A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics

This study uses a combination of satellite imagery and GIS data, a vegetation map, interview data, and on-site field studies to map detailed natural vegetation to land-use conversion pathways (~ 22,000 possible combinations) in the seasonal tropics of Santa Cruz Department in southeastern Bolivia from 1994 to 2008. We mapped a suite of land-use classes based on the seasonal phenology of double- and single season cropping regimes; pasture; and bare soil cropland (fallow). Analyses focus specifically on the Corredor Bioceánico, which bisects some of the most sensitive and poorly understood ecosystems in the world and indirectly creating one of the most important agricultural region-deforestation hotspots in South America at the present time. Training data to predict class membership were based on MODIS NDVI annual mean, maximum, minimum, and amplitude derived from field observations, semi-structured interviews, and aerial videography. Results show that over 8,000 km² of forest was lost during the 14-year study period. In the first years of cultivation, pasture is the dominant land use, but quickly gives way to cropland. The main findings according to forest type is that transitional forest types on deep and poorly drained soils of alluvial plains have lost the most in terms of percentage area cleared. The resulting transition pathways can potentially provide decision-makers with more detailed insight as to the proximate causes or driving forces of land change in addition to the most threatened forests remaining in the Tierras Bajas and those most likely to be cleared in the Brazilian Shield and Pantanal.     

中国温带地区植被物候期时空变化特征及对总初级生产力的影响

陆地表面物候(Land Surface Phenology,LSP)被定义为陆地表面植被的生命周期事件在区域或全球尺度上的各季节时间,对于中国温带地区的植被物候,已有研究大多仅针对某一地区的单一植被类型,对长时间、大尺度的陆表物候时空变化的研究较为少见,植被物候对总初级生产力(Gross Primary Productivity,GPP)的影响也有待明确。采用2001～2014年中等分辨率成像光谱仪MODIS的MCD12Q2数据以及MOD17A3 GPP数据,利用回归分析以及相关分析的方法对中国温带地区(>30°N)植被物候期时空变化特征进行分析,并研究物候变化对GPP的影响。结果表明:我国温带地区整体植被生长季开始日期(Start of the Season,SOS)、结束日期(End of the Season,EOS)和生长季长度(Length of the Season,LOS)的14 a平均值和标准差分别为第121±10 d、第270±12 d和153±12 d;SOS提前趋势(r=-0.36,p=0.21)、EOS滞后趋势(r=0.41,p=0.15)与LOS增长趋势(r=0.51,p=0.06)均不明显;但是年均LOS与GPP相关性显著(r=0.74,p=0.002 5),且呈正相关关系。2001～2014年LOS与GPP年均值的空间分布均呈现出由西北向东南增加的趋势;研究区SOS、EOS与LOS的年际变化呈现显著变化(p<0.05)的地区分别占研究区的13%、21%、13.2%。SOS、EOS、LOS与GPP显著相关(p<0.05)的地区分别占研究区的8.31%、9.33%、8.72%,三者与GPP的相关关系均以中度相关为主(p<0.05,0.5<|r|<0.8),二者高度相关(p<0.05,|r|≥0.8)的地区很少。研究区植被物候期变化显著影响(p<0.05)植被年均GPP的地区仅有4.29%。     

Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery.The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.     

基于信息融合的入侵检测方法

为了更全面地检测到在系统和网络中的入侵行为,本文将信息融合技术用于入侵检测．首先,利用支持向量机进行分类,将基于主机的审计数据和基于网络的流量数据包分别训练,然后利用D—s证据理论按照一定的规则对两个支持向量机的预测结果进行决策层的融合。把基于主机的入侵检测和基于网络的入侵检测结合起来将大大提升入侵检测的性能,降低漏报率,提高准确率。     

Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

A challenge in phenology studies is understanding what constitutes phenological change amidst background variation. The majority of phenological studies have focused on extracting critical points in the seasonal growth cycle, without exploiting the full temporal detail. The high degree of phenological variability between years demonstrates the necessity of distinguishing long-term phenological change from temporal variability. Here, we demonstrate the phenological change detection ability of a method for detecting change within time series. BFAST, Breaks For Additive Seasonal and Trend, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change. We tested BFAST by simulating 16-day NDVI time series with varying amounts of seasonal amplitude and noise, containing abrupt disturbances (e.g. fires) and long-term phenological changes. This revealed that the method is able to detect the timing of phenological changes within time series while accounting for abrupt disturbances and noise. Results showed that the phenological change detection is influenced by the signal-to-noise ratio of the time series. Between different land cover types the seasonal amplitude varies and determines the signal-to-noise ratio, and as such the capacity to differentiate phenological changes from noise. Application of the method on 16-day NDVI MODIS images from 2000 until 2009 for a forested study area in south eastern Australia confirmed these results. It was shown that a minimum seasonal amplitude of 0.1 NDVI is required to detect phenological change within cleaned MODIS NDVI time series using the quality flags. BFAST identifies phenological change independent of phenological metrics by exploiting the full time series. The method is globally applicable since it analyzes each pixel individually without the setting of thresholds to detect change within a time series. Long-term phenological changes can be detected within NDVI time series of a large range of land cover types (e.g. grassland, woodlands and deciduous forests) having a seasonal amplitude larger than the noise level. The method can be applied to any time series data and it is not necessarily limited to NDVI.     

Estimation of water and salt generation from unregulated upland catchments

Water and salt export to rivers is of particular importance in large catchments, such as Australia’s Murray-Darling Basin, where there are multiple users of the water resource. Comparing estimates of water and salt generated from upland catchments across large areas is difficult due to the lack of a comparable, consistent approach. River routing models are currently used to model water and salt movement along regulated reaches. However methods are still required to predict the individual contributions of water and salt from unregulated upland catchments to feed into these river routing models. The 2CSalt model has been developed to predict monthly water and salt export from these upland catchments. 2CSalt makes use of existing regional data sets such as topography (digital elevation models) and hydrogeology/salinity (Groundwater Flow Systems). 2CSalt was developed using the “TIME” modelling framework which allowed for a rapid development cycle through the reuse of existing and tested components. The results from current applications of the model show a strong match with measured data.     

一种混合式网络入侵检测系统

入侵检测系统通常采用单一的检测模式,难以有效地处理漏报和误报问题。该文分析不同类型网络流量的分布特征,提出一种将异常检测和误用检测相结合的混合式网络入侵检测系统,从总体上克服了单一模式的不足。实验结果表明,该方法能有效地提高入侵检测系统的检测率和准确率。     

基于分布式及协同式网络入侵检测技术综述

在当今信息化社会;网络入侵检测技术是信息安全保障领域的重点技术之一。随着大数据时代的到来;网络入侵检测技术正在向着多结构、多方法、多应用领域的方向发展。针对这个发展趋势;综述了网络入侵检测技术的最新研究情况;包括基本概念、系统模型、检测方法、应用领域等;其中重点分析了系统模型和检测算法的研究现状以及存在的问题;并提出发展趋势。同时;也介绍了大数据背景下网络入侵检测技术的新型应用领域。     

基于系统呼叫和网络数据包的两层入侵检测模型

由于每种检测手段都有各自的优缺点,任何一种单独的检测手段或方式都不能够检测出所有的入侵行为.为了弥补这些缺陷和不足,该文提出了一种基于系统呼叫和网络数据包的两层入侵检测模型,这种模型有机地把两种入侵检测技术融合在一起,极大地提高了系统的安全性能.结果也表明,这种检测模型具有更好的检测效果和健壮性.     

基于snort的校园分布式IDS

本文在分析现有分布式NIDS结构的基础上,结合校园网的特点,设计了一种区域集中整体层次化结构的NIDS,并使用免费的入侵检测工具snort予以实现。     

Detecting trend and seasonal changes in satellite image time series

A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01-0.07 σ) and seasonal amplitudes (0.1-0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur.     

入侵检测系统的性能问题研究

首先针对现有的入侵检测系统,讨论了影响入侵检测系统性能的一些因素,然后通过分析,找到了一些相应的解决方法,最后得出结论,如果想提高入侵检测系统的性能,要全面考虑这些因素。     

网络入侵检测技术浅析

随着网络技术的飞速发展,入侵检测技术已成为网络安全关注的热点。该文介绍了入侵检测的基本概念和入侵检测系统的通用模型,详细地阐述了入侵检测系统的分类,探讨了入侵检测技术面临的主要问题及其发展趋势。     

入侵检测系统发展简述

入侵检测系统是目前信息安全系统中的一个重要组成部分,该文简述了其二十多年的主要发展历程,对其使用的技术以及未来的发展方向进行了简要介绍。     

网络入侵检测系统的研究

入侵检测是提高网络安全性的有效手段。本文介绍了入侵检测的原理和方法,并详尽分析了几个实际的网络入侵检测系统,最后简要总结了网络入侵检测系统的发展方向。     

针对网络入侵检测系统的分析

入侵检测是提高网络安全性的有效手段。本文介绍了入侵检测的原理和方法,并详尽分析了几个实际的网络入侵检测系统,最后简要总结了网络入侵检测系统的发展方向。     

入侵检测中数据挖掘技术的应用

本文阐述了入侵检测,分析了入侵检测引入数据挖掘的优势,详细介绍了入侵检测中的数据挖掘研究方法,之后提出了一种自适应的入侵检测系统,该系统采用数据挖掘中关联／序列规则和分类算法,能使入侵检测更加自动化,提高检测效率和准确度,最后对入侵检测中数据挖掘技术进行了展望。     

入侵容忍系统设计

当入侵检测成为网络安全的热点的同时,入侵容忍的概念也悄然升起。论文主要通过对入侵容忍概念和常用入侵容忍方法进行总结,并根据研究提出了设计入侵容忍系统的必须具有的基础结构。该基础结构为设计入侵容忍系统提供了参考的标准。     

入侵防御系统的研究与设计

本文针对传统的入侵防御系统IPS(IntrusionPreventionSystem)存在的一些问题,提出了一种新型的入侵防御系统。该系统采用多台处理机并行处理的工作方式,并辅助以透明代理、内容过滤技术,能有效地防御DOS、DDOS网络攻击,抑制网络蠕虫病毒的传播。