Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets

The suitable spectral mode in remote sensing is often desirable to facilitate the inversion of ecological environment and landscape. This paper put forward an optimizing model based on variable precision rough sets (VPRS) for the land cover discrimination in wetland inventory. In the case study of Lake Baiyangdian which has important ecological functions to the northern China, this model is established successfully according to the domain-experts knowledge. The procedure is as follows. First step is data collection, including remote-sensing data (e.g., Landsat-5 TM bands), the digitized relief maps, and statistical yearbooks. Second, the remote sensing imagery (RSI) and relief maps are co-registered into the same resolution. Third, a condition set, including various attributes is derived from spectral bands, band math or ratio indices based on previous studies, at the same time, the decision set is derived from true land types after investigation and validation. Then, the remote sensing decision table (RSDT) is constructed by linking condition set with decision set according to the sequential pixels in RSI. Fourth, we create one forward greedy searching algorithm based on VPRS to handle this RSDT. After adjusting parameters such as β and knowledge granularity diameter (KGD), we obtain the stable optimized results. Comparative experiments and evaluation show that the discrimination or retrieval accuracy of VPRS model is satisfying (overall accuracy: 87.32% and KHAT: 0.84) and better than original data. Moreover, data dimension has been decreased dramatically (from 12 to 3) and key attributes found by the model may be useful for specific retrieval in wetland inventories.     

京津冀“以电代煤”替代大气污染物排放清单

本研究建立了基于人工神经网络的民用散煤燃用量估算模型,得到了京津冀地区“以电代煤”替代民用散煤大气污染物排放清单.结果表明：截止到2018年,京津冀地区“以电代煤”替代散煤用户约259万户,每年可减少散煤燃烧约706.6万t,减少PM_2.5、NO_x、SO₂的排放量分别约为2.81,0.76,2.17万t.其中,北京市和天津市实施效果较为明显,占京津冀地区“以电代煤”散煤替代总量的62.01%和22.82%.基于调研数据得到京津冀各市燃煤量月分布系数,1月份分布系数最大,燃煤量占比为27%~40%.     

基于LCA的钢铁联合企业CO_2排放影响因素分析

钢铁企业是CO2排放大户,减少吨钢CO2排放是钢铁企业节约能源、保护环境、走可持续发展道路的必然要求.本研究旨在对钢铁企业产品生命周期清单研究的基础上,识别钢铁企业CO2排放的主要影响因素,提出针对性的减排建议.以某钢铁联合企业的产品生命周期清单模型为平台,同时利用TornadoChart工具,计算得到对企业CO2排放影响较大的因素,然后提出了相应的减排措施.结果表明,转炉流程对于钢铁企业的影响要大于电炉流程;对该企业CO2排放有重大影响和相关影响的因素有:高炉煤气(BFG)的CO2排放系数、连铸坯的钢水单耗、热轧的板坯单耗、转炉的铁水比.减少钢铁联合企业CO2排放的有效措施是采取捕集BFG中的CO2、降低转炉的铁水比、加强副产煤气的回收以及优化企业的产品生产结构.     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

生命周期清单分析的数据质量评价

生命周期清单分析(LCI)数据质量的分析方法可概括为两类:采用诸如地理代表性、数据年代或数据获取方式等一系列指标来表示;根据不确定性来综合反映LCI质量.笔者在分析了这两类方法各自所存在缺陷的基础上,提出了将这两者相结合的评价方法:采用5个独立的反映数据质量的指标,根据系统各单元各数据属性对各指标从1～5进行打分,形成数据的质量指标向量元素.根据数据质量向量元素的算术平均在总指标范围中所占的百分数将质量指标向量转化为对应的综合数据质量指标(DQI),继而根据DQI可得出每个数据的随机分布,以便进行清单结果的不确定性随机模拟.最后将方法应用于钢铁生产生命周期清单数据中.      

区域高时空分辨率VOC天然源排放清单的建立

将中尺度气象模式MM5应用于估算VOC天然源排放的研究,建立了高时空分辨率VOC天然源排放清单的估算方法.根据方法需要,确定了我国部分树木排放异戊二烯和萜烯的标准排放因子,各植被类型排放各种VOC的标准排放因子,以及各植被类型季节平均的叶生物量密度.应用该方法估算了华南地区满足区域空气质量数值模拟要求的高时空分辨率VOC天然源排放清单.结果表明,华南地区夏季典型日的VOC天然源排放总量约1.12×10⁴t,VOC排放速率具有明显的时空分布,其中地理分布取决于植被类型及其分布,日变化规律则依赖于太阳辐射和温度的高低.并讨论了VOC天然源排放估算过程中误差的来源     

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.

2005年中国燃煤大气锑排放清单

采用基于燃料消耗的排放因子法,按照经济部门(电力、工业、生活消费及其他)、燃料类型(原煤、洗煤、焦炭、型煤)、燃烧方式(层燃炉、煤粉炉、流化床锅炉等)和除尘脱硫设施(机械式除尘器、湿式除尘器、电除尘器、袋式除尘器等)等将燃煤排放源进行分类,根据各省、市、自治区不同排放源类型的煤炭消耗量及平均锑含量,建立了2005年中国燃煤大气锑排放清单.结果表明,2005年中国燃煤导致的大气锑排放总量约为530.86t,排放量超过30t的省份依次为贵州(49.28t)、湖南(45.96t)、河北(37.36t)、山东(35.12t)、安徽(30.92t),表明中国燃煤大气锑排放主要集中在中东部地区.大部分排放来自工业和电力部门,分别占排放总量的47.2%和39.9%.     

图书馆书库清点方法初探

图书馆藏书大多存在实物与财产登记不符的状况,如情况较为严重则会影响到图书馆流通、采访等项工作的开展,因此适时进行书库清点具有重要意义。本文以中国环境管理干部学院图书馆的清点为实例,初步探讨了将条码作为清点切入点的书库清点工作的方法、步骤和注意事项。     

Validation of a hybrid life-cycle inventory analysis method

The life-cycle inventory analysis step of a life-cycle assessment (LCA) may currently suffer from several limitations, mainly concerned with the use of incomplete and unreliable data sources and methods of assessment. Many past LCA studies have used traditional inventory analysis methods, namely process analysis and input-output analysis. More recently, hybrid inventory analysis methods have been developed, combining these two traditional methods in an attempt to minimise their limitations. In light of recent improvements, these hybrid methods need to be compared and validated, as these too have been considered to have several limitations. This paper evaluates a recently developed hybrid inventory analysis method which aims to improve the limitations of previous methods. It was found that the truncation associated with process analysis can be up to 87%, reflecting the considerable shortcomings in the quantity of process data currently available. Capital inputs were found to account for up to 22% of the total inputs to a particular product. These findings suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependent upon data quality and availability. The use of input-output data assists in improving the system boundary completeness of life-cycle inventories. However, the use of input-output analysis alone does not always provide an accurate model for replacing process data. Further improvements in the quantity of process data currently available are needed to increase the reliability of life-cycle inventories.