Digital Photograph Analysis for Measuring Percent Plant Cover in the Arctic

Long-term satellite remote sensing data, when properly calibrated and validated against ground monitoring, could provide valuable data sets for assessing climate change impacts on ecosystems, wildlife, and other important aspects of life in the Arctic. Percent plant cover is ideal for seasonal and long-term ground monitoring because it can be observed non-destructively and is closely related to other key ecosystem variables, such as biomass and leaf area index (LAI). Accurately measuring percent plant cover in the Arctic, however, has been a challenge. Advances in digital photography and imageprocessing techniques have provided the potential to measure vegetation cover accurately. In this paper we report an adapted method for quantifying percent plant cover based on plot digital photograph classification (PDPC). In this digital image analysis, the red, green, and blue image channels and the intensity, hue, and saturation image channels were used together to ensure more accurate cover measurement and labeling of plant species. We evaluated the accuracy of the PDPC method and two other techniques, visual estimate and digital grid overlay, by testing against artificial plots with known percent cover, by comparing with destructively measured LAI, and by comparing results of the three methods. Our evaluation indicates that the PDPC method is the most accurate. In addition, PDPC has the advantages of being objective, quick in the field, and suitable for measuring percent plant cover in the Arctic at the level of functional types or species groups.Lorsqu'elles sont bien calibrées et qu'elles sont validées contre le dépistage terrestre, les données résultant de la télédétection satellitaire à long terme pourraient fournir d'importants ensembles de données en vue de l'évaluation des incidences du changement climatique sur les écosystèmes, la faune et d'autres aspects-clés de la vie dans l'Arctique. Le pourcentage de couverture végétale est idéal pour le dépistage saisonnier et le dépistage terrestre à long terme parce qu'il peut être observé sans qu'il n'y ait de destruction et parce qu'il est étroitement lié à d'autres variables-clés se rapportant aux écosystèmes, comme la biomasse et l'indice de surface foliaire (ISF). Toutefois, dans l'Arctique, la mesure exacte du pourcentage de couverture végétale représente un défi. Les progrès réalisés dans les domaines de la photographie numérique et des techniques de traitement d'images fournissent la possibilité de mesurer la couverture végétale avec précision. Dans cette communication, nous faisons état d'une méthode adaptée permettant de quantifier le pourcentage de couverture végétale en fonction de la classification de photographies numériques de parcelles. Dans le cadre de l'analyse d'images numériques, les canaux rouges, verts et bleus des images ainsi que les canaux d'intensité, de tonalité et de saturation des images ont été utilisés pour donner lieu à la mesure plus exacte de la couverture végétale et à l'étiquetage des espèces végétales. Nous avons évalué l'exactitude de la méthode de classification de photographies numériques de parcelles de même que celle de deux autres techniques, soit l'estimation visuelle et la superposition de grilles numériques en faisant des essais à la lumière de parcelles artificielles dont le pourcentage de couverture végétale était connu et en les comparant avec des ISF mesurés de manière destructive, puis en comparant les résultats des trois méthodes. Selon notre évaluation, la méthode consistant en la classification de photographies numériques de parcelles PDPC est la plus précise. La classification de photographies numériques de parcelles a également l'avantage d'être objective, d'être rapide sur le terrain et de se prêter à la mesure du pourcentage de couverture végétale dans l'Arctique en ce qui a trait aux types fonctionnels ou aux groupements d'espèces

Erosion-deposition patterns and depo-center movements in branching channels at the near-estuary reach of the Yangtze River

Channel evolution and depo-center migrations in braided reaches are significantly influenced by variations in runoff. This study examines the effect of runoff variations on the erosion-deposition patterns and depocenter movements within branching channels of the near-estuary reach of the Yangtze River. We assume that variations in annual mean duration days of runoff discharges, ebb partition ratios in branching channels, and the erosional/depositional rates of entire channels and sub-reaches are representative of variations in runoff intensity, flow dynamics in branching channels, and morphological features in the channels. Our results show that the north region of Fujiangsha Waterway, the Liuhaisha branch of Rugaosha Waterway, the west branch of Tongzhousha Waterway, and the west branch of Langshansha Waterway experience deposition or reduced erosion under low runoff intensity, and erosion or reduced deposition under high runoff intensity, with the depocenters moving upstream and downstream, respectively. Other waterway branches undergo opposite trends in erosion-deposition patterns and depo-center movements as the runoff changes. These morphological changes may be associated with trends in ebb partition ratio as the runoff discharge rises and falls. By flattening the intra-annual distribution of runoff discharge, dam construction in the Yangtze Basin has altered the ebb partition ratios in waterway branches, affecting their erosion-deposition patterns and depo-center movements. Present trends are likely to continue into the future due to the succession of large cascade dams under construction along the upper Yangtze and ongoing climate change

Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers

We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1,071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI)

Hydrologic Processes of Forested Headwater Watersheds Across a Physiographic Gradient in the Southeastern United States

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

Dark Matter in the Singlet Extension of MSSM: Explanation of Pamela and Implication on Higgs Phenomenology

As discussed recently by Hooper and Tait, the singlino-like dark matter in the Minimal Supersymmetric Standard Model (MSSM) extended by a singlet Higgs superfield can give a perfect explanation for both the relic density and the Pamela result through the Sommerfeld-enhanced annihilation into singlet Higgs bosons ($a$ or $h$ followed by $h->a a$) with $a$ being light enough to decay dominantly to muons or electrons. In this work we analyze the parameter space required by such a dark matter explanation and also consider the constraints from the LEP experiments. We find that although the light singlet Higgs bosons have small mixings with the Higgs doublets in the allowed parameter space, their couplings with the SM-like Higgs boson $h_{SM}$ (the lightest doublet-dominant Higgs boson) can be enhanced by the soft parameter $A_\kappa$ and, in order to meet the stringent LEP constraints, the $h_{SM}$ tends to decay into the singlet Higgs pairs $aa$ or $hh$ instead of $b\bar b$. So the $h_{SM}$ produced at the LHC will give a multi-muon signal, h_{SM} -> aa -> 4 muons or h_{SM} -> hh -> 4 a -> 8 muons.Comment: Version in JHE

Loop effects and non-decoupling property of SUSY QCD in gb→tH−g b\to tH^{-}

One-loop SUSY QCD radiative correction to $gb \to tH^{-}$ cross section is calculated in the Minimal Supersymmetric Standard Model. We found that SUSY QCD is non-decoupling if the gluino mass and the parameter $\mu$, $A_t$ or $A_b$ are at the same order and get large. The non-decoupling contribution can be enhanced by large $\tan\beta$ and therefore large corrections to the hadronic production rates at the Tevatron and LHC are expected in the large $\tan\beta$ limit. The fundamental reason for such non-decoupling behavior is found to be some couplings in the loops being proportional to SUSY mass parameters.Comment: 15 pages, 5 PS figures. A proof of non-decouplings of SUSY-QCD, Comments on corresponding QCD correction and references adde

Can MSSM with light sbottom and light gluino survive Z-peak constraints ?

In the framework of minimal supersymmetric model we examine the Z-peak constraints on the scenario of one light sbottom (2--5.5 GeV) and light gluino (12--16 GeV), which has been successfully used to explain the excess of bottom quark production in hadron collision. Such a scenario is found to be severely constrained by LEP Z-peak observables, especially by R_b, due to the large effect of gluino-sbottom loops. To account for the R_b data in this scenario, the other mass eigenstate of sbottom, i.e., the heavier one, must be lighter than 125 (195) GeV at 2-sigma (3-sigma) level, which should have been produced in association with the lighter one at LEP II and will probobaly be within the reach of Tevatron Run 2.Comment: discussion on SUSY-EW effects added, to appear in PR