. Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle, and to infer relationships between climate, land use, and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, with the aim of improving projections of fire regime characteristic and fire impacts on ecosystems and human societies under the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha), and global annual fire carbon emission (0.91–4.75 Pg C a−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent inter-annual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the northern hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.

/pdf/quantitative-assessment-of-fire-and-vegetation-properties-in-17l73xf2rk.pdf

Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project

Holocene warming and evergreen/deciduous forest replacement across eastern China

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model’s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model’s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model’s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model’s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively.

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

The frequent occurrence of forest fires in recent years has not only seriously damaged the forests’ ecological environments but also threatened the safety of public life and property. Smoke, as the main manifestation of the flame before it is produced, has the advantage of a wide diffusion range that is not easily obscured. Therefore, timely detection of forest fire smoke with better real-time detection for early warnings of forest fires wins valuable time for timely firefighting and also has great significance and applications for the development of forest fire detection systems. However, existing forest fire smoke detection methods still have problems, such as low detection accuracy, slow detection speed, and difficulty detecting smoke from small targets. In order to solve the aforementioned problems and further achieve higher accuracy in detection, this paper proposes an improved, new, high-accuracy forest fire detection model, the OBDS. Firstly, to address the problem of insufficient extraction of effective features of forest fire smoke in complex forest environments, this paper introduces the SimAM attention mechanism, which makes the model pay more attention to the feature information of forest fire smoke and suppresses the interference of non-targeted background information. Moreover, this paper introduces Omni-Dimensional Dynamic Convolution instead of static convolution and adaptively and dynamically adjusts the weights of the convolution kernel, which enables the network to better extract the key features of forest fire smoke of different shapes and sizes. In addition, to address the problem that traditional convolutional neural networks are not capable of capturing global forest fire smoke feature information, this paper introduces the Bottleneck Transformer Net (BoTNet) to fully extract global feature information and local feature information of forest fire smoke images while improving the accuracy of small target forest fire target detection of smoke, effectively reducing the model’s computation, and improving the detection speed of model forest fire smoke. Finally, this paper introduces the decoupling head to further improve the detection accuracy of forest fire smoke and speed up the convergence of the model. Our experimental results show that the model OBDS for forest fire smoke detection proposed in this paper is significantly better than the mainstream model, with a computational complexity of 21.5 GFLOPs (giga floating-point operations per second), an improvement of 4.31% compared with the YOLOv5 (YOLO, you only look once) model mAP@0.5, reaching 92.10%, and an FPS (frames per second) of 54, which is conducive to the realization of early warning of forest fires.

/pdf/omni-dimensional-dynamic-convolution-meets-bottleneck-21ug3yu6.pdf

Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model

Holocene fire history in southwestern China linked to climate change and human activities

Holocene fire history in China: Responses to climate change and human activities.

Vegetation biomass is a key and active component of the carbon cycle. Though China's vegetation biomass in recent decades has been widely investigated, only two studies have quantitatively assessed its century-scale changes so far and reported totally opposite trends. This study provided the first multi-model estimates of China's vegetation biomass change for the 20th century and its responses to historical changes in environmental and anthropogenic factors, based on simulations evaluated with the field observations from 3757 inventory plots in China and bias-corrected using machine learning (Gaussian process regression). A significant decline in vegetation biomass over the 20th century was shown by bias-corrected simulations from the six Dynamic Global Vegetation models (DGVMs) with trends ranging from −32.48 to −11.10 Tg C yr–1 and a mean trend of −17.74 Tg C yr–1. Land use and land cover change (LULCC) was primarily responsible for the simulated downward trend (−50.71 to −24.28 Tg C yr–1), while increasing atmospheric CO2 concentration lead to increased vegetation biomass (+9.27 to + 13.37 Tg C yr–1). Climate change had limited impacts on the long-term trend (−3.75 to + 5.06 Tg C yr–1). This study highlights the importance of LULCC for historical reconstruction and future projection of vegetation biomass over China. It also suggests that the incorrect change in China's forest area for 1980–2000 in the LULCC dataset used as model input data of many existing and ongoing model intercomparison projects (MIPs) has likely led to inaccurate estimations of historical vegetation biomass changes in China.

/pdf/vegetation-biomass-change-in-china-in-the-20th-century-an-4u0gvijo9t.pdf

Vegetation biomass change in China in the 20th century: an assessment based on a combination of multi-model simulations and field observations

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Holocene fire history in China: Responses to climate change and human activities.

Vegetation biomass change in China in the 20th century: an assessment based on a combination of multi-model simulations and field observations