Development of time series stacks of Landsat images for reconstructing forest disturbance history

Abstract

Forest dynamics is highly relevant to a broad range of earth science studies, many of which have geographic coverage ranging from regional to global scales. While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972, large quantities of Landsat images will need to be analysed for studies at regional to global scales. This will not only require effective change detection algorithms, but also highly automated, high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency, a status called imagery-ready-to-use (IRU). This paper describes a streamlined approach for producing IRU quality Landsat time series stacks (LTSS). This approach consists of an image selection protocol, high level preprocessing algorithms and IRU quality verification procedures. The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy. These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System (LEDAPS) designed for processing large quantities of Landsat images. Some characteristics of the LTSS developed using this approach are discussed.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

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.     

Forest cover trends from time series Landsat data for the Australian continent

In perennial and natural vegetation systems, monitoring changes in vegetation over time is of fundamental interest for identifying and quantifying impacts of management and natural processes. Subtle changes in vegetation cover can be identified by calculating the trends of a vegetation density index over time. In this paper, we apply such an index-trends approach, which has been developed and applied to time series Landsat imagery in rangeland and woodland environments, to continental-scale monitoring of disturbances within forested regions of Australia. This paper describes the operational methods used for the generation of National Forest Trend (NFT) information, which is a time-series summary providing visual indication of within-forest vegetation changes (disturbance and recovery) over time at 25 m resolution. This result is based on a national archive of calibrated Landsat TM/ETM+ data from 1989 to 2006 produced for Australia's National Carbon Accounting System (NCAS). The NCAS was designed in 1999 initially to provide consistent fine-scale classifications for monitoring forest cover extent and changes (i.e. land use change) over the Australian continent using time series Landsat imagery. NFT information identifies more subtle changes within forested areas and provides a capacity to identify processes affecting forests which are of primary interest to ecologists and land managers. The NFT product relies on the identification of an appropriate Landsat-based vegetation cover index (defined as a linear combination of spectral image bands) that is sensitive to changes in forest density. The time series of index values at a location, derived from calibrated imagery, represents a consistent surrogate to track density changes. To produce the trends summary information, statistical summaries of the index response over time (such as slope and quadratic curvature) are calculated. These calculated index responses of woody vegetation cover are then displayed as maps where the different colours indicate the approximate timing, direction (decline or increase), magnitude and spatial extent of the changes in vegetation cover. These trend images provide a self-contained and easily interpretable summary of vegetation change at scales that are relevant for natural resource management (NRM) and environmental reporting.     

Updating land cover automatically based on change detection using satellite images: case study of national forests in Southern California

Observing dynamic change patterns and higher-order complexities from remotely sensed images is warranted, but the main challenges include image inconsistency, plant phenological differences, weather variations, and difficulties of incorporating natural conditions into automatic image processing. In this study, we proposed a new algorithm and demonstrated it by producing 2002–2008 and 2010 land-cover maps in heterogeneous Southern California based on an existing 2009 land-cover map. The new algorithm improves the baseline land-cover map quality by discarding potential bad land-cover pixels and dividing each land-cover type into several subclasses. Time series Landsat images were used to detect changed and unchanged areas between baseline year and target year t. Subsequently, for each individual year t, each pixel that was identified as unchanged inherited the baseline classification. Otherwise, each pixel in the changed areas was classified by a similar surrogate majority classifier. The demonstration results in Southern California showed that the land-cover temporal pattern captured the observed successional stages of the ecosystem very well. The accuracy assessment had an overall classification accuracies ranging from 81% to 86% and overall kappa coefficients ranging from 0.79 to 0.83.     

Landsat长时间序列数据格式统一与反射率转换方法实现

介绍了一种长时间序列遥感影像预处理程序,即陆地卫星生态系统干扰自适应处理系统(landsat ecosystem disturbance adaptive processing system,LEDAPS)。该程序通过使用MODTRAN太阳能输出模型,校正太阳方位、日地距离、TM或ETM+带通以及太阳辐照度,将定标影像转换为表观(top-of-atmosphere,TOA)反射率影像,并将通过浓密植被(dark dense vegetation,DDV)算法插值生成的气溶胶光学厚度(aerosol optical thickness,AOT)以及通过相关资料获得的臭氧(O3)浓度、大气压及水汽值等用于6S辐射传输模型,生成地表反射率产品。以LEDAPS可处理的标准数据Landsat7 ETM+和统一格式后的非标准数据Landsat5 TM影像为例,介绍了长时间(1987—2011年)序列数据的选择、格式统一以及算法的实现过程,同时给出了校正后影像效果评价的方法。结果表明,标准数据和非标准数据经过LEDAPS处理后生成的地表反射率产品能有效降低大气中O3、水汽及气溶胶等对影像真实反射率的影响,为土地覆盖变化和干扰因素等的长时间序列监测和生物物理参数的遥感反演提供科学产品,有助于在国内形成处理长时间序列影像数据的准则。     

基于多通道奇异谱的GNSS坐标序列 粗差探测与数据插值

由于接收机故障、天线更换以及一些未知外界环境因素的影响,导致GPS时间序列中不可避免地存在数据缺失和粗差,数据缺失和粗差会产生各种问题,因此需要鲁棒探测粗差和缺失数据插值来获得连续完整统一的时间序列。传统方法可能需要针对不同类型的数据和不同长度的数据间隙研究不同的插值方法,这使得缺失数据的插值较为困难。针对上述问题,文中采用多通道奇异谱分析(Multichannel Singular Spectrum Analysis,MSSA)对时间序列进行粗差探测和缺失数据插值,重建非均匀采样时间序列的连续可靠模型,且不需要关于时间序列的任何先验信息。在该方法中,粗差探测与数据插值同时进行。模拟数据和实测GNSS时间序列数据测试结果都表明该方法的有效性。     

基于时序InSAR的东营地区地表沉降研究

黄河携带大量泥沙入海,河口三角洲覆盖20~50m厚的沉积层。沉积层的自然压实导致该地区的地表沉降。此外,黄河三角洲油气资源的开发和地下水的开采也加速地表的下陷。InSAR作为一种有效的空间大地测量工具,可以提供几十公里范围内的高精度地表形变场。时序InSAR技术在多幅雷达影像组成的干涉网络基础上,识别永久散射体(PS)等雷达回波信噪比高的像素。与传统的InSAR技术相比,时序InSAR技术削弱雷达影像去相干效应的影响,实现长期的形变序列提取。文中利用两组雷达影像:19幅ERS卫星1992年12月至1996年1月的SAR影像,17幅ENVISAT卫星2003年5月至2008年3月的SAR影像。影像主要覆盖山东省东营市及其周边部分区域。结果显示,东营地区存在每年15mm以上的地表沉陷,该地区的地表沉降与油气开采活动有关。     

Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring

Free and open access to the Landsat archive has enabled the implementation of national and global terrestrial monitoring projects. Herein, we summarize a project characterizing the change history of Canada’s forested ecosystems with a time series of data representing 1984–2012. Using the Composite2Change approach, we applied spectral trend analysis to annual best-available-pixel (BAP) surface reflectance image composites produced from Landsat TM and ETM+ imagery. A total of 73,544 images were used to produce 29 annual image composites, generating ～400 TB of interim data products and resulting in ～25 TB of annual gap-free reflectance composites and change products. On average, 10% of pixels in the annual BAP composites were missing data, with 86% of pixels having data gaps in two consecutive years or fewer. Change detection overall accuracy was 89%. Change attribution overall accuracy was 92%, with higher accuracy for stand-replacing wildfire and harvest. Changes were assigned to the correct year with an accuracy of 89%. Outcomes of this project provide baseline information and nationally consistent data source to quantify and characterize changes in forested ecosystems. The methods applied and lessons learned build confidence in the products generated and empower others to develop or refine similar satellite-based monitoring projects.     

基于时序Landsat数据的地理国情监测方法初探

为全面掌握地表自然和人文地理国情信息,基于北京市海淀区1986—2010年间8个不同时相的Landsat数据,分析了海淀区资源与环境要素的时空变化特点,讨论了其中的驱动因素。结果表明:在1986—2010年间,耕地面积减少了45.7%,建设用地面积增加了38.95%,林、草地面积增加了23.44%,水体和其他用地面积变化不明显;城市范围向西、向北方向扩展,在西北旺地区表现得尤为明显;城市质心向西北方向偏移;城市紧凑度逐渐减小,分形指数逐渐增大,反映出城市饱和程度降低,城市边界逐步复杂化,综合土地利用动态度呈先减小后增大的趋势。因此认为,人口的增长、经济的发展、基础设施的建设以及政策法规的制定等,都促进了北京市海淀区资源与环境要素的变化。     

Mapping rubber trees based on phenological analysis of Landsat time series data-sets

This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade (2006–2015) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r² = 0.1358) and secondly between rubber trees and oil palm trees during the growing period (r² = 0.2029). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated.     

基于多源空间数据的河道水域变化监测方法研究

近年来,随着经济社会的快速发展,河道水域占用情况日益增多,严重影响河道防洪、排涝等正常水域功能的发挥。以浙江省省级河道为例,以覆盖浙江全省的高分辨率航片、省级基础地理信息数据等多源空间数据为基础,结合测绘和水利标准,提出基于多源空间数据的河道水域变化监测方法。方法充分利用高精度的基础测绘空间数据,保证成果的可靠性,同时符合水利部门的规定,能够满足水利部门的实际需要。研究结果表明,2008-2012年间,浙江省省级河道水域减少情况严重,主要减少类型为建设用地。方法为水利部门全面、客观地掌握水域的无序占用情况提供了一种有效手段。     

基于时序分析的建筑物沉降数据处理

变形数据分析与预报是变形监测数据处理的重要内容。基于时间序列分析的特性,研究了应用AR模型对建筑物沉降数据进行分析处理和预报的方法步骤,并通过实例计算证明,该模型具有较好的拟合效果和预报精度。     

基于Landsat长时间序列数据估算树高和生物量

以Landsat长时间序列数据为研究对象,旨在以光谱序列信息反演森林参数为视角,应用Landtrendr算法从时间序列数据中提取森林扰动变量,使用随机森林计算方法建立扰动变量、反射率和GLAS激光点森林参数之间的关系模型,获取树高和生物量的空间分布信息。为多源遥感数据反演森林参数提供参考,研究证明基于Landsat长时间序列数据获得的森林扰动变量能够增强反射率和森林参数之间的相关性,可提高预测精度。     

用时间序列分析法进行建筑物沉降观测数据处理的研究

本文将时间序列分析法用于建筑物沉降观测数据处理,给出了用此法进行沉降观测数据处理时模型阶数确定的统计检验方法、模型参数估计以及预报分析的方法,以某建筑物沉降观测第30期的测量成果为例说明应用此法的全过程,并对计算结果进行了分析比较。     

一种新的时间序列变点算法在变形数据分析中的应用

本文在传统的变点分析理论的基础上,针对传统变点处理的方法,提出利用自适应神经网络,在原时间序列的基础上,适当选择滚动窗口的长度d和窗口每次移动的间隔为n0,形成新的序列作为自适应神经网络的输入,对新序列进行分类,得到变点个数和位置,在此基础判断变点的类型。本文用模拟数据和实测滑坡三维变形数据和坝体变形数据对算法的有效性进行了检验。     

基于TM影像的北京城乡结合部土地利用变化分析

利用遥感分类技术能够快速获取土地利用变化信息。基于1996年和2006年两时相的北京城乡结合部地区TM卫星影像数据,采用监督分类和分类后处理方法,对研究区10年间的土地利用变化情况进行了详细分析,得到如下结论：10年间北京城南地区城乡结合部的各种土地利用类型之间相互转化,并以耕地,林地和建设用地相互转化最为显著;耕地和大范围水域面积较大幅度减少,城市居民点及工矿用地和未利用土地面积大幅度地增加,城乡结合部的范围在10年间从北向南进行了大范围地移动。     

Monitoring the evolution of irrigated areas with Landsat images using backward and forward change detection analysis in the Kou watershed,Burkina Faso

The Kou watershed is characterized by important water resources used for drinking, agriculture (especially in the irrigated areas), industry and the preservation of aquatic fauna and flora. For several decades, there has been increasing pressure on the Kou's water resources, partly because of the expansion of the irrigated agricultural areas. This study was conducted to examine this issue, focusing on one specific irrigated area. In order to monitor the expansion of irrigated areas in developing countries, a low-cost remote sensing method based on Landsat images and aerial photographs was developed. The method is based on maximum-likelihood classification, followed by backward and forward change detection analysis requiring agronomic expertise. Using pixel trajectory analysis, the method connects all pixels to their consecutive states in order to correct their current states. The study showed that the irrigated area has expanded by almost 70% over 20 years, with most of this expansion occurring in the past 10 years. The method, if validated, could be used to obtain better information on past occupation in the rural irrigated areas for which there is currently no archived data, making temporal analyses impossible.     

A cloud detection method based on a time series of MODIS surface reflectance images

The Moderate Resolution Imaging Spectroradiometer (MODIS)-Terra surface reflectance product (MOD09A1), with bands 1 to 7, is a gridded, eight-day composite product derived from the MODIS-Terra top of atmosphere reflectance swaths. It performs cloud detection and corrects for the effects of atmospheric gases and aerosols. The cloud mask (CM) algorithms for MODIS are based on empirical thresholds on spectral reflectance and brightness temperature. Since the spatial resolution of the thermal band is 1000 m, while that of MOD09A1 is 500 m, many undetected and false clouds are observed in MOD09A1. These errors always result in temporal and spatial inconsistencies in higher-level products. In this paper, a cloud detection algorithm (TSCD) based on a MOD09A1 time series is introduced. Time series cloud detection (TSCD) algorithm is based on the relative stability of ground reflectance and the sudden variations in reflectance that result from cloud cover. The algorithm first searches the clear-sky reference data, and then discriminates clouded and unclouded pixels by detecting a sudden change of reflectance in the blue wavelength and spectral correlation coefficient at the pixel level. Compared with cloud cover assessments obtained from MODIS' original CM, TSCD provides similar or better discrimination in most situations when the land surface changes slowly.     

Quantifying shoreline change in Funafuti Atoll,Tuvalu using a time series of Quickbird,Worldview and Landsat data

Funafuti Atoll, Tuvalu is located in the southwestern Pacific Ocean, which has experienced some of the highest rates of global sea-level rise over the past 60 years. Atoll islands are low-lying accumulations of reef-derived sediment that provide the only habitable land in Tuvalu, and are considered vulnerable to the myriad possible impacts of climate change, especially sea-level rise. This study examines the shoreline change of twenty-eight islands in Funafuti Atoll between 2005 and 2015 using 0.65 m QuickBird, 0.46 m WorldView-2, and 0.31 m WorldView-3 imagery using an image segmentation and decision tree classification. Shoreline change estimates are compared to previous study that used a visual interpretation approach. The feasibility of estimating island area with Landsat-8 Operational Land Imager (OLI) data is explored using CLASlite software. Results indicate a 0.13% (0.35 ha) decrease in net island area over the study time period, with 13 islands decreasing in area and 15 islands increasing in area. Substantial decreases in island area occurred on the islands of Fuagea, Tefala and Vasafua, which coincides with the timing of Cyclone Pam in March, 2015. Comparison between the WorldView-2 shoreline maps and those created from Landstat-8 indicate that the estimates tend to be in higher agreement for islands that have an area > 0.5 ha, a compact shape, and no built structures. Ten islands had > 90% agreement, with percent disagreements ranging from 2.78 to 100%. The methods and results of this study speak to the potential of automated EoV shoreline monitoring through segmentation and classification tree approach, which would reduce down data processing and analysis time. With the growing constellation of high and medium spatial resolution satellite-based sensors and the development of semi or fully automated image processing technology, it is now possible to remotely assess the short and medium-term shoreline dynamics on dynamic atolls. Landsat estimates were reasonably matched to those derived from fine resolution imagery, with some caveats about island size and shape.     

基于嵌入式GIS的内外业一体化土地变更调查技术研究

针对目前土地变更调查中"直接外业调绘"方法的内外业分离的缺点,本文提出了将GPS、PDA、Arc-PAD三者相结合用于土地变更调查的模式,在需求分析、逻辑结构设计的基础上,提出了适合PDA存储的土地变更数据结构,研究了系统实现的开发方法,并进一步探讨了GPS采集时需要解决的坐标转换问题的解决方法。本文的研究对于改变现行变更调查模式,提高工作效率,减少因内外业分离引起的错误等进行了有益的探索。