NUV/Blue spectral observations of sprites in the 320-460 nm region: N2{\mathrm N_2} (2PG) Emissions

A near-ultraviolet (NUV) spectrograph (320-460 nm) was flown on the EXL98 aircraft sprite observation campaign during July 1998. In this wavelength range video rate (60 fields/sec) spectrographic observations found the NUV/blue emissions to be predominantly N2 (2PG). The negligible level of N2+ (1NG) present in the spectrum is confirmed by observations of a co-aligned, narrowly filtered 427.8 nm imager and is in agreement with previous ground-based filtered photometer observations. The synthetic spectral fit to the observations indicates a characteristic energy of ~1.8 eV, in agreement with our other NUV observations.Comment: 7 pages, 2 figures, 1 table, JGR Space Physics "Effects of Thunderstorms and Lightning in the Upper Atmosphere" Special Sectio

Tower Camera Handbook

The tower camera in Barrow provides hourly images of ground surrounding the tower. These images may be used to determine fractional snow cover as winter arrives, for comparison with the albedo that can be calculated from downward-looking radiometers, as well as some indication of present weather. Similarly, during spring time, the camera images show the changes in the ground albedo as the snow melts. The tower images are saved in hourly intervals. In addition, two other cameras, the skydeck camera in Barrow and the piling camera in Atqasuk, show the current conditions at those sites

EFFICIENCY OF PRODUCTION ON ARABLE LAND IN ORGANIC AND CONVENTIONAL FARMING

One of the most evident difference between conventional and organic farming is yield height. Differences predominantly depend on the crop species and the major drop in yield when organic farming comes in the period of conversion, lower variability feature low-input species. Combination of factors defines overall yield distinction between conventionally and organically grown crops. Even if the organic farming reaches lower yields, differences in costs and prices of products have very strong positive effect on it´s efficiency, which could be even higher when compared to conventional farming

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth

Double down on remote sensing for biodiversity estimation. A biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products

The relationship between spectral and plant diversity: Disentangling the influence of metrics and habitat types at the landscape scale

Biodiversity monitoring is crucial for ecosystem conservation, but ground data collection is limited by cost, time, and scale. Remote sensing is a convenient approach providing frequent, near-real-time information with fine resolution over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (i.e., metrics applied, habitat type, scale, and temporal effects) and conditions under which such a relationship exists. This study investigates the applicability of the SVH for monitoring plant diversity at the landscape scale by comparing the performance of three types of SD metrics. Species richness and functional diversity were calculated for >2000 grid cells of 5 ' x 3 ' covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying (i) the standard deviation of NDVI, (ii) Rao's Q entropy index and (iii) the richness of "spectral communities". Habitat type (i.e., land cover) was included in the models of the relationship between SD and ground biodiversity. Both species richness and functional diversity showed positive and significant relationships with each SD metric tested. However, SD alone accounted for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depended significantly on habitat type and was highest in natural areas with transitional bushy and herbaceous vegetation. Our results underline that despite the stability of the significance of the relationship between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is contextdependent and the factors mediating such a relationship must be carefully considered to avoid misleading conclusions

Under the mantra: ‘Make use of colorblind friendly graphs’

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors

Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

There is a lack of guidance on the choice of the spatial grain of predictor and response variables in species distribution models (SDM). This review summarizes the current state of the art with regard to the following points: (i) the effects of changing the resolution of predictor and response variables on model performance; (ii) the effect of conducting multi-grain versus single-grain analysis on model performance; and (iii) the role of land cover type and spatial autocorrelation in selecting the appropriate grain size. In the reviewed literature, we found that coarsening the resolution of the response variable typically leads to declining model performance. Therefore, we recommend aiming for finer resolutions unless there is a reason to do otherwise (e.g. expert knowledge of the ecological scale). We also found that so far, the improvements in model performance reported for multi-grain models have been relatively low and that useful predictions can be generated even from single-scale models. In addition, the use of high-resolution predictors improves model performance; however, there is only limited evidence on whether this applies to models with coarser-resolution response variables (e.g. 100 km2 and coarser). Low-resolution predictors are usually sufficient for species associated with fairly common environmental conditions but not for species associated with less common ones (e.g. common vs rare land cover category). This is because coarsening the resolution reduces variability within heterogeneous predictors and leads to underrepresentation of rare environments, which can lead to a decrease in model performance. Thus, assessing the spatial autocorrelation of the predictors at multiple grains can provide insights into the impacts of coarsening their resolution on model performance. Overall, we observed a lack of studies examining the simultaneous manipulation of the resolution of predictor and response variables. We stress the need to explicitly report the resolution of all predictor and response variables