatial resolution satellite data available in the GEE platform were used for this paper, such as bottom of atmosphere (BOA) multispectral image collection as Sentinel-2 level-2A (ID: COPERNICUS/S2_SR) [ 123 , 124 ]. Sentinel-2 satellites acquire 13 spectral bands with spatial resolutions that range from 10 to 60 m. The spectral channels include four bands at 10 m spatial resolution, six bands at 20 m spatial resolution, and three bands at 60 m spatial resolution.…”

A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data

“…Not only static layers, but also constantly updated maps are included among the ancillary data. These maps are produced by another processor, described in [34], which works on Sentinel-2 (S2) level-2A (L2A) data. In particular, a map of Normalized Difference Vegetation Index (NDV I) and a map of snow cover, both projected on the CLC reference grid, are used to search for flooded vegetation and to complement the exclusion mask.…”

Research challenges and opportunities for using big data in global change biology

“…The high velocity of space‐based data enables using satellite imagery to detect the abrupt occurrence of global change events and their biological impacts over large areas (Verbesselt, Zeileis, & Herold, 2012). Recently, some near real‐time monitoring systems based on space‐based data have been applied to forest conservation (Musinsky et al., 2018; Pratihast et al., 2016), flood event (Van Ackere et al., 2019), fire mapping (Pulvirenti et al., 2020), and tree mortality due to insect outbreak (He, Chen, Potter, & Meentemeyer, 2019; Olsson, Lindström, & Eklundh, 2016). Furthermore, global analyses of the big remote‐sensing data have revealed many emergent properties of ecosystems, such as the average optimum air temperature for ecosystem gross primary productivity (Huang, Piao, et al, 2019), high stability of evergreen broadleaf forests (Huang & Xia, 2019) and collapse of rain‐use efficiency in semi‐arid ecosystems (Du et al., 2018) under extreme droughts, diminishment of vegetation seasonality over northern lands (Xu et al, 2013), and constrained tropical photosynthetic seasonality by hydroclimate (Guan et al, 2015).…”