Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro

The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results

A climatology of particulate pollution in Christchurch

The research presented in this thesis provides a quantitative analysis of atmospheric influences on particulate matter pollution in Christchurch across a wide range of spatial and temporal scales. A complex interaction of low level flow characteristics that form in response to local and regional features of complex terrain, together with an urban setting that is characterised by low density housing, mostly comprised of single storey dwellings that are poorly insulated, regularly leads to nocturnal smog events during winter in Christchurch. Provided synoptic flow is weak, the above mentioned flow interaction promotes flow stagnation over the city, when nocturnal katabatic drainage flows and day-time north-easterly on-shore winds converge over the city. Additionally, undercutting of the density currents promotes highly stable atmospheric stratification close to the surface, so that, in combination, both horizontal and vertical air movement is suppressed. As particulate emission release from solid fuel burning for home heating coincides with this poor atmospheric dispersion potential, particle concentrations can increase substantially so that national air quality guidelines are regularly exceeded during winter in Christchurch. At the core of this thesis is a classification based approach that examines the day-to-day probabilities of breaches of the national air quality guideline for PM over the last decade at a single location in Christchurch as a result of variations in meteorological conditions alone. It is shown that, based on variations in temperature and wind speed, up to 85% of exceedence occurrence can be explained. From this, concentration trends over time, when meteorological variability is kept to a minimum, are assessed and evidence is found that recent regulatory measures to enhance air quality are beginning to show positive effects. Atmospheric processes that control pollution dispersion on the mesoscale are investigated through means of atmospheric numerical modelling in a novel approach that assimilates observational climatic wind field averages to drive low level flow for two idealised case studies. It is shown that this approach is able to reproduce the observed diurnal concentration patterns very well and that much of these patterns can be attributed to mesoscale circulation characteristics and associated atmospheric dispersion potential, namely flow stagnation and recirculation of contaminants. When timing of stagnation and subsequent recirculation is such that it occurs within a few hours after peak emission release, concentration increase is enhanced and dilution is delayed, thus severely exacerbating the problem. Links between exceedence probabilities and synoptic situations that favour the degradation of air quality are established and various synoptic transition scenarios are examined with regard to local air quality. The progression of anticyclones across the country is identified to be the dominant synoptic control mechanism and it is shown that latitudinal variation in the progression path determines the extent of expected exceedence probability. On interdecadal hemispheric scales, it is found that a particular combination of local and synoptic atmospheric conditions that favours air quality degradation, shows a re-occurring pattern of frequency maxima (and minima) with a periodicity of approximately 14 - 16 years. For the synoptic part of this interdecadal variability, a close relationship to Southern Hemispheric pressure anomalies in high latitudes is revealed. Finally, for verification of the combined findings and to assess their prediction capability, a validation case study is given which shows that the applied methodology is able to capture day-to-day variations in pollution levels with acceptable (statistically significant) accuracy

metvurst v0.0.1

<p>meteorological visualisation utilities using R for science and teaching</p

remote

In climate science, teleconnection analysis has a long standing history as a means for describing regions that exhibit above average capability of explaining variance over time within a certain spatial domain (e.g., global). The most prominent example of a global coupled ocean-atmosphere teleconnection is the El Nin ?o Southern Oscillation. There are numerous signal decomposition methods for identifying such regions, the most widely used of which are (rotated) empirical orthogonal functions. First introduced by van den Dool, Saha, and Johansson (2000), empirical orthogonal teleconnections (EOT) denote a regression based approach that allows for straight-forward interpretation of the extracted modes. In this paper we present the R implementation of the original algorithm in the remote package. To highlight its usefulness, we provide three examples of potential use- case scenarios for the method including the replication of one of the original examples from van den Dool et al. (2000). Furthermore, we highlight the algorithms use for cross- correlations between two different geographic fields (identifying sea surface temperature drivers for precipitation), as well as statistical downscaling from coarse to fine grids (using Normalized Difference Vegetation Index fields)

remote: Empirical Orthogonal Teleconnections in R

In climate science, teleconnection analysis has a long standing history as a means for describing regions that exhibit above average capability of explaining variance over time within a certain spatial domain (e.g., global). The most prominent example of a global coupled ocean-atmosphere teleconnection is the El Nin ?o Southern Oscillation. There are numerous signal decomposition methods for identifying such regions, the most widely used of which are (rotated) empirical orthogonal functions. First introduced by van den Dool, Saha, and Johansson (2000), empirical orthogonal teleconnections (EOT) denote a regression based approach that allows for straight-forward interpretation of the extracted modes. In this paper we present the R implementation of the original algorithm in the remote package. To highlight its usefulness, we provide three examples of potential use- case scenarios for the method including the replication of one of the original examples from van den Dool et al. (2000). Furthermore, we highlight the algorithms use for cross- correlations between two different geographic fields (identifying sea surface temperature drivers for precipitation), as well as statistical downscaling from coarse to fine grids (using Normalized Difference Vegetation Index fields)

A Comparative Study of Cross-Product NDVI Dynamics in the Kilimanjaro Region—A Matter of Sensor, Degradation Calibration, and Significance

While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In order to assess the extent of such differences in highly heterogeneous terrain, we analyze and compare intra-annual seasonal fluctuations and long-term monotonic trends (2003–2012) in the Kilimanjaro region, Tanzania. The considered NDVI datasets include the Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra and Aqua, Collections 5 and 6, and the 3rd Generation Global Inventory Modeling and Mapping Studies (GIMMS) product. The degree of agreement in seasonal fluctuations is assessed by calculating a pairwise Index of Association (IOAs), whereas long-term trends are derived from the trend-free pre-whitened Mann–Kendall test. On the seasonal scale, the two Terra-MODIS products (and, accordingly, the two Aqua-MODIS products) are best associated with each other, indicating that the seasonal signal remained largely unaffected by the new Collection 6 calibration approach. On the long-term scale, we find that the negative impacts of band ageing on Terra-MODIS NDVI have been accounted for in Collection 6, which now distinctly outweighs Aqua-MODIS in terms of greening trends. GIMMS NDVI, by contrast, fails to capture small-scale seasonal and trend patterns that are characteristic for the highly fragmented landscape which is likely owing to the coarse spatial resolution. As a short digression, we also demonstrate that the amount of false discoveries in the determined trend fraction is distinctly higher for p &lt; 0.05 ( 52.6 % ) than for p &lt; 0.001 ( 2.2 % ) which should point the way for any future studies focusing on the reliable deduction of long-term monotonic trends

Seasonal and long-term vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. Kilimanjaro, Tanzania

This dataset consists of GeoTIFF fille for GIMMS NDVI downscaled record (1982–2011) that was resampled from 8 km to 1 km spatial resolution

Raw climate station data for the southern slopes of Kilimanjaro, Tanzania

The climate station network was set up on the southern slopes of Kilimanjaro, Tanzania, in 2010 and presents the recorded characteristics of air temperature, air humidity, and precipitation from both a plot-based and area-wide perspectives. The station set-up followed a hierarchical approach covering an elevation as well as a land-use disturbance gradient. It consisted of 52 basic stations measuring ambient air temperature and above-ground air humidity and 11 precipitation measurement sites, with recording intervals of 5 min. With respect to precipitation observations, the network extended the long-term recordings of A. Hemp which has installed and maintained up to 117 multi-month accumulating rainfall buckets in the region since 1997

Monthly maps of air temperature and air humidity of the southern slopes of Kilimanjaro, Tanzania

Monthly maps of air temperature and air humidity of the southern slopes of Kilimanjaro, Tanzania. The dataset is part of our study on eco‐meteorological characteristics of the southern slopes of Kilimanjaro, Tanzania ([https://doi.org/10.1002/joc.4552]). (1) ta200_kriging.zip The dataset contains interpolate monthly air temperature maps using universal kriging with elevation, aspect, slope, sky‐view factor and mean monthly normalized difference vegetation index (NDVI) as external drift variables. This corresponds to step 5 in chapter 3.1 of [https://doi.org/10.1002/joc.4552]. (2) rh200_kriging.zip The dataset contains interpolate monthly air humidity maps using universal kriging with elevation, aspect, slope, sky‐view factor and mean monthly normalized difference vegetation index (NDVI) as external drift variables. This corresponds to step 5 in chapter 3.1 of [https://doi.org/10.1002/joc.4552]. (3) ta200_kriging_multi-year_average.zip and rh200_kriging_multi-year_average.zip The dataset contains multi-year monthly averages from (1) and (2) and a map of the multi-year annual mean air temperature and humidity. This corresponds to step 6 in chapter 3.1 of [https://doi.org/10.1002/joc.4552]. For the datasets (1) and (2), we used 5-min measurements between 2011 and 2014 of 52 climate stations that distributed across the southern slopes of Mt. Kilimanjaro. We aggregated the 5-min measurements to hourly observations and filled existing gaps of up to 1 year through multivariate regression using the five nearest stations. We aggregated the gap-filled hourly data to daily averages if at least 22 h of valid records exist for that day at a specific station. Finally, we aggregated the daily averages to monthly averages if a single month has at least 20 valid daily records