This paper assesses the combined effect of field plot size and LiDAR density on the estimation of four forest structure attributes: volume, total biomass, basal area and canopy cover. A total of 21 different plot sizes were considered, obtained by decreasing the field measured plot radius value from 25 to 5 m with regular intervals of 1 m. LiDAR data densities were simulated by randomly removing LiDAR pulses until reaching nine different density values. In order to avoid influence of the digital terrain model spatial resolution, eight different resolutions were considered (from 0.25 to 2 m grid size) and tested. A set of per-plot LiDAR metrics was extracted for each parameter combination. Prediction models of forest attributes were defined using forward stepwise ordinary least-square regressions. Results show that the highest R 2 values are reached by combining large plot sizes and high LiDAR data density values. However, plot size has a greater effect than LiDAR point density. In general, minimum plot areas of 500-600 m 2 are needed for volume, biomass and basal area estimates, and of 300-400 m 2 for canopy cover.Larger plot sizes do not significantly increase the accuracy of the models, but they increase the cost of fieldwork. OPEN ACCESSForests 2014, 5 937

Georeferencing field plots by means of GPS/GLONASS techniques is becoming compulsory for many applications concerning forest management and inventory. True coordinates obtained in a total station traverse were compared against GPS/GLONASS occupations computed from one navigation-grade and three survey-grade receivers. Records were taken under a high Pinus sylvestris L. forest canopy situated in a mountainous area in central Spain. The horizontal component of the absolute error was a better descriptor of the performance of GPS/GLONASS receivers compared to the precision computed by the proprietary software. The vertical component of absolute error also failed to show the effects revealed when the horizontal one was studied. These differences might be critical for applications involving high-demanding surveys, in which a comparison against a terrestrially surveyed ground truth is still mandatory for accuracy assessment in forested mountainous areas. Moreover, a comparison of diverse Differential GPS/GLONASS techniques showed that the effect of lengthening the baseline and lowering the logging rate was not significant in this study. Differences among methods and receivers were only observed for recording periods between 5 and 15 minutes. The hand-held receiver was inappropriate for plot establishment due to its inaccuracy and a low rate of fixed solutions, though it may be used for forest campaigns tolerating low precision or permitting the employment of periods of 20 minutes or longer for plot mensuration.Additional key words: forest inventory; georeferencing; global navigation satellite system (GNSS) (GLONASS); optimum observing time. Resumen Exactitud y precisión de receptores GPS bajo cubiertas forestales en ambientes montañososLa georreferenciación de trabajos de campo por medio de GPS/GLONASS es cada vez más necesaria para muchas aplicaciones en la gestión e inventario forestal. Se compararon coordenadas reales levantadas con estación total con las obtenidas por un navegador y tres equipos de calidad topográfica. Los registros se efectuaron bajo una masa de Pinus sylvestris L. del Sistema Central, España. La componente horizontal del error absoluto resultó ser un mejor descriptor de la calidad de las mediciones de los receptores GPS/GLONASS que los valores de precisión proporcionados por el software de los equipos. La componente vertical del error absoluto no mostró los efectos revelados por la componente horizontal. Estas diferencias pueden ser críticas para trabajos que requieran levantamientos topográficos de precisión, en los cuáles un contraste con itinerarios de validación sobre el terreno sigue siendo indispensable para calcular la exactitud en áreas forestales montañosas. Por otro lado, la comparación de diversas técnicas de GPS/GLO-NASS diferencial mostró que los cambios en la longitud de la línea base y de la tasa de registros no fueron significativos en este estudio. Sólo se observaron diferencias ente los métodos y receptores para tiempos de registro de 5 a 15 minutos. El navegador no resu...

The evaluation of accuracy is essential for assuring the reliability of ecological models. Usually, the accuracy of above-ground biomass () predictions obtained from remote sensing is assessed by the mean differences (), the root mean squared differences (), and the coefficient of determination (2) between observed and predicted values. In this article we propose a more thorough analysis of accuracy, including a hypothesis test to evaluate the agreement between observed and predicted values, and an assessment of the degree of overfitting to the sample employed for model training. Using the estimation of forest from LIDAR and spectral sensors as a case study, we compared alternative prediction and variable selection methods using several statistical measures to evaluate their accuracy. We showed that the hypothesis tests provide an objective method to infer the statistical significance of agreement. We also observed that overfitting can be assessed by comparing the inflation in residual sums of squares experienced when carrying out a cross-validation. Our results suggest that this method may be more effective than analysing the deflation in 2. We proved that overfitting needs to be specifically addressed since, in light of , and 2 alone, predictions may apparently seem reliable even in clearly unrealistic circumstances, for instance when including too many predictor variables. Moreover, Theil's partial inequality coefficients, which are employed to resolve the proportions of the total errors due to the unexplained variance, the slope and the bias, may become useful to detect averaging effects common in remote sensing predictions of. We concluded that statistical measures of accuracy, precision and agreement are necessary but insufficient for model evaluation. We therefore advocate for incorporating evaluation measures specifically devoted to testing observed-versuspredicted fit, and to assessing the degree of overfitting.

No abstract

Aim: Dispersal and environmental gradients shape marine microbial communities, yet the relative importance of these factors across taxa with distinct sizes and dispersal capacity in different ocean layers is unknown. Here, we report a comparative analysis of surface and deep ocean microbial beta diversity and examine how these patterns are tied to oceanic distance and environmental gradients.Location: Tropical and subtropical oceans (30°N-40°S).