PALSAR 50 m Mosaic Data Based National Level Biomass Estimation in Cambodia for Implementation of REDD+ Mechanism

Tropical countries like Cambodia require information about forest biomass for successful implementation of climate change mitigation mechanism related to Reducing Emissions from Deforestation and forest Degradation (REDD+). This study investigated the potential of Phased Array-type L-band Synthetic Aperture Radar Fine Beam Dual (PALSAR FBD) 50 m mosaic data to estimate Above Ground Biomass (AGB) in Cambodia. AGB was estimated using a bottom-up approach based on field measured biomass and backscattering (σ^o) properties of PALSAR data. The relationship between the PALSAR σ^o HV and HH/HV with field measured biomass was strong with R² = 0.67 and 0.56, respectively. PALSAR estimated AGB show good results in deciduous forests because of less saturation as compared to dense evergreen forests. The validation results showed a high coefficient of determination R² = 0.61 with RMSE  = 21 Mg/ha using values up to 200 Mg/ha biomass. There were some uncertainties because of the uncertainty in the field based measurement and saturation of PALSAR data. AGB map of Cambodian forests could be useful for the implementation of forest management practices for REDD+ assessment and policies implementation at the national level.     

Cost-Efficient Density Estimation Based on Nearest Individual Distances in a Natural Forest

One useful method of estimating tree density is based on point to individual distances. Estimators are available for data on i-th nearest individual distances, i = 1, 2, 3,…, etc. This paper provides a method of deciding the optimal value of i in the sense of providing the maximum likelihood estimator of density with minimum mean square error subject to given outlay. The result derived for a Poisson forest is extended to an aggregated forest.     

基于Bootstrap方法的基因表达谱数据互信息估计研究

基因调控网络模型为深入理解生命本质提供了一个新的研究框架和平台。作为基因调控网络模型的其中一种,互信息关联网络模型使用熵和互信息描述基因和基因之间的关联。本文描述了用互信息度量基因表达相似性的方法,提出基于Bootstrap的互信息估计算法,并对产生的偏离现象提出了改进策略。实验结果表明,改进的互信息估计方法可以有效提高基因表达相似性估计的精确度。     

Using LiDAR Data to Measure the 3D Green Biomass of Beijing Urban Forest in China

The purpose of the paper is to find a new approach to measure 3D green biomass of urban forest and to testify its precision. In this study, the 3D green biomass could be acquired on basis of a remote sensing inversion model in which each standing wood was first scanned by Terrestrial Laser Scanner to catch its point cloud data, then the point cloud picture was opened in a digital mapping data acquisition system to get the elevation in an independent coordinate, and at last the individual volume captured was associated with the remote sensing image in SPOT5(System Probatoired''Observation dela Tarre)by means of such tools as SPSS (Statistical Product and Service Solutions), GIS (Geographic Information System), RS (Remote Sensing) and spatial analysis software (FARO SCENE and Geomagic studio11). The results showed that the 3D green biomass of Beijing urban forest was 399.1295 million m³, of which coniferous was 28.7871 million m³ and broad-leaf was 370.3424 million m³. The accuracy of 3D green biomass was over 85%, comparison with the values from 235 field sample data in a typical sampling way. This suggested that the precision done by the 3D forest green biomass based on the image in SPOT5 could meet requirements. This represents an improvement over the conventional method because it not only provides a basis to evalue indices of Beijing urban greenings, but also introduces a new technique to assess 3D green biomass in other cities.     

南亚热带常绿阔叶林林下层植物的生物量及其测定方法的探讨

应用全收割法测定广东省鼎湖山南亚热带常绿阔叶林林下层植物生物量 ,林下植物总生物量为 12 9 58g/m2 ,其中茎、枝、叶、根的生物量占总生物量的比例约为 4 0 % ,9 0 % ,2 2 % ,2 9% .由部分实测数据建立林下植物个体生物量估算模型为W =0 0 0 4 2 ·H1 932 3.应用该模型得到的估算值 ,与收获实测值的相对误差仅为 1 8% ,具有良好的精度 .此外 ,还通过改变取样面积对该模型的适用性进行了探讨 .     

Estimation of Cell Proliferation Dynamics Using CFSE Data

Advances in fluorescent labeling of cells as measured by flow cytometry have allowed for quantitative studies of proliferating populations of cells. The investigations (Luzyanina et al. in J. Math. Biol. 54:57–89, 2007; J. Math. Biol., 2009; Theor. Biol. Med. Model. 4:1–26, 2007) contain a mathematical model with fluorescence intensity as a structure variable to describe the evolution in time of proliferating cells labeled by carboxyfluorescein succinimidyl ester (CFSE). Here, this model and several extensions/modifications are discussed. Suggestions for improvements are presented and analyzed with respect to statistical significance for better agreement between model solutions and experimental data. These investigations suggest that the new decay/label loss and time dependent effective proliferation and death rates do indeed provide improved fits of the model to data. Statistical models for the observed variability/noise in the data are discussed with implications for uncertainty quantification. The resulting new cell dynamics model should prove useful in proliferation assay tracking and modeling, with numerous applications in the biomedical sciences.     

基于ALOSPALSAR雷达影像的人工林蓄积量估算——以塞罕坝机械林场华北落叶松人工林为例

基于遥感影像精确估算区域人工林蓄积量对于人工林的管理具有重要意义。本研究以北半球面积最大的华北落叶松(Larix principis-rupprechtii)人工林林场——塞罕坝机械林场为研究区,采用逐步回归方法分别建立了华北落叶松人工林地面实测蓄积量与L波段ALOS PALSAR雷达数据、Landsat-8 OLI数据、以及共用ALOS PALSAR数据与Landsat-8OLI数据之间的不同模型,提出了一种基于雷达影像准确估算人工林蓄积量的方法。通过模型精度验证,找到落叶松人工林蓄积量估算最佳模型,并据此获得研究区不同林龄华北落叶松人工林蓄积量空间分布。结果表明:在建立的4个回归模型中,ALOS PALSAR雷达数据HV极化模式归一化后向散射系数与华北落叶松人工林蓄积量指数形式的回归模型预测精度最高(R2=0.67,相对RMSE=26.78%,P0.01);据此模型估算获得研究区2010年华北落叶松人工林总蓄积量为4.7×106m3及其相应的空间分布图;当单位蓄积量达到250~300 m3·hm-2时,ALOS PALSAR雷达数据HV极化模式归一化后向散射系数对蓄积量的变化不敏感,达到了饱和;林龄20 a、20~30 a、30~40 a和40 a的华北落叶松人工林单位蓄积量分别为78、97、136和127 m3·hm-2。本研究提出了一种基于雷达影像准确估算人工林蓄积量的方法。     

Effects of Initial Age Structure of Managed Norway Spruce Forest Area on Net Climate Impact of Using Forest Biomass for Energy

We investigated how the initial age structure of a managed, middle boreal (62°N), Norway spruce-dominated (Picea abies L. Karst.) forest area affects the net climate impact of using forest biomass for energy. The model-based analysis used a gap-type forest ecosystem model linked to a life cycle assessment (LCA) tool. The net climate impact of energy biomass refers to the difference in annual net CO₂ exchange between the biosystem using forest biomass (logging residues from final felling) and the fossil (reference) system using coal. In the simulations over the 80-year period, the alternative initial age structures of the forest areas were (i) skewed to the right (dominated by young stands), (ii) normally distributed (dominated by middle-aged stands), (iii) skewed to the left (dominated by mature stands), and (iv) evenly distributed (same share of different age classes). The effects of management on net climate impacts were studied using current recommendations as a baseline with a fixed rotation period of 80 years. In alternative management scenarios, the volume of the growing stock was maintained 20% higher over the rotation compared to the baseline, and/or nitrogen fertilization was used to enhance carbon sequestration. According to the results, the initial age structure of the forest area affected largely the net climate impact of using energy biomass over time. An initially right-skewed age structure produced the highest climate benefits over the 80-year simulation period, in contrast to the left-skewed age structure. Furthermore, management that enhanced carbon sequestration increased the potential of energy biomass to replace coal, reducing CO₂ emissions and enhancing climate change mitigation.     

Tree Biomass Estimation of Chinese fir (Cunninghamia lanceolata) Based on Bayesian Method

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most important conifer species for timber production with huge distribution area in southern China. Accurate estimation of biomass is required for accounting and monitoring Chinese forest carbon stocking. In the study, allometric equation was used to analyze tree biomass of Chinese fir. The common methods for estimating allometric model have taken the classical approach based on the frequency interpretation of probability. However, many different biotic and abiotic factors introduce variability in Chinese fir biomass model, suggesting that parameters of biomass model are better represented by probability distributions rather than fixed values as classical method. To deal with the problem, Bayesian method was used for estimating Chinese fir biomass model. In the Bayesian framework, two priors were introduced: non-informative priors and informative priors. For informative priors, 32 biomass equations of Chinese fir were collected from published literature in the paper. The parameter distributions from published literature were regarded as prior distributions in Bayesian model for estimating Chinese fir biomass. Therefore, the Bayesian method with informative priors was better than non-informative priors and classical method, which provides a reasonable method for estimating Chinese fir biomass.     

基于数据挖掘的门诊信息资源分析

目的：随着各级医院信息化建设的不断加强,医院的信息化水平也日益提高。目前各医院都有自己完善的信息化系统,在日常的门诊中,信息化系统积累了大量的门诊就诊数据,但长久以来这部分数据只是处于低层次的应用。对数据的深层次分析、加工以及对医院管理层的决策支持能力较弱。面对着这些宝贵的数据,医院迫切需要数据挖掘和分析工具从积累的就诊数据中分析出更深层次的、高价值的信息,从而为医院的管理决策提供高价值的决策信息。方法：以聚类算法进行数据挖掘建模,对某院门诊信息资源中有用字段进行挖掘分析。结果：根据数据挖掘模型进行挖掘分析,对有价值字段进行聚类分析,得到相关字段数据挖掘结果。结论：将得到相关字段数据挖掘结果进行分析,并将所分析的结果在医院管理决策和医疗质量管理等方面的应用进行探讨。     

Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability

Large external data sources may be available to augment studies that collect data to address a specific research objective. In this article we consider the problem of building regression models for prediction based on individual-level data from an “internal” study while incorporating summary information from an “external” big data source. We extend the work of Chatterjee et al. (J Am Stat Assoc 111(513):107–117, 2006) by introducing an adaptive empirical Bayes shrinkage estimator that uses the external summary-level information and the internal data to trade bias with variance for protection against departures in the conditional probability distribution of the outcome given a set of covariates between the two populations. We use simulation studies and a real data application using external summary information from the Prostate Cancer Prevention Trial to assess the performance of the proposed methods in contrast to maximum likelihood estimation and the constrained maximum likelihood (CML) method developed by Chatterjee et al. (J Am Stat Assoc 111(513):107–117, 2006). Our simulation studies show that the CML method can be biased and inefficient when the assumption of a transportable covariate distribution between the external and internal populations is violated, and our empirical Bayes estimator provides protection against bias and loss of efficiency.     

Ranking the Azorean Natural Forest Reserves for Conservation Using their Endemic Arthropods

Endemic arthropods were used to evaluate the conservation value of the 16 Natural Forest Reserves (NFRs) of the Azores (Macaronesia). For each of the 280 known Azorean endemic species of arthropods, a rarity index was calculated, using distribution and abundance data obtained from the literature. In addition, several scoring indices were used to rank the 16 NFRs. Frequency distributions of the rarity index indicated that there was a tendency for a greater proportion of the commonest species being represented in the NFRs in contrast with a lower representation of the rarest species. About 60% of the endemic arthropod species that were recorded from the NFRs are single NFR endemics, that is, are known from only one of the 16 NFRs. Species richness was considered to be a very good surrogate measure of the conservation value of the 16 NFRs under study. The fact that the six highest ranked NFRs (using a composite multi-criteria index) are located in different islands has some important conservation management implications; to preserve a large proportion of the Azorean arthropod biodiversity there is a need to protect sites in all islands. If the five highest ranked NFRs are correctly managed in terms of conservation, then at least 80% of the endemic arthropods known from the NFRs could be protected. Most of the tested taxa (Acari-Oribatei; Lepidoptera; Diptera; Coleoptera) are good surrogates of the overall total set of species present in the 16 NFRs when using a species richness index.     

Changes in Biomass Carbon and Soil Organic Carbon Stocks following the Conversion from a Secondary Coniferous Forest to a Pine Plantation

The objectives of this study were to estimate changes of tree carbon (C) and soil organic carbon (SOC) stock following a conversion in land use, an issue that has been only insufficiently addressed. For this study, we examined a chronosequence of 2 to 54-year-old Pinus kesiya var. langbianensis plantations that replaced the original secondary coniferous forest (SCF) in Southwest China due to clearing. C stocks considered here consisted of tree, understory, litter, and SOC (0–1 m). The results showed that tree C stocks ranged from 0.02±0.001 Mg C ha^-1 to 141.43±5.29 Mg C ha^-1, and increased gradually with the stand age. Accumulation of tree C stocks occurred in 20 years after reforestaion and C stock level recoverd to SCF. The maximum of understory C stock was found in a 5-year-old stand (6.74±0.7 Mg C ha^-1) with 5.8 times that of SCF, thereafter, understory C stock decreased with the growth of plantation. Litter C stock had no difference excluding effects of prescribed burning. Tree C stock exhibited a significant decline in the 2, 5-year-old stand following the conversion to plantation, but later, increased until a steady state-level in the 20, 26-year-old stand. The SOC stocks ranged from 81.08±10.13 Mg C ha^-1 to 160.38±17.96 Mg C ha^-1. Reforestation significantly decreased SOC stocks of plantation in the 2-year-old stand which lost 42.29 Mg C ha^-1 in the 1 m soil depth compared with SCF by reason of soil disturbance from sites preparation, but then subsequently recovered to SCF level. SOC stocks of SCF had no significant difference with other plantation. The surface profile (0–0.1 m) contained s higher SOC stocks than deeper soil depth. C stock associated with tree biomass represented a higher proportion than SOC stocks as stand development proceeded.     

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.     

落叶松人工林空间结构优化的探讨

基于落叶松(Larix olgensis Henry)人工林固定标准地ML707的每木检尺数据,针对人工林普遍具有混交度低,分布均匀的特点。主要从林分竞争机制和树冠营养空间利用情况等方面提出了利用Hegyi竞争指数、生长空间指数、生长空间竞争指数分析优化林分空间结构,进一步比较了不同择伐强度下林分空间结构变化的大小。研究结果表明：14%左右为林分最优的择伐强度,空间优化达到最佳,优化后林分竞争态势减小,营养空间利用程度增大,林分空间结构得到改善。     

Arthropod Diversity in Lama Forest Reserve (South Benin), a Mosaic of Natural, Degraded and Plantation Forests

Arthropod assemblages were examined in Lama forest reserve, a protected area situated in the Dahomey gap, southern Benin, composed of plantations, degraded forest and remnants of natural forest. The objectives were to compare assemblages in relation to forest type and use, to elucidate the value of forest plantations for biodiversity conservation and to identify indicator species for specific forest habitats. Arthropods were collected over an 11-month period, using standardized sets of traps (pitfall, emergence, Malaise and flight intercept traps). Nine different habitats were studied, including natural and degraded forest, forest plantations (Tectona grandis and Senna siamea) of different age, and isolated forest fragments. Our analysis focused on detritivorous and xylophagous arthropods but also included ground beetles and heteropterans, totalling 393 species. We found no differences in species richness among natural and degraded forest habitats in the centre of the reserve (Noyau central). Outside of the Noyau central, species richness was highest in old teak plantations and isolated forest fragments and lowest in young teak and fuelwood plantations. Detrended correspondence analysis (DCA) separated three main groups: (1) natural forest, (2) degraded forest and young plantations, and (3) old plantations and isolated forest fragments. Multiple regression of DCA scores of the first two axes on environmental variables identified one natural and three disturbance-related predictors of arthropod assemblages in Lama forest: soil type (texture), canopy height, naturalness (proportion of Guineo-Congolian plant species) and understorey vegetation cover. We identified 15 indicator species for six different forest habitats. The highest numbers were found in abandoned settlements and old teak plantations. β-diversity was similar among the three DCA ordination groups (degraded forest excluded). Values for β-diversity were relatively high, suggesting that all major forest habitats contribute significantly to regional species pools and should therefore be protected. To enhance arthropod diversity, we propose that management practices in Lama forest should aim to encourage the development of species-rich understorey vegetation of the Guineo-Congolian phytogeographical region.     

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km²). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.     

Coordinate Based Meta-Analysis of Functional Neuroimaging Data Using Activation Likelihood Estimation; Full Width Half Max and Group Comparisons

Coordinate based meta-analysis (CBMA) is used to find regions of consistent activation across fMRI and PET studies selected for their functional relevance to a hypothesis. Results are clusters of foci where multiple studies report in the same spatial region, indicating functional relevance. Contrast meta-analysis finds regions where there are consistent differences in activation pattern between two groups. The activation likelihood estimate methods tackle these problems, but require a specification of uncertainty in foci location: the full width half max (FWHM). Results are sensitive to FWHM. Furthermore, contrast meta-analysis requires correction for multiple statistical tests. Consequently it is sensitive only to very significant localised differences that produce very small p-values, which remain significant after correction; subtle diffuse differences between the groups can be overlooked. In this report we redefine the FWHM parameter, by analogy with a density clustering algorithm, and provide a method to estimate it. The FWHM is modified to account for the number of studies in the analysis, and represents a substantial change to the CBMA philosophy that can be applied to the current algorithms. Consequently we observe more reliable detection of clusters when there are few studies in the CBMA, and a decreasing false positive rate with larger study numbers. By contrast the standard definition (FWHM independent of the number of studies) is demonstrated to paradoxically increase the false positive rate as the number of studies increases, while reducing ability to detect true clusters for small numbers of studies. We also provide an algorithm for contrast meta-analysis, which includes a correction for multiple correlated tests that controls for the proportion of false clusters expected under the null hypothesis. Furthermore, we detail an omnibus test of difference between groups that is more sensitive than contrast meta-analysis when differences are diffuse. This test is useful where contrast meta-analysis is unrevealing.