共查询到20条相似文献，搜索用时 15 毫秒
1.
Driven by various natural and anthropogenic factors, Poyang Lake, the largest freshwater lake in China, has experienced significant land use/cover changes in the past few decades. The aim of this study is to investigate the spatial–temporal patterns of abrupt changes and detect their potential drivers in Poyang Lake, using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day maximum value composite vegetation indices between 2000 and 2012. The breaks for additive seasonal and trend (BFAST) method was applied to the smoothed time-series normalized difference vegetation index (NDVI), to detect the timing and magnitude of abrupt changes in the trend component. Large part of Poyang Lake (98.9% for trend component) has experienced abrupt changes in the past 13 years, and the change patterns, including the distributions in timing and magnitudes of major abrupt trend changes between water bodies and land areas were clearly differentiated. Most water bodies had abrupt increasing NDVI changes between 2010 and 2011, caused by the sequential severe flooding and drought in the two years. In contrast, large parts of the surrounding land areas had abrupt decreasing NDVI changes. Large decreasing changes occurred around 2003 at the city of Nanchang, which were driven by urbanization. These results revealed spatial–temporal land cover changing patterns and potential drivers in the wetland ecosystem of Poyang Lake. 相似文献
2.
Satellite derived vegetation vigour has been successfully used for various environmental modeling since 1972. However, extraction of reliable annual growth information about natural vegetation (i.e., phenology) has been of recent interest due to their important role in many global models and free availability of time-series satellite data. In this study, usability of Moderate Resolution Imaging Spectro-radiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) based products in extracting phenology information about evergreen, semi-evergreen, moist deciduous and dry deciduous vegetation in India was explored. The MODIS NDVI and EVI time-series data (MOD13C1: 5.6 km spatial resolution with 16 day temporal resolution—2001 to 2010) and GIMMS NDVI time-series data(8 km spatial resolution with 15 day temporal resolution—2000 to 2006) were used. These three differently derived vegetation indices were analysed to extract and understand the vegetative growth rhythm over different regions of India. Algorithm was developed to derive onset of greenness and end of senescence automatically. The comparative analysis about differences in the results from these products was carried out. Due to dominant noise in the values of NDVI from GIMMS and MODIS during monsoon period the phenology rhythm were wrongly depicted, especially for evergreen and semi-evergreen vegetation in India. Hence, care is needed before using these data sets for understanding vegetative dynamics, biomass cestimation and carbon studies. MODIS EVI based results were truthful and comparable to ground reality. The study reveals spatio-temporal patterns of phenology, rate of greening, rate of senescence, and differences in results from these three products. 相似文献
3.
多时相MODIS影像水田信息提取研究 总被引：5，自引：0，他引：5
水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。 相似文献
4.
Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved. 相似文献
5.
针对不同的数据源及时间和空间尺度会使植被覆盖度及其与气象因子影响的结果有所差别这一情况,该文基于青藏高原1982-2012年GIMMS NDVI和2001-2013年MODIS NDVI遥感数据集,结合研究区内12个典型的气象站点数据,进行了青藏高原地区植被覆盖时空动态变化规律及其与气象因子响应的时序分析,并利用重合时间段的数据对比分析了两种传感器在青藏高原地区对植被动态变化监测方面的差异.结果表明:近30年来,青藏高原地区植被呈整体改善趋势,尤其是高海拔地区;不同阶段植被的变化趋势有所不同;两种传感器在反映植被动态变化趋势上差异显著,但两者与气候因子的响应规律相同. 相似文献
6.
太湖水生植被NDVI的时空变化特征分析 总被引：2，自引：0，他引：2
为了明确太湖不同生态区水生植被长势的变化规律及其影响因子,利用MODIS传感器提供的NDVI数据,分析了太湖2000年—2015年NDVI的时间及空间变化特征。结果表明:太湖水生植被NDVI存在明显的季节变化和年际变化,NDVI每年最小值出现在冬季,最大值出现在植被生长旺盛的8月或9月,其值可达0.35;太湖全湖NDVI多年平均值为0.1,最大值为0.14,出现在2007年。太湖NDVI的空间差异可将太湖划分为不同的植被类型区,太湖西北部(竺山湾和梅梁湾)NDVI最大值可达0.2,植被类型主要以浮游藻类为主,东太湖区域最大值超过0.6,主要以沉水植被为主;太湖不同区域植被动态特征对气象因子的响应也不尽相同,沉水植物生长与平均气温有显著的正相关关系,而浮游植物区的生长状况受平均风速影响较大。 相似文献
7.
一种高时空分辨率NDVI数据集构建方法-STAVFM 总被引：1，自引：1，他引：0
ETM NDVI可以用来在30m的尺度上开展植被的监测,然而在Landsat卫星16天的重访周期和云污染等因素的影响下,常常会在相当长的一段时间内无法获取有效的ETM NDVI数据,给这一尺度下的植被动态监测带来了一定困难。相比之下,MODIS虽然在空间上只有250m分辨率的NDVI产品,却可以每天进行相同区域的监测。针对ETM空间分辨率高和MODIS时间分辨率高的特点,本研究选择实验区,基于对STARFM方法的改进,构建不同时空分辨率NDVI的时空融合模型-STAVFM,使用该模型对ETM NDVI与MODIS NDVI融合,构建了高时空分辨率NDVI数据集。研究结果表明,通过MODIS NDVI时间变化信息与ETM NDVI空间差异信息的有机结合,实现缺失高空间分辨率NDVI的有效预测（3景预测NDVI与实际NDVI的相关系数分别达到了0.82、0.90和0.91）,从而构建高时空分辨率NDVI数据集。所构建的高时空分辨率NDVI数据集在时间上保留了高时间分辨率数据的时间变化趋势,空间上又反映了高空间分辨率数据的空间细节差异。 相似文献
8.
This study empirically compared noise reduction techniques for the normalized difference vegetation index (NDVI) time-series based on a new absolute measure using a time-series of 16-day composite NDVI images extracted from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products covering the Poyang Lake area in China. We proposed an approach to accurately extract representative NDVI temporal profiles for the 12 land cover cluster types by clustering profiles, selecting optimal number of clusters, merging and labeling clusters, and selecting the representative NDVI profiles. The geometric average of the mean average distance between the reconstructed profile and the raw profiles, and the mean average distance between the reconstructed profile and the upper envelope (Dg(nr, c)) was selected as the most appropriate measure substitutive to RMSE for the evaluation of the noise reduction effects, when the ‘true’ profiles were not available. The running median, mean value, maximum operation, end point processing, and Hanning smoothing (RMMEH) filter and iterative Savitzky–Golay filter were the two most appropriate noise reduction techniques for the NDVI temporal profiles of the study area in the evaluation of noise reduction effects by the seven techniques. The robust framework using the proposed approach for the accurate extraction of representative NDVI temporal profiles and (Dg(nr, c)) in this study, is applicable in the evaluation of noise reduction effects using different techniques and in other study areas. 相似文献
9.
《地理信息系统科学与遥感》2013,50(1):117-138
This study investigated rice cropping practices and rice growing areas in the Vietnamese Mekong Delta using MODIS 250 × 250 m normalized difference vegetation index (NDVI) data acquired during the 2002 and 2007 rice cropping seasons. Data processing was conducted in five main steps: (1) constructing time-series MODIS NDVI data; (2) noise filtering of the time-series MODIS NDVI data using empirical mode decomposition (EMD); (3) extracting and evaluating phenological rice training patterns from the smooth time profiles of NDVI; (4) classifying rice cropping systems using support vector machines (SVMs); and (5) conducting an error analysis using ground reference data and government rice statistics. The results indicated that EMD was an efficient filter for noise removal in the time-series MODIS NDVI data. The filtered temporal NDVI profile characterized the distinct behaviors of the rice cropping systems. The estimated sowing and harvesting dates were compared with the field-survey data and indicated root mean square error (RMSE) values of 7.5 and 8.2 days, respectively. The comparison results between the 2002 classification map and the ground reference data indicated that the overall accuracy for the 2002 data was 92.9% with a Kappa coefficient of 0.89, while in 2007 these values were 93.8% and 0.90, respectively. At the district level, there was good agreement between the MODIS-based estimated areas and government rice statistics for 2002 and 2007 (R 2 ≥ 0.85). An investigation of changes in cropping practices from 2002 to 2007 showed that 12.9% of the area used for double-cropped irrigated rice in 2002 had been converted to triple-cropped irrigated rice by 2007, whereas 27.4% of the area used for triple-cropped irrigated rice in 2002 had been converted to double-cropped irrigated rice by 2007. 相似文献
10.
Alex O. Onojeghuo George A. Blackburn Qunming Wang Peter M. Atkinson Daniel Kindred Yuxin Miao 《地理信息系统科学与遥感》2018,55(5):659-677
Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales. 相似文献
11.
Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities. 相似文献
12.
A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring. 相似文献
13.
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
14.
Sébastien Rapinel Clémence Rozo Pauline Delbosc Damien Arvor Alban Thomas Jan-Bernard Bouzillé 《地理信息系统科学与遥感》2020,57(1):60-73
ABSTRACTMonitoring the structural and functional dimensions of natural vegetation is a critical issue to ensure effective management of biodiversity. While coarse-resolution satellite image time-series have been used extensively to monitor vegetation physiognomies, their potential to describe plant species composition remains understudied. The objective of this study is to assess the potential of annual time-series of MODIS images to discriminate combinations of plant communities, called “vegetation series,” and characterize their structural and functional dimensions at the landscape scale. Twelve vegetation series were mapped in a 16 574 ha study area in a Mediterranean context located in Corsica (France). First, the structural dimension of vegetation series was examined using a random forest (RF) model calibrated with a reference field map to (i) measure the importance of each MODIS image in discriminating vegetation series; (ii) quantify the influence of the number of dates on model accuracy; and (iii) map the vegetation series with the optimal subset of MODIS images. Second, the functional dimension of vegetation series was analyzed by ordinating three functional indices through principal component analysis. These indices were the annual sum of normalized difference vegetation index (NDVI), the annual amplitude of NDVI, and the date of maximum NDVI, considered as a proxy for annual primary production, seasonality of carbon fluxes, and vegetation phenology, respectively. Results showed that (i) vegetation series were mapped accurately (median Kappa index 0.70, median overall accuracy 0.76), preferably using images acquired from February to August; (ii) at least 10 MODIS images were required to achieve sufficient accuracy; and (iii) a functional gradient was detected, ranging from high annual net primary production with low seasonality of carbon fluxes and early phenology in Mediterranean vegetation series to low annual net primary production with high seasonality of carbon fluxes and late phenology in alpine vegetation series. 相似文献
15.
归一化植被指数（normalized difference vegetation index,NDVI）作为重要的植被生长状况植被指数,对其进行有效实时监测具有重要科学意义。选择4个大陆板块边界观测网（plate boundary observatory,PBO）观测站的GPS信噪比观测值,提取反射信号信噪比并计算归一化振幅,通过与MODIS（moderate resolution imaging spectroradiometer）NDVI产品时序频谱特征的相关性分析,建立GPS反射信号植被指数线性反演模型和BP神经网络反演模型。分析发现:GPS反射信号信噪比归一化振幅与NDVI指数存在显著年周期性和季候特性,NDVI线性反演模型相关系数均约为0.7,均方根误差处于0.05~0.09之间,BP神经网络反演模型相关系数提高了约5%。利用GPS反射信号反演NDVI变化趋势具有可行性,为获取高时间分辨率、低成本的NDVI指数提供了一种新思路。 相似文献
16.
MODIS增强型植被指数EVI与NDVI初步比较 总被引：31，自引：0，他引：31
利用东亚地区典型地带性植被和MODIS数据，对广泛使用的植被指数NDVI和新开发的增强型植被指数EVI进行了对比分析。由MODIS开发的NDVI和EVI对干旱-半湿润环境下低覆盖植被的描述能力相似，但对湿润环境下高密度植被的描述有明显差别：NDVI年时间过程的季节性不明显，表现为全年高平的曲线；而EVI仍然有季节性，表现为钟形曲线，与月平均温度关系更密切。EVI的这一特征为研究高覆盖植被的季节性变化提供了新的思路。 相似文献
17.
Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region 相似文献
18.
《International Journal of Digital Earth》2013,6(2):103-114
Abstract Due to spatial and temporal variability an effective monitoring system for water resources must consider the use of remote sensing to provide information. Synthetic Aperture Radar (SAR) is useful due to timely data acquisition and sensitivity to surface water and flooded vegetation. The ability to map flooded vegetation is attributed to the double bounce scattering mechanism, often dominant for this target. Dong Ting Lake in China is an ideal site for evaluating SAR data for this application due to annual flooding caused by mountain snow melt causing extensive changes in flooded vegetation. A curvelet-based approach for change detection in SAR imagery works well as it highlights the change and suppresses the speckle noise. This paper addresses the extension of this change detection technique to polarimetric SAR data for monitoring surface water and flooded vegetation. RADARSAT-2 images of Dong Ting Lake demonstrate this curvelet-based change detection technique applied to wetlands although it is applicable to other land covers and for post disaster impact assessment. These tools are important to Digital Earth for map updating and revision. 相似文献
19.
Abstract Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region. 相似文献
20.
草型湖泊总悬浮物浓度和浊度遥感监测 总被引：1，自引：0，他引：1
草型湖泊水质遥感监测中水生植物会造成“水体—水生植物”混合像元问题,针对因混合像元导致草型湖泊水生植物覆盖区域水质难以直接利用遥感监测的问题,本文以草型湖泊微山湖为研究对象,提出定性和定量相结合的总悬浮物浓度和浊度分区监测方法,实现微山湖水体总悬浮物浓度和浊度的时空变化监测。基于获取的2014年7月—2015年6月覆盖微山湖的多期高分一号（GF-1） WFV和HJ-1A/1B CCD影像,利用归一化水体指数将微山湖区分为水生植物覆盖区和水体区。针对水生植物覆盖区,利用时序MODIS NDVI数据获取微山湖主要水生植物的时谱曲线,识别不同水生植物的物候特征;基于不同物候期内的水生植物对总悬浮物浓度和浊度的指示作用,对微山湖水生植物覆盖区水体总悬浮物浓度和浊度进行定性监测。针对水体区,分别构建水体总悬浮物浓度和浊度的单波段/波段比值模型和偏最小二乘模型,定量反演微山湖水体区总悬浮物浓度和浊度。研究结果表明,微山湖中水生植物以光叶眼子菜、穗花狐尾藻和菹草等沉水植物为主,其中光叶眼子菜/穗花狐尾藻和菹草的空间分布和物候特征存在明显差异,不同水生植物在不同物候期内对水质具有不同的指示作用;微山湖水体总悬浮物浓度和浊度具有显著的空间变异性,基于定性和定量相结合的方法可以有效监测微山湖水体总悬浮物浓度和浊度的时空变化规律。本文提出的定性和定量相结合的监测方法为草型湖泊水质监测的业务化应用提供了新思路。 相似文献