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1.
准确掌握积雪覆盖信息对于气象、水文和全球气候变化研究都具有重要的意义。遥感技术在进行大范围、高频率的积雪覆盖监测中发挥着重要的作用。目前,SNOMAP算法是用于积雪遥感监测最普遍的技术手段,其核心是利用固定阈值的归一化差分积雪指数（Normalized Difference Snow Index,NDSI）进行积雪识别,但这种方法忽略了积雪光谱信息的时相变化,会产生积雪监测的误差。本文提出了一种动态NDSI阈值方法,以纯永久积雪像元的平均NDSI值作为参照系调整固定的NDSI阈值,从而削减影像光谱值波动对积雪识别的影响。以三江源地区作为研究区域,将基于每日MODIS数据进行积雪监测最佳的NDSI阈值与同日纯永久积雪像元的平均NDSI值作线性回归,通过每日纯永久积雪像元平均NDSI值的变化来调整用于积雪识别的NDSI阈值。结果表明：① 基于每日MODIS数据进行积雪覆盖监测最佳的NDSI阈值与同日纯永久积雪像元的平均NDSI值之间存在较好的线性关系,决定系数R 2达到0.86;② 三江源地区动态NDSI阈值的范围为0.29~0.37,其平均值在0.33左右,说明MODIS全球积雪面积产品中将NDSI阈值取为0.40会低估三江源地区的积雪面积;③ 与采用固定NDSI阈值0.33的监测方法相比,动态NDSI阈值法近似率、总体分类精度和F值的平均值分别提高了5.17%、0.70%、1.14%。 相似文献
2.
光谱混合分析能够提取亚像元信息,被广泛地应用于遥感影像目标探测之中。本文针对MODIS积雪遥感影像,基于光谱混合分析框架,利用渐进辐射传输模型建立不同粒径大小的雪反射率光谱库,提出了一种考虑端元变化及二次辐射的雪盖面积反演算法。此算法首先利用渐进辐射传输模型建立不同粒径大小积雪的反射率光谱库,然后使用序贯最大角凸锥方法获取植被、土壤与岩石、阴影的光谱库。在建立各种地物反射率光谱库之后,利用均方根误差最小的方法获取最优端元组合。在此基础上,考虑端元独立辐射以及积雪与其它地物的二次辐射过程,利用稀疏光谱混合模型获取积雪面积与雪粒径大小。实验结果表明：此方法能够同时反演雪粒径与积雪面积,反演的雪粒径相比单波段的渐进辐射传输模型小,反演的积雪面积相比MOD10A1产品精度略微提高。 相似文献
3.
《中国地理科学(英文版)》2019,(3)
The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer(MODIS)13 Q1 products are used, which include two vegetation indices data of NDVI(Normalized Difference Vegetation Index) and EVI(Enhanced Vegetation Index). Furtherly, after Quality Screening(QS) and Savizky-Golay(S-G) filtering of MODIS 13 Q1 data, four evaluation indices are obtained, which are NDVI with QS(QSNDVI), EVI with QS(QSEVI), NDVI with S-G filtering(SGNDVI) and EVI with S-G filtering(SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters. 相似文献
4.
基于几何光学模型的人工林叶面积指数遥感反演 总被引：1，自引：0，他引：1
MODIS等全球叶面积指数(LAI)产品空间分辨率偏低(250m~7km),不能满足高空间分辨率遥感应用的需求。为获取大区域高空间分辨率LAI,有必要对物理模型用于高空间分辨率遥感影像LAI反演的可行性进行探讨。本文基于4-scale模型LAI反演算法,以甘肃省张掖为研究区,利用TM 影像实现研究区人工林LAI反演。反演算法考虑了反射率入射-观测角度对LAI与植被指数关系的影响和植被冠层尺度的集聚程度。利用地面实测LAI数据对反演结果进行验证与分析,并与NDVI经验模型进行对比,同时分析LAI反演结果对波段反射率敏感性。结果表明: 4-scale模型LAI反演结果与实测LAI一致性良好(R2=0.67,RMSE=0.50),且优于NDVI经验模型(R2=0.59,RMSE=0.67);当LAI大于2时,4-scale模型LAI反演算法误差小于NDVI经验模型,能有效避免植被指数的饱和现象;红光波段反射率减小时,引起4-scale模型LAI反演结果的变化幅度比其增大时更高,且影响程度大于近红外波段反射率。研究表明,4-scale 模型LAI反演算法可用于TM数据反演人工林LAI,模型应用普适性较强。 相似文献
5.
Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China 总被引：3，自引：0，他引：3
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-... 相似文献
6.
《山地科学学报》2021,(9)
Landsat-8 spectral values have been used to map the earth's surface information for decades. However, forest types and other land-use/land-cover(LULC) in the mountain terrains exist on different altitudes and climatic conditions. Hence, spectral information alone cannot be sufficient to accurately classify the forest types and other LULC, especially in high mountain complex. In this study, the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types, and other LULC, using random forest(RF) classification algorithm for the Hindu Kush mountain ranges of northern Pakistan, was discussed. After prior-examination(multicollinearity) of spectral bands and ancillary variables, three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r20.7. The selected datasets were stepwise stacked together and six Input Datasets(ID) were created. The first ID-1 includes only the Surface Reflectance(SR) of spectral bands, and then in each ID, the extra one ancillary variable including Normalized Difference Vegetation Index(NDVI), Normalized Difference Water Index(NDWI), Normalized Difference Snow Index(NDSI), Land Surface Temperature(LST), and Digital Elevation Model(DEM) was added. We found an overall accuracy(OA) = 72.8% and kappa coefficient(KC) =61.9% for the classification of forest types, and other LULC classes by using the only SR bands of Landsat-8. The OA = 81.5% and KC=73.7% was improved by the addition of NDVI, NDWI, and NDSI to the spectral bands of Landsat-8. However, the addition of LST and DEM further increased the OA, and Kappa coefficient(KC) by 87.5% and 82.6%, respectively. This indicates that ancillary variables play an important role in the classification, especially in the mountain terrain, and should be adopted in addition to spectral bands. The output of the study will be useful for the protection and conservation, analysis, climate change research, and other mountains forest-related management information. 相似文献
7.
各类光学植被指数已成功地应用于各种植被监测与作物产量估算中,但这些指数易受大气状况的影响。由星载微波辐射计得到的植被光学厚度数据（VOD）与植被密度、含水量密切相关,数据可全天候获得,在农业遥感监测中呈现着巨大的潜力。作为来自不同传感器的遥感数据,微波遥感数据与光学遥感数据可以提供不同波长范围内的植被信息。为了更准确地进行作物产量估算,本研究提出将微波遥感数据与光学遥感数据共同应用于冬小麦单产估算中。研究选择L波段微波辐射计SMAP卫星的VOD数据与MODIS的标准归一化植被指数NDVI、增强型植被指数EVI、叶面积指数LAI、光合有效辐射分量FPAR数据作为研究变量,分别使用BP神经网络、GA-BP神经网络和PSO-BP神经网络建立冬小麦产量估算模型。结果表明： 3种神经网络回归模型的P值均小于0.001,通过了显著性检验。GA-BP神经网络回归模型的估算值与真实值在3种神经网络回归模型中表现了最高的相关性（R=0.755）与最低的均方根误差（RMSE=529.145 kg/hm2）,平均绝对误差(MAE=425.168 kg/hm2)和平均相对误差（MRE=6.530%）。为了分析多源遥感数据的结合在作物产量估算中的优势,研究同时构建了仅使用NDVI和LAI,使用NDVI、EVI、LAI、FPAR等光学数据进行冬小麦产量估算的3种GA-BP神经网络回归模型作为对比。结果表明,使用微波遥感数据与光学遥感数建立的GA-BP神经网络回归模型较上述3种作为对比的GA-BP神经网络回归模型的相关系数R值分别提高了0.163,0.229与0.056,均方根误差RMSE分别降低了122.334、158.462和46.923 kg/hm2,使用多源遥感数据的组合可以很好地提高作物产量估算的准确性。 相似文献
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干旱遥感监测方法及其应用发展（可下载全文） 总被引：3，自引：0，他引：3
干旱是世界上影响范围最广和造成社会经济损失最严重的一种自然灾害。本文从干旱遥感监测的不同角度出发,总结了目前干旱遥感监测的主要方法、应用状况及优缺点。主要包括针对裸土地表类型的热惯量法、微波法,针对植被覆盖地表类型的可见光、近红外、短波红外等波段反射率数据的归一化植被指数法、距平指数法、条件植被指数法、归一化差值水分指数、归一化干旱指数、植被供水指数等,以及热红外遥感数据的温度植被干旱指数、温度条件指数、作物缺水指数、水分亏缺指数等。最后,提出了加强干旱遥感监测技术研究的建议,同时指出将可见光和微波相结合的指数模型的研究是干旱遥感监测可能的发展方向。 相似文献
9.
植物吸收性光合有效辐射分量（FPAR）的遥感反演是生态环境领域的核心研究内容之一,但在复杂地形山区,其估算精度严重受到地形效应的影响（包括本影与落影）。本文利用能够消除地形阴影影响的阴影消除植被指数（SEVI）对山区遥感影像进行FPAR反演,并分别与基于不同影像预处理程度计算的归一化植被指数（NDVI）、比值型植被指数（RVI）反演的FPAR做对比分析,以评估复杂山区反演FPAR存在的地形效应。结果表明：在不做地形校正的情况下,基于NDVI与RVI反演FPAR会使得本影及落影区域的值远小于非阴影区域的值,它们的相对误差均大于70%;基于C校正后的NDVI与RVI反演FPAR可以较好地校正本影区域,相对误差降至约6.974%,但落影处的校正效果不明显,相对误差约为48.133 %;而基于SEVI反演FPAR无需DEM数据的支持,可以达到经FLAASH+C组合校正后NDVI与RVI反演FPAR相似的结果,且能改善落影区域的地形校正效果,相对误差降至约2.730%。 相似文献
10.
A simple method to extract tropical monsoon forests using NDVI based on MODIS data: A case study in South Asia and Peninsula Southeast Asia 总被引：1，自引：0，他引：1
Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation Index(NDVI) derived from the Moderate Resolution Imaging Spectroradiometer(MODIS) data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps(MODMF) from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products(MCD12Q1). The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMF which can be identified for 7 to 9 times between 2001 and 2009 account for 53.1%, while only 7.9% of MCD12Q1 pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000(GLC2000), MCD12Q1 and University of Maryland(UMD) products are compared with the statistical dataset at national level, which reveals that MODMF has the highest R2 of 0.95 and the lowest RMSE of 14 014 km2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques. 相似文献
11.
Watershed classification by remote sensing indices: A fuzzy c-means clustering approach 总被引：1，自引：0，他引：1
Bahram Choubin Karim Solaimani Mahmoud Habibnejad Roshan Arash Malekian 《山地科学学报》2017,14(10):2053-2063
Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean (FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups. The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently, the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures. 相似文献
12.
本文以福建省漳浦县沿海区域为研究对象,利用2005年的CBERS-02数据和2000年的ETM+数据,进行沿海防护林快速提取研究。通过分析沿海防护林和沿海地区其他典型地物在原始波段、归一化植被指数ND-VI和非线性波段比NLBR的光谱特征,提出了适用于不同传感器的沿海防护林快速提取方法。研究发现,综合利用NDVI大于阈值1和NLBR小于阈值2,可以实现沿海防护林的快速提取,而阈值可以根据NDVI和NLBR的散点图确定。该方法对具有绿、红和近红外3个波段的不同传感器数据均有一定的参考价值。研究区2000年2005年间,沿海防护林减少的面积是增加面积的1.46倍,政府相关部门应该加大对防护林的管理和建设力度,增强沿海地区防御自然灾害的能力。 相似文献
13.
Snow cover variation and streamflow simulation in a snow-fed river basin of the Northwest Himalaya 总被引：3，自引：2，他引：1
Snowmelt is an important component of any snow-fed river system.The Jhelum River is one such transnational mountain river flowing through India and Pakistan.The basin is minimally glacierized and its discharge is largely governed by seasonal snow cover and snowmelt.Therefore,accurate estimation of seasonal snow cover dynamics and snowmeltinduced runoff is important for sustainable water resource management in the region.The present study looks into spatio-temporal variations of snow cover for past decade and stream flow simulation in the Jhelum River basin.Snow cover extent(SCE) was estimated using MODIS(Moderate Resolution Imaging Spectrometer) sensor imageries.Normalized Difference Snow Index(NDSI) algorithm was used to generate multi-temporal time series snow cover maps.The results indicate large variation in snow cover distribution pattern and decreasing trend in different sub-basins of the Jhelum River.The relationship between SCE-temperature,SCE-discharge and discharge-precipitation was analyzed for different seasons and shows strong correlation.For streamflow simulation of the entire Jhelum basin Snow melt Runoff Model(SRM) used.A good correlation was observed between simulated stream flow and in-situ discharge.The monthly discharge contribution from different sub-basins to the total discharge of the Jhelum River was estimated using a modified version of runoff model based on temperature-index approach developed for small watersheds.Stream power - an indicator of the erosive capability of streams was also calculated for different sub-basins. 相似文献
14.
Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and climate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter (MODIS) time series data in Heilongjiang Province, China. The algorithm is divided into two steps: Firstly, the ‘core’ pixels were extracted to represent the most possible burned pixels based on the comparison of the temporal change of Global Environmental Monitoring Index (GEMI), Burned Area Index (BAI) and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ‘core’ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the highest accuracy of 96.61%. 相似文献
15.
Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression. 相似文献
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基于随机森林的遥感干旱监测模型的构建 总被引：1，自引：0，他引：1
利用遥感数据进行大面积旱情监测是现有干旱监测的重要方法之一,然而传统的遥感干旱监测方法主要侧重于对土壤湿度或植被状况等单一干旱响应因子进行监测,对综合多因子的干旱监测研究较为有限。随机森林是一种机器学习方法,具有学习过程快速、运算速度快、稳定性好、预测精度高的优点,近年来被应用于生态环境等多个领域。本文利用2001-2010年4-9月的MODIS数据提取的植被状态指数（VCI）、温度状态指数（TCI）和土地覆盖类型（LC）,TRMM降水资料计算的TRMM-Z指数及SRTM-DEM、土壤有效含水量（AWC）等多个遥感及土壤资料提取的干旱因子为自变量,以气象站点的综合气象干旱指数（CI）为因变量,利用随机森林模型构建遥感干旱监测模型,并以河南省为研究区进行了评价和分析。该模型在2009-2010年的监测值和实测CI值的具有显著的相关性,并且二者干旱等级的一致率为81%。在2001-2010年4-9月间,模型监测值与气象站点的标准降水蒸散发指数（SPEI）总体干旱等级一致率为74.9%,较为一致,其中9月的模型结果与SPEI的干旱等级一致率最高,达到82.4%,空评估率和漏评估率最低;与10 cm土壤相对湿度的相关系数在0.475-0.639之间,达到极显著水平。河南省2011年4-6月干旱事件同样验证了本文构建的模型旱情监测结果,说明本模型能较好地就应用于监测区域旱情监测。 相似文献
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遥感裸土识别制图为水土流失治理工作提供了科学依据。本文以SPOT-5影像为实验数据,提出一种以土壤指数NDSI和不透水面指数NDISI提取裸土的方法。通过热红外波段的亚像元分解技术,将同期120 m分辨率的TM 6波段细化为10 m分辨率的地表温度影像,为SPOT-5影像计算NDISI不透水面指数增加了必要的热红外波段。在此基础上,构建双重指数模型,获得10 m分辨率的裸土数据。研究表明,双重指数模型可较好地解决裸土提取中建筑用地与裸土相混淆的问题,提取裸土的总精度可达95.4%。通过比较10 m的SPOT-5和30 m的TM影像的裸土提取结果,发现影像分辨率的提升可使裸土信息提取结果更加准确、精细。因此,本文为更高分辨率裸土识别制图,提供了一种有效的方法。 相似文献
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叶面积指数(LAI)是衡量植被生态状况和估算作物产量的一个重要指标。LAI的反演是定量遥感研究的重要内容。传统的经验统计反演方法基于单一观测角度的遥感数据进行,忽略了地物反射率的方向性。若在反演中加入多观测角度的信息,则有可能提升LAI反演的精度。以2008年甘肃省张掖市玉米实验区为研究区,利用欧空局的CHRIS/PROBA多角度高光谱数据对比分析了传统植被指数NDVI、RVI、EVI的变化规律及其反演玉米叶面积指数LAI的精度,并根据NDVI随观测角度的变化规律,构造出新型多角度归一化植被指数MNDVI,分别对实测叶面积指数进行线性回归并利用实测数据对估算LAI进行精度验证,结果表明：新型MNDVI指数相比于传统NDVI、RVI、EVI对LAI的反演精度有了显著提升,估算模型决定系数R2达到0.716,精度验证均方根误差为0.127,平均减小了33.3%。 相似文献
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植被等效水厚度对路域生态环境的监测评估具有重要意义。本研究以湖南醴潭高速一段为研究对象,以地面实测光谱和等效水厚度以及PRO4SAIL模拟光谱和模拟等效水厚度为数据源,利用PRO4SAIL冠层模型模拟光谱与地面实测光谱建立12种常用水分指数,引入随机森林算法对水分指数与等效水厚度进行重要性分析,得到12种水分指数的重要性排序;利用调整R 2确定建立等效水厚度估算模型中输入水分指数的最佳个数;在优选水分指数基础上,以PRO4SAIL模拟光谱计算得到水分指数和等效水厚度为训练集,分别构建随机森林耦合偏最小二乘（RF-PLS）、随机森林耦合支持向量机（RF-SVM）和随机森林耦合遗传算法优化支持向量机（RF-GA-SVM）等效水估算模型,并用地面实测等效水厚度对估算模型进行精度验证与分析。结果表明：RF-SVM估算模型中输入重要性前9的水分指数（NDWI、NMDI、SRWI、SR、NDII、WI、DWI、MSI、SAVI）时,调整R 2最高,验证集决定系数为0.8877;RF-PLS和RF-GA-SVM估算模型中输入重要性前4的水分指数（NDWI、NMDI、SRWI、SR）时,调整R 2最高,验证集决定系数分别为0.8053、0.8952,其中RF-GA-SVM模型估算等效水厚度效果最佳,其精度满足路域植被等效水厚度监测要求。本文研究成果为等效水厚度估算提供一种有效且精确的方法,同时为发展基于高光谱遥感的路域环境监测提供重要支撑。 相似文献