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

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 fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This 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 China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We 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 1970.Following 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 database.The 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,respectively.Independent 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 study.

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

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 ecosystems.The carbon sink capacity of plantation forests contributes to the mitigation of climate change.Plantation forests throughout the world are intensively managed,and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics.Methods: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 parameters.Then,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 regimes.Results:1)The carbon stock of the oak plantation was lower at an early age(<50 years)but higher at an advanced 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 forest.Conclusions:This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO_(2)emissions and reach carbon neutrality.

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

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 measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The 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 management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four 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 performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 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 loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

Correction to: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

The article "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",written by Fangyan Zhu,Wenjuan Shen,Jiaojiao Diao,Mingshi Li and Guang Zheng,was originally pub-lished electronically on the publisher’s Internet portal(currently SpringerLink)on 14 May 2019 without open access.

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

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 plans.Methods: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 China.We 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,China.Results:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable manner.The 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 map.The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010.By 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 area.Conclusions: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 kriging),and the random forest model.The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography.The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987–2011)

Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker （VCT） model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks （LTSS）. The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM＋ scenes with different projec- tions due to distinct data sources （Beijing remote sensing ground station and the USGS EROS Center）. The valida- tion results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 （38.3 %） and 1992 （56.2 %） in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis- registration errors of the image pairs. Major forest distur- bance types existing in our study area were identified as agricultural expansion （39.8 %）, urbanization （24.9 %）, forest management practice （19.3 %）, and mining （12.8 %）. In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China＇s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.

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

Natural forces and anthropogenic activities greatly alter land cover,deteriorate or alleviate forest fragmentation and affect biodiversity.Thus land cover and forest fragmentation dynamics have become a focus of concern for natural resource management agencies and biodiversity conservation communities.However,there are few land cover datasets and forest fragmentation information available for the Dhorpatan Hunting Reserve(DHR)of Nepal to develop targeted biodiversity conservation plans.In this study,these gaps were filled by characterizing land cover and forest fragmentation trends in the DHR.Using 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 glaciers.Subsequently,two landscape process models and four landscape metrics were used to depict the forest fragmentation situations.Results showed that forest cover increased from 39.4%in 1993 to 39.8%in 2018.Conversely,grasslands decreased from 38.2%in 1993 to 36.9%in 2018.The forest shrinkage was responsible for forest loss during the period,suggesting that the loss of forest cover reduced the connectivity between forest and nonforested areas.Expansion was the dominant component of the forest restoration process,implying that it avoided the occurrence of isolated forests.The 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 fragmented.These 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.

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

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 regimes.This research focused on exploring the statistical relationships between meteorological and topographic variables and the recovery characteristics following disturbance of plantation forests in southern China.We 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 2016.Stepwise 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 temperature.The 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 algorithms.The RF regression yielded the best performance with an R2 value of 0.7348 for the validation accuracy.This indicated that slope and temperature,especially high temperatures,had substantial effects on post-disturbance vegetation recovery in southern China.For 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 measures.Additionally,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.