Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.     

Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data

Wildfire is an important disturbance agent in Canada's boreal forest. Optical remotely sensed imagery (e.g., Landsat TM/ETM+), is well suited for capturing horizontally distributed forest conditions, structure, and change, while Light Detection and Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to characterize post-fire conditions. The objective of this study is to compare changes in forest structure, as measured with a discrete return profiling LIDAR, to post-fire conditions, as measured with remotely sensed data. Our research is focused on a boreal forest fire that occurred in May 2002 in Alberta, Canada. The Normalized Burn Ratio (NBR), the differenced NBR (dNBR), and the relative dNBR (RdNBR) were calculated from two dates of Landsat data (August 2001 and September 2002). Forest structural attributes were derived from two spatially coincident discrete return LIDAR profiles acquired in September 1997 and 2002 respectively. Image segmentation was used to produce homogeneous spatial patches analogous to forest stands, with analysis conducted at this patch level.In this study area, which was relatively homogenous and dominated by open forest, no statistically significant relationships were found between pre-fire forest structure and post-fire conditions (r < 0.5; p > 0.05). Post-fire forest structure and absolute and relative changes in forest structure were strongly correlated to post-fire conditions (r ranging from − 0.507 to 0.712; p < 0.0001). Measures of vegetation fill (VF) (LIDAR capture of cross-sectional vegetation amount), post-fire and absolute change in crown closure (CC), and relative change in average canopy height, were most useful for characterizing post-fire conditions. Forest structural attributes generated from the post-fire LIDAR data were most strongly correlated to post-fire NBR, while dNBR and RdNBR had stronger correlations with absolute and relative changes in the forest structural attributes. Absolute and relative changes in VF and changes in CC had the strongest positive correlations with respect to dNBR and RdNBR, ranging from 0.514 to 0.715 (p < 0.05). Measures of average inter-tree distance and volume were not strongly correlated to post-fire NBR, dNBR, or RdNBR. No marked differences were found in the strength or significance of correlations between post-fire structure and the post-fire NBR, dNBR, RdNBR, indicating that for the conditions present in this study area all three burn severity indices captured post-fire conditions in a similar manner. Finally, the relationship between post-fire forest structure and post-fire condition was strongest for dense forests (> 60% crown closure) compared to open (26-60%) and sparse forests (10-25%). Forest structure information provided by LIDAR is useful for characterizing post-fire conditions and burn induced structural change, and will complement other attributes such as vegetation type and moisture, topography, and long-term weather patterns, all of which will also influence variations in post-fire conditions.     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes

Accurate landscape-scale maps of forests and associated disturbances are critical to augment studies on biodiversity, ecosystem services, and the carbon cycle, especially in terms of understanding how the spatial and temporal complexities of damage sustained from disturbances influence forest structure and function. Vegetation change tracker (VCT) is a highly automated algorithm that exploits the spectral-temporal properties of summer Landsat time series stacks (LTSSs) to generate spatially explicit maps of forest and recent forest disturbances. VCT performs well in contiguous forest landscapes with closed or nearly closed canopies, but often incorrectly classifies large patches of land as forest or forest disturbance in the complex and spatially heterogeneous environments that typify fragmented forest landscapes. We introduce an improved version of VCT (dubbed VCTw) that incorporates a nonforest mask derived from snow-covered winter Landsat time series stacks (LTSSw) and compare it with VCT across nearly 25 million ha of land in the Lake Superior (Canada, USA) and Lake Michigan (USA) drainage basins.Accuracy assessments relying on 87 primary sampling units (PSUs) and 2640 secondary sampling units (SSUs) indicated that VCT performed with an overall accuracy of 86.3%. For persisting forest, the commission error was 14.7% and the omission error was 4.3%. Commission and omission errors for the two forest disturbance classes fluctuated around 50%. VCTw produced a statistically significant increase in overall accuracy to 91.2% and denoted about 1.115 million ha less forest (− .371 million ha disturbed and − 0.744 million ha persisting). For persisting forest, the commission error decreased to 9.3% and the omission error was relatively unchanged at 5.0%. Commission errors decreased considerably to near 22% and omission errors remained near 50% in both forest disturbance classes.Dividing the assessments into three geographic strata demonstrated that the most dramatic improvement occurred across the southern half of the Lake Michigan basin, which contains a highly fragmented agricultural landscape and relatively sparse deciduous forest, although substantial improvements occurred in other geographic strata containing little agricultural land, abundant wetlands, and extensive coniferous forest. Unlike VCT, VCTw also generally corresponded well with field-based estimates of forest cover in each stratum. Snow-covered winter imagery appears to be a valuable resource for improving automated disturbance mapping accuracy. About 34% of the world's forests receive sufficient snowfall to cover the ground and are potentially suitable for VCTw; other season-based techniques may be worth pursuing for the remaining 66%.     

Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

We introduce and test LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery), a new approach to extract spectral trajectories of land surface change from yearly Landsat time-series stacks (LTS). The method brings together two themes in time-series analysis of LTS: capture of short-duration events and smoothing of long-term trends. Our strategy is founded on the recognition that change is not simply a contrast between conditions at two points in time, but rather a continual process operating at both fast and slow rates on landscapes. This concept requires both new algorithms to extract change and new interpretation tools to validate those algorithms. The challenge is to resolve salient features of the time series while eliminating noise introduced by ephemeral changes in illumination, phenology, atmospheric condition, and geometric registration. In the LandTrendr approach, we use relative radiometric normalization and simple cloud screening rules to create on-the-fly mosaics of multiple images per year, and extract temporal trajectories of spectral data on a pixel-by-pixel basis. We then apply temporal segmentation strategies with both regression-based and point-to-point fitting of spectral indices as a function of time, allowing capture of both slowly-evolving processes, such as regrowth, and abrupt events, such as forest harvest. Because any temporal trajectory pattern is allowable, we use control parameters and threshold-based filtering to reduce the role of false positive detections. No suitable reference data are available to assess the role of these control parameters or to test overall algorithm performance. Therefore, we also developed a companion interpretation approach founded on the same conceptual framework of capturing both long and short-duration processes, and developed a software tool to apply this concept to expert interpretation and segmentation of spectral trajectories (TimeSync, described in a companion paper by Cohen et al., 2010). These data were used as a truth set against which to evaluate the behavior of the LandTrendr algorithms applied to three spectral indices. We applied the LandTrendr algorithms to several hundred points across western Oregon and Washington (U.S.A.). Because of the diversity of potential outputs from the LTS data, we evaluated algorithm performance against summary metrics for disturbance, recovery, and stability, both for capture of events and longer-duration processes. Despite the apparent complexity of parameters, our results suggest a simple grouping of parameters along a single axis that balances the detection of abrupt events with capture of long-duration trends. Overall algorithm performance was good, capturing a wide range of disturbance and recovery phenomena, even when evaluated against a truth set that contained new targets (recovery and stability) with much subtler thresholds of change than available from prior validation datasets. Temporal segmentation of the archive appears to be a feasible and robust means of increasing information extraction from the Landsat archive.     

Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization

Forest dynamics are characterized by both continuous (i.e., growth) and discontinuous (i.e., disturbance) changes. Change detection techniques that use optical remotely sensed data to capture disturbance related changes are established and commonly applied; however, approaches for the capture of continuous forest changes are less mature. Optical remotely sensed imagery is well suited for capturing horizontally distributed conditions, structures, and changes, while Light Detection And Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to fully characterize forest canopy attributes and dynamics.The study described in this paper captures forest conditions along a corridor approximately 600 km long through the boreal forest of Canada. Two coincident LIDAR transects, representing 1997 and 2002 forest conditions respectively, are compared using image segments generated from Landsat ETM+ imagery. The image segments are used to provide a spatial framework within which the attributes and temporal dynamics of the forest canopy are estimated and compared. Segmented and classified Landsat imagery provides a context for the comparison of sufficiently spatially related LIDAR profiles and for the provision of categories to aid in the application of empirical models requiring knowledge of land cover.Global and local approaches were employed for characterizing changes in forest attributes over time. The global approach, emphasized the overall trend in forest change along the length of the entire transect, and indicated that key canopy attributes were stable, and transect characteristics, including forest canopy height, did not change significantly over the five-year period of this study (two sample t-test, p = 0.08). The local approach analyzed segment-based changes in canopy attributes, providing spatially explicit indications of forest growth and depletion. The local approach identified that 84% of the Landsat segments intercepted by both LIDAR transects either have no change, or have a small average increase in canopy height (0.7 m), while the other 16% of segments have an average decrease in canopy height of 1.6 m. As expected, the difference in the magnitude of the changes was markedly greater for depletions than it was for growth, but was less spatially extensive. Growth tends to occur incrementally over broad areas; whereas, depletions are dramatic and spatially constrained. The approach presented holds potential for investigating the impacts of climate change across a latitudinal gradient of boreal forest.     

Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation

Availability of free, high quality Landsat data portends a new era in remote sensing change detection. Using dense (~ annual) Landsat time series (LTS), we can now characterize vegetation change over large areas at an annual time step and at the spatial grain of anthropogenic disturbance. Additionally, we expect more accurate detection of subtle disturbances and improved characterization in terms of both timing and intensity. For Landsat change detection in this new era of dense LTS, new detection algorithms are required, and new approaches are needed to calibrate those algorithms and to examine the veracity of their output. This paper addresses that need by presenting a new tool called TimeSync for syncing algorithm and human interpretations of LTS. The tool consists of four components: (1) a chip window within which an area of user-defined size around an area of interest (i.e., plot) is displayed as a time series of image chips which are viewed simultaneously, (2) a trajectory window within which the plot spectral properties are displayed as a trajectory of Landsat band reflectance or index through time in any band or index desired, (3) a Google Earth window where a recent high-resolution image of the plot and its neighborhood can be viewed for context, and (4) an Access database where observations about the LTS for the plot of interest are entered. In this paper, we describe how to use TimeSync to collect data over forested plots in Oregon and Washington, USA, examine the data collected with it, and then compare those data with the output from a new LTS algorithm, LandTrendr, described in a companion paper (Kennedy et al., 2010). For any given plot, both TimeSync and LandTrendr partitioned its spectral trajectory into linear sequential segments. Depending on the direction of spectral change associated with any given segment in a trajectory, the segment was assigned a label of disturbance, recovery, or stable. Each segment was associated with a start and end vertex which describe its duration. We explore a variety of ways to summarize the trajectory data and compare those summaries derived from both TimeSync and LandTrendr. One comparison, involving start vertex date and segment label, provides a direct linkage to existing change detection validation approaches that rely on contingency (error) matrices and kappa statistics. All other comparisons are unique to this study, and provide a rich set of means by which to examine algorithm veracity. One of the strengths of TimeSync is its flexibility with respect to sample design, particularly the ability to sample an area of interest with statistical validity through space and time. This is in comparison to the use of existing reference data (e.g., field or airphoto data), which, at best, exist for only parts of the area of interest, for only specific time periods, or are restricted thematically. The extant data, even though biased in their representation, can be used to ascertain the veracity of TimeSync interpretation of change. We demonstrate that process here, learning that what we cannot see with TimeSync are those changes that are not expressed in the forest canopy (e.g., pre-commercial harvest or understory burning) and that these extant reference datasets have numerous omissions that render them less than desirable for representing truth.     

A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests

Insects are important forest disturbance agents, and mapping their effects on tree mortality and surface fuels represents a critical research challenge. Although various remote sensing approaches have been developed to monitor insect impacts, most studies have focused on single insect agents or single locations and have not related observed changes to ground-based measurements. This study presents a remote sensing framework to (1) characterize spectral trajectories associated with insect activity of varying duration and severity and (2) relate those trajectories to ground-based measurements of tree mortality and surface fuels in the Cascade Range, Oregon, USA. We leverage a Landsat time series change detection algorithm (LandTrendr), annual forest health aerial detection surveys (ADS), and field measurements to investigate two study landscapes broadly applicable to conifer forests and dominant insect agents of western North America. We distributed 38 plots across multiple forest types (ranging from mesic mixed-conifer to xeric lodgepole pine) and insect agents (defoliator [western spruce budworm] and bark beetle [mountain pine beetle]). Insect effects were evident in the Landsat time series as combinations of both short- and long-duration changes in the Normalized Burn Ratio spectral index. Western spruce budworm trajectories appeared to show a consistent temporal evolution of long-duration spectral decline (loss of vegetation) followed by recovery, whereas mountain pine beetle plots exhibited both short- and long-duration spectral declines and variable recovery rates. Although temporally variable, insect-affected stands generally conformed to four spectral trajectories: short-duration decline then recovery, short- then long-duration decline, long-duration decline, long-duration decline then recovery. When comparing remote sensing data with field measurements of insect impacts, we found that spectral changes were related to cover-based estimates (tree basal area mortality [R²_adj = 0.40, F_1,34 = 24.76, P < 0.0001] and down coarse woody detritus [R²_adj = 0.29, F_1,32 = 14.72, P = 0.0006]). In contrast, ADS changes were related to count-based estimates (e.g., ADS mortality from mountain pine beetle positively correlated with ground-based counts [R²_adj = 0.37, F_1,22 = 14.71, P = 0.0009]). Fine woody detritus and forest floor depth were not well correlated with Landsat- or aerial survey-based change metrics. By characterizing several distinct temporal manifestations of insect activity in conifer forests, this study demonstrates the utility of insect mapping methods that capture a wide range of spectral trajectories. This study also confirms the key role that satellite imagery can play in understanding the interactions among insects, fuels, and wildfire.     

An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks

A highly automated algorithm called vegetation change tracker (VCT) has been developed for reconstructing recent forest disturbance history using Landsat time series stacks (LTSS). This algorithm is based on the spectral-temporal properties of land cover and forest change processes, and requires little or no fine tuning for most forests with closed or near close canopy cover. It was found very efficient, taking 2-3 h on average to analyze an LTSS consisting of 12 or more Landsat images using an average desktop PC. This LTSS-VCT approach has been used to examine disturbance patterns with a biennial temporal interval from 1984 to 2006 for many locations across the conterminous U.S. Accuracy assessment over 6 validation sites revealed that overall accuracies of around 80% were achieved for disturbances mapped at individual year level. Average user's and producer's accuracies of the disturbance classes were around 70% and 60% in 5 of the 6 sites, respectively, suggesting that although forest disturbances were typically rare as compared with no-change classes, on average the VCT detected more than half of those disturbances with relatively low levels of false alarms. Field assessment revealed that VCT was able to detect most stand clearing disturbance events, including harvest, fire, and urban development, while some non-stand clearing events such as thinning and selective logging were also mapped in western U.S. The applicability of the LTSS-VCT approach depends on the availability of a temporally adequate supply of Landsat imagery. To ensure that forest disturbance records can be developed continuously in the future, it is necessary to plan and develop observational capabilities today that will allow continuous acquisition of frequent Landsat or Landsat-like observations.     

基于时间序列和任务调度的Web数据聚类算法

为了实现Web服务请求数据的快速聚类,并提高聚类的准确率,提出一种基于增量式时间序列和任务调度的Web数据聚类算法,该算法进行了Web数据在时间序列上的聚类定义,并采用增量式时间序列聚类方法,通过数据压缩的形式降低Web数据的复杂性,进行基于服务时间相似性的时间序列数据聚类。针对Web集群服务的最佳服务任务调度问题,通过以服务器执行能力为标准来分配服务任务。实验仿真结果表明,相比基于网格的高维数据层次聚类算法和基于增量学习的多目标模糊聚类算法,提出的算法在聚类时间、聚类精度、服务执行成功率上均获得了更好的效果。     

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.     

Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data

During the period 1973-1999, the effects of air pollution on the terrestrial ecosystems in the border areas of Norway and Russia have been investigated by the use of satellite remote sensing. Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data, in combination with field work data collected in the period 1970-2000, were applied to produce land cover maps for seven different years, with an overall accuracy of 75-83%. On the basis of this monitoring, we can conclude that the main effect of air pollution was that the areas of lichen (Cladina spp.)-dominated forests and mountain heaths were reduced from 37% in 1973 to 10% in 1994, followed by a slight increase to 12% in 1999. The lichen-dominated vegetation types were changed into barrens, partly damaged vegetation entities, and dwarf shrub (e.g., Vaccinium myrtillus)-dominated vegetation. We found a significant negative relationship (r=−0.94, p=0.001) between the extent of the area of mixed forests with lichen content and the emissions of SO₂ during the period. The area of the category industrial barrens had a significant negative relationship (r=−0.95, p=0.001) to the emissions of SO₂ during the same period.     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

Using a model of cellular automata and classification methods for prediction of time series with memory

The paper deals with the problem of prediction of time series with memory for which classical prediction methods are frequently inadequate. A method is proposed that is based on a model of cellular automata, classification methods, and fuzzy set theory. The accuracy of models based on this method is estimated. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 43–54, November–December 2006.     

Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain)

The Ayora region, situated about 60 km southwest of the city of Valencia/Spain, was chosen to demonstrate pathways of characterizing fire events and post-fire succession in Mediterranean ecosystems using multi-temporal satellite imagery. A corresponding time series of 6 Landsat MSS, 13 Landsat-5 TM and 1 Landsat-7 ETM + images, covering the period 1975—2000, was processed to account for geometric and radiometric distortions as well as sensor calibration. Spectral Mixture Analysis was applied to derive estimates of photosynthetic active green vegetation cover as a primary indicator.A combination of pixel-based linear trend analysis and diachronic thresholding was employed to procure a fire perimeter data base and characterize post-fire dynamics based on a temporally stratified trend analysis. The results were integrated with auxiliary information to evaluate driving factors and further interpreted in relation to field-based information on plant communities and post-fire succession patterns.Reflecting typical recovery phases with an initial establishment of grasses and herbaceous species, followed by a gradual development of a shrub layer and the subsequent colonization of tree individuals, temporal trajectories could be described by exponential functions and were related to plot-based botanical information.     

BP神经网络进行时间序列预测的不足及改进

BP算法是应用广泛的神经网络算法,具有较强的非线性拟合能力,可以用来预测非线性时间序列数据的发展趋势,在实际应用和仿真过程中,由于算法本身的限制和不足,对于仿真和计算都会带来这样那样的问题,比如网络训练过程中程序异常中止、训练时间过长、仿真精度不高等.针对这样的情况,通过分析算法本身和训练仿真过程,找到了相应的原因和解决方法,并通过完善训练过程,使问题得到了一定程度的改善.最后通过在Matlab仿真环境下的实际仿真过程,验证了改善效果.     

Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat

Remote sensing of forest vertical structure is possible with lidar data, but lidar is not widely available. Here we map tropical dry forest height (RMSE = 0.9 m, R² = 0.84, range 0.6-7 m), and we map foliage height profiles, with a time series of Landsat and Advanced Land Imager (ALI) imagery on the island of Eleuthera, The Bahamas, substituting time for vertical canopy space. We also simultaneously map forest disturbance type and age. We map these variables in the context of avian habitat studies, particularly for wintering habitat of an endangered Nearctic-Neotropical migrant bird, the Kirtland's Warbler (Dendroica kirtlandii). We also illustrate relationships between forest vertical structure, disturbance type and counts of forage species important to the Kirtland's Warbler. The ALI imagery and the Landsat time series are both critical to the result for forest height, which the strong relationship of forest height with disturbance type and age facilitates. Also unique to this study is that seven of the eight image time steps are cloud-cleared images: mosaics of the clear parts of several cloudy scenes. We created each cloud-cleared image, including a virtually seamless ALI image mosaic, with regression tree normalization. We also illustrate how viewing time series imagery as red-green-blue composites of tasseled cap wetness (RGB wetness composites) aids reference data collection for classifying tropical forest disturbance type and age. Our results strongly support current Landsat Program production of co-registered imagery, and they emphasize the value of seamless time series of cloud-cleared imagery.     

时间序列分析法用于常减压炼油装置测量数据校正

提高常减压炼油装置过程数据校正精度的有效手段是增加测量数据的冗余。通过对影响测量数据校正的因素进行分析,建立了时序平均值模型,提出了使用时间序列分析法来增加数据校正过程时间冗余的新方法,可在不增加测量点的前提下改善校正数据精度。采用常减压炼油生产装置的实际测量数据,对时间序列分析法的过失误差侦破能力、数据校正精度受时域值、过失误差大小的影响等进行了研究,探讨了时序法的可行性和实用性。结果表明,时序法可以快速有效地用于该装置的实时测量数据校正。