Prototyping an artificial neural network for burned area mapping on a regional scale in Mediterranean areas using MODIS images

Each year thousands of ha of forest land are affected by forest fires in Southern European countries such as Spain. Burned area maps are a valuable instrument for designing prevention and recovery policies. Remote sensing has increasingly become the most widely used tool for this purpose on regional and global scales, where a large variety of techniques and data has been applied. This paper proposes a semiautomatic method for burned area mapping on a regional scale in Mediterranean areas (the Iberian Peninsula has been used as a study case). A Multi-layer Perceptron Network (MLPN) has been designed and applied to MODIS/Terra Surface Reflectance Daily L2G Global 500m SIN Grid multitemporal composite monthly images. The compositing criterion was based on maximum surface temperature. The research covered a six year period (2001–2006) from June to September, when most of the forest fires occur. The resulting burned area maps have been validated using official fire perimeters and compared with MODIS Collection 5 Burned Area Product (MCD45A1). The MLPN shown as an effective method, with a commission error of 29.1%, in the classification of the burned areas, while the omission error was of 14.9%. The results were compared with the MCD45A1 product, which had a slightly higher commission error (30.2%) and a considerably higher omission error (26.2%), indicating a high underestimation of the burned area.     

Land cover classification of VHR airborne images for citrus grove identification

Managing land resources using remote sensing techniques is becoming a common practice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006-2007). The citrus GIS inventory, created in 2001, needs to be regularly updated in order to monitor changes quickly enough, and allow appropriate policy making and citrus production forecasting. Automatic methods are proposed in this work to facilitate this update, whose processing scheme is summarized as follows. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution aerial images (0.5 m). Next, several automatic classifiers (decision trees, artificial neural networks, and support vector machines) are trained and combined to improve the final classification accuracy. Finally, the citrus GIS is automatically updated if a high enough level of confidence, based on the agreement between classifiers, is achieved. This is the case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert photo-interpreters in order to guarantee the high accuracy demanded by policy makers.     

陕焦化甲醇生产项目雷击风险评估

为了使人工增雨更有针对性,需要在作业方案制定阶段就对未来影响天气系统有一个明确的判断,需要总结出一套适合山西省进行人工增雨作业的天气概念模型。为此,普查了1991年-2000年欧亚范围高空及地面天气图,筛选出影响山西省并产生降水的天气系统,加以分析归纳,建立了4种有利于山西省人工增雨作业的天气概念模型,即西风槽型、东北冷涡型、切变线型及副高型,并分别给出了未来12h～24h影响山西省的警戒区及最佳人工增雨作业区。     

华能岳阳电厂泵房取水头部水下施工测量

文章通过该工程泵房取水头部水下施工过程,简述水下施工测量方法。     

爆炸离心模型试验系统研究与初步试验

依托清华大学50 g-t土工离心机,开发了离心模型爆炸试验系统。该系统由模型箱系统、数据采集系统、数据存储系统、通信系统、分析系统以及引爆系统6部分组成,能克服高速离心力场的干扰,采集到高质量的数据。该系统能同时测量应力、应变和加速度等相关参数。使用该系统对水平地基进行了爆炸荷载初步试验。试验结果表明,该系统具有测试爆炸条件下相关参数的能力,为进行更复杂的爆炸试验奠定了基础。     

Trend estimation and univariate forecast of the sunspot numbers: Development and comparison of ARMA, ARIMA and Autoregressive Neural Network models

In the present study, a prominent 11-year cycle, supported by the pattern of the autocorrelation function and measures of Euclidean distances, in the mean annual sunspot number time series has been observed by considering the sunspot series for the duration of 1749 to 2007. The trend in the yearly sunspot series, which is found to be non-normally distributed, is examined through the Mann-Kendall non-parametric test. A statistically significant increasing trend is observed in the sunspot series in annual duration. The results indicate that the performance of the autoregressive neural network-based model is much better than the autoregressive moving average and autoregressive integrated moving average-based models for the univariate forecast of the yearly mean sunspot numbers.     

Marine artificial micro‐reserves: a possibility for the conservation of endangered species living on artificial substrata

The installation of urban and commercial infrastructures on the coast has a severe impact on the marine environment. This impact is mostly negative but, simultaneously, some of the species that colonize artificial substrata (sometimes rare or threatened) find refuge in man‐made structures. The protection of endangered species is usually provided by marine protected areas or by laws that punish their damage or collection, but effective enclosure and custody must be achieved in the case of easily accessible intertidal species. We used the ferruginous limpet, Patella ferruginea, as a case study because it is the most endangered invertebrate in the Mediterranean. This species, which lives in the intertidal zone and is very accessible to people, is a free‐spawning, protandric hermaphrodite, highly dependent on population density and individual size for its reproductive success. Previous work by the authors found the species is especially abundant on artificial substrates on both shores of the Strait of Gibraltar, where people collect fewer of them. Spanish laws of coastal management do not allow the enclosure of the natural coastline, but artificial installations like harbour breakwaters may be closed or guarded, and visits for educational purposes may be carried out under control. The possibility of declaring some kind of environmental protection for these artificial areas would be a very helpful tool for conservation and management purposes. Protected individuals would grow larger and eventually produce exponentially more offspring, which could be transported by currents to adjacent habitats. Here we propose a protection concept for artificial sites termed Artificial Marine Micro‐Reserve (AMMR), where some endangered species experience a refuge from human disturbance. This concept is a modification of the land micro‐reserves that have been recently declared in small natural sites that keep rare plant species. A network of AMMRs would be the final objective, allowing the creation of a great metapopulation of P. ferruginea and also giving refuge to other endangered species that have been found to grow in this type of substratum. Critically, the declaration of a micro‐reserve cannot be used as a reason in favour of new construction because of the negative influence of such structures on the marine environment, together with the fact that there is no certainty that valuable species will settle on them.     

Wave height forecasting in Dayyer, the Persian Gulf

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.     

城市旅游开发规划研究进展评述

在总结国内旅游开发规划工作经验的基础上,从理论和实践两方面探讨了综合与整体规划、配套建设、滚动开发的操作模式以及当前旅游开发重点等,强调要突出旅游文化背景和旅游形象策划,并归纳了旅游开发规划需要研究的一系列问题。     

利用人工神经网络预测电离层F2层骚扰

利用人工神经网络技术，提出预报离散随机的电离层骚扰事件的新方案.本文重点讨论了预报电离层骚扰的人工神经网络的构造，采用模糊理论和模式识别的思想构造了网络的输入层和输出层.将与电离层骚扰相关的日面现象如太阳耀斑、黑子等的日面位置、强度等参量作为网络的输入，该方案预报结果检验中，使传统方法难以预报的小型和中型电离层(骚扰达到80%以上)的预报准确率有所提高.最后还提出了利用人工神经网络识别单一型别骚扰事件的方案，预报准确率在95%以上。