Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 algorithm performance contest

This paper presents the initial results of the Algorithm Performance Contest that was organized as part of the 5th IAPRWorkshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation. © 2008 IEEE

Performance of built-up area classifications using high-resolution SAR data

Identification of the built-up area from satellite imagery can provide a crucial information layer in disaster mitigation and management and for monitoring urban sprawl e.g. in developing countries. Spaceborne radar imagery is at an advantage in regions where environmental conditions impede the acquisition of optical image data. Automated exploitation procedures are imperative for efficient, large area coverage. However, methodologies must be developed or adapted to account for the specific characteristics of synthetic aperture radar (SAR) data. This study evaluates the identification of the built-up area on RADARSAT-1 Fine Mode and ENVISAT Image Mode data using the texture-based, anisotropic, rotation-invariant built-up presence index. Data selection and processing parameters are discussed. User’s accuracies of up to 77.5%, with overall accuracies of up to 81.3%, were achieved in this comparative study without any post-classification editing

Biomass estimation to support pasture management in Niger

Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998–2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R2 = 0.55, R2cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available

The need for improved maps of global cropland

Food security is a key global concern. By 2050, the global population will exceed 9 billion, and a 50% increase in annual agricultural output will be required to keep up with demand. There are significant additional pressures on existing agricultural land through increased competition from the biofuel sector and the need to elevate feed production, which is being driven by higher levels of meat consumption in low- and middle-income countrie