A novel method for area frame stratification based on geospatial crop planting frequency data layers
This paper proposes a novel method for land cover area frame stratification based on corn planting frequency and percent cultivation. South Dakota U.S. geospatial crop frequency (2008-2013) and cultivation (2013) data layers created from NASS Cropland Data Layers are utilized to develop a novel area sampling frame (ASF) stratification design. Eight corn planting frequency strata are derived using a k-means clustering method based on mean corn planting frequency calculated at the NASS ASF primary sampling unit level. The corn planting frequency strata are then sub stratified based on percent cultivation, which, together, provide more crop specific information than the current NASS ASF based on percent cultivation alone. Using 2014 Farm Service Agency Common Land Unit Data as in situ validation, it is found that this novel ASF design predicts crop specific planting patterns well. These results indicate that the new stratification method has potential to improve ASF accuracy, efficiency and crop estimates.
US geospatial crop frequency data layers
Information on future crop specific planting is valuable for improving agricultural survey estimates. This information is critical for agricultural production planning, agricultural product commodity inventory control, natural resource allocation and conservation, etc. However, future crop planting details are generally unavailable. This paper proposes to use crop specific planting frequency data as indicators to indirectly provide information regarding future crop planting. A methodology to derive crop planting frequency data layers based on 2008-2013 Cropland Data Layers has been presented in this paper. Crop frequency layers for corn, soybeans, wheat and cotton were successfully built at the national level and for two states including Indiana and Mississippi, USA. Multi-year (2008-2013) Farm Service Agency (FSA) Common Land Unit (CLU) data were utilized to assess the accuracy of the derived crop frequency data layers. The accuracies of the national scale crop frequency data layers are 91.00%, 90.13%, 87.67% and 85.96% for corn, cotton, soybean and wheat respectively.
Service-oriented applications for environmental models: Reusable geospatial services
Environmental modelling often requires a long iterative process of sourcing, reformatting, analyzing, and introducing various types of data into the model. Much of the data to be analyzed are geospatial data-digital terrain models (DTM), river basin boundaries, snow cover from satellite imagery, etc.-and so the modelling workflow typically involves the use of multiple desktop GIS and remote sensing software packages, with limited compatibility among them. Recent advances in service-oriented architectures (SOA) are allowing users to migrate from dedicated desktop solutions to on-line, loosely coupled, and standards-based services which accept source data, process them, and pass results as basic parameters to other intermediate services and/or then to the main model, which also may be made available on-line. This contribution presents a service-oriented application that addresses the issues of data accessibility and service interoperability for environmental models. Key model capabilities are implemented as geospatial services, which are combined to form complex services, and may be reused in other similar contexts. This work was carried out under the auspices of the AWARE project funded by the European programme Global Monitoring for Environment and Security (GMES). We show results of the service-oriented application applied to alpine runoff models, including the use of geospatial services facilitating discovery, access, processing and visualization of geospatial data in a distributed manner.