共查询到20条相似文献，搜索用时 31 毫秒
1.
毒草型退化草地具有群落演替特点,通过高光谱遥感技术反演毒杂草分布与退化草地群落结构能对该类退化草地进行有效监测,而光谱特征分析是毒杂草与优良牧草遥感识别的基础。本文选取了三江源区毒草型退化草地的8种典型毒杂草和4种优良牧草的地面实测高光谱数据作为研究样本,经过SG平滑、包络线去除、导数变换和光谱参量化对毒杂草种和优良牧草种的光谱特征进行了分析,并通过马氏距离法提取其特征识别波段。结果表明：① 8种典型毒杂草和4种优良牧草的 “近红外峰值”差异较大,其中鹅绒萎陵菜的“近红外峰值”达到60.07%,而最小者早熟禾仅为17.53%;② 经包络线去除处理后,植被光谱曲线中吸收谷和反射峰光谱差异更加明显,且可减少环境背景对植被光谱的影响,如沼泽草甸的鹅绒委陵菜和驴蹄草,其“绿峰幅值”分别为6.46%和6.89%,经处理后其“绿峰指数”分别为0.2866和0.3671,而在2种环境下生长的同一草种（狼毒草1和狼毒草2）的峰谷特征差异不明显;③ 基于马氏距离法提取的毒杂草与优良牧草的敏感识别波段主要分布在680~750 nm和900~1000 nm波长范围内,以醉马草与矮嵩草为例,其基于反射率的敏感识别波段为713.1~737.1 nm和934.6~965.6 nm。该研究可为利用高光谱遥感进行大面积毒杂草草种识别和植被群落生长监测提供重要科学依据,对于三江源区毒杂草的监测防治和畜牧业的可持续发展具有重要意义。 相似文献
2.
WorldView-2近红外光谱波段反演马尾松植被信息的比较研究 总被引：1，自引：0，他引：1
WorldView-2卫星自2009年发射至今,已为用户提供了大量高性能的影像产品。与众多高分辨率卫星影像不同,WorldView-2有2个近红外波段,即近红外1(Near-infrared1,NIR1)和近红外2(Near-infrared2,NIR2),但目前这2个波段在应用上的区别并不清楚。因此,本文以福建省长汀县河田地区的马尾松林为例,采用NIR1和NIR2这2个近红外波段分别构建了3种植被指数(NDVI、ARVI和NDMVI),以探索二者在植被信息反演方面的差异。结果表明,NIR1构建的植被指数在马尾松林提取精度上高于NIR2,并具有更丰富的植被信息量。经统计可知,NIR1所构建的植被指数信息量比NIR2分别大8.0%(NDVI)、12.3%(ARVI)和7.3%(NDMVI);在反演植被覆盖度方面,NIR1也比NIR2具有更高的精度,其模拟的植被覆盖度与实际植被覆盖度的拟合度更高,误差更小。NIR1和NIR2所表现出的差异是因为马尾松在这2个近红外波段的光谱反射不同,其反射在NIR1的波长范围内达到最强,而在NIR2的波长范围内则出现了小幅下降。 相似文献
3.
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
4.
基于Google Earth Engine的红树林年际变化监测研究 总被引：1，自引：0，他引：1
遥感技术已广泛应用于红树林资源调查与动态监测中,但仍然存在遥感数据获取困难、数据预处理工作量大、监测时间长而周期过大等问题,影响了学者对红树林演变过程的精细刻画与理解。本文基于Google Earth Engine（GEE）云遥感数据处理平台,选取Landsat系列卫星数据,生成长时间序列年际极少云影像集（云量少于5%）,利用3个红外波段反射率（NIR、SWIR1、SWIR2）和3个特征指数（NDVI、NDWI、NDMI）建立阈值规则集,实现对实验区越南玉显县红树林、红树林-虾塘、不透水面-裸地、水体4种目标地物的专家知识决策树分类和土地覆盖的制图,并基于分类结果监测该区域1993-2017年的红树林年际动态变化。结果表明：GEE平台可满足多云多雨地区红树林的长时间序列年际变化监测需求;本文阈值分类方法可以有效提取红树林及红树林-虾塘,实验区有86%年份的影像分类精度达到80%以上;年际变化监测可精细刻画实验区红树林面积先增后减再增的变化过程,也能准确反映红树林与红树林-虾塘养殖系统面积之间的负相关关系。红树林年际动态监测结果可以降低红树林演变分析的不确定性,并能更精细地量化红树林与其他土地覆盖类型的转化过程,从而评估经济发展、政策等因素对红树林演变的影响。 相似文献
5.
从增强型水体指数分析遥感水体指数的创建 总被引：6，自引：0，他引：6
徐涵秋 《地球信息科学学报》2008,10(6):776-780
本文对新近提出的增强型水体指数(EWI)进行了分析和讨论,分别用经过大气校正和未经大气校正的两种影像来对该指数作了验证,并与改进的归一化差值水体指数(MNDWI)进行比较。结果表明该指数在经过大气校正的影像中对水体的增强和提取效果不理想,许多水体影像特征不但未能得到增强,反而受到抑制而被漏提。显然,该指数在创建时忽略了大气因素的影响。另外,该指数在构建上重复选择近红外和中红外波段也是造成提取效果不理想的原因之一。因此,为了创建正确的水体指数,避免水体增强和提取结果的偶然性和不确定性,水体指数的创建必须用经过大气校正的影像进行验证,而构建指数的波段也要避免不合理的重复选择,这样才能使所创建的指数具有更广的适应性。 相似文献
6.
Zhaofei Wen Ce Zhang Shuqing Zhang Changhong Ding Chunyue Liu Xin Pan Huapeng Li Yan Sun 《中国地理科学(英文版)》2012,22(2):188-195
Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index (NDVI) and its related bands variable images, namely red and near infrared (NIR), on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that: 1) in high fractal vegetation cover (H-FVC) area, NDVI and NIR variables display a similar ability in detecting the spatial heterogeneity caused by vegetation growing status structure; 2) in low fractal vegetation cover (L-FVC) area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3) generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data. 相似文献
7.
《山地科学学报》2021,(4)
The Fraction of Absorbed Photosynthetically Active Radiation(FPAR) is an important indicator of the primary productivity of vegetation. FPAR is often used to estimate the assimilation of carbon dioxide in vegetation. Based on MOD15 A2 H/FPAR data product, the temporal and spatial variation characteristics and variation trend of FPAR in different vegetation types in 2001 to 2018 were analyzed in the Hengduan Mountains. The response of FPAR to climate change was investigated by using Pearson correlation analytical method and partial least squares regression analysis. Results showed that the FPAR in Hengduan Mountains presented an increasing trend with time. Spatially, it was high in the south and low in the north, and it also showed obvious vertical zonality by elevation gradient.The vegetation FPAR was found to be positively correlated with air temperature and sunshine duration but negatively correlated with precipitation. Partial least squares regression analysis showed that the influence of sunshine duration on vegetation FPAR in Hengduan Mountains was stronger than that of air temperature and precipitation. 相似文献
8.
Chao Zhan Junbao Yu Qing Wang Yunzhao Li Di Zhou Qinghui Xing Xiaojing Chu 《中国地理科学(英文版)》2017,27(6):934-947
Accurate assessment of surface suspended sediment concentration(SSSC) in estuary is essential to address several important issues: erosion, water pollution, human health risks, etc. In this study, an empirical cubic retrieval model was developed for the retrieval of SSSC from Yellow River Estuary. Based on sediments and seawater collected from the Yellow River and southeastern Laizhou Bay, SSSC conditions were reproduced in the laboratory at increasing concentrations within a range common to field observations. Continuous spectrum measurements of the various SSSCs ranging from 1 to 5700 mg/l were carried out using an Ava Field-3 spectrometer. The results indicated the good correlation between water SSSC and spectral reflectance(Rrs) was obtained in the spectral range of 726–900 nm. At SSSC greater than 2700 mg/L, the 740–900 nm spectral range was less susceptible to the effects of spectral reflectance saturation and more suitable for retrieval of high sediment concentrations. The best correlations were obtained for the reflectance ratio of 820 nm to 490 nm. Informed by the correlation between Rrs and SSSC, a retrieval model was developed(R2 = 0.992). The novel cubic model, which used the ratio of a near-infrared(NIR) band(740–900 nm) to a visible band(400–600 nm) as factors, provided robust quantification of high SSSC water samples. Two high SSSC centers, with an order of 103 mg/l, were found in the inversion results around the abandoned Diaokou River mouth, the present Yellow River mouth to the abandoned Qingshuigou River mouth. There was little sediment exchange between the two high SSSC centers due to the directions of the residual currents and vertical mixing. 相似文献
9.
多云多雾现象是农作物遥感分类经常遇到的问题,影响分类精度。为解决此类问题,本文提出一种基于时间序列GF-1号遥感影像识别水稻方法。利用多时相时间序列的GF-1号遥感影像提取中稻、晚稻的近红外波段（NIR）反射率、红光（R）波段反射率、归一化植被指数（NDVI）特征;拟合光谱和植被指数时间序列特征曲线;分析多时相影像离散近红外波段 、红光波段、NDVI值落在拟合中稻、晚稻近红外波段、红光波段、NDVI时间序列曲线两侧的敏感性区域的比例,该区域也可以视为水稻作物识别特征的目标特征区域,只有达到一定的比例才能视为某类水稻作物。在此情形下,需要综合3种情况进行集中投票决定其最终分类结果。研究表明：该方法可以在多云雾地区对中稻和晚稻精确识别,中稻和晚稻用户精度可达95.97%和95.95%,总体精度为95.76%,kappa系数为0.9335。实验结果表明了NIR、R、NDVI时间序列曲线拟合的有效性,以及拟合曲线目标特征区域设置的合理性。 相似文献
10.
铅污染水稻的冠层高光谱特征研究 总被引：10，自引：1，他引：9
本研究通过铅污染土壤中的水稻盆钵栽培试验,考察了水稻对土壤重金属铅的吸收以及铅对水稻生长的胁迫,并借助地面高光谱辐射仪器获取多个生育期（苗期、分蘖前期、分蘖盛期、拔节期和孕穗期）的水稻冠层高光谱反射数据。在进行光谱测量的同时测定了水稻植株体内的铅含量与冠层叶片叶绿素含量。分析结果表明:铅污染胁迫下水稻冠层叶片叶绿素含量与叶绿素a、b组成变化明显,可见光区间520nm～560nm和630nm～690nm处是铅污染水稻对冠层反射高光谱敏感的特征波段。通过模拟高光谱分辨率遥感传感器MODIS的相应波段（第4通道:545nm～564nm、第1通道附近:620nm～670nm）以及考虑叶绿素的荧光特征（760nm）,本研究分别选择敏感波段中的552nm,672nm与760nm构造了复合归一化污染指数CNDPI（Compos-ite Normalized Difference Pollution Index）,分析发现CNDPI能够明显地区分不同铅污染水平的水稻。在分蘖前期采用适当的冠层光谱反射率形式（敏感波段、CNDPI）可以实现水稻铅污染的遥感监测。 相似文献
11.
为研究不同波段宽度遥感数据对监测水体叶绿素a含量的影响,以太湖水体实测高光谱遥感反射率数据为基础,分析计算不同波段宽度下遥感反射率的归一化值与叶绿素a浓度之间的相关系数。随着波段宽度在75.93nm范围内不断递增,最大相关系数逐渐减小,最大正相关波段向长波方向移动,最大负相关波段向短波方向移动。而波段宽度在31.6nm范围内变化时,最大正相关波段和最大负相关波段都会保持相对稳定。通过对不同波段处相关系数平均值和标准差的对比分析认为,718.77~34.58nm为叶绿素a遥感监测的最佳波段范围。这将对遥感传感器的波段设置,以及实际水体叶绿素a遥感监测时的波段选择,具有重要的参考价值。 相似文献
12.
自动子空间划分在高光谱影像波段选择中的应用 总被引：2，自引：0，他引：2
针对高光谱遥感影像数据量大、维数高的特点,结合联合熵波段选择算法,提出了一种自动子空间划分的改进方案。该方法充分利用了影像各波段数据之间的局部相关性,根据波段间相关系数矩阵图像的"分块"特点,将整个波段空间自动划分为若干个子空间,然后再进行波段选择。实现了在删减冗余信息的同时选择出含有主要信息的特征波段组合的目的。将此方法得到的结果与用联合熵得到的结果进行了比较分析,结果表明自动子空间划分的联合熵波段选择方法具有较好的效果。 相似文献
13.
线性光谱分离技术可以有效地提取像元水平上植被或其他端元(影像中的地物)的相对百分比,但是目前该技术在多光谱宽波段影像数据应用中,由于波段数量、波段宽度等的限制,估算精度离定量研究的水平仍有一定差距。鉴于此,本文提出了一种改进的线性光谱分离方法,该方法在对影像进行土地覆盖分类基础上进行分离,一方面同类土地覆盖类型内同种地物的光谱变异相对较小,更有利于端元选取;另一方面,分影像的地物种类数量明显少于整幅影像,更容易满足模型的适用条件,从而突破了波段数量限制,同时使地物光谱分离更具针对性,经过验证,该方法较传统分离方法相比植被覆盖度的反演精度可提高6.4%,用该方法实现了研究区的植被覆盖度的定量反演并对研究区植被覆盖度的空间结构进行了分析。 相似文献
14.
芒萁是南方红壤侵蚀区生态恢复重要的地带性草本植物,对生态系统修复具有重要作用,监测其叶绿素含量能有效诊断生长健康状况。本文以福建省长汀县朱溪流域6个不同生态恢复年限下的芒萁叶片高光谱反射数据以及实测叶绿素含量为数据源,借助高光谱遥感技术分析不同恢复年限芒萁叶片原始光谱特征,筛选出光谱敏感波段并构建光谱指数,基于相关性分析,建立芒萁叶绿素单变量以及多元逐步回归模型,并确定最佳估算模型。结果表明：高光谱指数建立的单变量估算模型中,改进红边归一化植被指数（mNDVI705）、叶面叶绿素指数（LCI）、红边指数（Vog）、比值光谱指数（RVI603/407）、NDVI[603,407]高光谱指数建立的二次模型精度高,建模决定系数R2均超过了0.8,其中以高光谱指数为自变量建立的多元回归模型拟合R2值（0.886）最高。综合建模精度和模型验证精度,LCI指数构建的单变量模型以及基于高光谱指数的多元回归模型是估算芒萁叶片叶绿素含量最佳模型。本研究建立的叶绿素高光谱估算模型对快速、无损地监测水保植物芒萁生长具有重要意义。 相似文献
15.
遥感水深反演具有非接触测量和省时省力等优点,能够为航海、岛礁工程与珊瑚礁生态调查等活动提供重要参考。随着高光谱遥感卫星数量的增长,基于高光谱遥感影像的水深反演具有良好的发展与应用潜力。HOPE（Hyperspectral Optimization Process Exemplar）算法是比较常用的高光谱水深反演算法。鉴于HOPE算法在低遥感反射率海域会出现水深被高估的问题,本文基于Hyperion高光谱遥感影像提出一种改进的水深反演算法。该算法针对危险或难以到达海域往往具有水体光学性质较为均一的特点,利用深水区遥感反射率的观测值来估计整个研究区域内的水体光学性质参数并将其固定,以便减少未知参数数量,解决水深被高估的问题,最终达到提高水深反演整体精度的目的。塞班岛和中业岛的实验结果表明,改进算法能够有效克服常规HOPE算法在低遥感反射率水域高估水深的问题。改进算法能够将平均遥感反射率小于0.0075sr-1（塞班岛）和0.001 sr-1（中业岛）范围内的水域的水深反演平均绝对误差从常规HOPE算法的2.94 m和6.44 m分别降低至2.56 m和4.99 m,从而能够相应地将整体的均方根误差从3.18 m和5.39 m分别降低至2.30 m和3.32 m,而将整体的平均相对误差从32.4%和27.1%分别降低至30.6%和23.9%。因此,改进算法在提高卫星高光谱遥感影像水深反演效果方面具有可行性和有效性。 相似文献
16.
Effect of Temporal Resolution of NDVI on Potential Evapotranspiration Estimation and Hydrological Model Performance 总被引：1，自引：0，他引：1
LI Xianghu REN Liliang 《中国地理科学(英文版)》2007,17(4):357-363
Normalized difference vegetation index (NDVI) data, obtained from remote sensing information, are essential in the Shuttleworth-Wallace (S-W) model for estimation of evapotranspiration. In order to study the effect of temporal resolution of NDVI on potential evapotranspiration (PET) estimation and hydrological model performance, monthly and 10-day NDVI data set were used to estimate potential evapotranspiration from January 1985 to December 1987 in Huangnizhuang catchment, Anhui Province, China. The differences of the two calculation results were analyzed and used to drive the block-wise use of the TOPMODEL with the Muskingum-Cunge routing (BTOPMC) model to test the effect on model performance. The results show that both annual and monthly PETs estimated by 10-day NDVI are lower than those estimated by monthly NDVI. Annual PET from the vegetation root zone (PETr) lowers 9.77%-13.64% and monthly PETr lowers 3.28%-17.44% in the whole basin. PET from the vegetation interception (PETi) shows the same trend as PETr. In addition, temporal resolution of NDVI has more effect on PETr in summer and on PETi in winter. The correlation between PETr as estimated by 10-day NDVI and pan measurement (R2= 0.835) is better than that between monthly NDVI and pan measurement (R2 = 0.775). The two potential evapotranspiration estimates were used to drive the BTOPMC model and calibrate parameters, and model performance was found to be similar. In summary, the effect of temporal resolution of NDVI on potential evapotranspiration estimation is significant, but trivial on hydrological model performance. 相似文献
17.
Retrieval of chlorophyll a and suspended solid concentrations by hyperspectral remote sensing in Taihu Lake, China 总被引：1，自引：0，他引：1
Chlorophyll α（ch1-α） and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of ch1-α and suspended solids, requiring laborious laboratory work, which is often expensive and time consuming. Hyperspectral remote-sensing measurement provides a fast and easy tool for estimating water trophic status. In situ hyperspectral data on March 7-8, July 6-7, September 20 and December 7-8, 2004 and the corresponding water chemical data were used to regress the algorithm of water quality parameters. Results showed that the peak of water leaving radiance around 700 nm （R700） varied proportionally with ch1-α concentration, and moved to infrared when algal bloom occurred. The reflectance ratio of R702/R685 was well correlated with ch1-α when water surface in no algal bloom case and the correlation coefficient was better if absorption of phycocyanin was considered. The reflectance ratio R620/R531 was highly correlated to the concentration of suspended solids. The relationship between suspended solids and other band groups were also compared. Secchi disk depth could be calculated by non-linear correlation with suspended solids concentration. 相似文献
18.
Landsat系列卫星数据是对地观测研究中应用最为广泛的遥感数据源之一,但是Landsat数据易受云及云影的影响,因此,在Landsat数据的应用中,云和云影的识别十分关键。美国地质调查局（United States Geological Survey,USGS）在其分发的最新的Landsat 8 数据中新增了一个质量评估（Quality Assessment）波段,能快速提供高精度的云掩膜,然而并不能识别云影。本文在Landsat 8 QA波段云识别基础上,对影像的近红外和短波红外波段进行种子填充变换,提取影像中的潜在云影,采用非监督分类的方法识别影像中的水体,将水体从潜在云影中去除。利用太阳方位角和太阳高度角对云及云影相对位置的影响,对云和云影进行匹配,识别真实的云影。利用全球云和云影验证数据集对本文的云影识别结果进行了精度评价,结果表明：不同生态区域云影识别精度达到87%以上。与Fmask云影检测方法相比,本文方法所需波段数更少,流程简单,简化了云高度估算和视角问题,可以快速、准确地识别云影,对基于Landsat 8数据的定量分析或时序研究有重要价值。 相似文献
19.
通过对归一化差异水体指数NDWI中的绿波段修正,提出了不依赖于中红外波段的伪归一化差异水体指数FNDWI（False NDWI）。使用NDWI和FNDWI分别在背景地物为城市、城郊、乡镇、村落和山区的遥感影像上进行河流水体提取,实验表明,FNDWI影像中城镇建筑用地与河流水体的可分离性较NDWI有所提升,提升率为116%~335%不等;相关性分析表明,河流宽度与可分离性提升率具有明显的负相关关系,相关系数为-0.82;分类结果显示,在城市和城郊区域,NDWI提取的水体中混杂有较多城镇建筑用地信息,而FNDWI提取的水体中基本未见混杂。总体上,FNDWI提高了2种地物的可分离性,剔除了NDWI影像混入的城镇建筑用地信息,较好地解决了NDWI城镇建筑用地与河流水体的混淆问题,尤其适用于城镇周边的细小河流。 相似文献
20.
Vegetation NPP Distribution Based on MODIS Data and CASA Model —— A Case Study of Northern Hebei Province 总被引：2，自引：0，他引：2
1 Introduction Vegetation is an important component of terrestrial eco- system, it plays an important role in global matter and energy cycle, carbon balance and climate change. CO2 has effects on global warming, photosynthesis function, Net Primary Productivity (NPP) and earth environmental condition. NPP is one of the important biophysical variables of vegetation activity, and is a beginning link of biogeochemical carbon cycle. Vegetation absorbs CO2 from atmosphere through photosynthesi… 相似文献