遥感影像用于城市扩展监测的研究

通过焦作市城市扩展监测的实践,对以多时相遥感影像监测城市扩展进行了研究,阐述了以地形图及TM遥感影像为数据源,利用RS和GIS软件进行图像处理,提取城区扩展的变化信息,并对其扩张变化的主要方向、速度及规律进行监测和分析;论述了交通建设、政策法规、经济投资区的建设和当地的自然地理环境对城市的扩展速度和方向的影响。     

HJ-1A星辐射定标及近海水体大气校正的精度验证

以应用HJ-1A星数据进行近海水体定量反演为目的,对HJ-1A星CCD的星上定标系数的精度进行验证,并基于Landsat-5TM数据对其进行交叉定标。在此基础上反演近海水体的反射率,将反演结果与实测水体反射率进行比较发现:对HJ-1A星CCD数据进行交叉定标能在一定程度上能提高近海水体反射率反演的精度。结果表明,HJ-1A星CCD影像的星上定标系数不够精确,在利用该数据进行近海水体的定量反演研究时,需要利用交叉定标对星上定标参数进行修正。     

干旱区ETM遥感图像蚀变异常信息提取方法研究

含矿热液蚀变带是成矿作用发生的重要标志之一,它作为矿产资源快速评价和定位预测的一个主要参数,在遥感地质找矿中具有重要意义。本文在分析干旱区ETM遥感影像地物识别所依据的光谱特征的基础上,提出一种基于小波自适应增强及主分量分析相结合的蚀变遥感异常信息提取方法,结合MATLAB编程软件和ERDAS 8.6遥感图像处理软件进行蚀变信息提取试验,并分别与传统的基于波段选择的主成分提取法以及基于主成分分析和比值增强相结合的提取法进行对比分析和效果查证。实验结果表明,研究区内利用小波自适应增强与主分量分析相结合提取的遥感异常与已知矿床、矿（化）点的吻合率达到80%,新发现矿化点一处和外围找矿线索多处,能取得较为满意的提取效果,具有快速、高效、 经济的特点,较之传统方法具有很大的优越性。     

2003-2011年青藏高原佩枯错相对水量变化及其对气候变化的响应

湖泊的退缩与扩张是全球气候变化的指示器.利用2003-2011年Landsat ETM数据和2003-2009年ICESat激光测高数据, 分别对青藏高原佩枯错湖泊的面积和高程变化进行了分析, 并进一步估算了湖泊2003-2009年相对水量变化.结果表明: 佩枯错面积年内变化明显, 湖泊面积冬季最小, 春季出现小峰值, 秋季达到最大; 面积年内波动明显(1.18%), 但在冬季、 春季和秋季相对稳定, 波动范围分别为0.26%、 0.1%和0.29%. 2003-2011年湖泊呈退缩趋势, 冬季、 春季和秋季面积年际变化率分别为-0.52 km²·a^-1、-0.35 km²·a^-1和-0.61 km²·a^-1; 2003-2009年间湖泊水位下降了1.17 m, 变化率为-0.05 m·a^-1; 2003-2010年, 冬季总水量减少了2.51×10⁸ m³, 春季总水量减少了1.74×10⁸m³, 秋季总水量减少了2.80×10⁸ m³, 平均相对水量变化率分别为-0.35×10⁸ m³·a^-1、-0.21×10⁸ m³·a^-1、-0.37×10⁸ m³·a^-1. 从空间上看, 湖泊退缩主要发生在东北角、 东南角和西南角.气候因素分析表明, 佩枯错湖泊退缩秋季主要是因为夏半年平均气温的升高, 冬季和春季则主要是因为冬半年降水量的减少.     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.     

LANDSAT-7 ETM+影像大气校正算法IDL的实现

基于IDL语言平台,实现一种较为成熟的适于LANDSAT-7号卫星ETM 传感器获得的影像的大气校正算法.结果表明所采用的校正算法简便实用,可操作性强.     

基于ERDAS IMAGING软件的TM影像几何精校正方法初探--以塔里木河流域为例

遥感图像的几何精校正一直是人们所关注的问题。本文初步探讨了利用ERDAS IMAGING软件对塔里木河流域TM影像进行几何精校正的方法,ERDAS IMAGING软件进行几何精校正具有操作简便、实用性强、速度快、几何精度高等特点。几何精校正为TM影像的进一步处理提供了技术准备。     

Estimating soil erosion using MODIS and TM images based on support vector machine and à trous wavelet

To date, there is little work concerning the application of fusing images with significantly different spectral and spatial resolutions. In this paper, a novel method based on support vector machine (SVM) is proposed to quickly estimate soil erosion using the fused results produced from fusing such multisensor images by à trous wavelet transform (AWT). In the proposed method, the AWT is used to derive the high-resolution vegetation coverage image (HVCI) while the SVM overlays the HVCI and the slope image to derive the soil erosion map. By taking MODIS and TM images as an example, the potential of the proposed method is evaluated both quantitatively and qualitatively. The results show that it is feasible to perform the fusion of MODIS and TM images and the soil erosion map produced from the fused images by the proposed method can be achieved with an accuracy level comparable to that solely from the TM images. The merging of MODIS and TM images partly solves the constrains associated with the TM data availability which is caused by the lower revisit frequency and narrower spatial coverage.     

An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso,Brazilian Amazon

Forest cover disturbances due to processes such as logging and forest fires are a widespread issue especially in the tropics, and have heavily affected forest biomass and functioning in the Brazilian Amazon in the past decades. Satellite remote sensing has played a key role for assessing logging activities in this region; however, there are still remaining challenges regarding the quantification and monitoring of these processes affecting forested lands. In this study, we propose a new method for monitoring areas affected by selective logging in one of the hotspots of Mato Grosso state in the Brazilian Amazon, based on a combination of object-based and pixel-based classification approaches applied on remote sensing data. Logging intensity and changes over time are assessed within grid cells of 300 m × 300 m spatial resolution. Our method encompassed three main steps: (1) mapping forest/non-forest areas through an object-based classification approach applied to a temporal series of Landsat images during the period 2000–2015, (2) mapping yearly logging activities from soil fraction images on the same Landsat data series, and (3) integrating information from previous steps within a regular grid-cell of 300 m × 300 m in order to monitor disturbance intensities over this 15-years period. The overall accuracy of the baseline forest/non-forest mask (year 2000) and of the undisturbed vs disturbed forest (for selected years) were 93% and 84% respectively. Our results indicate that annual forest disturbance rates, mainly due to logging activities, were higher than annual deforestation rates during the whole period of study. The deforested areas correspond to circa 25% of the areas affected by forest disturbances. Deforestation rates were highest from 2001 to 2005 and then decreased considerably after 2006. In contrast, the annual forest disturbance rates show high temporal variability with a slow decrease over the 15-year period, resulting in a significant increase of the ratio between disturbed and deforested areas. Although the majority of the areas, which have been affected by selective logging during the period 2000–2014, were not deforested by 2015, more than 70% of the deforested areas in 2015 had been at least once identified as disturbed forest during that period.     

二维大地电磁资料频域逆散射反演

逆散射反演是由已知散射波场反演地下介质中散射体各参数了分布的方法,本文从这一思想出发,针对二维大地电磁资料,在BORN近似的前提下,提出了一种新的反地下电导率参数的方法,经模型试算与实际资料的反演验证正确,这一方法特别适于地下无强电性分界面的地区。