关于图的升分解研究的进展

１９８７年阿拉维（ＡｌａｖｉＹｏｕｓｅｆ）等人定义了图的一种新分解，即“升分解”（ＡｓｃｅｎｄｉｎｇＳｕｂｇｒａｐｈＤｅｃｏｍｐｏｓｉｔｉｏｎ），并且猜想：任意有正数条边的图都可升分解．该文综述了升分解问题研究的进展情况。

Spatio-temporal Variations in Plantation Forests'Disturbance and Recovery of Northern Guangdong Province Using Yearly Landsat Time Series Observations(1986-2015)

Forest disturbance plays a vital role in modulating carbon storage,biodiversity and climate change.Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study.A Landsat time series stack(LTSS) was fed to the vegetation change tracker model(VCT) to map long-term changes in plantation forests' disturbance and recovery,followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations,magnitudes and rates of plantations' disturbance and recovery.And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km^2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%.A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%.Forest disturbance rates' change trend was characterized by a decline first,followed by an increase,then giving way to a decline again.An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001,disturbance rate of 4.51% in 1994 was a notable anomaly,while after 2001 there was a sharp ascending change,forest disturbance rate spiked in 2007(5.84%).After that,there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015(2.59%).Two obvious spikes in post-disturbance recovery rates occurred in 1995(0.26%) and 2008(0.41%).Overall,forest recovery rates were lower than forest disturbance rates.Moreover,forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery(LandT rendr) algorithms in Fogang County have been conducted,with LandT rendr finding mostly much more disturbance than VCT.Overall,disturbances and recoveries in northern Guangdong were triggered mostly by timber needs,policies and decisions of the local governments.This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.

Using multispectral landsat and sentinel-2 satellite data to investigate vegetation change at Mount St. Helens since the great volcanic eruption in 1980

Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. WilcoxRank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes-only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.

Review of Shadow Detection and De-shadowing Methods in Remote Sensing

Shadow is one of the major problems in remotely sensed imagery which hampers the accuracy of information extraction and change detection.In these images,shadow is generally produced by different objects,namely,cloud,mountain and urban materials.The shadow correction process consists of two steps:detection and de-shadowing.This paper reviews a range of techniques for both steps,focusing on urban regions(urban shadows),mountainous areas(topographic shadow),cloud shadows and composite shadows.Several issues including the problems and the advantages of those algorithms are discussed.In recent years,thresholding and recovery techniques have become important for shadow detection and de-shadowing,respectively.Research on shadow correction is still an important topic,particularly for urban regions(in high spatial resolution data) and mountainous forest(in high and medium spatial resolution data).Moreover,new algorithms are needed for shadow correction,especially given the advent of new satellite images.

Evaluation on the Two Filling Functions for the Recovery of Forest Information in Mountainous Shadows on Landsat ETM + Image

In general,topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images.In this paper,information in shadow was synthesized by using two filling techniques,namely,roifill and imfill,in order to improve the accuracy of forest mapping over mountainous regions.These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County,Zhejiang Province,China.The performance of these methods was compared with two conventional techniques,including cosine correction and multisource classification.The results showed that by applying filling approaches,average overall accuracy of classification was improved by 14 percent.However,through conventional methods this value increased only by 9 percent.The results also revealed that estimated forest area on the basis of shadow-corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms.Apart from this finding,our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions.

Forest-height inversion using repeat-pass spaceborne polInSAR data

Forest-height inversion using airborne double-antenna synthetic aperture radar(SAR)systems has been widely researched,leading to increasing accuracy.Polarimetric SAR Interferometry(PolInSAR)data from spaceborne single-antenna SAR systems,which are influenced by temporal decorrelation,have difficulty inverting forest height.Given the temporal decorrelation effect,the classical random volume over ground(RVoG)model has been proven to invert forest height with significant errors,using repeat-pass PolInSAR data.In consideration of this problem,the temporal decorrelation RVoG(TD-RVoG;based on the RVoG)model was proposed.In this study,an improved TD-RVoG model is presented,with a new temporal decorrelation function.Compared with TD-RVoG,the new model has fewer unknown parameters and can be applied in a three-stage inversion procedure.Validity of the new model is demonstrated by Advanced Land Observing Satellite/Phased Array type L-band SAR(ALOS/PALSAR)data.Results show that the improved TD-RVoG has better accuracy,with inversion error less than 1.5m.