Yuanzheng Shao

Application of a Fast Linear Feature Detector to Road Extraction From Remotely Sensed Imagery

This paper presents a fast and effective algorithm for detecting ridge- or ribbon-like linear features from remote sensing imagery. To judge if a pixel is at the center of a linear feature, the first step is to find several biggest pixels by their grey values within orthogonal directional windows, and store them into an evaluation window. A simple evaluation method is then applied to make the yes or no decision on whether the pixel is a linear feature point. Aerial images were used to test the algorithms ability to extract roads. This algorithm was compared with the Multiple Directional Non Maximum Suppression (MDNMS) algorithm. The experimental results indicate that with the proposed algorithm the processing of road details could improve and the processing time decrease.

Implementation of Geospatial Data Provenance in a Web Service Workflow Environment With ISO 19115 and ISO 19115-2 Lineage Model

Data provenance, also called data lineage, records the derivation history of a data product. In the earth science domain, geospatial data provenance is important because it plays a significant role in data quality and usability evaluation, data trail audition, workflow replication, and product reproducibility. The generation of the geospatial provenance metadata is usually coupled with the execution of geo-processing workflow. Their symbiotic relationship makes them complementary to each other and promises great benefit once they are integrated. However, the heterogeneity of data and computing resources in the distributed environment constructed under the service-oriented architecture (SOA) brings a great challenge to resource integration. Specifically, the issues, such as the lack of interoperability and compatibility among provenance metadata models and between provenance and workflow, create obstacles for the integration of provenance, and geo-processing workflow. In order to tackle these issues, on one hand, this paper breaks the provenance heterogeneity through recording provenance information in a standard lineage model defined in ISO 19115:2003 and ISO 19115-2:2009 standards. On the other hand, this paper bridges the gap between provenance and geo-processing workflow through extending both workflow language and service interface, making it possible for the automatic capture of provenance information in the geospatial web service environment. The proposed method is implemented in the GeoBrain, a SOA-based geospatial web service system. The testing result from implementation shows that the geospatial provenance information is successfully captured throughout the life cycle of geo-processing workflows and properly recorded in the ISO standard lineage model.

GEOSS Component and Service Registry: Design, Implementation and Lessons Learned

Petabytes of Earth science data have been accumulated through space- and air-borne Earth observation programs during the last several decades. The data are valuable both scientifically and socioeconomically. The value of these data could be further increased significantly if the data from these programs can be easily discovered, accessed, integrated, and analyzed. The Global Earth Observation System of Systems (GEOSS) is addressing this need. In particular, the GEOSS Component and Service Registry maintains the descriptive information about available Earth Observation resources that are self-nominated by providers. This registry provides the following capabilities for data providers: user registration, resource registration, and service interface registration. It enables data users to discover these resources through the dedicated graphical user interfaces. It also exposes machine-to-machine interfaces to other GEOSS core components. The captured functional requirements, system design and implementation details of this registry are introduced in this paper, followed by the analysis of the registered resources and the system accessing information. The discussion on the strengths of this registry, the limitations in its current form, and the lessons learned from the system maintenance and upgrade over the last five years may be useful to others building a centralized managed resource catalog to enable a large-scale integrated system following a system-of-systems approach.

Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data

Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, and Accumulated Growing Degree Days (AGDDs). In our case, these features are global variable, and measured in the state-level. Moreover, global feature in each Day of Year (DOY) would be impacted by multiple progress stages. Therefore, a mixture model is employed to model the observation probability distribution with all possible stage components. Then, a filtering based algorithm is utilized to estimate the proportion of each progress stage in the real-time. Experiments are conducted in the states of Iowa, Illinois and Nebraska in the USA, and our results are assessed and validated by the Crop Progress Reports (CPRs) of the National Agricultural Statistics Service (NASS). Finally, a quantitative comparison and analysis between our method and spectral pixel-wise based methods is presented. The results demonstrate the feasibility of the proposed method for the estimation of corn progress stages. The proposed method could be used as a supplementary tool in aid of field survey. Moreover, it also can be used to establish the progress stage estimation model for different types of crops.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Abstract Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer (MODIS) weekly normalized difference vegetation index (NDVI) product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels (>90%) masked by the United States Department of Agriculture (USDA) Cropland Data Layer (CDL). The NDVI-based time-series difference between flood years and normal year (median of years 2000–2014) was used to detect flood occurrences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error (4.8%) of leave-one-out cross validation (LOOCV) along with smaller statistical error indicators (root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE)), further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

GIS in the Cloud: Implementing a Web Coverage Service on Amazon Cloud Computing Platform

With the continuously increment of the available amount of geospatial data, a huge and scalable data warehouse is required to store those data, and a web-based service is also highly needed to retrieve geospatial information. The emergence of Cloud Computing technology brings a new computing information technology infrastructure to general users, which enable the users to requisition compute capacity, storage, database and other service. The Web Coverage Service supports retrieval of geospatial data as digital geospatial information representing space and time varying phenomena. This paper explores the feasibility of utilizing general-purpose cloud computing platform to fulfill WCS specification through a case study of implementing a WCS for raster image on the Amazon Web Service. Challenges in enabling WCS in the Cloud environment are discussed, which is followed by proposed solutions. The resulting system demonstrates the feasibility and advantage of realizing WCS in Amazon Cloud Computing platform.

Agent-as-a-service-based geospatial service aggregation in the cloud

An Agent-as-a-Service (AaaS)-based geospatial service aggregation is proposed to build a more efficient, robust and intelligent geospatial service system in the Cloud for flood emergency response. It involves an AaaS infrastructure, encompassing the mechanisms and algorithms for geospatial Web Processing Service (WPS) generation, geoprocessing and aggregation. The method has the following advantages: 1) it allows separately hosted services and data to work together, avoiding transfers of large volumes of spatial data over the network; 2) it enriches geospatial service resources in the distributed environment by utilizing the agent cloning, migration and service regeneration capabilities of the AaaS, solving issues associated with lack of geospatial services to a certain extent; 3) it enables the migration of services to target nodes to finish a task, strengthening decentralization and enhancing the robustness of geospatial service aggregation; and 4) it helps domain experts and authorities solve interdisciplinary emergency issues using various Agent-generated geospatial services. Display Omitted Agent-as-a-Service (AaaS)-based geospatial service aggregation on the Cloud is proposed.It allows separately-hosted services and data to work together, which avoids transferring large volume of spatial data.It enriches geospatial service resources in the distributed environment and solves the issue of lack of geospatial services.It strengthens decentralization and enhances robustness of the geospatial service aggregation.It provides experts assistance in solving the interdisciplinary emergency issues with agent-generated geospatial services.

Persistent WCS and CSW services of GOES data for GEOSS

The Global Earth Observation System of Systems (GEOSS) is a new international effort to provide an integrated system on top of the distributed, diverse legacy systems to create an open, public infrastructure for worldwide Earth observation research and applications. One key approach for enabling GEOSS is that legacy systems must support GEOSS-endorsed public standards. This paper presents a case study consisting of providing a persistent data customization service through the OGC Web Coverage Service (WCS) protocol and a data discovery service through the OGC Catalog Service-Web Profile (CSW) protocol for NOAA GOES data. These two services talk to the legacy NOAA data systems on the back end, and provide open standards-compliant interfaces on the front end. These two service prototypes make NOAA GOES data queryable and accessible in GEOSS to serve different societal benefit areas (SBA). The lessons learned may be useful to others in upgrading legacy data systems to support open standards.

Investigating metrics of geospatial web services: The case of a CEOS federated catalog service for earth observation data

Abstract Geospatial Web Services (GWS) make geospatial information and computing resources discoverable and accessible over the Web. Among them, Open Geospatial Consortium (OGC) standards-compliant data, catalog and processing services are most popular, and have been widely adopted and leveraged in geospatial research and applications. The GWS metrics, such as visit count, average processing time, and user distribution, are important to evaluate their overall performance and impacts. However, these metrics, especially of federated catalog service, have not been systematically evaluated and reported to relevant stakeholders from the point of view of service providers. Taking an integrated catalog service for earth observation data as an example, this paper describes metrics information retrieval, organization, and representation of a catalog service federation. An extensible and efficient log file analyzer is implemented to retrieve a variety of service metrics from the log file and store analysis results in an easily programmable format. An Ajax powered Web portal is built to provide stakeholders, sponsors, developers, partners, and other types of users with specific and relevant insights into metrics information in an interactive and informative form. The deployed system has provided useful information for periodical reports, service delivery, and decision support. The proposed measurement strategy and analytics framework can be a guidance to help GWS providers evaluate their services.

Use of Geographically Weighted Regression Model for Exploring Spatial Patterns and Local Factors Behind NDVI-Precipitation Correlation

Normalized Difference Vegetation Index (NDVI)-precipitation correlation has long been studied. In previous studies, the correlation was usually based on global regression model, which assumed such correlation be constant across the space. However, NDVI-precipitation correlation is spatially dependent and affected by local factors (e.g., soil background). In this paper, geographically weighted regression model is utilized to analyze the NDVI-precipitation correlation on three land use types (i.e., 1) grassland, 2) fallow/idle land, and 3) winter wheat land) within U.S. central great plain area. Results suggest that geographically weighted regression model has better performances than global regression models. Specifically, higher average R2 (0.81) and lower proportion (9%) of residuals with spatial autocorrelation has been achieved under geographically weighted regression in comparison with lower average R2 (0.68) and higher proportion (38%) of residual with spatial autocorrelation under global regression models. In addition, the spatially dependent correlation between NDVI and precipitation has been revealed with geographically weighted regression model. From the north to south, the increasing unit rate of NDVIs change with precipitation has been found through spatially varying regression slopes. Moreover, local factors affecting NDVI-precipitation correlation, such as soil permeability and thickness, have been identified through analyzing the local goodness of fitting under geographically weighted regression model. In summary, unveiled spatial patterns of NDVI-precipitation correlation provide another perspective for studying correlations between NDVI and climatic factors. This work should also be helpful to better understand crop responses to precipitation in agricultural management.