Estimates of Wildfire Emissions in Boreal Forests of China

Yi, Kunpeng; Bao, Yuhai

doi:10.3390/f7080158

Cited by 11 publications

(4 citation statements)

References 54 publications

(55 reference statements)

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“…The values of aboveground biomass density were based on research on province-specific aboveground biomass density in China for various forest types [20,21], as shown in Table 1, which came from direct field measurements and reflected spatial variations in forest type. Burning efficiency is an uncertain parameter in fire emissions estimation due to the high-spatial variability of both the burning process and fuel availability, and it has usually been set as a constant in previous works [9,12,27]. In this paper, specific forest types were derived from MCD12Q1 data, and the burning efficiency of all forest types was defined as a constant value of 0.25 according to the report of Michel et al [28].…”

Section: Biomass Density and Burning Efficiencymentioning

confidence: 99%

Estimation of Forest Fire Emissions in Southwest China from 2013 to 2017

et al. 2019

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Forest fire emissions have a great impact on local air quality and the global climate. However, the current and detailed regional forest fire emissions inventories remain poorly studied. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate monthly emissions from forest fires at a spatial resolution of 500 m × 500 m in southwest China from 2013 to 2017. The spatial and seasonal variations of forest fire emissions were then analyzed at the provincial level. The results showed that the annual average emissions of CO 2 , CO, CH 4 , SO 2 , NH 3 , NO X , PM, black carbon, organic carbon, and non-methane volatile organic compounds from forest fires were 1423.19 × 10 3 , 91.66 × 10 3 , 4517. 08, 881.07, 1545.04, 1268.28, 9838.91, 685.55, 7949.48, and 12,724.04 Mg, respectively. The forest fire emissions characteristics were consistent with the characteristics of forest fires, which show great spatial and temporal diversity. Higher pollutant emissions were concentrated in Yunnan and Tibet, with peak emissions occurring in spring and winter. Our work provides a better understanding of the spatiotemporal representation of regional forest fire emissions and basic data for forest fire management departments and related research on pollution and emissions controls. This method will also provide guidance for other areas to develop high-resolution regional forest fire emissions inventories.Atmosphere 2020, 11, 15 2 of 12 Although they are an important type of biomass burning, forest fire emissions have been commonly analyzed only as a part of overall biomass burning emissions, and detailed analysis of the spatial and temporal changes of the forest fire emissions has been neglected [1,[13][14][15]. Tian et al. [16,17] noted that forest fires are influenced by many factors and have more complex characteristics than other biomass types. Huang et al. [18] indicated that compared with forest fires, agricultural field burnings have small sizes and temporal impermanency. Few studies have aimed to differentiate the type of biomass burning and most typically focus on just emissions from forest fires. It is unreasonable to calculate forest fire emissions as a part of the total fire emissions because of their highly complex nature. For example, to simplify the calculations, a fixed biomass density or emission factor value has been applied for all forest types in many studies [13,19]. Above-ground biomass density values in different regions are quite different [2,20]. Therefore, it is necessary to use local biomass density to estimate regional forest fire emissions. Moreover, large uncertainties in the form of varied emission factor values can be found for different forest fuels in forest fire emissions estimation [20,21]. Therefore, the accurate estimation and detailed analysis of forest fire emissions still need to be improved.Streets et al. [8] found that forest fire emissions were the primary source of biomass emissions in Asia, and that China had the highest biomass burning emissions in Asia. Some researc...

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Section: Biomass Density and Burning Efficiencymentioning

confidence: 99%

Estimation of Forest Fire Emissions in Southwest China from 2013 to 2017

et al. 2019

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“…Overall, national forest protection policies in different periods were the main factors affecting forest change. Natural disasters [42,43] such as fire, insects, wind, snow and frost also caused some loss of forest resources.…”

Section: The Impact Of Forest Protection Policies On Forest Changementioning

confidence: 99%

Investigation of Long-Term Forest Dynamics in Protected Areas of Northeast China Using Landsat Data

Wang

Feng

et al. 2022

Remote Sensing

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Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer’s accuracy = 85.21%; User’s accuracy = 84.26%) and gain (Producer’s accuracy = 87.74%; User’s accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in “forest gain after loss (forest recovery)” and “forest gain with no loss (forest newborn)”, accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection.

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“…In order to understand the above changes, it is necessary to have maps of Essential Climate Variables (ECV)-such as the burned area-readily available [8][9][10]. The georeferenced information must be as exact and consistent as possible and should encompass large periods of time (above 30 years) so that it can be introduced in climate forecast and vegetation dynamics models.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

Moreno-Ruiz

García-Lázaro

Arbeló

2019

Forests

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Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

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Estimates of Wildfire Emissions in Boreal Forests of China

Cited by 11 publications

References 54 publications

Estimation of Forest Fire Emissions in Southwest China from 2013 to 2017

Estimation of Forest Fire Emissions in Southwest China from 2013 to 2017

Investigation of Long-Term Forest Dynamics in Protected Areas of Northeast China Using Landsat Data

A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

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