GIS Based Soil Loss Estimation Using RUSLE Model: The Case of Jabi Tehinan Woreda, ANRS, Ethiopia

Soil degradation in the form of soil erosion is a serious and continuous environmental problem in Jabi Tehinan Woreda. Uncontrolled land use, deforestation, over cultivation, overgrazing and exploitation of biomass for firewood, construction and other household uses due to increasing population ultimately lead to severe soil erosion. The impact of natural hazards like erosion can be minimized and ultimately controlled by disaster preparedness maps. Therefore, the overall objective of this paper is to quantify and map an estimated soil loss by examining different topographic and anthropogenic factors for the planning and implementations of sustainable soil conservation and management system in the study area. This study had integrated Geographic Information System (GIS), Remote Sensing (RS) and Multi-Criteria Evaluation (MCE) techniques to quantify and map erosion vulnerable areas using RUSLE model. Slope gradient, slope length, soil type, soil conservations techniques, cover management and rainfall variables were used as input model parameters/variables. The data had been collected and analyzed from different land sat imageries, SRTM data, topomaps and point interpolations of primary data. Finally, the aggregated effects of all parameters had been analyzed and soil loss from the area was calculated using RUSEL models. After analyzing all model parameters, areas in steeper slope with Lithosols, Eutric Nitosols, Orthic Luvisols, croplands, bare lands and river banks have been identified as the most erosion vulnerable areas. Quantitatively, an estimated annual soil loss in Jabi Tehinan Woreda ranges from nearly 0 in south and central parts of the area to 504.6 t/ha/yr in steeply sloping mountainous areas of the north and north-eastern parts of the catchments.

TCM analysis and strategy for COVID-19 associated loss of smell and taste

Coronavirus disease 2019(COVID-19)is caused by SARS-CoV2 and is still not yet under complete control and no cure is available until this moment.It is still unpredictable to expect vaccination available worldwide in the near future.Early diagnosis hence becomes extremely important.Various papers have proven that early identification of loss of smell and taste is of significance in the diagnosis of this disease.Some patients of COVID-19 may carry on suffering from loss of smell and taste even long after the other symptoms of the infection subside.The aetiology,pathology,and mechanism of loss of smell and taste are discussed in this article,pointing out that impairment of meridians,disturbed Brain-Shen and dysfunction of the Zang-Fu organs are the key pathology of loss of smell and taste by COVID-19.Traditional Chinese medicine resolution with acupuncture and Chinese herbs are therefore demonstrated accordingly,aiming to establish some practical and effective strategies to deal with loss of smell and taste.This unique presentation in the treatment illustrate that even though loss of smell and taste being a single symptom,it requires a profound understanding on the complicated and sophisticated background mechanism of the COVID-19,which is urgently in need of comprehensive treatment in time.Loss of smell and taste by COVID-19 is not only a minor suffering of the infection,but also a very critical part of a fully recovery of COVID-19,it shall not be left untreated.

Integrated Approach of Universal Soil Loss Equation (USLE) and Geographical Information System (GIS) for Soil Loss Risk Assessment in Upper South Koel Basin, Jharkhand

Soil erosion is a growing problem especially in areas of agricultural activity where soil erosion not only leads to decreased agricultural productivity but also reduces water availability. Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. Remote sensing and GIS techniques have become valuable tools specially when assessing erosion at larger scales due to the amount of data needed and the greater area coverage. The present study area is a part of Chotanagpur plateau with undulating topography, with a very high risk of soil erosion. In the present study an attempt has been made to assess the annual soil loss in Upper South Koel basin using Universal Soil Loss Equation (USLE) in GIS framework. Such information can be of immense help in identifying priority areas for implementation of erosion control measures. The soil erosion rate was determined as a function of land topography, soil texture, land use/land cover, rainfall erosivity, and crop management and practice in the watershed using the Universal Soil Loss Equation (for Indian conditions), remote sensing imagery, and GIS techniques. The rainfall erosivity R-factor of USLE was found as 546 MJ mm/ha/hr/yr and the soil erodibility K-factor varied from 0.23 - 0.37. Slopes in the catchment varied between 0% and 42% having LS factor values ranging from 0 - 21. The C factor was computed from NDVI (Normalized Difference Vegetative Index) values derived from Landsat-TM data. The P value was computed from existing cropping patterns in the catchment. The annual soil loss estimated in the watershed using USLE is 12.2 ton/ha/yr.

Soil Erosion Changes over the Past Five Decades in the Red Soil Region of Southern China

This paper reports the dynamic changes of soil and water loss in the red soil region of Southern China since the 1950s. The red soil region covers eight provinces: Jiangxi, Zhejiang, Fujian, Anhui, Hubei, Hunan, Guangdong and Hainan. From the 1950s to 1986, the annual rate of soil erosion increased by 3.4%. From 1986 to 1996 and from 1996 to 2000, the annual rates of soil erosion decreased by 2.0% and 0.32%, respectively. Field surveys showed that from 2000 to 2005, the area of soil and water loss decreased annually by 1.2%. This decrease was a result of large-scale erosion control activities across China. Although the eroded soil has been restored, the restoration process is very slow and full restoration will take a long time. Our report suggests that controlling soil and water loss is a challenging task, and additional measures must be taken to effectively control the soil erosion in the red soil region.

The Extent and Effectiveness of Protected Areas in the Russian Federation

To tackle environmental challenges and in particular,the problems associated with the high demand for forests and resources,the consequence of which is the degradation of ecological systems,Russia has developed the biggest network of specially protected natural areas in the world.Although the scale of SPNA may look impressive,a recent comprehensive study of the period from 2001 to 2018 brought the spotlight on protected areas in Russia experiencing significant tree loss annually.Since Russia is confronting unprecedented fires that break records every year,the problem of preserving rare animal and plant species,their habitats,and natural ecosystems is acute.This paper is intended to assess the extent and effectiveness of SPNAs with regard to wilderness conservation in the Russian Federation based on the latest data;the main focus being on the period of 2014—2019,before and after the launch of the environmental safety strategies,along with additional analysis of available data for the following year of 2020.With due consideration of unique geographical,economic,and socio-cultural circumstances,as well as historical background and legislative reality of SPNAs in Russia,we have conducted a statistical analysis of data from the official annual reports from the Federal State Statistics Service on SPNAs by territories and significance,expenditures on maintenance and ecotourism,followed by RGB analysis of satellite imagery via means of GIS software for visualization of obtained data and further analytics.Despite the growth of the SPNA network in Russia,resulting in an astonishing 2402 Mha in 2020,an estimated 4 Mha of tree loss accounted for SPNAs over the period of 2014—2019,and 134 Mha in 2020 alone.Our findings indicate a need for the application of a comprehensive GIS approach for further development and effective management of the SPNA network in Russia.Furthermore,the results include recommendations on legislative changes,engagement of locals in SPNA protection,and popularization of ecotourism,all of which can be valuable for policymakers and SPNA development.

A New Software for GIS Image Pixel Topographic Fac－tors in Remote Sensing Monitoring of Soil Losses

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Spatial and seasonal dynamics of soil loss ratio in mountain rangelands of south-western Kyrgyzstan

Vegetation cover is the main factor of soil loss prevention.The C-factor of the RUSLE(Revised Universal Soil Loss Equation) was predicted with NDVI,ground data and exponential regression equation for mountain rangelands of Kyrgyzstan.Time series of C-factor,precipitation and temperature were decomposed into seasonal and trend components with STL(seasonal decomposition by loess) to assess their interrelations.C-factor,precipitation and temperature trend components indicated significant lagged correlation,whereas seasonal components indicated more complex relations with climate factors which can be promoting as well as limiting factors for vegetation development,depending on the season.Rainy springs and hot summers may increase soil loss dramatically,whereas warm and dry springs with rainy summers can decrease it.Steep slopes indicated higher soil loss ratio,whereas flat areas were better protected by vegetation.

Effect of drug therapies on self-reported chemosensory outcomes after COVID-19

Objective:The aim of this study was to assess the relative efficacy of medications used following severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection on self-reported alterations in taste and/or smell function.Methods:Seven hundred and fourteen persons with self-reported postcoronavirus disease 2019(post-COVID-19)chemosensory disorders were personally interviewed regarding specific medications they were administered following the acute phase of the disease.The dependent measure—self-reported total recovery of chemosensory symptoms—was subjected to stepwise logistic regression.Independent predictors included demographic and clinical variables,in addition to specific medications used to mitigate disease symptoms(i.e.,systemic corticosteroids,oseltamivir,vitamin C,ibuprofen,hydroxychloroquine,azithromycin,ivermectin,nitazoxanide,anticoagulants,and zinc).Results:The median time between COVID-19 symptom onset and the interviews was 81 days(interquartile range:60-104).Of the 714 subjects,249(34.9%)reported total recovery of their chemosensory function;437(61.2%)had at least one treatment since the beginning of the disease.Women and those with more comorbidities had undergone more treatments.The recovery rates of the treated and nontreated groups did not differ significantly.Nonetheless,respondents who had used nitazoxanide tended to have a higher rate of self-reported taste or smell recovery.Those who took oral zinc were less likely to improve.Conclusions:No medication employed during the first months after SARS-CoV-2 infection had a clear positive effect on returning self-reported smell or taste function to normal,although nitrazoxide trended in a positive direction.Oral zinc had a negative effect on the reported recovery of these senses.

Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China

China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China’s hotspot regions, the Guangdong-Hong KongMacao Greater Bay Area(GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to2017 were used to derive forest, and landscape metrics and geographic information system(GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in1987–2017;meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60%(1034.42 km2) was converted to urban land;2) the percentages of forest loss to urban land in Dongguan(19.14%), Guangzhou(18.35%) and Shenzhen(15.81%) were higher than those in other cities;3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from2007 to 2017);4) the landscape responses to forest changes varied with the scale;and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong.Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.

Design of an Efficient Cooperative Spectrum for Intra-Hospital Cognitive Radio Network

This paper presents a new low delay centralized cooperative spectrum sensing method based on dynamic voting rule using multiple threshold level,for indoor hospital environment,where fading and direct path loss both are together responsible for variation in signal strength.In the proposed algorithm,weights of Cognitive Radios(CRs)in terms of assigned votecount are estimated based on the sensed energy values with respect to multiple fixed threshold levels,so that the direct path loss can be dealt without further increasing the error,which occurs due to fading.For indoor hospital like environment,Rician fading model is more appropriate.Therefore,formulation,evaluation and analysis of various sensing performance parameters have been carried out considering Rician fading model.Comparative analysis and tradeoff among three optimum parameters-energy threshold,total number of CRs participating in sensing and total error rate,are presented with respect to signal to noise ratio(SNR).Finally,the Monte Carlo simulation is designed and implemented considering the two conditions-only fading environment and fading along with direct path loss environment.It can be observed that in both cases,the proposed spectrum sensing rule outperforms the other contemporary rules.

从络脉探讨新型冠状病毒感染后嗅觉异常

新型冠状病毒感染(COVID-19)从发生到现在为止已3年有余,疫情期间中医中药发挥了重要的作用,取得了很好的疗效,但随着时间推移,感染过的人数增多,发现感染后嗅觉异常患者越来越多,西医对其治疗以刺激神经或使其自愈为主,但效果因人而异且治疗周期长,而嗅觉异常会使患者生活体验感降低、心理状态改变,如焦虑、抑郁等,还会诱发其他系统疾病,所以对于嗅觉异常的情况不容小觑,中医对其认识、治疗有独特之处,故文章从COVID-19致嗅觉异常的现代研究、中医认识及药物应用等方面阐述了嗅觉异常的原因及从络脉入手如何拓展治疗思路进行治疗。

神经科护士工作投入的现状及影响因素分析

目的分析神经科护士工作投入现状及其影响因素。方法采用便利抽样的方法,于2022年4月—5月对北京某三级甲等综合医院神经科护士进行横断面调查。采用自行设计的一般资料调查问卷、工作投入量表、中文版知觉压力量表,对神经科护士进行调查问卷。结果本研究共发放问卷660份,回收630份,回收率95.45%;有效问卷630份,有效率100.00%。630名神经科护士工作投入得分为(35.15±5.34)分,各个维度的平均分对比,差异有统计学意义(P<0.05)。单因素分析结果显示,神经科护士的工作投入评分在年龄、聘用关系、技术职称、工作年限和子女情况上的差异有统计学意义(P<0.05)。神经科护士的知觉压力总分为(36.76±8.10)分,工作投入总分与失控感评分、知觉压力总分呈正相关(P<0.05),与紧张感评分呈负相关(P<0.05)。多因素分析结果显示,失控感、聘用关系、工作1~10年、紧张感是工作投入的独立影响因素(P<0.05)。结论神经科护士工作投入的影响因素众多。作为临床护理管理者可以通过有效的科学管理办法,采取有效措施缓解护士心理压力,降低反刍思维水平,正向引导建立正向的应急心理,从而提高护士工作投入程度,最终达到提高护理质量和患者满意度的目的。

基于纹理特征的深度学习云和云阴影检测

针对云和云阴影检测过程中存在边界不准确以及易与地表混淆等问题,构建一种融合纹理特征模块的卷积神经网络模型对Landsat 8遥感图像进行云和云阴影检测。引入基于统计特性的纹理特征模块进行纹理特征的提取和学习,在训练过程采用焦点损失函数削弱样本不均衡带来的影响。实验结果表明,该模型细化了云和云阴影的边界等纹理细节,减少了云和云阴影的误检和漏检现象,提高了云和云阴影的检测精度。

一种传感图像特征快速挖掘算法设计

传感图像采集因曝光而损失图像特征,导致特征错误挖掘率高和精准率召回率(Precision-Recall,PR),曲线表现较差,针对该问题,提出一种新的传感图像特征快速挖掘算法。该方法将参数化修正线性单元函数作为激活函数,融合修补图和输入图像,并在训练阶段使用L1损失函数和结构相似性指数(Structural Similarity Index,SSIM)损失函数约束图像,采用softmax回归挖掘图像特征,构建卷积神经网络传感图像特征挖掘结构,在训练过程中,使用自适应梯度更新结构参数,设计挖掘步骤,实现传感图像特征快速挖掘。实验结果表明,设计的卷积神经网络图像特征挖掘方法的PSNR值较高、SSIM值接近1,错误挖掘率为1.0%,PR曲线最接近右上角,面积最大,并且图像视觉效果更好,特征挖掘时间最高仅为1.3 s,实现了快速挖掘。

基于区域掩码对比蒸馏的遥感图像目标检测

为解决遥感图像中存在背景干扰以及目标密集分布的问题,采用了一种以区域掩码对比蒸馏为基础的目标检测方法,旨在提高遥感图像目标检测性能.首先,通过对目标特征区域进行掩码操作,以区分前景和背景并捕捉目标的细节纹理,生成细致的特征掩码.其次,结合对比蒸馏算法,通过对教师网络和学生网络的区域掩码进行对比学习,使学生网络更加充分地吸收教师网络在目标特征纹理检测方面的知识.同时,在检测阶段引入了一种旋转定位损失算法,该算法通过量化真实边界框和预测边界框之间的格点向量差来进行损失估计,从而减小了预测边界框与真实边界框之间的旋转损失.结果表明,改进算法的均值平均精度在DOTA和HRSC2016数据集上分别较传统算法提高了3.57%和5.22%.

基于改进U-Net模型的遥感影像道路提取方法研究

针对高分辨率遥感影像背景信息复杂,道路提取难度大,自动化程度低等问题,论文提出了一种改进的U-Net的道路提取方法。首先,编码器使用VGG16网络结构替代原始U-Net编码器结构;然后,在每个编码器和解码器块后加入特征压缩激活模块(SENet)增强网络特征学习能力;最后,使用Dice损失函数和二分类交叉熵损失函数复合的损失函数进行训练,减轻了道路提取任务中的样本不平衡问题。在Massachusetts Road数据集上的结果表明,改进后的算法对道路提取结果得到了有效的提升。所提方法在测试集上的精确度、召回率、F1-score和mIoU评价指标分别达到82.5%、77.8%、80.0%及82.1%,在测试影像中对错综交叉的道路具有更好的识别效果。

基于改进PatchSVDD的田间异常区域检测方法

利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,对检测方法的适用性提出了更高的要求。针对以上问题,本文提出基于改进PatchSVDD (Patch-level Support Vector Data Description)模型的田间异常区域检测方法,该方法仅使用田间正常区域的标注信息,即可对田间异常区域进行检测和定位。首先,改进方法引入不相邻图像块之间的边界损失函数,从而提升了正常与异常样本边界的判别性,改进了检测的鲁棒性;第二,引入外部记忆组件,通过压缩存储正常样本特征,从而在保证检测精度的基础上有效减少了测试阶段的时间和空间消耗;第三,构建了包含杂草簇、种植缺失、障碍物、双倍种植和积水共5个异常类型的田间异常数据集。本文方法在平均检测AUC(Area Under Curve)值和平均定位AUC值上分别达到了96.9%和94.6%,相比于原算法分别提升1.2%和1.6%,从而验证了方法的有效性。

基于回音壁模式微泡腔的流速传感应用

基于黏性流体的伯努利效应原理,利用回音壁模式微泡腔高品质因子与具有天然微流控通道的特性,提出一种基于回音壁模式微泡腔的流速传感器。理论分析了黏性流体流动时由于黏滞损耗引起的压强损失,仿真分析了不同流速范围内流动速度和压强之间的关系,发现二者之间表现出良好的线性依赖,沿程损耗是引起压强损失的主要因素;恒定流速下微腔内部为均匀的正压分布,会引起谐振波长红移。实验制备了壁厚约为2μm的微泡腔,并搭建流速传感实验测试系统。在3~106μL/min流速范围内,流速增加时,谐振波长发生红移,并且波长偏移与流速之间满足良好的线性关系,拟合得到流速传感灵敏度为0.047 pm/(μL/min),检测极限为0.635μL/min。该流速传感器结构简单、易于制备、低成本且检测极限较低。

森林食叶害虫空间分布格局遥感定量反演研究

以Sentinel–2A多光谱影像为数据源,利用卷积神经网络模型提取的受害树种空间分布和多时相PROSAIL模型叶面积指数反演差值确定的失叶率耦合的虫口密度,定量获取长白山南麓虫害空间格局。结果表明:2018—2020年共7个时相LAI反演整体精度在88%以上;红松的适宜参考时相为2019年6月,预测与实测拟合R^(2)为0.82,其余树种及全样本2018年6月最佳;虫口密度与失叶率耦合采用线性函数,R^(2)为0.755;落叶松遭虫害面积6174 hm2最大,云杉受害面积比65.19%最大。虫害导致失叶率计算采用的参考时相为受灾前一年6月;虫口密度与失叶率呈线性关系;不同树种受灾空间格局不同,常绿树种重度灾害比例普遍高于落叶树种。

基于增强特征提取的森林遥感图像行人小目标检测网络

林业作业人员常常由于复杂地貌而面对坠落、森林火灾等威胁,卷积神经网络结合无人机巡查的方法已经成为主流防范、搜救措施,但遥感图片中的行人小目标有特征少、定位精度要求高以及极易受背景信息干扰的特点。为了能够使森林遥感图片行人小目标检测的精度达到预期,在YOLOv4方法的基础上针对上述特点设计了增强特征提取的目标检测网络(EFEN),通过构建感受野增强模块(RFBA)并结合CBAM注意力机制,在充分利用遥感图片中的丰富上下文信息之余,对相关信息进行动态选择,增强特征的表示能力;基于高斯分布思想,将归一化Wasserstein距离与CIOU结合,提出了一种新的损失函数(GKCLOSS),降低了小目标检测任务中对位置偏差的敏感性;引入一种自适应分割训练检测策略,平衡正负样本,提高目标检测的准确性,进一步提高了检测精度。以河北省张家口市崇礼区采集的无人机行人图像为研究对象,实验表明,EFEN框架在小目标检测方面优于现有的深度学习网络,在与SSD、YOLOv5、YOLOv7等算法比较中平均查准率(mAP)均有所提升,在上述数据集上,mAP高达39.10%,证明了此方法对行人小目标数据的有效性。