遥感技术在呼和浩特市地表水污染调查中应用

应用现代监测技术即遥感技术，依据地表水和各种水文地质现象对电磁波响应特性，结合地面调查，综合分析影像信息。快速、准确、及时了解和掌握呼和浩特市地表水的污染范围与污染程度，查明浅层水污染情况及地下水开采现状，合理规划地下水的开采，防止由于地下水位下降引起大面积地面下沉。为保护水资源，为呼和浩特市可持续发展，也为决策部门合理利用与管理水资源，提供科学依据和可靠数据。     

一种适用于模式分类的模糊粗隶属函数神经网络

在设计模式分类器时,由于很难甚至不可能获得实现精确分类的所有必要的本质特征属性,使得在进行分类时出现模糊不确定性和粗糙不确定性并存的情况.基于模糊粗糙集理论,本文构建了一种模糊粗隶属函数神经网络FRMFN.该网络融合了模糊信息和粗糙信息的处理能力,在对加拿大Norman Wells地区的红外波段合成图像进行分类的测试中,显示FRMFN网络具有比相应RBF网络更好的分类精度,同时保留了RBF网络学习速度快的优点.     

花生冠层APAR和FAPAR高光谱遥感估测模型研究

为了快速、无损监测花生生长发育，建立整个生育期内花生冠层吸收性光合有效辐射（APAR）和光合有 效辐射吸收系数（FAPAR）的高光谱遥感估测模型，本试验利用高光谱遥感技术，测定沈阳地区5种不同生态类型的 花生冠层光谱数据，同期获取APAR、FAPAR；并对原始光谱数据进行logρ、1/ρ、ρ′变换，构建6种植被指数，分别与 APAR和FAPAR进行Pearson相关分析，并建立估测模型，对模型进行检验与评价。研究结果表明：4种变换形式的 光谱数据中最优波段与APAR 和FAPAR 均达极显著相关（r≥0.3969，P<0.01），以ρ′在759nm 波段处与APAR（r= 0.7574）和FAPAR（r=0.6276）的相关性最好，ρ′759nm处的高光谱参数与APAR、FAPAR建立的估测方程y = 797.3846 e271.4883x（R=0.5512，P<0.01；RE=0.1213）和y =0.756e85.21x（R=0.4204，P<0.01，RE=0.0788）拟合系数最高、预测精度较 好，估测效果很好。比值植被指数（RVI）、差值植被指数（DVI）、归一化植被指数（NDVI）、复归一化差值植被指数 （RDVI）、垂直植被指数（PVI）和修改土壤调整植被指数（MSAVI）这6种植被指数的最优波段与APAR的相关性优 于与FAPAR的相关性，MSAVI[723,761]与APAR所建立的对数函数y = 1554ln（x）+ 1631（R=0.7566，P<0.01；RE= 0.0870）和RDVI[731,764]与FAPAR建立的多项式函数y = 1.027x2 + 0.713x + 0.729（R=0.6194，P<0.01；RE=0.0699） 的模拟值和实测值均达到了极显著、预测精度较高，MSAVI对APAR和RDVI对FAPAR估测效果很好。一阶微分光 谱和植被指数可以较好地估测花生冠层APAR和FAPAR。     

Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

This paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheat fields using multi-spectral QuickBird satellite imagery for site-specific weed management. The research was conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and 2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluate the wild oat patches mapping at the field level, both pixel- and object-based image analyses were tested with six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The results showed that weed patches could be accurately detected with both analyses obtaining global accuracies between 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate for both the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis, all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentation based on two scales. The first segmentation scale was classified using the MD and ML algorithms to discriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and 88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentation scale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based on the most accurate results obtained in the field-level study, pixel-based classifications using the MD, ML and SVM algorithms were applied to the wheat fields identified. The results of these broad-level analyses showed that wild oat patches were accurately discriminated in all the wheat fields present in the entire images with accuracies greater than 91% for all the classifiers tested.     

Production of acute hepatopancreatic necrosis disease toxin is affected by addition of cell‐free supernatant prepared from AI‐2‐producing Vibrio harveyi mutant

Acute hepatopancreatic necrosis disease (AHPND) causes massive mortality in shrimp ponds within the first month poststocking. The causative agent is a specific strain of Vibrio parahaemolyticus (VP_AHPND) that has acquired the capability to produce virulent binary toxins called ToxA and ToxB. This study aims to test the effect of the addition of an autoinducer‐2‐containing cell‐free supernatant (CFS) from the mutant Vibrio harveyi (VH) on growth and toxin production of VP_AHPND. The relative AI‐2‐like activity in CFS was detected by luminescence assay. The effect of CFS (5 and 9%) on growth and toxin production of VP_AHPND was evaluated. Compared to the control culture (without CFS‐VH addition), the addition of either 5 or 9% CFS‐VH affected the growth at the initial stage of VP_AHPND. Similar growth profiles of VP_AHPND were found with the addition of CFS‐VH at both concentrations. Western blot analysis suggests that the addition of CFS‐VH affected the production of both toxins. ToxA could be detected at the early hour post‐CFS‐VH inoculation, whereas the high amount of ToxB was detected when 5% CFS‐VH was added. However, interfering with the AI‐2 function with furanone, the AI‐2 antagonist resulted in a slight delay in the production of both toxins. Results from this study will help to design a novel strategy to control AHPND in shrimp culture.     

新疆土壤侵蚀类型图的编制研究

本文根据新疆水土保持规划任务要求，对新疆土壤侵蚀类型图进行了研究编制，采用卫星遥感技术，并结合气候，水文，地形，地貌，植被，土壤等诸多影响土壤侵蚀类型和强度的资料及因子图，进行研究判读，垒置，野外考察核定，用计算机技术和先进的技术设备完成了１／１００万的新疆土壤侵蚀类型图和土壤侵蚀数据库。     

基于高分一号遥感影像的绿地信息提取

对高分一号卫星影像进行大气校正、几何校正、裁剪等,利用Libsvm 4.0在Matlab平台里编程进行交叉验证网格法寻优,最终获得支持向量机分类的最佳惩罚系数为45,不敏感系数为0.31。改进支持向量机分类器绿地分类精度为94.6%,该提取精度能满足高分辨率遥感影像在城市绿地动态监测。     

Mapping Prosopis juliflora in West Somaliland with Landsat 8 Satellite Imagery and Ground Information

Prosopis juliflora is a drought‐tolerant fast‐growing tree species originating from South and Central America with a high invasion potential in arid and semi‐arid areas in Africa. It was introduced in Somaliland in the 1980s and is reported to have spread vigorously since. Despite being recognized as a serious issue in the country, the actual scale of the problem is unknown. In this study, we mapped the species in a study area that includes the capital, Hargeisa, using Landsat 8 satellite imagery. During a field campaign in 2015, we collected canopy‐level spectral signatures of P . juliflora and native trees to analyse the potential use of spectral data in discriminating the invasive species. P. juliflora was found to be generally distinguishable because of its greater vigour during the dry season. We tested the accuracy of the random forest classifier and different classification set‐ups, varying the spatial resolution (original 30 m vs pan‐sharpened 15 m) and image acquisition dates (during the wet season, the dry season and a combination of the two). Best overall accuracy (84%) was achieved by using pan‐sharpened data from the two seasons. About 30 years since its introduction, the invasive species was detected in 9% of the total investigated area with highest occurrence in the proximity of human settlements and along seasonal water courses. © 2016 The Authors. Land Degradation and Development published by John Wiley & Sons, Ltd.     

敦煌盆地环境地质变化趋势及治理对策探讨

指出了环境地质问题是水资源开发利用过程中出现的主要问题。通过不同时期的遥感解译结果,分析了敦煌盆地区内环境地质问题的演变特点,探讨了区内水资源与生态环境的相互作用,提出了通过水资源管理与合理利用来促进生态环境好转的对策。