A land-use map at the regional scale is a heavy computation task yet is critical to most landowners, researchers, and decision-makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating land classification maps at the regional scale: the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time. Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens. As one of the most well-known VGI initiatives, OpenStreetMap (OSM), contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns. Whereas, most large-scale mapping approaches-including regional and national scales-confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover data-sets, in this study, we clearly distinguished and differentiated land-use from land cover. By focusing on our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery. Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work. Furthermore, our novel approach in regional scale land-use mapping produced robust results in our study area: the overall internal accuracy of the classifier was 95.2% and the external accuracy of the classifier was measured at 74.8%.

This paper uses GeoEye-1 imagery and airborne lidar (Light Detection and Ranging) data to map buildings and their rubble in Port-au-Prince caused by the Haiti earthquake on 12 January 2010. This is achieved by performing an objectbased one-class-at-a-time land cover classification of the image and lidar data using spectral, textural and height information. Classification accuracy is about 87 percent overall, and approximately 80 percent for buildings and rubble. Comparison of manually-selected 200 actual damaged buildings within an area of two sq. km in the city center shows an accuracy of over 90 percent for building and rubble mapping. 3D building models for approximately 55,000 buildings covering an area of 30 sq. km over Port-au-Prince were generated. It is found that most of the damage is to the concrete and masonry structures in the well planned areas of the city and very little damage to the shelters and the temporary type houses with metal sheet roofs. The study demonstrates that fusing optical imagery and lidar data can effectively map the nature, severity, extent and damage patterns caused by earthquakes in densely populated urban areas like Port-au-Prince.

Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software.

Hurricane Harvey (2017) caused widespread flash flooding by extremely heavy rainfall and resulted in tremendous damage, including 82 fatalities and huge economic loss in the Houston, Texas area. To reduce hazards, loss, and to improve urban resilience, it is important to understand the factors that influence the occurrence of flooding events. People rely on natural resources and different land uses to reduce the severity of flood impacts and mitigate the risk. In this study, we focused the impacts of land use on Hurricane-Harvey-induced flooding inside and outside the Houston city center. With the recent trend that more citizen scientists serve in delivering information about natural disaster response, local residents in Houston areas participated in delineating the flooded areas in Hurricane Harvey. The flooding information used here generated a published map with citizen-contributed flooding data. A regional model framework with spatial autocovariates was employed to understand those interactions. Different land use patterns and types affected the potential of flooding events differently inside and outside Houston's city center. Explicitly, we found agricultural and open space were associated with high risk of flooding outside the city center, industrial lands increased the high risk of flooding in city center, and residential areas reduced the potential of flooding both inside and outside the city center. The results can assist with future land use strategy in Houston and other areas, and mitigate potential flash flooding. This study also highlighted the contribution of citizen science to responses to natural hazards.

In Reply Dr Scheer and colleagues have 3 concerns about our study: questionable definitions, inconsistent methods, and a data set that includes missing values. The definition of "severe sepsis" that we used may not capture all patients in this category, particularly those with organ dysfunction and normal lactate levels. However, the definition of "organ dysfunction" in severe sepsis is equivocal. 1 If we defined severe sepsis as any SOFA component of at least two, 127 more patients would have been classified as having severe sepsis, giving an area under the receiver operating curve (AUROC) for prediction of in-hospital mortality of 0.73. qSOFA still had better prognostic accuracy (incremental AUC, 0.07; 95% CI, 0.02-0.12). Scheer and colleagues suggest these 2 classifications performed equally because there was little difference in their positive and negative predictive values. Because our study was not powered to detect such differences, we do not believe this is a valid conclusion. Furthermore, the requirement for 2 elements of SIRS in severe sepsis resulted in poor sensitivity (47%), ie, in a substantial proportion of seriously ill patients misclassified as not having severe sepsis. This risk of misclassification has previously been reported. 2 For these reasons, among others, the Sepsis-3 task force removed the SIRS criteria and focused only on organ dysfunction. 3 Our results support these changes. What Scheer and colleagues suggest for severe sepsis is actually what was done with the new definition of sepsis that focuses on organ dysfunction, without the SIRS criteria.The primary objective of our study was to assess the prognosis accuracy of qSOFA in patients in the emergency department with suspected infection. Because qSOFA only has 3 components, we decided that we could not analyze patients with any missing components. Also, it seemed artificial to perform multiple imputation. One component is binary (altered mental state) and respiratory rate is highly skewed, so multiple imputation would be subject to bias. 4 We accepted missing SOFA data because this is a pragmatic approach and reflects how patients would be managed in an emergency department. We do not agree that this represents an inconsistency in our methods.Missing SOFA data could have led to the normal value assumption being inaccurate. However, we built a multiple imputation model that resulted in a larger proportion of patients with a SOFA score of 2 or higher (324 vs 297) and a nonsignificant improvement of SOFA AUROC (0.80 vs 0.77). We did not conclude that qSOFA was superior to SOFA, but that both scores performed well for the prediction of in-hospital mortality. The advantage of qSOFA over SOFA in the emergency department lies in its rapidity, simplicity, and absence of requirement of laboratory variables.