Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

A new methodology for estimating forest NPP based on forest in-ventory data——a case study of Chinese pine forest

Accurately estimating forest net primary productivity (NPP) plays an important role in study of global carbon budget. A NPP model reflecting the synthetic effects of both biotic (forest stand age, A and stem volume, V) and climatic factors (mean annual actual evapotranspiration, E) was developed for Chinese pine (Pinus tabulaeformis) forest by making full use of Forest Inventory Data (FID) and dynamically assessing forest productivity. The NPP of Chinese pine forest was estimated by using this model and the fourth FID (1989–1993), and the spatial pattern of NPP of Chinese pine forest was given by Geography Information System (GIS) software. The results indicated that mean NPP value, of Chinese pine forest was 7.82 t m?2·a?1 and varied at the range of 3.32–11.87 t hm?2·a?1. NPP distribution of Chinese pine forests was significantly different in different regions, higher in the south and lower in the north of China. In terms of the main distribution regions of Chinese pine, the NPPs of Chinese pine forest in Shanxi and Shaanxi provinces were in middle level, with an average NPP of 7.4 t hm?2·a?1, that in the southern and the eastern parts (e.g. Shichuang Hunan, Henan, and Liaoning provinces) was higher (over 7.7 t hm?2·a?1), and that in the northern part and western part (e.g. Neimenggu and Ningxia provinces) was lower (below 5 t hm?2·a?1). This study provides an efficient way for using FID to understand the dynamics of foest NPP and evaluate its effects on global climate change. Keywords Forest NPP - Forest inventory data - Chinese pine forest - Climatic and biotic NPP model - Spatial distribution pattern CLC number S727.22 - S757.2 Document code A Foundation item: This study was supported by the National Natural Science Foundation of China (Nos. 30028001, 49905005), National Key Basic Research Specific Foundation (G1999043407); the Chinese Academy of Sciences (KSC2-1-07).Biography: ZHAO Min (1973-), female, Ph. D. in Laboratory of Quantitative Vegetation Ecology, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, P. R. China.Responsible editor: Zhu Hong

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.

Land cover mapping of deciduous forest regions using ETM+ data: a case study of Azerbaijan Province, Iran

Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM＋ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.

A Data-Driven Car-Following Model Based on the Random Forest

The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re- presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em- ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car- following behavior with better performance under multiple performance indicators.

Forest Change and Its Effect on Biomass in Yok Don National Park in Central Highlands of Vietnam Using Ground Data and Geospatial Techniques

This paper assesses the changes in forest cover in Yok Don National Park of Vietnam between 2004 and 2010, and the implications of such changes on the biomass stocks of this national park. Remote sensing and GIS tools along with the ground truth data collected from the field were employed for classifying the forest types of the study area from SPOT HRV satellite imagery for years 2004 and 2010. The total area considered in this study is 115.5 thousand ha. Five different categories of forests were identified. The results demonstrated that between 2004 and 2010, the Evergreen broad leaved rich quality forest decreased by 11.2 thousand ha (3.5 Mega tons of biomass) and the Dry open dipterocarps medium quality forest decreased by 15.3 thousand ha (2.5 Mega tons of biomass). In that time period, the Evergreen broad leaved medium quality forest increased by 3.2 thousand ha (0.8 Mega tons of biomass), the Evergreen broad leaved poor quality forest increased by 2.5 thousand ha (0.24 Mega tons of biomass), and the Dry open dipterocarps poor quality forest increased by 3.2 thousand ha (0.69 Mega tons of biomass). Total biomass of the study area decreased by 4.3 Mega tons.

Classifying forest inventory data into species-based forest community types at broad extents： exploring tradeoffs among supervised and unsupervised approaches

Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis （FIA） plots. Methods： Each plot had three forest community type labels： （1） ＂FIA＂ types were assigned by the FIA program using a supervised method; （2） ＂USNVC＂ types were assigned via a key based on the U.S. National Vegetation Classification; （3） ＂empirical＂ types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. Results： The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The Iongleaf pine forest community type stood out as the exception： it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as Ioblolly pine types, indicator species metrics were generally high for the USNVC, suggesting that LJSNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variables. Conclusions： The classifications have similarities and differences that reflect their differing approaches and Dbjectives. They are most consistent for forest community types that occur in a relatively narrow range of Invironmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, guggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers.

大小兴安岭地区森林生态城市带林业经济增长动力分析——基于Panel Data模型

大小兴安岭地区森林生态城市带具有大兴安岭地区、伊春市和黑河市3个核心发展区辐射周边区域,该城市带的建立有利于整合大兴安岭地区森林资源,发展区域林业经济优势产业。文章运用Panel Data模型对大小兴安岭地区林业经济增长动力进行分析,通过根据固定影响变系数模型计算,测试大小兴安岭地区林业经济增长的主动力,分别测算林业三次产业内部增长动力,根据大小兴安岭地区林业经济发展的实际情况,给出若干提升区域林业经济增长动力,促进林业经济发展的建议。

Forest biomass carbon storage from multiple inventories over the past 30 years in Gansu Province, China: implications from the age structure of major forest types

We used the forest inventory data of Gansu Province, China to quantify carbon storage and carbon density changes by regional forest cover and by typical forest types in 1979-2006. Total forest area increased from 1.77 x 106 ha in 1979 to 2.32 x 106 ha in 2006, and the forest carbon storage, estimated by the continuous biomass expansion factor method, increased from 83.14 to 100.66 Tg, equivalent to a carbon accumulation rate of 0.0071 Tg per year during the period. Mean carbon densities were 44.83-48.50 t ha-1 and the values decreased slightly over the time period. Natural forests generated greater car- bon storage and density than did plantations. By regression analysis, forest stand age was an important parameter incarbon density studies. We developed various regression equations between carbon density and stand age for major types of natural forests and plantations in the region. Our results can be used for proper selection of re-forestation species and efficient management of young and middle-aged forests, offering great potential for future carbon sequestra- tion, especially in arid and semi-arid regions.

Effects of Afforestation on Carbon Storage in Boyang Lake Basin,China

By using field survey data from the sixth forest inventory of Jiangxi Province in 2003,the biomass and carbon storage for three studied species(Pinus massoniana,Cunninghamia lanceolata,and Pinus elliottii)were estimated in Taihe and Xingguo counties of Boyang Lake Basin,Jiangxi Province,China.The relationship between carbon density and forest age was analyzed by logistic equations.Spatio-temporal dynamics of forest biomass and carbon storage in 1985–2003 were also described.The results show that total stand area of the three forest species was 3.10×105ha,total biomass 22.20 Tg,vegetation carbon storage 13.07 Tg C,and average carbon density 42.36 Mg C/ha in the study area in 2003.Carbon storage by forest type in descending order was:P.massoniana,C.lanceolata and P.elliottii.Carbon storage by forest age group in descending order was:middle stand,young stand,near-mature stand and mature stand.Carbon storage by plantation forests was 1.89 times higher than that by natural forests.Carbon density of the three species increased 8.58 Mg C/ha during the study period.The carbon density of Taihe County was higher in the east and west,and lower in the middle.The carbon density of Xingguo County was higher in the northeast and lower in the middle.In general,the carbon density increased with altitude and gradient.Afforestation projects contribute significantly to increasing stand area and carbon storage.Appropriate forest management may improve the carbon sequestration capacity of forest ecosystems.

Forest biodiversity, relationships to structural and functional attributes, and stability in New England forests

Background： Forest biodiversity is the foundation of many ecosystem services, and the effect of biodiversity on ecosystem functioning and processes （BEF） has been a central issue in biodiversity studies. Although many hypotheses have been developed to interpret global gradients of biodiversity, there has not been complete agreement on mechanisms controlling biodiversity patterns and distributions. Differences may be due to limited observation data and inconsistencies of spatial scales in analysis. Methods： In this study, we take advantage of USDA Forest Service forest inventory and analysis （FIA） data for exploring regional forest biodiversity and BEF in New England forests. The FIA data provide detailed information of sampled plots and trees for the region, including 6000 FIA plots and more than 33,000 individual trees. Biodiversity models were used to analyze the data. Results： Tree species diversity increases from the north to the south at a rate about 2-3 species per latitudinal degree. Tree species diversity is better predicted by tree height than forest age or biomass. Very different distribution patterns of two common maple species, sugar maple （Acer sdcchorum） and red maple （Acer rubrum）, highlight the vulnerability of sugar maple and its potential replacement by red maple on New England landscapes. Red maple generally already outperforms sugar maple, and will likely and continuously benefit from a changing climate in New England. Conclusions： We conclude that forest structure （height） and resources （biomass） are more likely foundational characteristics supporting biodiversity rather than biodiversity determining forest productivity and/or biomass. The potential replacement of red maple for sugar maple in the New England areas could affect biodiversity and stability of forest ecosystem functioning because sugar maple plays important ecological roles distinct from red maple that are beneficial to other tree species in northern hardwood forests. Such a change may not affect forest resilience in terms of forest productivity and biomass as these are similar in red maple and sugar maple, however, it would almost certainly alter forest structure across the landscape.

Impact of Natural Forest Protection Program policies on forests in northeastern China

This study analyzes the effects of the Natural Forest Protection Program （NFPP） on the restoration of forest resources in the northeastern forest region of China, an important region because of its ecological and geographical location and timber supply in China, by using panel data sets of 35 forest farms of state-owned forest enterprises. Panel data analysis results show that related policies under the NFPP have had positive effects on forest resources. Planting activity had a statistically significant effect on forest growth and could be reinforced continually by planting tree species suitable for a warming climate in temperate forests. Forest management and protection played a powerful role in increasing the growing stock of the forests. In NFPP areas timber harvests, after implementation of the NFPP, were a positive factor in increasing forest resources if the harvests combined effective tending and thinning under a carefully guided management plan.

基于iForest和LOF的流量异常检测

异常检测在现代大规模分布式系统的安全管理中起着重要作用,而网络流量异常检测则是组成异常检测系统的重要工具。网络流量异常检测的目的是找到和大多数流量数据不同的流量,并将这些离群点视为异常。由于现有的基于树分离的孤立森林(iForest)检测方法存在不能检测出局部异常的缺陷,为了克服这个缺陷,提出一种基于iForest和局部离群因子(LOF)近邻集成的无监督的流量异常检测方法。首先,改进原始的iForest与LOF算法,在提升检测精度的同时控制算法时间;然后分别使用两种改进算法进行检测,并将结果进行融合以得到最终的检测结果;最后在自制数据集上对所提方法进行有效性验证。实验结果表明,所提方法能够有效地隔离出异常,获得良好的流量异常检测效果。

考虑载客状态的改进孤立森林浮动车异常数据检测算法

为提高浮动车数据中异常数据检测能力及不同载客状态下的模型检测分析能力,提出基于S-DTA-IIForest(Summation&Difference Third Order Average&Improvement-Isolation Forest)的浮动车数据异常检测算法。构建由相邻两项求和(S)、三阶求和平均差分(DTA)的二维度空间SDTA特征向量;提出差额累计更新和动态区分辨识的改进孤立森林IIForest算法,通过设置停止阈值参数,避免当出现新样本异常值分数大于停止阈值时,仅更新样本不更新孤立森林模型的问题,设计每个二叉树区分辨识度参数,区分辨识度位于停止区间时停止二叉树生长,提高算法收敛性能,以ROC(Receiver Operating Characteristic)曲线下面积AUC(Area Under ROC Cure)、F1-score为指标对模型精度进行对比分析,并以重庆市中心城区学府大道开展实例验证。结果表明:本文S-DTA-IIForest组合算法AUC、F1-score分别为86.63%、0.89,AUC较传统孤立森林IForest(Isolation Forest)提高32.4%,运行效率提高1.29%,具有收敛速度更快、精度更高的优势,载客条件下模型AUC、F1-score较未载客分别提高7.7%、10.8%,组合算法对载客数据有更高的检测精度,且未载客状态数据异常率较载客状态增加71.4%,未载客数据异常率更高。

The global rainforest mapping project JERS-1: a paradigm of international collaboration for monitoring land cover change

The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.

基于车联网大数据的重型货车载重估计方法

针对当前货车载重计算方法普遍存在的成本高昂及泛化性能不明确的问题,提出一种创新的重型货车载重估计方法,方法融合了车辆行驶动力学理论与机器学习算法,通过有监督学习,利用高速通行大数据对模型进行训练与验证.首先采用聚类分析,确定车辆空、半、满载判断阈值,为后续的计算提供了重要依据.随后,利用随机森林算法训练分类模型,用以判断车辆在一段行驶过程中的基本载重情况.在此基础上,进一步在车辆行驶数据中筛选出稳定行驶的小片段,根据车辆系统动力学理论,对这些小片段车重进行计算.最后,根据载重状态的判断结果,对小片段车重结果进行筛选与计算,得到最终车辆载重计算结果.研究表明,在高速通行大数据的验证下,该方法对于空载及满载状态下趟次车重计算结果的整体平均绝对百分比误差(mean absolute percent error,MAPE)均可控制在10%以内,展现了较高的准确性.相比于现有技术,由于该方法无需安装额外传感器,对数据采集、存储、运算设备的要求也相对较低,因此在成本方面具有显著优势.在交通监管、物流运输、基于大数据的产品开发方面具有快速广泛推广的潜力.

Adaptive Optimization Swarm Algorithm Ensemble Model Applied to the Classification of Unbalanced Data

In order to solve the problem that, the hyper-parameters of the existing random forest-based classification prediction model depend on empirical settings, which leads to unsatisfactory model performance. We propose a based on adaptive particle swarm optimization algorithm random forest model to optimize data classification and an adaptive particle swarm algorithm for optimizing hyper-parameters in the random forest to ensure that the model can better predict unbalanced data. Aiming at the premature convergence problem in the particle swarm optimization algorithm, the population is adaptively divided according to the fitness of the population, and an adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. The main steps of the model are as follows: Normalize the data set, initialize the model on the training set, and then use the particle swarm optimization algorithm to optimize the modeling process to establish a classification model. Experimental results show that our proposed algorithm is better than traditional algorithms, especially in terms of F1-Measure and ACC evaluation standards. The results of the six-keel imbalanced data set demonstrate the advantages of our proposed algorithm.

基于多源数据与随机森林方法的城市建成区提取——以郑州市为例

基于夜间灯光数据的阈值分割法在城镇建成区提取研究中被广泛应用,但由于夜间灯光数据分辨率低、灯光溢出和阈值分割法无法顾及区域差异等问题,一定程度上影响了该方法的提取精度。以郑州市为例,以LJ1-01与NPP/VIIRS两种夜间灯光影像为主要数据源,结合Landsat8中分辨率遥感影像、网络城市兴趣点(POI)及路网数据,利用随机森林分类方法对郑州市2018年建成区进行提取,参考土地利用数据,对RF分类法与NTL、VANUI、BANUI、PANUI、RANUI指数等阈值法进行对比实验和精度评价,评估基于多源数据的随机森林分类方法在城市建成区提取中的优势。实验表明,RF比阈值法提取的建成区更接近真实建成区且提取精度更高,具有更好适用性;LJ1-01数据提取的效果和精度总体优于NPP/VIIRS数据;在采用RF分类时,各类特征的重要性在不同夜光数据源中表现差异较大。

基于Isolation Forest和Random Forest相结合的智能电网时间序列数据异常检测算法

智能电网的信息系统是保障电力行业正常运行的基础,而智能电网中各种时间序列数据的分析结果是衡量信息系统稳定运行的重要依据。传统的时间序列数据异常检测算法很难同时兼顾准确性和实时性。本文引入基于Isolation Forest和Random Forest相结合的智能电网时间序列数据异常检测算法,结合无监督学习算法和有监督学习算法的优点,实现机器自动标注和自动学习阈值,人工标注少量特征值,从一定程度上提高了时间序列数据异常检查准确性和实时性,可以满足智能电网时间序列数据异常检测需求,从而达到提升智能电网信息安全的目的。