The Big Pine Creek watershed and climate change: A trend analysis of Landsat surface reflectance and PRISM datasets over the last 3 decades

Recent variations in normal meteorological conditions indicate the earth’s climate is changing in ways that may impact delicate ecological balances in sensitive regions. Identifying how those changes are affecting the biosphere is essential if we are going to be able to adapt to those changes and to potentially mitigate their harmful consequences. This paper presents a time series study of an alpine ecosystem in the Big Pine Creek watershed in California’s Eastern Sierra Nevada Mountain’s. Raw Landsat data covering the years 1984 through 2011 is converted to observed surface reflectance and analyzed for trends that would indicate a change in the ecosystem. We found that over the time period of the study, observed surface reflectance shows a general decline across the spectrum while our analysis of environmental data demonstrates statistically significant increases in temperatures. While declining reflectance in the visible and short wave bands are indicators of increased surface cover, the fact that the IR band also shows declines is consistent with a decline in tree density. This study provides a useful insight into the ecological response of the Big Pine Creek watershed to recent climate change. These findings suggest that alpine ecosystems are particularly sensitive to increasing temperatures. If these results are replicated in other alpine watersheds it will demonstrate that the biosphere is already showing the effects of a warmer environment.     

基于多特征的高分遥感图像分割算法研究

针对传统的图像分割算法不能完全适用具有多种特征〖BF〗（〖BFQ〗光谱特征、纹理特征和几何特征〖BF〗）〖BFQ〗的高分辨率遥感图像的问题,提出了一种基于多特征的遥感图像分割算法。算法基于改进的均值漂移滤波和自动标记分水岭分割方法来实现最终分割。首先利用自动标记分水岭分割方法对遥感图像进行分割,进而采用仿射不变矩形状特征算子提取图像几何特征;其次对图像进行主成分分析,计算第一主成分灰度共生矩阵,分析矩阵特性得出纹理特征;然后结合光谱特征通过改进的均值漂移方法得到多特征滤波结果;最后利用分水岭分割方法实现高分辨率遥感图像分割。为了表明算法的分割效果,利用基于多光谱信息熵方法对算法和单一的分水岭分割方法进行非监督评价。研究结果表明,算法可较好地改善遥感图像的过分割问题,是一种适合高空间分辨率多光谱遥感图像的分割算法。     

基于图像特征分析的物体轮廓提取

对物体的轮廓进行分析提取,是计算机视觉方向的基础问题之一,对其进行研究对于复杂场景的分析理解至关重要。本文对室内场景图像进行研究,基于图像特征进行图像分割,提取物体轮廓。在彩色场景图像全局轮廓后验边界概率（gPb）提取算法的基础上,加入深度图像信息,对室内场景的彩色、深度（RGB-D）图像中的物体轮廓进行分析。通过多尺度信息融合,计算得到多尺度轮廓后验概率（mPb）和谱后验概率（sPb）,两后验概率加权综合得到gPb。而后结合超度量轮廓图与分水岭算法,对基于方向特征变化的gPb图像融合处理,最终得到清晰的物体轮廓。本文所提方法在通用的RGB-D数据库基础上进行实验。实验结果表明,本文所提出的方法能提取出清晰的室内物体轮廓图。      

Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.     

稀疏泡状流中变形气泡的三维图像测量技术

研究气泡变形对于分析泡状流中气泡的受力和运动具有重要意义.设计了一种可确定稀疏泡状流中气泡空间坐标与变形参数的图像测量技术:原始图像经预处理和增强后,结合形态学方法识别出变形气泡投影的闭合轮廓.改进了分水岭方法对气泡投影进行分割,并用对称直线法确定投影中心改进Hough变换,得到椭球气泡模型的投影椭圆参数.提出了一种由两幅投影轮廓重构三维气泡模型的算法并分析了实验误差.该技术可较准确地测得气泡空间坐标和变形参数:投影椭圆的参数误差小于3个像素,气泡识别误差小于15%.     

基于分水岭与图割的自动分割方法

为快速、准确的提取CT序列图像中目标物体,把分水岭和图割相结合.首先选择目标物体的内外轮廓,对内外轮廓之间的区域用分水岭算法预分割为若干小区域,把每一个小区域作为一个节点,建立图结构.把多源点和多汇点简化成单源点和单汇点,建立新的图结构.然后利用最大流/最小割定理进行切割,提取目标物体.最后把上一张CT目标物体的轮廓映射到下一张CT上,分别扩大和缩小该轮廓作为该CT的内外轮廓.根据上述方法提取轮廓,对整个CT序列依次循环操作.通过实验证明该算法在分割效果和分割时间上优于其它传统算法,同时,实现了三维空间上序列轮廓的自动提取.