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朱爱萍 《测绘与空间地理信息》2018,(3):83-86
煤田自燃一直是煤矿生产不容忽视的问题。本文利用热红外遥感技术检测因煤火导致地面温度异常的地区,采用两种不同的遥感反演温度算法(单窗算法、基于影像的算法),使用ETM+数据并通过阈值分割法提取2010年乌尊布拉克煤田火区的温度。结果表明,两种遥感反演算法得到的地下煤火区地表温度的空间分布趋势一致,反演结果符合要求。据此对地下煤火的变化类型进行判断,并确定煤火的生成状况,从而为煤田火灾防治提供必要的决策支持。 相似文献
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煤田火区地表形态变化是煤火监测和分析的可用指标之一。由于矿区地表严重的去相干噪声,使得星载合成孔径雷达干涉测量技术应用于煤田火区地表形变检测比较困难。结合煤田火区地表形变特点,利用L波段的ALOS PALSAR数据进行差分干涉处理,利用干涉条纹频率精确估计基线,并用自适应滤波方法降低去相干噪声的影响。在去除平地相位和参考地形相位后,获取煤田火区的地表形变。研究表明:地表形变与煤火燃烧具有一定的相关关系,通过地表形变分析有助于对地下煤火燃烧情况的判断;利用差分干涉SAR技术检测煤田火区地表形变,进而对煤火进行监测和分析是可行的。 相似文献
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Landsat-8 TIRS数据第10波段和第11波段是热红外波段,两个波段数据空间分辨率是100 m。本文选取乌鲁木齐大泉湖煤田火区进行了实验,分别获取了2015年5月和2017年5月大泉湖煤田火区两期遥感影像,采用辐射传输方程方法进行了温度反演。对反演温度数据进行密度分割,提取了乌鲁木齐大泉湖煤田火区范围,并和经过物探方法确定的火区范围进行了叠加,矢量范围重合度达83%。结果显示,基于Landsat-8 TIRS数据煤田火区识别方法可行,对于煤田火区识别和监测将是一种重要的方法。 相似文献
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西南喀斯特山区地形起伏较大,地物分布较为破碎,致使传统的光谱特征一次分类方法的精度较低。本文基于高分辨率无人机正射影像和地形指标,充分利用无人机遥感影像空间特征、光谱特征、纹理特征及地形特征,采取面向对象CART决策树算法与分层策略提取了研究区土地覆盖类型。研究表明,结合空间地形因子和分层策略的方法减少了破碎区地物间的相干扰,故具有较高的分类精度,总体分类精度达91.2%,Kappa系数为0.87,较传统一次分类精度提高了9.8%,Kappa系数提高了0.13。该方法对西南喀斯特地区土地覆盖解译精度较好,可为土地利用监测提供参考。 相似文献
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为了有效地提取大范围地形复杂区域的土地利用/土地覆盖遥感信息,以位居青藏高原与黄土高原过渡地带的青海东部地区为研究区,研究基于蚁群智能优化算法(ant colony intelligent optimization algorithm,ACIOA)的土地利用/土地覆盖遥感智能分类。首先选用TM图像、DEM、坡度和坡向数据作为分类的特征波段;然后利用归一化植被指数NDVI对实验区数据进行植被分区;最后利用ACIOA算法进行分类规则挖掘,并依据分类规则进行土地利用/覆盖信息的提取。研究表明,基于植被分区的多特征蚁群智能分类的总体精度为88.85%,Kappa=0.86,优于传统的遥感图像分类方法,为大范围地形复杂区域的土地利用/土地覆盖遥感信息提取提供了有效的方法。 相似文献
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通过航空高光谱遥感地面定标、波谱特征分析、火区特征地物和热异常信息提取,确定了用于温度反演的热红外波段、拟合关系式和热异常对应辐射温度,探测精度达到了1∶2 000比例尺精度;准确圈定了宁夏汝箕沟煤田火区范围,查清了火区燃烧强度,分析了热扩散规律以及遥感探测热异常与地下煤火热异常的对应关系,实现了将遥感定量调查结果直接用于灭火工程设计的目标。 相似文献
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木里煤田地处青藏高原典型生态环境脆弱地带的大通河源头,本文以Landsat影像为数据源,基于归一化植被指数像元二分模型估算木里煤田矿区1990-2016年植被覆盖度,监测其动态变化及时空发展规律。研究发现,1990-2016年矿区裸土及低植被覆盖面积增加156.60 km^2,中植被覆盖面积增加153.37 km^2,高植被覆盖面积减少309.99 km^2。动态监测结果表明,1990-2016年木里煤田植被覆盖呈现严重退化趋势,退化最明显区域出现在矿区周边;时空格局变化分析结果表明,矿区植被覆盖等级逐渐向低植被覆盖等级转变,植被覆盖区域面积逐渐缩小。通过监测木里煤田矿区植被覆盖动态变化并分析其时空变化特征,为研究区生态环境修复,土地复垦等工作提供相关数据参考及技术支撑。 相似文献
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The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images. 相似文献
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This study aims to increase the accuracy of the object based classification approach to differentiate the spectrally similar land cover types to create thematic maps depicting the current land use status in rangeland. Firstly, the multispectral and panchromatic bands of a WorldView-2 MS and Pan images are fused. The fused WV-2 image is then classified with object based approach using Support Vector Machines (SVMs) classifier (Method 1). The overall classification accuracy for Method 1 is found to be 88.6%. Secondly, UAV ortho-image is utilised for segmentation process, which is required for the object based SVM classification of the WV-2 MS image (Method 2). The overall classification accuracy for Method 2 is obtained as 92.4%. It is realised that the Method 2 increases the object based classification accuracy by 4%, compared to Method 1. This result reveals that the object based classification of the UAV and WV-2 MS images makes significant contribution to the classification accuracy. 相似文献
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利用决策树和支持向量机分类方法,基于多期Landsat MSS,TM and ETM+遥感图像和其他辅助数据,对1970s以来近40年半干旱的老哈河流域土地利用变化(land use and land cover change,LUCC)进行动态监测,并利用GIS方法对LUCC进行了定量分析和空间分布制图.结果显示,利用支持向量机分类方法对该地区1976年、1989年、1999年和2007年土地覆盖类型分类可达到较满意的效果;近40年老哈河流域土地利用变化显著,水体和草地减少,城乡用地持续扩张,耕地大幅增加,林地和未利用地大幅度波动、总体减少.LUCC主要发生在林地、草地和耕地之间,表明农、林、牧用地之间转换显著,且在各个时期的空间分布差别较大.从变化强度来看,土地利用的年综合变化率最大值渐趋增大,年均土地动态度在空间分布上差异很大,另外在各研究期赤峰市区周边动态度都很大,反映了赤峰市持续性的城市化进程. 相似文献
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为提高土地覆被分类精度,采用非参数权重特征提取(nonparametric weighted feature extraction,NWFE)结合纹理特征的支持向量机(support vector machines,SVM)的分类法,对新疆玛纳斯河流域绿洲区2006年的土地覆被进行分类,并将该方法与主成分分析(principal component analysis,PCA)结合纹理特征的SVM分类、原始波段结合纹理特征的SVM分类进行对比。结果表明,NWFE结合纹理特征的SVM分类结果优于其他2种分类结果,不仅反映了土地覆被分布的整体情况,而且使不同土地覆被类型得到较好的区分,总体分类精度达89.17%。 相似文献
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区域尺度土地利用/覆盖分类研究是当今国际上开展土地利用/覆盖变化研究的重要领域之一。及时、准确地获取地球表面特性对于掌握人类和自然现象之间的关系和相互作用至关重要。本文根据土地覆盖遥感分类方法特点，从硬分类方法、软分类方法以及最新的软硬分类方法出发，总结了国内外的各研究成果，分析了各种方法的分类策略与特点及其方法适用性。研究结果表明：软硬分类方法能够灵活适用于遥感图像上纯净、混合像元并存的特点，可以有效解决光谱的异质性，在土地覆盖遥感监测中具有广阔的应用潜力。文中提出了基于变端元的软硬分类土地覆盖制图方法框架，并指出了今后的研究重点。 相似文献
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多时相双极化合成孔径雷达干涉测量土地覆盖分类方法 总被引：5，自引：1，他引：4
综合采用时相、极化和干涉3种维度的SAR数据进行土地覆盖分类。以黑龙江省逊克县多时相ALOS PALSAR数据覆盖区为研究区,利用不同时相极化SAR、干涉SAR信号对地物特征的敏感性,结合后向散射强度和干涉相干的时变特征进行地物解译,发展了基于多时相、多极化、干涉SAR数据的SVM土地覆盖分类方法。研究结果表明,引入双极化SAR中不同极化(HH-HV)间的相干系数,并结合所选择的时相特征、极化特征以及干涉相干特征进行分类,可解决双极化SAR影像中林地与城市及建设用地的混分问题,得到更高精度的土地覆盖分类结果。 相似文献
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Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines – SVM), and hybrid (unsupervised–supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different depending on land use/cover classes. Early-growth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land use/cover classes were mapped with producer's and user's accuracies of ∼90%. Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes, thus having great potential to contribute to climate change mitigation schemes, conservation initiatives, and the design of management plans and rural development policies. 相似文献
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针对GLAS地学激光测高系统是冰、云和陆地高程卫星(ICESat)的唯一监测工具,能够记录地表光斑内的地物信息,是否能应用于黄土高原土地覆盖分类的问题进行了研究。利用粒子群和最小二乘法相结合的方法对GLAS波形数据进行高斯分解,获取高斯波个数、波形总能量、波形信号起始和信号结束位置4个波形参数;基于波形自动分类方法对黄土高原水体、森林、城市用地、其他地类(裸地、低矮植被等)进行分类。通过基于覆盖相同研究区域的30 m地表覆盖数据(Globe Land30),验证分类的准确性。结果表明,GLAS大光斑波形数据对黄土高原的4种地类能够很好地进行区分,总分类精度高达87.68%,Kappa系数为65.79%。研究表明,GLAS波形数据可以作为获取土地覆盖信息的有效数据源,为研究黄土高原土地覆盖变化提供更丰富的数据支持。 相似文献