Suivi de la dégradation des forêts d’Afrique centrale et de la cartographie des routes dans la région

Un atelier a eu lieu au Centre Commun de Recherche (CCR), à Ispra (Italie), du 27 au 31 Mars 2017. Les activités de cet atelier se sont appuyées sur le projet ReCaREDD financé par la DG DEVCO et le Projet Roadless financé par DG CLIMA. L'atelier a réuni un groupe d'experts des pays partenaires du bassin du Congo en provenance du Cameroun, de la République Démocratique du Congo, et de la République du CongoJRC.D.1-Bio-econom

Supervised Burned Areas delineation by means of Sentinel-2 imagery and Convolutional Neural Networks

Wildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods

Influence of fire on the carbon cycle and climate

Purpose of Review: Understanding of how fire affects the carbon cycle and climate is crucial for climate change adaptation and mitigation strategies. As those are often based on Earth system model simulations, we identify recent progress and research needs that can improve the model representation of fire and its impacts. Recent Findings New constraints of fire effects on the carbon cycle and climate are provided by the quantification of the carbon ages and effects of vegetation types and traits. For global scale modelling the low understanding of the human-fire relationship is limiting. Summary Recent developments allow improvements in Earth system models with respect to the influences of vegetation on climate, peatland burning and the pyrogenic carbon cycle. Better understanding of human influences is required. Given the impacts of fire on carbon storage and climate, thorough understanding of the effects of fire in the Earth system is crucial to support climate change mitigation and adaptation

Double-Step U-Net: A Deep Learning-Based Approach for the Estimation of Wildfire Damage Severity through Sentinel-2 Satellite Data

Wildfire damage severity census is a crucial activity for estimating monetary losses and for planning a prompt restoration of the affected areas. It consists in assigning, after a wildfire, a numerical damage/severity level, between 0 and 4, to each sub-area of the hit area. While burned area identification has been automatized by means of machine learning algorithms, the wildfire damage severity census operation is usually still performed manually and requires a significant effort of domain experts through the analysis of imagery and, sometimes, on-site missions. In this paper, we propose a novel supervised learning approach for the automatic estimation of the damage/severity level of the hit areas after the wildfire extinction. Specifically, the proposed approach, leveraging on the combination of a classification algorithm and a regression one, predicts the damage/severity level of the sub-areas of the area under analysis by processing a single post-fire satellite acquisition. Our approach has been validated in five different European countries and on 21 wildfires. It has proved to be robust for the application in several geographical contexts presenting similar geological aspects

Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020

The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves

Contribution of ‘human induced fires’ to forest and savanna land conversion dynamics in the Luki Biosphere Reserve landscape, western Democratic Republic of Congo

peer reviewedHuman-induced fire is one of the most important determinants of forest cover and change in tropical and subtropical regions of the world. Yet its impact on forest cover and forest cover change remains unclear, as fires in Africa generally do not spread over very large area. This is particularly the case in the Democratic Republic of Congo (DRC), a region of the world that is still poorly investigated. Here, we propose to study the effect of humaninduced fire on land use and land cover change in a protected area of the DRC, i.e. the Luki Biosphere Reserve (LBR). We investigate tree cover changes in and around the reserve between 2002 and 2019 using Landsat 7 ETM+, Landsat 8 OLI/TIRS and MODIS MCD12Q1 images and quantify human induced fires using MODIS MCD64A1 images. The study combines land use and land cover (LULC) change detection analysis of four images, two acquired in 2002 and two acquired in 2019, with multi-temporal assessment of annual burnt area acquired between 2002 and 2019 from MODIS MCD64A1 to assess the role of fire in LULC changes and the sensitivity of different LULC types to fire. The results show a dynamic conversion of primary forest to secondary forest over about 16% of the area, the evolution of savanna to secondary forest over 9.6% (Landsat image) and the replacement of secondary forest by savanna over 8.1% (MODIS image) of the total area of Luki Reserve. Of the total area undergoing land use change, 34.1% (Landsat image) and 35.7% (MODIS image) were caused by fire, which however did not cause a significant LULC change. For the LULC types that experienced fire events, the least stable type was KEYWORD

A distinct ecotonal tree community exists at central African forest-savanna transitions

1. Global change is expected to increase savanna woody encroachment as well as fire spreading into forest. Forest‐savanna ecotones are the frontier of these processes and can thus either mitigate or enhance the effects of global change. However, the ecology of the forest‐savanna ecotone is poorly understood. In this study, we determined whether a distinct ecotonal tree community existed between forest and savanna. We then evaluated whether the ecotonal tree community was more likely to facilitate fire spreading into the forest, woody encroachment of the savanna, or the stabilisation of both forest and savanna parts of the landscape. 2. We sampled twenty‐eight vegetation transects across forest‐savanna ecotones in a central African forest‐savanna mosaic. We collected data on the size and species of all established (basal diameter >3cm) trees in each transect. Split moving window dissimilarity analysis detected the location of borders delineating savanna, ecotone, and forest tree communities. We assessed whether the ecotonal tree community was likely to facilitate fire spreading into the forest by burning experimental fires and evaluating shade and grass biomass along the transects. To decide if the ecotone was likely to facilitate woody encroachment of the savanna we evaluated if ecotonal tree species were forest pioneers. 3. A compositionally distinct and spatially extensive ecotonal tree community existed between forest and savanna. The ecotonal tree community did not promote fire spreading into forest and instead acted as a fire buffer, shading out flammable grass biomass from the understorey and protecting the forest from 95% of savanna fires. The ecotone helped stabilise the forest‐savanna mosaic by allowing the fire‐dependant savanna to burn without exposing the fire‐sensitive forest to lethal temperatures. 4. The ecotonal tree community was comprised of many forest pioneer species that will promote woody encroachment in the savanna, especially if fire frequency is decreased. SYNTHESIS: A distinct fire‐buffering ecotonal tree community in this forest‐savanna mosaic landscape illustrated that savanna fires are unlikely to compromise forest integrity. Conversely, suppression of fire in this landscape will likely lead to loss of savanna as the ecotone becomes the frontier of woody encroachment. Regular burning is essential for the preservation of this forest‐savanna mosaic

A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine

A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those candidates are then used to estimate the burn probability for each scene. The burning dates are identified by analyzing the temporal evolution of burn probabilities. The algorithm was processed, and its quality assessed globally using reference data from 2019 derived from Sentinel-2 data at 10 m, which involved 369 pairs of consecutive images in total located in 50 20 × 20 km2 areas selected by stratified random sampling. Commissions were around 10% with both satellites, although omissions ranged between 27 (Sentinel-2) and 35% (Landsat), depending on the selected resolution and dataset, with highest omissions being in croplands and forests; for their part, BA from Sentinel-2 data at 20 m were the most accurate and fastest to process. In addition, three 5 × 5 degree regions were randomly selected from the biomes where most fires occur, and BA were detected from Sentinel-2 images at 20 m. Comparison with global products at coarse resolution FireCCI51 and MCD64A1 would seem to show to a reliable extent that the algorithm is procuring spatially and temporally coherent results, improving detection of smaller fires as a consequence of higher-spatial-resolution data. The proposed automatic algorithm has shown the potential to map BA globally using medium-spatial-resolution data (Sentinel-2 and Landsat) from 2000 onwards, when MODIS satellites were launched.This research was funded by the Vice-Rectorate for Research of the University of the Basque Country (UPV/EHU) through a doctoral fellowship (contract no. PIF17/96)

Apes, protected areas and infrastructure in Africa

[Extract] Equatorial Africa sustains the continent's highest levels of biodiversity, especially in the wet and humid tropical forests that harbor Africa's apes. This equatorial region, like much of sub-Saharan Africa, is facing dramatic changes in the extent, number and environmental impact of large-scale infrastructure projects. A key concern is how such projects and the broader land use changes they promote will affect protected areas—a cornerstone of wildlife conservation efforts. This chapter assesses the potential impact of new and planned infrastructure projects on protected areas in tropical Africa, particularly those harboring critical ape habitats. It focuses on Africa not because tropical Asia is any less important, but because analyses of comparable detail are available only for certain parts of the Asian tropics (Clements et al., 2014; Meijaard and Wich, 2014; Wich et al., 2016). Such knowledge gaps underscore the importance of future work on infrastructure impacts in Asia