基于分布式计算的遥感图像水体识别研究

为了提高遥感数据的处理速度,解决遥感信息提取中的数据密集与计算密集问题,将并行计算的思想引入到遥感图像的处理与信息提取中,构建基于Landsat ETM+影像的分布式遥感图像水体提取模型。以渭干河流域为研究区,利用单波段阈值法、多波段谱间关系法、水体指数法等方法进行水体信息自动提取的实验。实验结果表明,该模型具有较高的识别精度,能够快速识别水体,并具有稳定的可扩展性和伸缩性。     

利用TM资料提取森林信息的波段选择及图像增强方法探讨

本文通过对陕西省黄陵县桥山林业局Landsat TM除热红外6波段外各波段的研究、对比、分析,探讨了可供西北地区森林遥感调查和研究参考的TM各波段组合的方案及影像增强方法。     

森林火灾后生态变化监测中植被指数提取方法的研究

一、前言动态监测是遥感的一大特长。尤其是TM数据，具有较高的辐射测量灵敏度、几何精度和空间分辨率，较航空遥感资料成本低，易获取。多时相的TM数据，既包含着森林植被的空间分布信息，又反映了火灾后森林生态变化和迹地更新的动态信息，故在“八五”的《森林火灾后生态变化遥感监测评价技术》研究课题中，选用TM图像数据作为遥感信息源，从TM数据中提取各迹地的生态变化信息，与各生态因子的模糊综合评判结果和火烧年限一起构建成动态监测数学模型，经过5个迹地的监测试验和验证，其功能达到了预期的攻关目标。应用TM数据监测火灾…     

融合红边波段的森林火烧迹地提取方法

针对森林火灾火烧迹地提取精度不够高的问题,基于Sentinel-2等新型卫星提供的对植被敏感的红边波段,分别构建了基于主成分分析法和新型植被指数的火烧迹地提取算法。第一种是基于第三主成分分析法(PC3),选择可见光及近红外波段进行主成分分析,根据变换后的第三主成分提取火烧迹地,将3个红边波段分别替换可见光红波段,根据得到的PC3_(red-dege1)、PC3_(red-dege2)、PC3_(red-dege3)提取火烧迹地。第二种是基于新型植被指数法,根据NDVI及将3个红边波段分别替换可见光红波段计算获取的NDVI_(re1)、NDVI_(re2)、NDVI_(re3),分别提取火烧迹地。以2017年毕拉河特大火灾火烧迹地为研究区。实验结果表明,基于PC3的火烧迹地取效果整体优于基于植被指数的提取效果;红边波段参与的火烧迹地的提取精度高于红波段参与的提取精度,其中PC3_(red-dege1)精度最高,PC3_(red-dege2)提取精度次之。研究证明利用红边波段采用PC3的方法能满足火烧迹地的高精度快速提取。     

马尾藻遥感信息提取

以Landsat数据为基础,分析马尾藻的图像和波谱特征,对比分析单波段提取法、双波段比值法、双波段差值法和归一化植被指数法对马尾藻信息的提取结果,并利用IKONOS数据来验证4种方法的提取精度.结果表明:马尾藻在Landsat真彩色(TM3、TM2、TM1)和假彩色(TM4、TM3、TM1)合成图上均呈黄色,其生长边界在假彩色合成图上更为清晰.马尾藻水体与非藻类水体在TM4的差异最大,在TM3也存在细小差异,单波段提取法(TM4)、双波段比值法(TM4/TM3)、双波段差值法(TM4-TM3)和归一化植被指数法((TM4-TM3)/(TM4+TM3))都可以从自然水体中提取出马尾藻信息,与IKONOS的提取结果相比,归一化植被指数法的提取精度最高.     

HJ-1星多光谱影像火烧迹地信息提取研究——以百色市为例

为识别火烧迹地等地类,以广西百色市为研究区,采用HJ-1星多光谱影像数据近红外波段光谱值、林火发生前后两时相各自NDVI值以及NDVI变化值,基于先验知识和统计分析构建决策树分类模型,通过与传统最大似然分类提取结果的比较分析,表明基于多特征的决策树模型能够有效地对HJ-1星多光谱遥感数据进行火烧迹地等地类提取,在研究区并具有良好的推广性。     

基于TM影像的几种常用水体提取方法的比较和分析

随着遥感技术的飞速发展,利用遥感数据来进行水资源的监测、调查和分析已成为一种必然的趋势。从遥感影像中快速、准确地提取水体信息,是进行水资源调查和监测的一种重要的方法和手段。目前进行水体提取的方法有很多,本文选取了常用的3种水体提取方法,即单波段阈值法、基于阈值的多波段谱间关系法、基于阈值的水体指数法,然后分别选取典型的武汉平原地区和宜昌山地地区为研究区,以Landsat5TM影像为数据源,通过实验来比较和分析这3种水体提取方法分别在平原地区和山地地区的优势和不足。     

基于TM遥感图像的近海岸带水深反演研究

以江苏南通小洋口港外辐射沙脊群海域为研究区,在TM数据地表反射率反演的基础上,通过实测水深数据与TM4波段反射率、TM4/TM2波段反射率比值因子的相关研究,分别建立了线性、对数、指数和幂指数的水深反演模型,研究不同模型对该研究区0~5m、5~10m、10~15m不同水深的适用情况以及水深反演精度分析。研究结果表明:针对本研究区,以TM4/TM2波段反射率比值建立的线性水深反演模型的精度最好,并依据该模型对研究区进行遥感水深制图。     

一种基于遥感数据快速提取水体信息的新方法

“数字流域”建设已成为水利现代化的重要组成部分。利用遥感技术进行水体信息提取可为构建数字流域提供重要的数据来源。在对Landsat ETM+影像上的水体及其背景地物进行光谱特征分析的基础上,发现水体在近红外和中红外波段（对应于Landsat数据的Band 4、Band 5和Band 7）同时具有强吸收这一典型特征,据此提出了一种新型的水体指数NWI（New Water Index）。其中,将Band 7用于水体指数模型的构建和水体信息的提取目前较少见于文献报道。以厦门岛为研究区,将该指数在经过大气校正的遥感影像上进行实验的结果表明：NWI可用于水体信息的快速有效提取,且具有较高的精度。     

森林叶面积指数遥感反演模型构建及区域估算

基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。     

Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image

Logistic regression modeling was applied, as an alternative classification procedure, to a single post-fire Landsat-5 Thematic Mapper image for burned land mapping. The nature of the classification problem in this case allowed the structure and application of logistic regression models, since the dependent variable could be expressed in a dichotomous way. The two logistic regression models consisted of the TM 4, TM 7, TM 1 and TM 4, TM 7, TM 2 presented an overall accuracy of 97.37% and 97.30%, respectively and proved to be the most well performing three-channel color composites. The discriminator ability in respect to burned area mapping of each one of the six spectral channels of Thematic Mapper, which was achieved by applying six logistic regression models, agreed with the results taken from the separability indices Jeffries-Matusita and Transformed Divergence.     

多光谱数据波段选择方法试验研究——以湖北神农架林区为例

本文以神农架林区植被信息提取为例,从统计特征的角度出发,采用最佳指数因子、联合熵与类间、类内可分性判别准则三种波段选择方法。在对三种波段选择方法计算结果综合分析的基础上,结合试验区地物光谱特征和TM传感器不同波段功能,采用逐步逼近的思路,从候选波段组合中确定了最佳波段组合。试验得出TM传感器453波段组合为神农架林区植被信息提取的最佳波段组合。     

Temporal burn scar evolution in tallgrass prairie based on field spectroscopy

In situ field spectroscopy samples were used to simulate several Moderate Resolution Imaging Spectroradiometer (MODIS) bands and indices commonly used for burned area detection. Each band or index was tested for its ability to differentiate between burned and unburned tallgrass prairie during several time periods from spring (when burning took place) to late summer (peak biomass) with three analysis of variance tests. The normalized difference vegetation index (NDVI), global environmental monitoring index (GEMI), global environmental monitoring index – burn scar (GEMI-B), and normalized burn ratio (NBR) indices, as well as MODIS band 7 (longwave mid-infrared; LWMIR), showed virtually no promise for differentiating burned from unburned areas for more than several days after the burn. Others, including the burned area index (BAI), Mid-infrared burn index (MIRBI), and MODIS bands 3 (red), 4 (near-infrared; NIR), 5 (longwave near-infrared; LWNIR), and 6 (shortwave mid-infrared; SWMIR) were able to differentiate between burned and unburned areas well into the growing season – in some cases, even through its entire length. The performance of particular bands and indices often depended on grazing, vegetation phenology, ash/char/soil reflectance, and factors that influenced pre-burn biomass.     

基于MODIS时间序列森林扰动监测指数比较研究

森林扰动是影响陆地生态系统的重要因素之一,遥感可定期地获得大面积森林覆盖数据,成为定期和连续森林扰动监测的重要手段,基于时间序列数据的森林监测成为主要方式。研究利用2001~2013年MODIS时间序列遥感影像,以福建省为例,利用NDVI、NBRI、NDMI、IFZ和DI 5种森林扰动监测指数,结合植被变化追踪算法提取森林扰动区域,并从光谱响应特征和对不同扰动类型的响应能力等方面,分析了对我国南方森林扰动的监测能力。结果表明:DI对森林砍伐、森林病虫害和植树造林3种扰动类型的响应能力较强,NBR对森林火灾最为敏感,NDVI对4种扰动类型的响应能力相对较弱;5种指数中DI对森林扰动的响应能力较强,森林扰动提取精度最高,IFZ次之,NDMI和NBR监测精度相当,且优于NDVI。     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

Detection of forest harvest type using multiple dates of Landsat TM imagery

A simple and relatively accurate technique for classifying time-series Landsat Thematic Mapper (TM) imagery to detect levels of forest harvest is the topic of this research. The accuracy of multidate classification of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were compared and the effect of the number of years (1–3, 3–4, 5–6 years) between image acquisition on forest change accuracy was evaluated. When Landsat image acquisitions were only 1–3 years apart, forest clearcuts were detected with producer's accuracy ranging from 79% to 96% using the RGB-NDMI classification method. Partial harvests were detected with lower producer's accuracy (55–80%) accuracy. The accuracy of both clearcut and partial harvests decreased as time between image acquisition increased. In all classification trials, the RGB-NDMI method produced significantly higher accuracies, compared to the RGB-NDVI. These results are interesting because the less common NDMI (using the reflected middle infrared band) outperformed the more popular NDVI. In northern Maine, industrial forest landowners have shifted from clearcutting to partial harvest systems in recent years. The RGB-NDMI change detection classification applied to Landsat TM imagery collected every 2–3 years appears to be a promising technique for monitoring forest harvesting and other disturbances that do not remove the entire overstory canopy.     

The utility of multi-sensor data for mapping eroded lands

The availability of remote sensing data with improved spatial, spectral and radiometric resolution is now available to fully exploit their potential for a specific application subject to the relative merits and the limitations of each sensor's data. Presented here is a case study where Landsat MSS and TM; and SPOT MLA data for part of the Bijapur district, southern India, which were acquired on the same day, have been evaluated for mapping eroded lands. The approach involves the geometric registration of all three data to a common map grid using tie points and third order polynomial transform; and resampling the MSS and TM data to a 20m by 20 m pixel dimension and radiometric normalization. Thematic maps showing eroded lands were generated on a micro-VAXbased DIPIX system using a maximum likelihood classifier. Accuracy estimates were made for the thematic maps following stratified unaligned random sampling technique, and subsequently, computing overall accuracy and Kappa coefficient. Spectral separability and classification accuracy was maximum from SPOT-MLA data followed by a combination of Landsat MSS band 1, SPOT-MLA band 2 and Landsat TM band 4; Landsat TM, a combination of Landsat MSS, TM and SPOT MLA; and Landsat MSS data.     

高光谱数据的光谱信息特点及面向对象的特征参数选择——以北京顺义区为例

波段宽度为纳米级的高光谱数据,具有几十乃至几百个光谱通道,它们各有不同的特点。如何根据具体的应用目的,在这众多的波段中选择出最佳波段和特征参数,对于有效地进行高光谱数据的处理、分析及信息提取至关重要。以北京顺义区高光谱数据为例,首先分析了通道间的相关性,根据通道的相关性大小和分组块状结构特点,将其分为若干组;然后全面分析了高光谱数据的光谱信息特征,在综合考虑各波段的信息含量、波段间的相关性以及地物光谱的吸收特性和可分性等因素
的基础上,提出了面向对象的分层多次选择高光谱数据最佳波段和提取特征参数的基本思路和方法;最后用其它地区的成像光谱数据对此方法进行了验证。     

Using Landsat TM data to estimate carbon release from burned biomass in an Alaskan spruce forest complex

Fire disturbance in boreal forests can release carbon to the atmosphere stored in both the aboveground vegetation and the organic soil layer. Estimating pyrogenic emissions of carbon released during biomass burning in these forests is useful for understanding and estimating global carbon budgets. In this work, we have developed a method to estimate carbon efflux for the burned black spruce in an Alaskan forest by combining information derived from Landsat Thematic Mapper (TM) data and field measurements. We have used the spatial and spectral information of TM data to identify and measure two important factors: pre-burn black spruce density and burn severity. Field measurements provided estimates of aboveground and ground layer carbon per unit area for the pre-burn Landsat spectral classes, and percentage of carbon consumed for the post-burn Landsat spectral classes. Carbon release estimates for the burned black spruce were computed using field data and the co-occurrence of the pre-burn and post-burn spectral classes. The estimated carbon released was 39.9tha-1, which is 57% greater than an estimate computed using AVHRR data and estimates of pre-burn biomass and carbon fractions consumed that were not site specific or spatially varying. We conclude that the spectral bands and spatial resolution of Landsat TM data provide the potential for improved estimates of pyrogenic carbon efflux relative to the coarser spectral and spatial resolution of other multispectral sensors.     

Detection of annual burned forest area using change metrics constructed from MODIS data in Manitoba,Canada

The monitoring of annual burned forest area is commonly used to evaluate forest fire carbon release and forest recovery and can provide information on the evolution of carbon sources and sinks. In this work, a new method for mapping annual burned area using four types of change metrics constructed from Moderate Resolution Imaging Spectroradiometer (MODIS) data for Manitoba, Canada, was developed for the 2003–2007 period. The proposed method included the following steps: (1) four types of change metrics constructed from MODIS composite data; (2) Stochastic Gradient Boosting algorithm; and (3) two thresholds to ascertain the final burned area map. Fire-event records from the Canadian National Fire Database (CNFDB) for Manitoba were used to train and validate the proposed algorithm. The predicted burned area was within 91.8% of the CNFDB results for all of the study years. The results indicate that the presented metrics could retain spectral information necessary to discriminate between burned and unburned forests while reducing the effects of clouds and other noise typically present in single-date imagery. A visual comparison to Thematic Mapper (TM) images further revealed that in some areas the mapping provided improvement to the CNFDB data set.