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Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province,southwestern China

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Forest Ecosystems 2024年第1期11卷 87-99页

作者： Yuwen Peng Huiyi Su Min Sun mingshi li Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037China College of Forestry Nanjing Forestry UniversityNanjing 210037China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from ***,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite *** study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern ***,the forest fire danger index(FFDI)was improved by supplementing slope and aspect *** compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to *** this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger *** results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,*** validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this stu

关键词： Forest fire risk reconstruction MFFDI Time series models LSTM ARIMA Prophet Anusplin

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基于随机森林协同克里金法的区域森林地上生物量制图——以粤北森林为例

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南京林业大学学报（自然科学版） 2024年第1期48卷 169-178页

作者：周友锋谢秉楼李明诗南京林业大学林草学院、水土保持学院南方现代林业协同创新中心江苏南京210037 浙江省森林资源监测中心浙江杭州310020

【目的】森林地上生物量(aboveground biomass,AGB)是森林生态系统健康状态和碳汇潜力评估的重要指标,本研究提出了一种快速、准确地获取区域尺度AGB信息的制图方法。【方法】基于Landsat 5 TM、ALOS-1 PALSAR-1、STRM DEM和国家森林资... 详细信息

【目的】森林地上生物量(aboveground biomass,AGB)是森林生态系统健康状态和碳汇潜力评估的重要指标,本研究提出了一种快速、准确地获取区域尺度AGB信息的制图方法。【方法】基于Landsat 5 TM、ALOS-1 PALSAR-1、STRM DEM和国家森林资源清查数据,提取光谱特征、纹理指数、后向散射系数和地形因子等特征因子,采用随机森林进行AGB建模。考虑到AGB是典型的具有空间自相关特性的生物物理参量,针对模型残差,使用以高程为协变量的克里金空间插值法分离残差项中的结构化成分,并将其叠加到随机森林模型预测值上形成最终的AGB预测值,从而提高区域尺度的AGB制图精度。【结果】协同克里金法将高程数据作为协变量,在预测AGB残差结构化成分方面的性能优于普通克里金法,协同克里金法与随机森林协同的AGB预测性能明显提升。经独立AGB数据的模型验证表明,随机森林模型预测的AGB与实际观测的AGB间的决定系数R^(2)为0.46,随机森林结合普通克里金的验证R^(2)提高到了0.51,而随机森林结合协同克里金模型的验证R^(2)为0.57。相应的均方根误差(RMSE)分别从32.48 t/hm^(2)降低到31.58、29.80 t/hm^(2),平均绝对误差(MAE)从27.28 t/hm^(2)降低到26.63、25.12 t/hm^(2),相对改进指数为0.03和0.08。【结论】总体而言,本研究提出的随机森林协同克里金模型提供了一种更准确、可靠地进行复杂地形区域的亚热带森林AGB制图新方法,所生成的AGB专题图有助于发展针对固碳效能的森林经营方法,为全球气候变化背景下的森林碳增汇和森林可持续经营提供参考。

关键词：森林地上生物量随机森林协同克里金 ALOS-1 PALSAR-1 Landsat 5 TM 国家森林资源连续清查粤北地区

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Characterizing changes in land cover and forest fragmentation from multitemporal Landsat observations(1993-2018)in the Dhorpatan Hunting Reserve,Nepal

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Journal of Forestry Research 2022年第1期33卷 159-170页

作者： Yali Zhang Sandeep Sharma Manjit Bista mingshi li College of Forestry Nanjing Forestry UniversityLongpan Road 159Nanjing 210037People’s Republic of China Department of National Parks and Wildlife Conservation Madanbhandari PathKathmandu 44600Nepal Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityLongpan Road 159Nanjing 210037People’s Republic of China

Natural forces and anthropogenic activities greatly alter land cover,deteriorate or alleviate forest fragmentation and affect *** land cover and forest fragmentation dynamics have become a focus of concern for natural resource management agencies and biodiversity conservation ***,there are few land cover datasets and forest fragmentation information available for the Dhorpatan Hunting Reserve(DHR)of Nepal to develop targeted biodiversity conservation *** this study,these gaps were filled by characterizing land cover and forest fragmentation trends in the *** five Landsat images between 1993 and 2018,a support vector machine algorithm was applied to classify six land cover classes:forest,grasslands,barren lands,agricultural and built-up areas,water bodies,and snow and ***,two landscape process models and four landscape metrics were used to depict the forest fragmentation *** showed that forest cover increased from 39.4%in 1993 to 39.8%in ***,grasslands decreased from 38.2%in 1993 to 36.9%in *** forest shrinkage was responsible for forest loss during the period,suggesting that the loss of forest cover reduced the connectivity between forest and nonforested *** was the dominant component of the forest restoration process,implying that it avoided the occurrence of isolated *** maximum value of edge density and perimeter area fractal dimension metrics and the minimum value of aggregation index were observed in 2011,revealing that forests in this year were most *** specific observations from the current analysis can help local authorities and local communities,who are highly dependent on forest resources,to better develop local forest management and biodiversity conservation plans.

关键词： Land cover Forest fragmentation Spatial process model Fragstats

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Spatio-temporal Variations in Plantation Forests'Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations(1986-2015)

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Chinese Geographical Science 2017年第4期27卷 600-613页

作者： SHEN Wenjuan li mingshi WEI Anshi College of Forestry Nanjing Forestry UniversityNanjing 210037China Co-Innovation Center for Sustainable Forestry in Southern-Cht'na Nanjing Forestry UniversityNanjing 210037China Guangdong Provincial Center for Forest Resources Monitoring Guangzhou 510173China

Forest disturbance plays a vital role in modulating carbon storage,biodiversity and climate *** Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study.A Landsat time series stack(LTSS) was fed to the vegetation change tracker model(VCT) to map long-term changes in plantation forests' disturbance and recovery,followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations,magnitudes and rates of plantations' disturbance and *** the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km^2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%.A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%.Forest disturbance rates' change trend was characterized by a decline first,followed by an increase,then giving way to a decline *** undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001,disturbance rate of 4.51% in 1994 was a notable anomaly,while after 2001 there was a sharp ascending change,forest disturbance rate spiked in 2007(5.84%).After that,there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015(2.59%).Two obvious spikes in post-disturbance recovery rates occurred in 1995(0.26%) and 2008(0.41%).Overall,forest recovery rates were lower than forest disturbance ***,forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery(LandT rendr) algorithms in Fogang County have been conducted,with LandT rendr finding mostly much more distu

关键词： plantation Landsat dense time series remote sensing forest disturbance and recovery driving forces northern Guangdong

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基于多分类器集成的土地覆盖分类及预测研究

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南京林业大学学报(自然科学版) 2024年第5期48卷 255-266页

作者：蒋路凡苏慧毅张银刘琴琴张小伟李明诗南京林业大学林草学院南方现代林业协同创新中心浙江省森林资源监测中心浙江省林业勘测规划设计有限公司

【目的】为发挥不同单分类器各自的优势进而采用集成学习方式提高土地覆盖分类精度，据此比较不同土地覆盖变化模拟模型性能从而执行最优的土地覆盖变化预测，为土地资源合理开发与利用决策制定提供参考。【方法】基于南京市江宁区2000... 详细信息

【目的】为发挥不同单分类器各自的优势进而采用集成学习方式提高土地覆盖分类精度，据此比较不同土地覆盖变化模拟模型性能从而执行最优的土地覆盖变化预测，为土地资源合理开发与利用决策制定提供参考。【方法】基于南京市江宁区2000、2010和2020年的Landsat TM/Oli影像，结合研究区实际定义了水体、建筑、林地、草地、耕地和未利用地等6种土地覆盖分类体系，在测试了最大似然法、马氏距离法、最小距离法、神经网络和支持向量机等基分类器性能基础上，采用随机森林和证据理论2种不同的集成学习方法对5种基分类器的分类结果进行集成，比较了集成性能后构建了最终的土地覆盖分类结果。然后，基于2000和2010年的最优集成土地覆盖分类图，运用CA-Markov、PLUS和ANN-CA模型分别对2020年研究区的土地覆盖格局进行模拟，并将不同的模拟结果与2020年真实集成分类结果进行了空间一致性检验，以此确定土地覆盖变化预测的最佳模型并用其预测2030年江宁区的土地覆盖模式。【结果】在单分类器分类结果中，2000年支持向量机算法取得了最佳分类效果，总体精度达到了88.75%,Kappa系数为0.77;2010年神经网络方法表现最佳，总体精度为88.75%,Kappa系数为0.83;2020年最大似然法取得了最佳分类效果，总体精度为82.75%,Kappa系数为0.74。在2种集成方法中，随机森林在2000年取得了最佳集成分类效果，总体精度和Kappa系数分别为91.25%和0.85;证据理论在2010年取得了最佳集成效果，总体精度和Kappa系数分别为90.80%和0.86;随机森林在2020年取得了最佳集成效果，总体精度和Kappa系数分别为93.75%和0.91。就土地覆盖预测而言，PLUS模型获得了98.54%的空间一致性。根据PLUS模型预测2030年土地覆盖结果可知，江宁区各土地覆盖类型变化较小，建设用地略有扩张但范围有限，耕地稍减少但在可控范围内。林地、草地和未利用地变化较小，水体面积相对减少。【结论】恰当的集成策略能明显改进土地覆盖分类精度，合适的、适应局部土地利用状态和政策调控的预测模型可有效预测区域未来土地覆盖分布模式，它们能为区域土地利用、城市管理决策等提供更可靠的方法和数据支持。

关键词： Landsat影像地表特征土地覆盖分类集成学习土地覆盖变化模拟

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Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

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Journal of Forestry Research 2020年第5期31卷 1743-1758页

作者： Fangyan Zhu Wenjuan Shen Jiaojiao Diao mingshi li Guang Zheng College of Forestry Nanjing Forestry UniversityNanjing 210037People’s Republic of China Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037People’s Republic of China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023People’s Republic of China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection *** spatial resolution remote sensing images can be used to detect subtle vegetation *** major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable *** used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in *** pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection ***,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation *** accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection *** results showed that the MIICA method was the best pixel-based change detection *** the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based *** 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation *** 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of veget

关键词： High spatial resolution satellite images Vegetation change Direct detection method Objectoriented Purple Mountains

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工业实时操作系统关键技术及发展趋势研究

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新型工业化 2024年第3期14卷 14-22页

作者：孟嫣李明时邓昌义国家工业信息安全发展研究中心北京100040

工业实时操作系统(RTOS)广泛应用于工业自动化、军事、电力、新能源等重要场景中,发挥着中枢神经系统的关键作用。当前,我国工业实时操作系统产业规模持续提升,但整体水平仍落后于发达国家,关键领域国产化率较低,缺乏完善的生态系统建... 详细信息

工业实时操作系统(RTOS)广泛应用于工业自动化、军事、电力、新能源等重要场景中,发挥着中枢神经系统的关键作用。当前,我国工业实时操作系统产业规模持续提升,但整体水平仍落后于发达国家,关键领域国产化率较低,缺乏完善的生态系统建设。本文阐述了工业实时操作系统的概念及关键作用,分析了国内外整体市场现状及代表性产品,梳理了工业实时操作系统内核、中间件、辅助设计工具等关键技术及相关现有研究基础,并进一步指出其轻量化、虚拟化、跨平台等发展趋势,为国产工业实时操作系统加速发展应用提供参考。

关键词：工业控制嵌入式软件实时操作系统

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Active forest management accelerates carbon storage in plantation forests in lishui,southern China

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Forest Ecosystems 2022年第1期9卷 33-46页

作者： Jiaojiao Diao Jinxun liu Zhiliang Zhu Xinyuan Wei mingshi li College of Forestry Nanjing Forestry UniversityNanjing 210037China Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037China U.S.Geological Survey Western Geographic Science Center Moffett FieldCA 94035USA U.S.Geological Survey RestonVA 20192USA Environmental Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37830USA

Background:China has committed to achieving peak CO_(2)emissions before 2030 and carbon neutrality before 2060;therefore,accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial *** carbon sink capacity of plantation forests contributes to the mitigation of climate *** forests throughout the world are intensively managed,and there is an urgent need to evaluate the effects of such management on long-term carbon ***:We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species(Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.),oak(Cyclobalanopsis glauca(Thunb.)Oerst.),and pine(Pinus massoniana Lamb.))in lishui,southern China,by using an integrated biosphere simulator(IBIS)tuned with localized ***,we used the state-and-transition simulation model(STSM)to study the effects of active forest management(AFM)on carbon storage by combining forest disturbance history and carbon cycle ***:1)The carbon stock of the oak plantation was lower at an early age(50 years)than that of the Chinese fir and pine plantations.2)The carbon densities of the pine and Chinese fir plantations peaked at 70 years(223.36 Mg⋅ha^(‒1))and 64 years(232.04 Mg⋅ha^(‒1)),respectively,while the carbon density in the oak plantation continued increasing(>100 years).3)From 1989 to 2019,the total carbon pools of the three plantation ecosystems followed an upward trend(an annual increase of 0.16–0.22 Tg C),with the largest proportional increase in the aboveground biomass carbon pool.4)AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations,respectively,but did not result in higher growth in the oak plantation.5)The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the ***:This study provide

关键词： Active forest management Carbon storage IBIS Plantations STSM

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Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

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Forest Ecosystems 2020年第4期7卷 851-870页

作者： Huiyi Su Wenjuan Shen Jingrui Wang Arshad Ali mingshi li College of Forestry Nanjing Forestry UniversityNanjing 210037China. Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037China. Shenzhen Academy of Environmental Sciences Shenzhen 518000China.

Background:Aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management ***:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of *** used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National Forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,***:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable *** root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB *** spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in *** adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study ***:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary krigi

关键词： Forest aboveground biomass Random forest co-kriging ALOS PALSAR Landsat TM National forest inventory Digital elevation model

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Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations(1988-2016)

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Frontiers of Earth Science 2020年第4期14卷 816-827页

作者： Fangyan Zhu Heng Wang mingshi li Jiaojiao Diao Wenjuan Shen Yali Zhang Hongji Wu College of Forestry Nanjing Forestry UniversityNanjing210037China Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing210037China

Climate change,a recognized critical environmental issue,plays an important role in regulating the structure and function of forest ecosystems by altering forest disturbance and recovery *** research focused on exploring the statistical relationships between meteorological and topographic variables and the recovery characteristics following disturbance of plantation forests in southern *** used long-term Landsat images and the vegetation change tracker algorithm to map forest disturbance and recovery events in the study area from 1988 to *** multiple linear regression(MLR),random forest(RF)regression,and support vector machine(SVM)regression were used in conjunction with climate variables and topographic factors to model short-term forest recovery using the normalized difference vegetation index(NDVI).The results demonstrated that the regenerating forests were sensitive to the variation in *** fitted results suggested that the relationship between the NDVI values of the forest areas and the post-disturbance climatic and topographic factors differed in regression *** RF regression yielded the best performance with an R2 value of 0.7348 for the validation *** indicated that slope and temperature,especially high temperatures,had substantial effects on post-disturbance vegetation recovery in southern *** other mid-subtropical monsoon regions with intense light and heat and abundant rainfall,the information will also contribute to appropriate decisions for forest managers on forest recovery ***,it is essential to explore the relationships between forest recovery and climate change of different vegetation types or species for more accurate and targeted forest recovery strategies.

关键词： climate change forest disturbance forest recovery vegetation change tracker

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