Petr Votava

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities. D 2002 Published by Elsevier Science Inc.

Large seasonal swings in leaf area of Amazon rainforests

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation–atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of ≈25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.

Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine

Application of remote sensing data to extrapolate evapotranspiration (ET) measured at eddy covariance flux towers is a potentially powerful method to estimate continental-scale ET. In support of this concept, we used meteorological and flux data from the AmeriFlux network and an inductive machine learning technique called support vector machine (SVM) to develop a predictive ET model. The model was then applied to the conterminous U.S. In this process, we first trained the SVM to predict 2000-2002 ET measurements from 25 AmeriFlux sites using three remotely sensed variables [land surface temperature, enhanced vegetation index (EVI), and land cover] and one ground-measured variable (surface shortwave radiation). Second, we evaluated the model performance by predicting ET for 19 flux sites in 2003. In this independent evaluation, the SVM predicted ET with a root-mean-square error (rmse) of 0.62 mm/day (approximately 23% of the mean observed values) and an R2 of 0.75. The rmse from SVM was significantly smaller than that from neural network and multiple-regression approaches in a cross-validation experiment. Among the explanatory variables, EVI was the most important factor. Indeed, removing this variable induced an rmse increase from 0.54 to 0.77 mm/day. Third, with forcings from remote sensing data alone, we used the SVM model to predict the spatial and temporal distributions of ET for the conterminous U.S. for 2004. The SVM model captured the spatial and temporal variations of ET at a continental scale

Terrestrial Observation and Prediction System: integration of satellite and surface weather observations with ecosystem models

Satellite data are widely used in land surface models to compute carbon and water exchange processes. However, much of this work is retrospective in nature. To better represent current land surface conditions in weather/climate models or to provide timely information on ecosystem conditions for natural resource management, one must move from retrospective to real-time analysis. A number of advances allow us to develop a system that would allow such real-time assimilation. These include consistent and timely availability of land surface products from EOS/MODIS, and on-line availability of weather data from a number or surface weather stations. We have developed a data assimilation system, terrestrial observation and prediction system, that integrates satellite data, surface weather observations and weather/climate forecasts with a terrestrial ecosystem model. TOPS produces daily 1 km estimates of carbon and water fluxes using MODIS derived LAI, land cover and gridded meteorological data created using more than 2000 surface weather stations over the conterminous U.S. Daily outputs are expressed as anomalies from historical normals that were computed using 20 years (1982-2001) of satellite and surface weather data. TOPS is also capable of using short/mid-term weather/climate forecasts to produce forecasts of land surface conditions (snow pack, runoff, soil moisture and primary production) that are useful in resource management.

Distributed anomaly detection using 1-class SVM for vertically partitioned data

There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are collected from modern satellites, in situ sensors and different climate models. Similarly, huge amount of flight operational data is downloaded for different commercial airlines. These different types of data sets need to be analyzed for finding outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only because of the massive volume of data but also because these data sets are physically stored at different geographical locations with only a subset of features available at any location. Moving these petabytes of data to a single location may waste a lot of bandwidth. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the entire data without moving all the data to a single location. The method we propose only centralizes a very small sample from the different data subsets at different locations. We analytically prove and experimentally verify that the algorithm offers high accuracy compared to complete centralization with only a fraction of the communication cost. We show that our algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of the algorithm is demonstrated on two large publicly available data sets: (i) the NASA MODIS satellite images and (ii) a simulated aviation data set generated by the ‘Commercial Modular Aero-Propulsion System Simulation’ (CMAPSS).

A Semiautomated Probabilistic Framework for Tree-Cover Delineation From 1-m NAIP Imagery Using a High-Performance Computing Architecture

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps.

A Technique of Analyzing Trust Relationships to Facilitate Scientific Service Discovery and Recommendation

Most of the existing service discovery methods focus on finding candidate services based on functional and non-functional requirements. However, while the open science community engenders many similar scientific services, how to differentiate them remains a challenge. This paper proposes a trust model that leverages the implicit human factor to help quantify the trustworthiness of candidate services. A hierarchical Knowledge-Social-Trust (KST) network model is established to draw hidden information from various publication repositories (e.g., DBLP) and social networks (e.g., Twitter). As a proof of concept, a prototyping service has been developed to help scientists evaluate and visualize trust of services. The performance factor is studied and experience is reported.

A Workflow Model for Earth Observation Sensor Webs

An Earth science sensor web consists of a distributed collection of sensors, Earth science models, human scientists and information technologists, and data archives. The scientific use of the sensor web consists broadly of seeking to improve the understanding of natural processes occurring on the Earth’s surface or in the atmosphere. Sensor measurements serve to quantify aspects of these processes that allow Earth science models to make predictions of scientific and social value. The management problem for sensor webs considered here is the problem of reconfiguring the sensor web in order to answer new science questions. The notion of reconfiguration is used broadly here to describe a set of actions for retargeting sensors, querying databases for image data, or executing functions for analyzing acquired data. This paper describes a workflow model and architecture for a workflow management system for reconfiguring sensor webs.

A Community-Driven Workflow Recommendations and Reuse Infrastructure

NASA Earth Exchange (NEX) aims to provide a platform to enable and facilitate scientific collaboration and knowledge sharing in the Earth sciences, as current satellite measurements rapidly magnify the accumulation of more than 40 years of NASA datasets. One of the main objectives of NEX is to help Earth scientists leverage and reuse various data processing software modules developed by their peers, in order to quickly run value-added executable experiments (workflows). Toward this goal, this paper reports our efforts of leveraging social network analysis to intelligently extract hidden information from data processing workflows. By modeling Earth science workflow modules as social entities and their dependencies as social relationships, this research opens up new vistas for applying social science to facilitate software reuse and distributed workflow development. As a proof of concept, a prototyping system has been developed as a plug-in to the NEX workflow design and management system (VisTrails) to aid Earth scientists in discovering and reusing workflow modules and extending them to solve more complex science problems.

Bridging VisTrails Scientific Workflow Management System to High Performance Computing

NASA Earth Exchange (NEX) is a collaboration platform whose goal is to accelerate Earth science research, by leveraging NASAs vast collections of global satellite data together with access to NASAs High-End Computing (HEC) facilities. NEX also aims to facilitate the sharing of experimental results as well as scientific processes (workflows) with the Earth science community through integration with VisTrails workflow management system. While VisTrails is used internally, it is not easily accessible from remote computers without directly logging into the NASA HEC systems through twofactor authentication and a bastion host. This paper describes the initial design of an extensible architecture that facilitates easier workflow interaction on NEX, by enabling users to develop and execute workflows in a supercomputing environment directly from their local VisTrails installation. This architecture helps domain scientists seamlessly leverage distributed computing and storage resources and it is potentially applicable to other scientific workflow management software. We further describe the architecture of the VisTrails-HEC plugin (as well as the VisTrails-Amazon plugin) and the implementation of a working prototype to demonstrate the feasibility of our solution.