共查询到20条相似文献，搜索用时 281 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.
Liliana Perez Trisalyn Nelson Nicholas C. Coops Fabio Fontana C. Ronnie Drever 《International Journal of Digital Earth》2016,9(7):676-696
Climate drives ecosystem processes and impacts biodiversity. Biodiversity patterns over large areas, such as Canada's boreal, can be monitored using indirect indicators derived from remotely sensed imagery. In this paper, we characterized the historical space–time relationships between climate and a suite of indirect indicators of biodiversity, known as the Dynamic Habitat Index (DHI) to identify where climate variability is co-occurring with changes in biodiversity indicators. We represented biodiversity using three indirect indicators generated from 1987 to 2007 National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer images. By quantifying and clustering temporal variability in climate data, we defined eight homogeneous climate variability zones, where we then analyzed the DHI. Results identified unique areas of change in climate, such as the Hudson Plains, that explain significant variations in DHI. Past variability in temperatures and growing season index had a strong influence on observed vegetation productivity and seasonality changes throughout Canada's boreal. Variation in precipitation, for most of the area, was not associated with DHI changes. The methodology presented here enables assessment of spatial–temporal relationships between biodiversity and climate variability and characterizes distinctive zones of variation that may be used for prioritization and planning to ensure long-term biodiversity conservation in Canada. 相似文献
3.
基于遥感的植被年际变化及其与气候关系研究进展 总被引：61，自引：0，他引：61
植被具有明显的年际变化和季节变化特点,对植被的动态监测可以从一定程度上反映气候变化的趋势,因此监测植被动态变化以及分析这种变化与气候的关系已经成为全球变化研究的一个重要领域.随着遥感卫星获得长时间系列逐日观测数据,许多国际组织和机构制定了全球卫星数据接收、处理和生成数据集计划,所产生的标准数据集则极大地促进了该项研究.大量研究在全球尺度、洲际尺度（北美洲和欧亚大陆）以及区域尺度上广泛开展.在阅读国内外大量文献的基础上,比较分析了常用于植被监测的卫星传感器和主要数据集,汇总了植被年际变化及其与气候关系研究的主要研究方法和研究结果.结果表明近20年来全球植被活动明显增强,表现为北半球普遍存在增加的趋势,南半球干旱半干旱区出现降低的植被光合作用,但这些变化因空间位置不同和研究尺度不一样体现出不同的动态变化特征.气温和降水是影响植被变化的最主要的因素. 相似文献
4.
Ignacio San-Miguel David W. Andison Nicholas C. Coops 《International Journal of Digital Earth》2019,12(5):544-565
Free and open access to the Landsat archive has enabled the detection and delineation of an unprecedented number of fire events across the globe. Despite the availability and potential of these data, few studies have analysed residual vegetation patterns and/or partial mortality of fire across the Canadian boreal forest, and those available, are either incomplete or inaccurate. Further, they all differ in the methods and spatial language, which makes it difficult for managers to interpret fire patterns over large areas. There is an urgent need for methods to help unify fire pattern observations across the Canadian boreal forest. This study explores the capacity of the Landsat data archive when coupled with a recently developed fire mapping approach and a robust spatial language to characterize and compare tree mortality patterns across the boreal plains ecozone, Canada. With 507 fires 2.5?Mha mapped, this study represents the most comprehensive analysis of mortality patterns for study area. Summaries from this demonstration generated an accurate characterization of the fire patterns the various ecoregions based on seven key fire metrics. The comparison between ecoregions revealed differences in the amount of residual vegetation, which in turn suggested various climate, topography and/or vegetation ecosystem drivers. 相似文献
5.
长时间序列多源遥感数据的森林干扰监测算法研究进展 总被引：2，自引：2，他引：2
时空意义明确的森林干扰和恢复信息是评价森林生态系统碳动态的关键因素之一。然而由于诸多的现实困难,多尺度的森林干扰定量化时空信息相对缺乏。Landsat数据具备光谱、时间和空间分辨率上的优势,以及可以免费获取的特点,使其成为主要的长时间序列动态监测的遥感数据源之一,为长时间周期内提供具有合适的空间细节和时间频率的森林干扰信息成为可能。特别是基于Landsat时间序列堆栈(LTSS)的森林干扰自动分析算法的出现,更为森林生态系统的近实时监测提供强有力的工具。本文全面评述了长时间序列遥感数据准备和预处理技术以及国内外基于遥感数据源的多时相森林干扰监测方法,重点分析了基于Landsat的多种指数监测和自动化方法的优缺点,并总结了其与多源数据结合的扩展应用,最后就现有方法与国内外新的数据、技术手段的关联进行了展望,以期为推广中国本土卫星影像应用于森林干扰监测提供理论借鉴。 相似文献
6.
Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May–September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming. 相似文献
7.
遥感估算叶面积指数(LAI)时空动态变化对全球气候变化研究具有重要的意义,为了提高遥感估算时间序列叶面积指数的精度,需要耦合遥感观测数据与LAI动态过程模型。本文提出一种基于双集合卡尔曼滤波(Dual EnKF)的时间序列LAI反演方法,同时更新LAI估计值和LAI动态过程模型中的敏感性参数,得到LAI和动态过程模型敏感参数的最优估计值来优化动态过程模型。一方面使得动态过程模型可以更好地描述LAI随时间的变化过程,降低模型预测误差,从而提高LAI动态过程模型的预测能力;另一方面通过耦合动态过程模型和辐射传输模型,集成遥感观测数据与动态过程模型的预测值,进而得到优化的LAI估计值。为检验算法,分别选取作物、草地和林地等典型植被验证站点进行Dual EnKF LAI时间序列估算,并分别与MODIS LAI产品及其SG滤波曲线、集合卡尔曼滤波方法反演LAI、未优化的动态过程模型模拟LAI结果进行比较,并配以一些站点地面实测点数据作为参考。结果表明,采用Dual EnKF方法得到的LAI不但保持了时间上的连续性,而且通过改善动态过程模型的预测能力,即使在缺乏高质量遥感观测数据时,也能够获得符合LAI发展趋势的估算值,没有出现跳跃、波动现象,时间序列曲线较稳定,更符合植被LAI变化规律,表明基于Dual EnKF的时间序列LAI遥感估算方法是提取LAI时间廓线的一种有效途径。 相似文献
8.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI. 相似文献
9.
With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images. 相似文献
10.
《Geoscience and Remote Sensing Letters, IEEE》2009,6(3):403-407
11.
Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of “optical types” (OT), meaning “optically distinguishable functional types”, as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation.This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MODIS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found.Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability. 相似文献
12.
Urban event detection with big data of taxi OD trips: A time series decomposition approach 
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下载免费PDF全文 Big urban mobility data, such as taxi trips, cell phone records, and geo‐social media check‐ins, offer great opportunities for analyzing the dynamics, events, and spatiotemporal trends of the urban social landscape. In this article, we present a new approach to the detection of urban events based on location‐specific time series decomposition and outlier detection. The approach first extracts long‐term temporal trends and seasonal periodicity patterns. Events are defined as anomalies that deviate significantly from the prediction with the discovered temporal patterns, i.e., trend and periodicity. Specifically, we adopt the STL approach, i.e., seasonal and trend decomposition using LOESS (locally weighted scatterplot smoothing), to decompose the time series for each location into three components: long‐term trend, seasonal periodicity, and the remainder. Events are extracted from the remainder component for each location with an outlier detection method. We analyze over a billion taxi trips for over seven years in Manhattan (New York City) to detect and map urban events at different temporal resolutions. Results show that the approach is effective and robust in detecting events and revealing urban dynamics with both holistic understandings and location‐specific interpretations. 相似文献
13.
冰流速是反映在全球气候变化条件下南极冰盖变化及其稳定性最直接和最基本的指标之一,也是精确估算南极冰盖对全球海平面上升贡献的关键数据之一。光学遥感影像因其空间覆盖广、时间和空间分辨率高等优势,是南极冰流速大规模提取的重要数据源。本文首先对现有利用光学遥感影像进行南极冰流速提取的方法进行了综述,介绍了相关的软件和工具。然后,总结了20世纪60年代以来基于光学遥感影像生成的南极冰流速产品,并对南极典型区域的冰流速产品在物质平衡估算、冰架长时序变化监测等方面的应用进行了分析。最后,总结了光学遥感影像用于南极冰流速提取的优势及未来的发展趋势。 相似文献
14.
The ubiquity of movement data has led to new research interest in aspects of temporal scale. Few of the approaches analyzing movement data have been developed specifically for scale-oriented temporal analysis. To overcome this limitation, this article proposes a series-based approach to perform scale-oriented temporal analysis of movement data. Key to the proposed approach is the construction of four types of series (one type of identical-scale series and three types of cross-scale series) based on the continuous triangular model (CTM). Two distinct research goals are derived from this: to investigate the changes in motion attributes of moving individuals across different temporal scales, and to detect the time intervals during which active events might have occurred. The results based on real football movement data show that finer changes in motion attributes can be found and more accurate time intervals can be detected through the proposed approach. 相似文献
15.
水汽在全球水文和气候变化上扮演着非常重要的角色。选取了中国台湾GPS水汽时间序列作为算例数据,分析了水汽与地理环境的对应关系,得到中国台湾地区的大气水汽分布主要受纬度,地形特征和气候条件控制的结论。利用经验模态分解（EMD）和小波分解（WD）联合算法将中国台湾GPS站水汽的长时间序列和短时间序列进行分解,探测出每个GPS站都存在周年、半周年、天、半天的周期振荡,再结合地理、气候因素分析周期振荡产生的物理原因,得出结论:年周期的水汽振荡主要是由于在中国台湾特殊地形条件下年季风周期变化引起的;半周年的振荡主要是由于夏季风与冬季风对中国台湾地区的交替控制所导致的;而导致水汽日变化的原因则是海陆风环流、海陆风-山谷风叠加环流;天顶可降水量（PWV）半日振荡的振幅较小,则主要是因为受到了太阳辐射加热引起的局地热对流的影响所导致。 相似文献
16.
Wei Yang Hideki Kobayashi Xuehong Chen Kenlo Nishida Nasahara Rikie Suzuki Akihiko Kondoh 《International Journal of Digital Earth》2018,11(10):981-1000
Three-dimensional (3-D) Monte Carlo-based radiative transfer (MCRT) models are usually used for benchmarking in intercomparisons of the canopy radiative transfer (RT) simulations. However, the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests, due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales. Fortunately, some of important tree structure parameters such as canopy height and tree density distribution have been available globally. This enables to run the intermediate complexities of the 3-D MCRT models. We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density. It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms. The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA, respectively. Results demonstrated that the simulations of bidirectional reflectance factor (BRF) based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error (RMSE) and relative RMSE (rRMSE) ranging from 0.002 to 0.006 and from 0.7% to 19.8%, respectively. Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%, respectively. Although the results from the current study are limited in two boreal forest stands, our approach has the potential to generate stand structures for different forest biomes. 相似文献
17.
The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984–2012, with a time series representing 1984–2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r ≥ 0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984–2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time series change detection and update approach followed here, science outcomes or reports representing one temporal epoch can be considered stable and will not be altered when a time series is updated with newly available data. 相似文献
18.
Engdahl M.E. Pulliainen J.T. Hallikainen M.T. 《Geoscience and Remote Sensing Letters, IEEE》2004,1(3):228-231
The performance of interferometric synthetic aperture radar (INSAR)-based boreal forest stem volume retrieval is strongly affected by weather conditions around the time of the SAR image acquisitions. Since weather conditions cannot be controlled, the suitability of a particular interferometric pair for stem volume retrieval can only be assessed afterward. In this letter, four objective measures based on observed forest coherence were compared in assessing the suitability of interferometric pairs for stem volume retrieval. These suitability measures can be used to identify the best and worst pairs, i.e., the ones with the most and least favorable weather conditions. Stem volume retrievals were performed using single European Remote Sensing (ERS-1/2) Tandem interferometric pairs by inverting a backscattering-coherence model for boreal forests. A total of 14 ERS Tandem image pairs acquired in varying weather conditions were studied, and the stem volume retrieval performance was assessed against ground-based stem volume estimates on 134 boreal forest stands. Stem volume retrieval performance as measured by R/sup 2/-values between INSAR-estimated stem volumes and ground truth was found to be directly proportional to boreal forest coherence. The interferometric coherence-contrast (ICC), i.e., the difference in coherence between sparsest and densest boreal forest stands was found to be the best of the four studied suitability measures. The ICC could be used as a suitability parameter in the selection of the best interferometric pairs for operational boreal forest stem volume retrieval. 相似文献
19.
遥感时间序列影像变化检测研究进展 总被引：2，自引：0，他引：2
同一区域、不同时期大量历史数据的积累,以及同一区域能够方便地获取高时间分辨率遥感数据,使遥感时间序列影像变化检测成为近年来遥感技术与应用的研究热点。本文系统总结和评述了当前遥感时间序列影像变化检测的相关研究进展和应用状况,在阐明遥感时间序列分析的意义,以及时间序列影像在变化检测中的优势的基础上,从非遥感领域时间序列变化检测方法出发,针对遥感时间序列影像变化检测的需求,明确和归纳了遥感时间序列变化检测的问题与类型,并对当前最新研究进行了综述,总结了各种方法的优点与不足,重点介绍了基于经验模态分解的遥感时间序列影像异常信息检测方法和基于隐马尔可夫模型的土地利用/覆盖变化检测方法,以期能够为相关研究提供参考。最后总结了该研究领域的发展趋势和存在问题,并对今后的研究工作和未来发展方向进行了展望。 相似文献
20.
An algorithm to classify and monitor seasonal variations in vegetation phenologies and their inter-annual change 总被引：1，自引：0，他引：1
The algorithm presented in this paper classifies vegetation from annual Normalized Difference Vegetation Index (NVDI) time series according to the shape of the temporal cycle. Shape is described using the Fourier components’ magnitude and phase. The degree of an NDVI cycle’s similarity to a predefined reference cycle is measured by the similarity in their amplitude ratios and in their phase differences. Tolerable deviations from the ideal ratios and differences can be set by the user depending on individual accuracy requirements. Tolerable vegetation coverage variation within a shape class is another user defined variable. The algorithm is invariant to cycle modifications including temporal shifts, vertical displacements, and intensity variations, modifications that may be caused by differences in climate, soil (-type, -water, -fertility), or topography, but are unrelated to the vegetation type. The output is a highly consistent clustering of NDVI cycles according to their shapes, which can be linked to distinct vegetation types or land use practices. Intra-class coverage variations in the form of continuous fields measured relative to the reference cycle provide additional information about vegetation covers. Based on the same principles, inter-annual vegetation changes can be monitored with the possibility to distinguish between coverage fluctuations and phenological variations/changes.The algorithms are independent from scene statistics and can be used to create spatially and temporally comparable classifications. Their potential is demonstrated using a 250 m MODIS NDVI time series (version 4) from the Middle East. 相似文献