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1.
Zhong-ping Sun Suhong Liu Fei Cao Yuanli Shi Chang-zuo Wang 《Arabian Journal of Geosciences》2017,10(22):495
Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68). 相似文献
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以辽宁省双台子河口湿地为研究对象,以Landsat 8和HJ-1-A/HJ-1-B的多时相遥感影像为数据源,根据研究区现状,将研究区分为旱地、芦苇、水田、碱蓬、混合植被、水面、滩涂、居民点、养殖塘九个类型.利用时间序列的归一化植被指数提取植被与非植被的分类阈值,采用粗糙集理论和多时相遥感影像,对植被和非植被分别进行分类规则的获取,建立了研究区决策树分类模型.为了进行精度评价,利用相同的训练点又进行了同样基于像元的最大似然法分类.最后利用混淆矩阵对上述两种方法进行了精度评估,基于粗糙集的决策树分类法与最大似然法总体分类精度分别为93.70%和91.62%,Kappa系数分别为0.92和0.90,两项指标值基于粗糙集理论法均比最大似然法有所提高.这为构建决策树分类模型进行湿地地表分类信息提取提供了一条新的研究思路. 相似文献
3.
高光谱遥感探测技术已成为探测油气藏的前沿新技术之一.研究以油气微渗漏地表共生异常理论为基础, 采用基于小波主成份分析(principal component analysis, PCA)最大似然分类、端元提取分类、光谱库典型蚀变光谱分类和植被指数决策树分类方法, 对榆林典型稀疏植被地区的进行油气勘探, 提取了与烃异常相关的粘土、碳酸盐、植被异常等相关的专题信息产品, 得出综合异常区图.对照分析已知气井与油气异常区分布, 证明了油气微渗漏信息的提取与识别方法的有效性. 相似文献
4.
遥感技术在植物物候研究中的应用综述 总被引：24，自引：1，他引：24
通过遥感技术研究植物物候现象的机理分析,认为植被指数可反映植被各物候期的特征。国内外在探测植物生长季始末日期、花期变化、净第一性生产力变化、全球碳收支等方面的研究促进了植物物候的发展;同样物候研究也可提高遥感影像植物分类和作物估产的精度,同时可促进高光谱遥感的发展。通过我国物候研究从传统的农林业应用转向注重遥感探测、生态学应用的现状分析,展望了我国物候发展方向：关注植物生长季始末时间的时空分布规律;遥感监测植物季相变化;遥感监测植物花期;注重探讨植物生理和生态特征;植物高光谱遥感物候研究;重视物候科普普及工作。 相似文献
5.
植被覆盖度遥感估算研究进展 总被引：24，自引：0，他引：24
植被覆盖度是刻画地表植被覆盖的重要参数,在全球变化研究、地表过程模拟和水文生态模型中发挥着重要作用.遥感能够反映不同空间尺度的植被覆盖信息及其变化趋势,是获取区域及全球植被覆盖度参数的一个重要手段.综合分析了用于植被覆盖度估算的遥感数据源,包括高光谱数据、多光谱数据、微波数据和激光雷达数据.而且分析了各种常用的植被覆盖度遥感估算方法及其优缺点,并评价了现有基于遥感数据的植被覆盖度产品及存在问题.最后,针对目前研究中存在的问题,讨论了植被覆盖度遥感估算研究的发展趋势,指出高时空分辨率长时间序列的全球植被覆盖度数据集、多源遥感数据融合和同化技术是未来植被覆盖度遥感估算研究的主要方向. 相似文献
6.
面向对象的喀斯特地区土地利用遥感分类信息提取 总被引：1，自引：0，他引：1
传统的面向像元分类方法虽然对光谱差异较为明显的遥感影像信息提取具有较好的效果,但会不可避免地产生“椒盐现象”,同时对纹理和形状信息不能充分应用,造成了大量信息损失。为了提高喀斯特地区土地利用遥感信息提取的精度,本文采用面向对象的分类方法,对贵州省毕节地区开展了土地利用遥感信息自动提取研究。首先对该地区Landsat-5 TM影像进行多尺度分割,形成影像对象层,然后综合应用基于知识决策树分类和基于样本的最邻近分类等技术对喀斯特地区进行遥感解译。结果表明,面向对象分类技术能较好地对喀斯特地区土地利用信息进行提取,同时避免了“椒盐现象”的产生,经野外采集样点数据验证,一级类分类精度为91.7 %,二级类分类精度为89.4 %,表明该方法在贵州省毕节地区应用效果良好。 相似文献
7.
Bulent Bayram Dursun Zafer Seker Akhtar Jamil Hatice Catal Reis Nusret Demir Salih Bozkurt Abdulkadir Ince Turgay Kucuk 《Arabian Journal of Geosciences》2018,11(12):319
Obtaining information about tree species distribution in agricultural lands is a topic of interest for various applications, such as tree inventory, forest management, agricultural land management, crop estimation, etc. This information can be derived from images obtained from modern remote sensing technology, which is the most economical way as compare to field surveys covering large geographic areas. Therefore, in this study, a new method is proposed for extraction and counting of sparse and regular distributed individual pistachio trees from agricultural areas on large scale from high-resolution digital ortho-photo maps, which were obtained using an airborne sensor (Ultracam-X). The input images were first smoothed by applying Gaussian filter to reduce the impact of noise. Normalized difference vegetation indices (NDVI) were then derived to obtain vegetation areas followed by Otsu’s global thresholding algorithm to obtain candidate tree areas. Further, connected component (CC) analysis was applied to segregate each object. Morphological processing was performed to fill holes within tree objects and get smooth contours, which were obtained by using the Moore-neighbor tracing method (MNTM) for each CC, while geometrical constraints were applied to undermine possible non-tree elements from output image. To further improve the segmentation results for sparse trees, a new method was applied, called quadratic local analysis (QLA). QLA helped to segment the trees, which were missed by the Otsu method due to low contrast and resulted in improved accuracy (3–6%). The obtained results were compared with well-known support vector machine (SVM) classifier. Proposed method produced slightly better results (1–5%) than SVM for extraction of pistachio trees and obtained accuracy for QLA and SVM were 96 and 91% for region 1, while 91 and 90% for region 2 respectively. 相似文献
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三峡库区岩体上方覆盖着厚实的土壤和茂密的植被,是高植被覆盖区,岩性信息弱,因此岩性识别和分类困难,没有成熟的方法可循。针对三峡库区进行岩性分析,选择三峡库区巴东城区作为研究区域,采用2000年5月成像的ETM+遥感影像,构造纹理、光谱、植被覆盖等17个分类因子,将遥感影像与地质图叠加,选取1 101个样本点,采用决策树C4.5算法,挖掘出三峡库区巴东县处岩性的解译规则和知识,决策树的学习精度为96.6%,剪枝后精度为95.9%,规则提取的精度为93.1%,提取的规则置信度很高,并基于知识驱动和规则匹配实现了岩性的智能分类,分类精度较高为90.11%;将分类结果与IsoData方法、K-Means方法、马氏距离法、最大似然法、最小距离法、平行六面体方法等6种方法的分类结果进行比较,试验结果证明,决策树方法的分类结果最好,精度明显高于其他6种方法。 相似文献
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11.
基于ASTER数据遥感影像的决策树分类 总被引：6，自引：0，他引：6
以黑龙江省北安市为研究区域,尝试利用ASTER视反射率值进行便利、准确的土地利用分类研究。对ASTER数据进行波段相关分析,确定最佳组合波段;然后重点分析转换为视反射率值的影像特征和光谱特征,从中提取各种典型地物的光谱曲线; 并依据提取的光谱曲线建立基于地物反射率值大小关系或阈值的决策树模型,对研究区不同地物类型进行分类,并对结果进行精度评价。应用效果表明,该方法简单有效,但对于混合光谱容易错分。 相似文献
12.
鉴于盐湖水体矿化度含量定量反演研究较少,且中、低空间分辨率遥感数据反演的矿化度含量精度偏低,文章以柴达木盆地西部尕斯库勒盐湖为例,基于WorldView-Ⅱ高空间分辨率遥感数据和实测矿化度数据,开展了盐湖湖表水体矿化度含量定量反演技术研究。通过开展盐湖水际线提取、盐湖水体矿化度光谱诊断特征分析、矿化度识别遥感指数构建、矿化度遥感信息分离和线性回归模拟,构建了盐湖矿化度含量定量反演模型。经评价,模型反演精度为96.61%。研究结果表明,基于WorldView-II高分数据定量反演盐湖矿化度含量的方法是有效的,该方法对于快速定量识别盐湖矿化度含量,降低盐湖矿化度调查和分析成本,预测富矿水域具有十分重要的意义。 相似文献
13.
A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map 总被引：7，自引：0，他引：7
Spatial decision support system (SDSS) is an interactive, computer-based system designed to support a user in achieving a higher effectiveness of decision-making while solving a semi-structured spatial data. Satellite Remote Sensing and Digital Elevation Modeling are providing a systematic, rational framework for advancing scientific knowledge of our SDSS of geophysical phenomena that, often lead to observe the natural hazards or resources. Taking the advantage of these, more specifically, our study focused on using these to collect and measure the landslide data on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Our source data includes (1) Digital Elevation Modeling is also used to investigate the landform, and (2) remote sensing image data are also employed to analyze the vegetation conditions. In addition, the process of generating landslide susceptibility maps involved on how to effectively extract the site-condition dominant attributes and thresholds for displaying the landslide occurrence accurately. Thus, the information from landslide must be categorized and thoroughly evaluated by an Advanced Data Mining Technique — Entropy-based classification method to construct the landslide knowledge rules. The knowledge scope with regards to core factors and thresholds are solved. Then, the susceptibility hazard maps are drawn and verifications are made. On the other hand, the conventional statistical method of Logistic Regression is used for comparison. 相似文献
14.
夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。 相似文献
15.
为了深化遥感监测方法在生态环境调查中的应用,本文以吉林西部为试验区,设计了一种多时相遥感数据分类方案。该方案以物候信息为主,结合地物特征变量(植被、水体和土地信息)构建的多维特征空间数据集用于土地覆被分类。该遥感分类方案提取了9种地表覆被类型,结果表明:地表植被季节变化信息和土地利用信息的引入能明显改善土地覆被的分类精度;与基于原始波段的分类方案相比,多时相遥感数据分类方案的分类精度最好,总体分类精度为95.50%,Kappa系数为95.04%。 相似文献
16.
Selma Etteieb Mounir Louhaichi Chariton Kalaitzidis Ioannis Z. Gitas 《Arabian Journal of Geosciences》2013,6(12):5017-5032
Mediterranean forests are characterized by spatiotemporal heterogeneity that is associated with Mediterranean climate, floristic biodiversity and topographic variability. Satellite remote sensing can be an effective tool for characterizing and monitoring forest vegetation distribution within these fragmented Mediterranean landscapes. The heterogeneity of Mediterranean vegetation, however, often exceeds the resolution typical of most satellite sensors. Hyper-spectral remote sensing technology demonstrates the capacity for accurate vegetation identification. The objective of this research is to determine to what extent forest types can be discriminated using different image analysis techniques and spectral band combinations of Hyperion satellite imagery. This research mapped forest types using a pixel-based Spectral Angle Mapper (SAM), nearest neighbour and membership function classifiers of the object-oriented classification. Hyperion classification was done after reducing Hyperion data using nine selected band combinations. Results indicate that the selection of band combination while reducing the Hyperion dataset improves classification results for both the overall and the individual forest type accuracy, in particular for the selected optimum Hyperion band combination. One shortcoming is that the performance of the best selected band combination was superior in terms of both overall and individual forest type accuracy when applying the membership classifier of the object-oriented method compared to SAM and nearest neighbour classifiers. However, all techniques seemed to suffer from a number of problems, such as spectral similarity among forest types, overall low energy response of the Hyperion sensor, Hyperion medium spatial resolution and spatiotemporal and spectral heterogeneity of the Mediterranean ecosystem at multiple scales. 相似文献
17.
Seyed Armin Hashemi Mir Mozaffar Fallah Chai Shohreh Bayat 《Arabian Journal of Geosciences》2013,6(9):3363-3369
Vegetation indices have been introduced for analyzing and assessing the status of quantitative and qualitative characteristics of vegetation using satellite images. However, choosing the best indices to be used in forest biodiversity and vegetation is one of the important problems faced by the users. The purpose of this research is to evaluate six vegetation indices in the analysis of tree species diversity in the northern forests of Iran. The present research uses LISS III sensor data from IRS-P6 satellite. Geometric rectification of images was performed using ground control points, and Chavez model was used for atmospheric correction of the data. The six spectral vegetation indices included NDVI, IPVI, Ashburn Vegetation Index (AVI), TVI, TTVI, and RVI. Shannon–Wiener species diversity index was used to analyze diversity, and the value of the index was calculated in each sample plot. Then, the spectral values of each sample plot were extracted from different bands. The best subset regression was used to analyze the relationship between species diversity and the related bands. The results obtained from the regression showed that polynomial equations under scrutiny as independent variables can assess tree and shrub species diversity better than other bands and compounds used (R 2?=?0.47). The obtained results also indicated a higher capacity in the case of the AVI index for estimating tree species diversity in the under study area. 相似文献
18.
Hickmat Hossen Mona G. Ibrahim Wael Elham Mahmod Abdelazim Negm Kazuo Nadaoka Oliver Saavedra 《Arabian Journal of Geosciences》2018,11(5):93
This study assesses the changes in surface area of Manzala Lake, the largest coastal lake in Egypt, with respect to changes in land use and land cover based on a multi-temporal classification process. A regression model is provided to predict the temporal changes in the different detected classes and to assess the sustainability of the lake waterbody. Remote sensing is an effective method for detecting the impact of anthropogenic activities on the surface area of a lagoon such as Manzala Lake. The techniques used in this study include unsupervised classification, Mahalanobis distance supervised classification, minimum distance supervised classification, maximum likelihood supervised classification, and normalized difference water index. Data extracted from satellite images are used to predict the future temporal change in each class, using a statistical regression model and considering calibration, validation, and prediction phases. It was found that the maximum likelihood classification technique has the highest overall accuracy of 93.33%. This technique is selected to observe the changes in the surface area of the lake for the period from 1984 to 2015. Study results show that the waterbody surface area of the lake declined by 46% and the area of floating vegetation, islands, and land agriculture increased by 153.52, 42.86, and 42.35% respectively during the study period. Linear regression model prediction indicates that the waterbody surface area of the lake will decrease by 25.24% during the period from 2015 to 2030, which reflects the negative impact of human activities on lake sustainability represented by a severe reduction of the waterbody area. 相似文献
19.
Kaixiang Zhang Xueling Wu Ruiqing Niu Ke Yang Lingran Zhao 《Environmental Earth Sciences》2017,76(11):405
Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management. 相似文献
20.
面向对象的喀斯特地区石漠化遥感信息提取研究——以贵州省大方地区为例 总被引：1，自引：1，他引：0
针对现有基于像素的监督和非监督分类方法在地质环境复杂、地形起伏较大、阴影明显的喀斯特石漠化地区难以满足石漠化信息提取精度要求的问题,采用基于纹理特征数据和地形数据辅助面向对象方法进行喀斯特地区石漠化信息的提取。该方法首先依据石漠化分布在TM/ETM+影像面积大小不均匀的特征,利用纹理和地形因子计算最优分割参数进行多尺度分割;然后根据植被覆盖率、岩石裸露率以及坡度因子构建石漠化分级指标;最后参照石漠化分级标准、光谱信息以及纹理特征等建立的分类规则提取喀斯特地区石漠化信息。选取贵州省石漠化严重的大方县时序TM/ETM+影像进行石漠化信息提取试验,结果表明:与基于像素的监督分类和非监督分类方法相比,基于面向对象的分类可以有效地减少因复杂地形导致石漠化信息提取结果"椒盐化"现象,提取精度明显优于基于像素的监督分类和非监督分类方法。 相似文献