Dataset Information

Regional scale high resolution ?18O prediction in precipitation using MODIS EVI.

ABSTRACT: The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict ?(18)O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated ?(18)O are highly correlated and thus the EVI is a good predictor of precipitated ?(18)O. We then test the predictability of our EVI-?(18)O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale ?(18)O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and ?(18)O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape.

SUBMITTER: Chan WP

PROVIDER: S-EPMC3446878 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.

Chan Wei-Ping WP Yuan Hsiao-Wei HW Huang Cho-Ying CY Wang Chung-Ho CH Lin Shou-De SD Lo Yi-Chen YC Huang Bo-Wen BW Hatch Kent A KA Shiu Hau-Jie HJ You Cheng-Feng CF Chang Yuan-Mou YM Shen Sheng-Feng SF

PloS one 20120919 9

The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develo ...[more]

PMID: 23029053

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Project description:African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 =0.79, relative Root Mean Square Error, rRMSE=1.9%) and tree cover (R2 =0.78, rRMSE=0.3%). EVI provided the best model for shrub density (R2 =0.82) and shrub cover (R2 =0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 =0.76), shrubs (R2 =0.83), and grass (R2 =0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Project description:OBJECTIVES:We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP). MATERIALS AND METHODS:Thirty-three patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of 2 thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence-based guidelines for computed tomography diagnosis of UIP. Two cardiothoracic fellows with 1 year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple-detector computed tomography images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multidirectional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes. RESULTS:We compared the predictions on the basis of regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the 2 fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic. CONCLUSIONS:Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its associated risks.

Dataset Information

Regional scale high resolution ?18O prediction in precipitation using MODIS EVI.

Publications

Regional scale high resolution δ18O prediction in precipitation using MODIS EVI.

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