共查询到20条相似文献，搜索用时 15 毫秒
1.
Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data. 相似文献
2.
植被净初级生产力NPP(Net Primary Production)遥感估算与分析,有赖于高时空分辨率的遥感数据,但目前中高分辨率的遥感数据受卫星回访周期及天气的影响,在中国南方地区难以获取连续时间序列的数据,从而影响了高精度的区域植被净初级生产力的遥感估算。为此,提出一种基于多源遥感数据时空融合技术与CASA模型估算高时空分辨率NPP的方法。首先,利用多源遥感数据,即Landsat8 OLI数据与MODIS13Q1数据,采用遥感数据时空融合方法,获得了时间序列的Landsat8 OLI融合数据;然后,基于Landsat8 OLI时空融合数据,并采用CASA模型,以长株潭城市群核心区为例,进行区域植被NPP的遥感估算。研究结果表明,基于时间序列Landsat融合数据估算的30m分辨率的NPP具有良好的空间细节信息,且估算值与实测值的相关系数达0.825,与实测NPP数据保持了较好的一致性。 相似文献
3.
Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models. 相似文献
4.
Liegang Xia Jiancheng Luo Weihong Wang Zhanfeng Shen 《Journal of the Indian Society of Remote Sensing》2014,42(3):505-515
This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods. 相似文献
5.
应用面向对象的决策树模型提取橡胶林信息 总被引：4，自引：0，他引：4
橡胶林的无序和不合理种植引发了一系列的生态问题,快速监测橡胶林空间分布及动态变化,对橡胶的合理种植、区域生态环境保护以及有关部门的规划决策有重要的指导意义。以MODIS归一化植被指数NDVI时间序列数据和多时相的Landsat TM数据为基础分析橡胶林的季相和光谱特征,确定橡胶识别的关键时期和特征参数,构建面向对象的决策树分类模型,开展橡胶信息提取研究。结果表明,多时相的遥感数据可反映橡胶的季相特征,以TM数据为基础计算得到的陆表水分指数LSWI和归一化植被指数NDVI可作为橡胶识别的光谱特征参数,橡胶休眠期是利用遥感方法进行橡胶提取的最佳时期。相比于单时相数据,利用包含橡胶关键物候期的多时相遥感数据能得到更高的橡胶林提取精度。 相似文献
6.
高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集,目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响,提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model),VISTFM采用模糊C聚类算法,对存量时序VI数据按土地利用类型划分为若干子类,从高低分辨率影像中随土地覆被类的变化规律提取子类,结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法,测试表明,VISTFM能够较好的捕获VI的中间变化过程,与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比,利用VISTFM获得的植被指数数据集具有更高的时空分辨率。 相似文献
7.
综合多特征的Landsat 8时序遥感图像棉花分类方法 总被引：3，自引：0，他引：3
传统的多时相遥感图像分类大多拘泥于单一特征,本文基于多时相的Landsat 8遥感数据,开展了综合多特征的特征提取与特征选择方法研究。综合了NDVI时间序列、最佳时相反射率光谱特征以及纹理特征作为初始分类特征,并采用基于属性重要度的粗糙集特征选择算法对其进行特征约简。分类结果表明:(1)利用初始分类特征,分类的总体精度达到92.81%,棉花提取精度达87.4%,与仅利用NDVI时间序列相比,精度分别提高5.53%和5.05%;(2)利用粗糙集选择后的特征分类,分类总体精度可达93.66%,棉花分类精度达92.73%,与初始分类特征提取结果相比,棉花分类精度提高5.33%。基于属性重要度的粗糙集特征选择不仅提高了分类精度,同时有效降低了分类器的计算复杂度。 相似文献
8.
融合多源遥感数据的高分辨率城市植被覆盖度估算 总被引：2，自引：0，他引：2
准确获取城市植被覆盖定量信息对城市生态环境评价,城市规划及可持续城市发展具有重要意义。遥感技术的发展为获取区域及全球植被覆盖信息提供了有效手段,目前基于单传感器、单时相遥感数据的城市植被覆盖度估算方法得到较为广泛的应用。然而,由于城市地表覆盖的复杂性、植被类型的多样性,在一定程度上影响了城市植被覆盖信息提取的精度。为此,本文提出一种基于多源遥感数据与时间混合分析的城市植被覆盖度估算方法。首先,通过时空融合、植被物候特征分析获得最佳时序的GF-1 NDVI数据;其次,基于时间序列的GF-1 NDVI及Landsat 8 SWIR1、SWIR2数据,采用时间混合分析方法以长沙市为例估算城市植被覆盖度。实验研究表明,基于多源遥感数据与时间混合分析方法获得了较高精度的城市植被覆盖度估算（RMSE为0.2485,SE为0.1377,MAE为0.1889）,相对于单时相光谱混合分析、传统的像元二分法,本文提出的方法更为稳定,在低、中、高不同植被覆盖区均能获得较高的估算精度,为城市植被覆盖度定量估算提供了有效方法。 相似文献
9.
提出了一种基于深度学习技术的遥感分类方法,它能有效解决中分辨率影像在分类过程中出现的像元混分问题。研究选用2016年5月12日武汉市Landsat 7 ETM+遥感影像,基于GoogleNet模型中的Inception V3网络结构,借助迁移学习方法,构建出遥感分类模型,实现了对武汉市主城区4类典型地物（不透水层、植被、水体和其他用地）的自动分类提取,并将分类结果与传统最大似然分类（ML）结果进行了对比分析。研究表明：基于深度学习方法的遥感影像总体分类精度高达88.33%,Kappa系数为0.834 2,明显优于传统ML方法总体分类精度83%和Kappa系数0.755 0,而且有效抑制了地物在分类过程中出现的像元混分现象。 相似文献
10.
Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km2, experienced changes in land cover over the nine years considered. 相似文献
11.
Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物 总被引：2，自引：0，他引：2
本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。 相似文献
12.
由于中高分辨率遥感影像数据时序性不强,分类过程中无法准确记录地物的时序特征。为增加地物时序变化特征,本文使用时空融合模型重建高时序高分辨率遥感影像,分析加入时相特征对分类结果的影响。以河北省石家庄市中部地区为例,本文采用3种时空融合模型重建高时序的30 m分辨率的遥感影像,增加影像时序分类特征,采用随机森林对年度重建时序影像分类,分析不同重建时序影像数量和不同时间跨度对分类结果的影响。试验表明,通过重建年度时序影像分类比单一影像分类精度增强;分类精度随着时序影像数量增加而增大,当时序影像数量选定为12景,也就是1月1景时,分类精度趋于稳定;不同时间段对分类结果影像程度不同,引入植被变化期间的时序影像,分类精度最高。 相似文献
13.
Inderjit Singh 《Journal of the Indian Society of Remote Sensing》1988,16(4):43-52
LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.) 相似文献
14.
针对传统遥感影像解译效率较低、人力物力需求量大等问题,该文以谷歌地球引擎为依托平台,利用Landsat5TM影像,采用分类回归树算法对2010年北京市土地覆被/土地利用类型开展了解译研究,并从类型构成、类型混淆和空间一致性3个方面将解译所得LUC-2010产品与Globeland30-2010产品进行空间一致性分析。研究表明,谷歌地球引擎(GEE)平台通过编程运算,数据处理速度极快,大幅提高工作效率。解译产品与训练样本交叉验证的学习精度为94.2%。两套产品总体对比发现,林地、水体和耕地的空间一致性比率分别为84.28%、74.75%和73.56%;林地、水体和人工地表的地类纯净度分别为87.23%、77.04%和72.97%;总体分布空间一致性为74.0%。两套产品局部对比发现,LUC-2010产品分类结果更准确和精细,精度更高。 相似文献
15.
A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors 总被引：1，自引：0，他引：1
Fernando Sedano Pieter KempeneersPeter Strobl Jan KuceraPeter Vogt Lucia SeebachJesús San-Miguel-Ayanz 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):588-596
This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow. 相似文献
16.
Uttam Kumar Anindita Dasgupta Chiranjit Mukhopadhyay T. V. Ramachandra 《Journal of the Indian Society of Remote Sensing》2018,46(3):407-422
Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. Multi-resolution remote sensing (RS) data coupled with additional ancillary topographical layers (both remotely acquired or derived from ground measurements) with appropriate classification strategies would be more effective in capturing LC dynamics and changes associated with the natural resources. Ancillary information would make the decision boundaries between the LC classes more widely separable, enabling classification with higher accuracy compared to conventional methods of RS data classification. In this work, we ascertain the possibility of improvement in classification accuracy of RS data with the addition of ancillary and derived geographical layers such as vegetation indices, temperature, digital elevation model, aspect, slope and texture, implemented in three different terrains of varying topography—urbanised landscape (Greater Bangalore), forested landscape (Western Ghats) and rugged terrain (Western Himalaya). The study showed that use of additional spatial ancillary and derived information significantly improved the classification accuracy compared to the classification of only original spectral bands. The analysis revealed that in a highly urbanised area with less vegetation cover and contrasting features, inclusion of elevation and texture increased the overall accuracy of IKONOS data classification to 88.72% (3.5% improvement), and inclusion of temperature, NDVI, EVI, elevation, slope, aspect, Panchromatic band along with texture measures, significantly increased the overall accuracy of Landsat ETM+ data classification to 83.15% (7.6% improvement). In a forested landscape with moderate elevation, temperature was useful in improving the overall accuracy by 6.7 to 88.26%, and in a rugged terrain with temperate climate, temperature, EVI, elevation, slope, aspect and Panchromatic band significantly improved the classification accuracy to 89.97% (10.84% improvement) compared to the classification of only original spectral bands, suggesting selection of appropriate ancillary data depending on the terrain. 相似文献
17.
卫星遥感技术可用于海岛资源调查。Sentinel-2A与Landsat 8两颗卫星都可免费提供空间分辨率较高的多光谱遥感影像,在海岛调查中的应用潜力较大。本文以浙江舟山普陀山岛为例开展了针对这两种影像在海岛植被分类中的应用效果的研究,分别利用Sentinel-2A多光谱成像仪（MSI）和Landsat 8陆地成像仪（OLI）影像基于最大似然法分类获得了该岛阔叶林、针阔混交林、针叶林、灌丛、草丛等植被及其他地物的分布情况,并进行了精度检验,结果表明MSI的总体分类精度略高于OLI。 相似文献
18.
Land cover mapping forms a reference base for resource managers in their decision-making processes to guide rural/urban growth and management of natural resources. The aim of this study was to map land cover dynamics within the Upper Shire River catchment, Malawi. The article promotes innovation of automated land cover mapping based on remote sensing information to generate data products that are both appropriate to, and usable within different scientific applications in developing countries such as Malawi. To determine land cover dynamics, 1989 and 2002 Landsat images were used. Image bands were combined in transformations and indices with physical meaning; together with spatial data, to enhance classification accuracy. A maximum likelihood classification for each image was computed for identification of land cover variables. The results showed that the combination of spatial and digital data enhanced classification accuracy and the ability to categorise land cover features, which are relatively inhomogeneous. 相似文献
19.
Xia Zhang Rui Sun Bing Zhang Qingxi Tong 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):476-484
Moderate Resolution Imaging Spectroradiometer (MODIS) data have played an important role in global environmental and resource research. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results. In this research, the high temporal resolution of MODIS was employed to improve the accuracy of land cover classification of the North China Plain using MODIS_EVI time series from 2003. Harmonic Analysis of Time Series (HANTS) was performed on the MODIS_EVI image time series to reduce cloud and other noise effects. The improved MODIS_EVI time series was then classified into 100 clusters by the Iterative Self-Organizing Data Analysis Technique (ISODATA). To distinguish ambiguous land cover classes, a decision tree was built on five phenological features derived from EVI profiles, Land Surface Temperature (LST) and topographic slope. The overall accuracy of the final land cover map was 75.5%, indicating the promise of using MODIS EVI time series and decision trees for broad area land cover classification. 相似文献
20.
Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed ±4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%–10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics. 相似文献