Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Detection of Burned Areas through Spectral Indices Analysis of Sentinel-2A Satellite Images in the Abokouamékro Wildlife Reserve (Central, Côte D’Ivoire)

In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.

Spatiotemporal changes in forest loss and its linkage to burned areas in China

Fire-induced forest loss has substantially increased worldwide over the last decade.In China,the connection between forest loss and frequent fi res on a national scale remains largely unexplored.In this study,we used a data set for a time-series of forest loss from the Global Forest Watch and for a MODIS-derived burned area for 2003–2015 to ascertain variations in forest loss and to explore its relationship with forest fi res(represented by burned areas)at the country-and forest-zone levels.We quantifi ed trends in forest loss during 2003–2015 using linear regression analysis and assessed the relation between forest loss and burned areas using Spearman’s correlation.Forest loss increased signifi cantly(264.8 km 2 a−1;R 2=0.54,p<0.01)throughout China,with an average annual increase of 11.4%during 2003–2015.However,the forest loss trend had extensive spatial heterogeneity.Forest loss increased mainly in the subtropical evergreen broadleaf forest zone(315.0 km 2 a−1;R 2=0.69,p<0.01)and tropical rainforest zone(38.8 km 2 a−1;R 2=0.66,p<0.01),but the loss of forest decreased in the cold temperate deciduous coniferous forest zone(−70.8 km 2 year−1;R 2=0.75,p<0.01)and the temperate deciduous mixed broadleaf and coniferous forest zone(−14.4 km 2 a−1;R 2=0.45,p<0.05).We found that 1.0%of China’s area had a signifi cant positive correlation(r≥0.55,p<0.05)with burned areas and 0.3%had a signifi cant negative correlation(r≤−0.55,p<0.05).In particular,forest loss had a signifi cant positive relationship with the burned area in the cold temperate deciduous coniferous forest zone(16.9% of the lands)and the subtropical evergreen broadleaf forest zone(7.8%).These results provide a basis for future predictions of fi re-induced forest loss in China.

Factorial analysis on forest canopy density restoration in the burned area of northern Great Xing＇an Mountains, China

The restoration of forest landscape has drawn much attention since thecatastrophic fire took place on the northern slope of Great Xing'an Mountains in 1987. Forest canopydensity, which has close relation to forest productivity, was selected as a key factor to find howmuch the forest quality was changed 13 years after fire, and how fire severity, regeneration way andterrain factors influenced the restoration of forest canopy density, based on forest inventory datain China, and using Kendall Bivariate Correlation Analysis, and Distances Correlation Analysis. Theresults showed that fire severity which was inversely correlated with forest canopy density gradewas an initial factor among all that selected. Regeneration way which did not remarkably affectforest canopy density restoration in short period, may shorten the cycle of forest succession andpromote the forest productivity of conophorium in the future. Among the three terrain factors, theeffect of slope was the strongest, the position on slope was the second and the aspect was the last.

A MODIS Time Series Data Based Algorithm for Mapping Forest Fire Burned Area

Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and climate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter (MODIS) time series data in Heilongjiang Province, China. The algorithm is divided into two steps: Firstly, the′core′pixels were extracted to represent the most possible burned pixels based on the comparison of the temporal change of Global Environmental Monitoring Index (GEMI), Burned Area Index (BAI) and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the′core′pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre-and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm

DISCUSSIONS ON FOREST RECOVERY IN THE BURNED AREA IN DAXING'ANLING FOREST REGION

The disastrous fire occurred in northern area of Daxing’anling forest region on May 6, 1987, destroyed a large area of forests. The broad-leaved tree species, such as poplar,birch had regenerated in a great quantity after the fire, but the coniferous species such as larch and scotch pine had difficult to regenerate naturally, This paper put forward that the coniferous forest could be recovered by the planting method of effect strip and effect islet based on the principle of borderline effects and by making full use of the condition of broad-leaved trees.

削痂清创与水刀清创治疗中面积深Ⅱ度烧伤创面的临床效果比较

目的:比较中面积深Ⅱ度烧伤患者应用削痂清创与水刀清创治疗的临床效果。方法:选取2021年1月—2023年3月吉安市中心人民医院收治的中面积深Ⅱ度烧伤患者,共82例,按照随机数字表法分成水刀清创组(41例)与削痂清创组(41例)。水刀清创组采用水刀清创治疗,削痂清创组采用削痂清创治疗。比较两组临床指标、预后情况、创面愈合质量、炎症因子及疼痛介质。结果:水刀清创组每1%面积清创时间、创面愈合时间、住院时间均较削痂清创组短,创面细菌菌落数更少,差异均有统计学意义(P<0.05)。相较削痂清创组,水刀清创组二次清创率更低,差异有统计学意义(P<0.05)。相较削痂清创组,水刀清创组改良温哥华瘢痕评估量表(mVSS)评分均更低,差异均有统计学意义(P<0.05)。治疗后,两组降钙素原(PCT)、C反应蛋白(CRP)水平均较治疗前下降,相较削痂清创组,水刀清创组均更低,差异均有统计学意义(P<0.05)。治疗后,两组5-羟色胺(5-HT)、神经肽Y(NPY)水平均较治疗前下降,相较削痂清创组,水刀清创组均更低,差异均有统计学意义(P<0.05)。结论:与削痂清创比较,中面积深Ⅱ度烧伤患者应用水刀治疗的效果更好,能够改善临床指标及预后情况,促进创面良好愈合,调节炎症因子、疼痛介质水平。

大兴安岭重度火烧迹地不同林龄落叶松人工林土壤团聚体养分特征

[目的]为探寻大兴安岭重度火烧迹地植被恢复后不同林龄落叶松人工林下土壤团聚体养分含量与分布的变化。[方法]以大兴安岭1987年发生特大森林火灾形成的重度火烧迹地上分别在1989年、2000年、2010年种植的落叶松人工林(11,21,32年)作为研究对象,通过测定土壤团聚体各粒级有机碳、全氮、全磷、碱解氮、速效磷和速效钾的含量,开展土壤团聚体养分分布特征研究。[结果](1)在重度火烧迹地上不同林龄的落叶松人工林中,土壤各粒级团聚体有机碳、全氮、全磷、碱解氮和速效钾的含量均随着粒径的减小逐渐上升,<0.5 mm粒级含量最大。(2)随着落叶松人工林林龄的增长,土壤各粒级团聚体有机碳、全氮、全磷、碱解氮和速效磷含量均逐渐上升。32年落叶松人工林土壤团聚体有机碳、全氮、全磷、碱解氮和速效磷含量相较于11年落叶松人工林分别显著增加77.35%~130.24%,77.61%~143.36%,105.84%~147.98%,94.58%~155.96%,206.98%~537.09%(p<0.05)。21年落叶松人工林土壤各粒级团聚体有机碳含量高于11年落叶松人工林,但二者无明显差异。(3)土壤各粒级团聚体有机碳与全氮和碱解氮极显著相关(p<0.01),土壤有机碳和碱解氮含量与<0.25 mm粒级含量相关性最强,全氮、全磷、速效磷和速效钾与>0.25 mm粒级含量极显著相关(p<0.01)。[结论]落叶松人工林土壤团聚体养分呈表聚现象。大兴安岭重度火烧迹地上种植的落叶松人工林,随着林龄的增大土壤团聚体养分含量增加,种植32年的落叶松人工林显著提升土壤团聚体的养分含量,研究结果可为大兴安岭重度火烧迹地人工植被恢复工作提供科学依据。

大兴安岭火烧迹地天然次生林土壤微生物生物量及酶活性特征研究

探究火烧迹地植被恢复过程中土壤微生物生物量及酶活性的变化规律,为火烧迹地森林恢复途径选择及恢复成效评价提供科学依据。以大兴安岭重度火烧迹地落叶松母树-白桦林(MB)、林下抚育-白桦林(FB)、无干扰白桦林(BB)3种不同类型的白桦林为研究对象,落叶松人工林(LL)为对照,测定0~5、5~10、10~20 cm土壤微生物生物量碳(MBC)、土壤微生物生物量氮(MBN)和脲酶(URE)、酸性磷酸酶(ACP)、蔗糖酶(SUC)、过氧化氢酶(CAT)活性。结果表明,1)土壤MBC、MBN含量变化为239.16~852.09 mg/kg和37.08~114.99 mg/kg,除10~20 cm土层外MBC、MBN含量由大到小依次为MB、FB、LL、BB。2)MB土壤ACP、CAT和URE活性最高,FB的ACP、SUC、CAT活性均值要高于LL,BB的URE、ACP活性均值低于LL。3)冗余分析表明,0~5 cm土层土壤SOC、SAP、MBN对土壤酶活性的解释率达到了46.8%、24.9%和4.5%,5~10 cm土层土壤NO-3-N、pH、MBC的解释率分别为61.8%、11.4%和4.0%。10~20 cm土层土壤NO-3-N、pH、TN的解释率为53.3%、14.7%和12.4%。研究表明火后保留具有繁殖能力的树木作为母树恢复森林和及时森林抚育管理对土壤微生物生物量和酶活性具有促进作用。

内蒙古大兴安岭林火时空动态的变化特征

内蒙古大兴安岭森林火灾频发,研究其季节性变化趋势及空间动态分布特征,对今后该地区森林防火期划分和防火资源的合理分配具有重要意义。利用Mann-Kendall趋势检验法、滑动t检验法对1981—2018年内蒙古大兴安岭森林火灾数据进行趋势和突变分析,使用分位数回归法研究影响森林火灾过火面积的时间与空间因素,得出内蒙古大兴安岭森林火灾在时间和空间上的动态变化特征。结果表明:内蒙古大兴安岭森林火灾整体呈先下降(1981—1999年)再上升(2000—2007年)又下降(2008—2018年)的趋势。森林火灾主要发生在春、夏、秋三季,春季森林火灾变化较复杂,波动明显,在1991年发生突变;秋季森林火灾波动较平缓,但在1994年发生突变;2004—2011年期间夏季森林火灾明显增长,火险期向夏季发生偏移和延长;冬季发生火灾较少。森林火灾过火面积与火灾持续时间呈正相关,尤其在高分位点处,正相关性更加显著。内蒙古大兴安岭森林火灾主要发生在鄂伦春自治旗等地,从空间横向动态变化来看,整体呈现由西南向东北递增的趋势;从空间纵向动态变化来看,海拔对森林火灾过火面积的影响并不显著。建议应加强内蒙古大兴安岭东北地区及春、夏两季的林火监测和防火宣传,适当调整森林防火期和防火资源时空分配,将有助于减少内蒙古大兴安岭森林火灾的发生。

火烧迹地恢复后不同稳定机制土壤有机碳库特征研究

以大兴安岭重度火烧迹地经过不同方式恢复的林分土壤为研究对象,采用物理-化学联合分组方法,研究未保护、物理保护、化学保护、生物化学保护、物理-化学保护和物理-生物化学保护有机碳组分含量特征及其与土壤总有机碳含量间的关系。结果表明,1)人工恢复的林分土壤总有机碳、未保护有机碳、物理保护有机碳、物理-化学保护有机碳、物理-生物化学保护有机碳和生物化学保护有机碳含量均显著高于自然恢复。化学保护有机碳含量在不同恢复方式下变化不显著。除化学保护有机碳和物理-化学保护有机碳外,其余各保护态有机碳均表现为上层土壤含量显著高于下层(P<0.05)。2)2种恢复方式下的土壤有机碳组分均以未保护有机碳占比最高,物理-化学保护有机碳占比最低。与天然恢复相比,人工恢复下的土壤物理保护有机碳含量占比显著提高2.80%~3.19%(P<0.05)。3)回归分析表明,除物理-化学保护有机碳和化学保护有机碳组分外,其余各保护态有机碳组分与总有机碳含量之间均呈极显著的正相关关系(P<0.001)。大兴安岭重度火烧迹地经过32 a的人工恢复,更利于土壤总有机碳积累,土壤未保护有机碳的积累更多。研究结果为该区域重度火烧迹地植被恢复提供科学依据。

基于AGREE算法与BURN-IN算法的平原灌区DEM河网提取问题剖析

基于数字高程模型(DEM)的区域水系提取应用较为广泛,当提取对象为平原灌区时存在突出问题,如河道断裂、平行河道的出现以及末端支流的提取未果等造成的子流域划分结果与实际轨迹不符等现象,数字化河网的精确程度直接影响后续的区域水文分析,故该问题值得关注。【目的】优化DEM河网提取结果。【方法】基于Agree算法和Burn-in算法下的提取河网,并从浅表、DEM因子与河网提取的相关关系等方面进行原因分析,进一步提出解决方法。【结果】2种算法下平原灌区河网提取问题的主要原因是DEM离散误差、栅格像元值的真实程度、栅格像元的精度以及地表的无渗漏假设;提出的解决方法为栅格像元水平分辨率的提高与主河道断裂部分的栅格重计算。对于坡度范围为0°～10°的平原灌区,坡度对地表水流方向的形成影响较小,不具备影响河网提取问题的能力;栅格像元水平分辨率提高10 m×10 m时,平原灌区2种算法下数字河道提取首要问题成功解决;主河道断裂部分的栅格重计算虽成功提取原断裂部分河道,但有新的断裂部分生成,未从根本解决。【结论】Agree算法和Burn-in算法下的基于DEM的平原灌区河网提取问题,经一系列原因剖析和解决方法的提出与应用,提取结果较为满意,提倡学者能够结合2种方法思路作为解决类似平原灌区的提取数字河网出现的问题。

高精度位场边界识别方法及在煤矿火烧区边界解释的应用

场源边界识别是位场数据解释必不可少的任务,早先人们使用数据的分布特征来获取场源的边界信息,难以在较强的背景异常条件下识别弱异常,为此使用基于一定窗口尺寸的自动控制滤波器来识别场源的分布,然而这种方法的结果对于窗口尺寸有较大的依赖,且对于复杂异常难以良好应用。近几年,人们主要采用位场数据导数的特征点反映场源边界信息的特征,然后利用成像结果与边界的对应关系来识别场源体的水平边界。其中,磁异常水平导数的极值、垂直导数的零值与地质体边界相对应,现有边界识别方法主要采用一阶水平和垂直导数的比值所组成的均衡边界识别滤波器来完成地质体位置的圈定,但方法分辨率和泛用性较低。基于此,提出将基于不同阶导数比值的边界检测滤波器与多尺度无监督深度学习相结合,利用不同阶导数比值来获得更高分辨率的边缘成像结果,同时建立Deep Image Prior(DIP)与Generative Adversarial Network-None Local(GAN-NL)网络相结合的多尺度无监督深度学习,根据边缘成像结果的极值来获取源水平位置。利用多尺度DIP网络来识别源位置,在DIP网络中加入自注意机制神经网络增强其学习能力,可以在不需要大量数据标签的情况下去除噪声,利用GAN-NL网络对极值点进行分类,给出极值点的位置信息。与其他边缘检测滤波器比较的结果表明:所开发的边界识别方法具有更高的分辨能力,其能够更精确、更清晰地显示场源的边缘,相对常规方法精度提升15%左右。多尺度无监督深度学习可以根据边缘成像结果自动给出源边缘,且结果与真实边缘一致,具有良好的泛用性。通常情况下,煤层与围岩之间不存在明显的磁性差异。而煤层自燃时,高温使煤层顶板地层中的黄铁矿、菱铁矿等结核体受热变质,形成含铁磁性矿物的烧成岩。温度降低后,保留了强烈的热剩余物,磁化强度比燃烧前高几十倍,因此煤矿燃烧区存在明显的磁异常。这一特性为利用磁测法圈定燃烧区域边界提供了物理前提。针对山东某矿煤田燃烧区开展磁法勘探来查明其分布,将所开发的边界识别方法用于获取火烧区的分布范围,后期打钻验证结果准确率85%。

基于Landsat 8遥感数据的森林火灾过火面积估算——以贵州毕节市赫章县“3·18”火灾为例

以贵州省毕节市赫章县2021年3月18日较大森林火灾为例,利用火灾前后Landsat 8遥感数据及ENVI遥感数据处理分析软件,通过图像预处理、计算归一化植被指数和燃烧面积指数等方法,提取森林火灾过火区域,计算过火面积。结果表明,利用Landsat 8数据能够较好地提取森林火灾过火区域,过火面积估算准确率达96.2%。

成批大面积烧伤患者的组织救治效果分析

目的 分析成批大面积烧伤患者的组织救治效果。方法 19批(79例)烧伤面积>30%的大面积烧伤患者,均接受补液等药物治疗、气管切开、创面治疗以及其他治疗。记录治疗结果,并比较治疗前后患者的生长因子[血管内皮生长因子(VEGF)、S100钙结合蛋白A9(S100A9)、碱性成纤维细胞生长因子(bFGF)]水平、疼痛因子[P物质(SP)、前列腺素E2(PGE2)、环氧化酶2(COX-2)]水平。结果 79例患者治愈69例,死亡10例,其中4例死于创面感染,2例死于重度吸入性损伤,1例死于后期气管切开的无名动脉出血,1例死于肾衰竭,1例死于继发性肺炎,1例死于导管败血症。治疗后,患者的VEGF(265.73±18.49)μg/L、S100A9(176.49±10.18)μg/L、bFGF(274.85±18.63)μg/L均高于治疗前的(110.54±7.63)、(80.53±3.12)、(101.52±7.24)μg/L,差异有统计学意义(P<0.05)。治疗后,患者的SP、PGE2、COX-2分别为(46.53±5.11)ng/ml、(124.39±10.67)pg/ml、(53.47±3.42)ng/ml,均低于治疗前的(75.29±10.24)ng/ml、(238.74±20.15)pg/ml、(126.79±10.82)ng/ml,差异有统计学意义(t=22.337、44.576、57.429,P=0.000、0.000、0.000<0.05)。结论 针对成批大面积烧伤患者实施组织救治的效果显著,有利于减轻疼痛程度,调节生长因子水平,临床可进一步推广应用。

基于正交设计对新疆地区露天煤矿富水火烧区影响边坡稳定性的敏感分析

露天煤矿边坡所处地质环境在煤层自燃后发生变化,选取密度、黏聚力、内摩擦角、渗透系数、坡高、坡角、水位高度,采用正交设计方法设计“7因素5水平”试验方案,应用SEEP/W与SLOPE/W计算边坡稳定系数,将其作为评价指标,通过极差分析、方差分析方法分析7种因素的敏感性。研究表明:7种因素的敏感性为坡角>水位高度>黏聚力>渗透系数>坡高>内摩擦角>密度,方差分析的影响效应与极差分析顺序一致,黏聚力、渗透系数、坡角、水位高度是主要影响因素,坡高、密度、内摩擦角是次要影响因素。

阔叶红松林火烧迹地天然更新特征分析

通过对天然更新阔叶混交林的林分密度、树种组成、径阶结构和主要树种生长规律4个方面探讨天然更新阔叶混交林林木更新特征,为火烧迹地植被恢复和天然林保护提供理论基础。结果表明,其阔叶红松林火烧后80 a,天然更新形成的林分主要树种是榆树和白桦,虽公顷蓄积量不低,但经济价值不高,且缺少针叶树种,如要加快林分的演替进程,需要采用人工促进天然更新或人工引入本土红松的措施,加速火烧迹地向顶极群落的恢复。

西昌安保公司后山火后泥石流成灾特征

火后泥石流是指火烧迹地附近发生的与林火密切相关的泥石流。以四川省凉山州西昌市安保公司后山沟为研究对象,通过无人机航拍、现场勘查和室内分析等方法,对火烧迹地、沟道流域和物源特征进行阐述分析,对火后泥石流成灾特征进行论证研究。结果表明,安保公司后山沟过火面积达到流域总面积的100%,其中重度火烧区占流域的51.8%,灰烬层厚度在3~5cm,扰动土层厚度4~6cm;中度火烧区占流域的44.4%,灰烬层厚度在2~4cm,扰动土层厚度3~5cm;火烧迹地灰烬层和火烧扰动土层厚度与火烧烈度呈正比。火后泥石流的启动方式由坡面径流冲刷和浅层滑坡组成,两种启动方式共同作用引发。火后泥石流表现出暴发频率高,火灾后第一个汛期就暴发了4次泥石流灾害,且多为当地常见的中到大雨激发,说明火后泥石流沟启动降雨阈值降低、暴发频率高。通过对火后泥石流沟成灾特征的研究,为火后泥石流的治理提供科学依据。