Orthomosaics derived from consumer grade digital cameras on board unmanned aerial vehicles (UAVs) are increasingly being used for biodiversity monitoring and remote sensing of the environment. To have lasting quantitative value, remotely sensed imagery should be calibrated to physical units of reflectance. Radiometric calibration improves the quality of raw imagery for consistent quantitative analysis and comparison across different calibrated imagery. Moreover, calibrating remotely sensed imagery to units of reflectance improves its usefulness for deriving quantitative biochemical and biophysical metrics. Notwithstanding the existing radiometric calibration procedures for correcting single images, studies on radiometric calibration of UAV-derived orthomosaics remain scarce. In particular, this study presents a cost-and time-efficient radiometric calibration framework for designing calibration targets, checking scene illumination uniformity, converting orthomosaic digital numbers to units of reflectance, and accuracy assessment using in situ mean reflectance measurements (i.e. the average reflectance in a particular waveband). The empirical line method was adopted for the development of radiometric calibration prediction equations using mean reflectance values measured in only one spot within a 97 ha orthomosaic for three wavebands, i.e. red, green and blue of the Sony NEX-7 camera. A scene illumination uniformity check experiment was conducted to establish whether 10 randomly distributed regions within the orthomosaic experienced similar atmospheric and illumination conditions. This methodological framework was tested in a relatively flat terrain semi-arid woodland that is invaded by Harrisia pomanensis (the Midnight Lady). The scene illumination uniformity check results showed that at a 95% confidence interval, the prediction equations developed using mean reflectance values measured from only one spot within the scene can be used to calibrate the entire 97 ha RGB orthomosaic. Furthermore, the radiometric calibration accuracy assessment results showed a correlation coefficient r value of 0.977 (p < 0.01) between measured and estimated reflectance values with an overall root

The geographic distribution of a species is governed by climatic conditions, topography, resources and habitat structure determining the fundamental niche, while the local distribution expressed via home range occupation may be compressed by biotic interactions with competitors and predators, restricting the realised niche. Biotic influences could be especially important for relatively rare species. We investigated how rainfall, geology, land type and abundance of other ungulate species serving as competitors or prey for predators contributed to the patchy distribution of sable antelope herds within Kruger National Park. Data were provided by annual aerial surveys of ungulate populations conducted between 1978 and 1988. Sable herds were more commonly present on granitic and sandstone substrates than on more fertile basalt. They occurred both in the moist south‐west and dry north of the park. They were most abundant in sour bushveld and mopane savanna woodland, and mostly absent from knob thorn‐marula parkland. The presence of sable was negatively associated with high concentrations of impala and wildebeest, less consistently related to the abundance of zebra, and positively associated with the occurrence of buffalo herds. Best supported models included the separate effects of the most abundant grazers along with land type. Interspecific relationships seemed more consistent with vulnerability to predation as the underlying mechanism restricting the distribution of sable herds than with competitive displacement. Sable favoured land types distinct from those where wildebeest, the most preferred prey of lions, and impala, numerically the most important resident prey species, were most abundant. Hence the risk of predation, associated with habitat conditions where abundant prey species are most concentrated, can exert an overriding influence on the distribution of rarer species in terms of their home range occupation.

Sable antelope numbers in the Kruger National Park have declined substantially since the mid-1980s and have shown little recovery despite improved rainfall conditions. We used aerial survey records to investigate how changes in herd numbers, herd sizes, calf proportions and consequent changes in the distribution range of breeding herds contributed to this situation. Both herd sizes and herd numbers decreased in the drier northern half of the park, coupled with low calf proportions, especially during and following a severe drought, while declines in these measures became evident later in the wetter southern half. Herd extirpations led to a 25% contraction in the local distribution range of sable antelope, although some of these herds had only become established during the high rainfall period in the late 1970s. Local population trends were not related to increases in zebra numbers potentially attracting more predators. Compensatory density dependence in the population growth rate was no longer evident after 1986 during the period of the population decline. The population trajectory modelled from rainfall relationships indicated that the depressed population growth rate relative to rainfall apparent after 1986 has persisted, implicating either enduring habitat degradation or continuing high predation pressure. The pattern of the population decline and subsequent lack of recovery raises the possibility that an Allee effect could be operating, mediated through the consequences of reduced herd sizes for exposure to predation. Our findings suggest how delays in responding to declining population numbers could prejudice population recovery well before the overall population size drops to levels threatening population viability. bs_bs_banner Animal Conservation. Print ISSN 1367-9430 Animal Conservation 15 (2012) 195-204 Shrinking sable antelope numbers N. Owen-Smith et al.

Documenting current species distribution patterns and their association with habitat types is important as a basis for assessing future range shifts in response to climate change or other influences. We used the adaptive local convex hull (a-LoCoH) method to map distribution ranges of 12 ungulate species within the Kruger National Park (KNP) based on locations recorded during aerial surveys (1980–1993). We used log-linear models to identify changes in regional distribution patterns and chi-square tests to determine shifts in habitat occupation over this period. We compared observed patterns with earlier, more subjectively derived distribution maps for these species. Zebra, wildebeest and giraffe distributions shifted towards the far northern section of the KNP, whilst buffalo and kudu showed proportional declines in the north. Sable antelope distribution contracted most in the north, whilst tsessebe, eland and roan antelope distributions showed no shifts. Warthog and waterbuck contracted in the central and northern regions, respectively. The distribution of impala did not change. Compared with earlier distributions, impala, zebra, buffalo, warthog and waterbuck had become less strongly concentrated along rivers. Wildebeest, zebra, sable antelope and tsessebe had become less prevalent in localities west of the central region. Concerning habitat occupation, the majority of grazers showed a concentration on basaltic substrates, whilst sable antelope favoured mopane-dominated woodland and sour bushveld on granite. Buffalo showed no strong preference for any habitats and waterbuck were concentrated along rivers. Although widespread, impala were absent from sections of mopane shrubveld and sandveld. Kudu and giraffe were widespread through most habitats, but with a lesser prevalence in northern mopane-dominated habitats. Documented distribution shifts appeared to be related to the completion of the western boundary fence and widened provision of surface water within the park. Conservation implications: The objectively recorded distribution patterns provide a foundation for assessing future changes in distribution that may take place in response to climatic shifts or other influences.

Globally, Smallholder farming systems (SFS) are recognized as one of the most important pillars of rural economic development and poverty alleviation because of their contribution to food security. However, support for this agricultural sector is hampered by lack of reliable information on the distributions and acreage of smallholder fields. This information is essential in not only monitoring food security and informing markets but also in guiding the determination of levels of support required from government by individual farmers. There is urgent need for robust techniques that can be used to cost-effectively and time-efficiently map smallholder crop fields especially in Sub-Saharan Africa and Asia. This study attempts to do this by using an approach in which optical and Synthetic Aperture Radar (SAR) data are systematically combined and classified using Extreme Gradient Boosting (Xgboost). We also investigated model stacking as another technique to improve classification accuracy. We combined Xgboost with Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB). The combined use of multi-temporal Sentinel-2 bands, spectral indices, and Sentinel-1 produced better results than exclusive use of optical data (α = 0.95, p = 0.0005). Furthermore, stacking of classification algorithms based on model comparisons achieved higher accuracy than stacking the algorithms indiscriminately (α = 0.95, p = 0.0100). Through systematic fusion of SAR and optical data and hyper-parameter tuning of Xgboost, we achieved a maximum classification accuracy of 97.71%, while achieving a maximum accuracy of 96.06% through model stacking. This highlights the importance of multi-sensor data fusion and multi-classifier systems when mapping fragmented agricultural landscapes.

Restoration of polycyclic aromatic hydrocarbon- (PAH-) polluted sites is presently a major challenge in agroforestry. Consequently, microorganisms with PAH-degradation ability and soil fertility improvement attributes are sought after in order to achieve sustainable remediation of polluted sites. This study isolated PAH-degrading bacteria from enriched cultures of spent automobile engine-oil polluted soil. Isolates' partial 16S rRNA genes were sequenced and taxonomically classified. Isolates were further screened for their soil fertility attributes such as phosphate solubilization, atmospheric nitrogen fixation, and indoleacetic acid (IAA) production. A total of 44 isolates were obtained and belong to the genera Acinetobacter, Arthrobacter, Bacillus, Flavobacterium, Microbacterium, Ochrobactrum, Pseudomonas, Pseudoxanthomonas, Rhodococcus, and Stenotrophomonas. Data analysed by principal component analysis showed the Bacillus and Ochrobactrum isolates displayed outstanding IAA production. Generalized linear modelling statistical approaches were applied to evaluate the contribution of the four most represented genera (Pseudomonas, Acinetobacter, Arthrobacter, and Rhodococcus) to soil fertility. The Pseudomonas isolates were the most promising in all three soil fertility enhancement traits evaluated and all isolates showed potential for one or more of the attributes evaluated. These findings demonstrate a clear potential of the isolates to participate in restorative bioremediation of polluted soil, which will enhance sustainable agricultural production and environmental protection.

Abstract:We evaluated unified algorithms for remote sensing of chlorophyll-a (Chla) and turbidity in eutrophic and ultra-turbid waters such as Japan's Lake Shinji and Lake Nakaumi (SJNU) and the Vaal Dam Reservoir (VDR) in South Africa. To realize this objective, we used 38 remote sensing reflectance (R rs ), Chla and turbidity datasets collected in these waters between July 2016 and March 2017. As a result, we clarified the following items. As a unified Chla model, we obtained strong correlation (R 2 = 0.7, RMSE = 2 mg m −3 ) using a two-band model (2-BM) and three-band model (3-BM), with R rs (687)/R rs (672) and [R rs −1 (687) − R rs −1 (672)] × R rs (832). As a unified turbidity model, we obtained strong correlation (R 2 = 0.7, RMSE = 260 NTU) using 2-BM and 3-BM, with R rs (763)/R rs (821) and R rs (810) − [R rs (730) + R rs (770)]/2. When targeting the Sentinel-2 Multispectral Imager (MSI) frequency band, we focused on MSI Bands 4 and 5 (R rs (740) and R rs (775)) for the Chla algorithm. When optically separating SJNU and VDR data, it is effective to use the slopes of MSI Bands 3 and 4 (R rs (560) and R rs (665)) and the slopes of MSI Bands 7 and 9 (R rs (775) and R rs (865)).

When assessing distribution range shifts, precise information is required on distribution limits, densities in occupied regions, unoccupied gaps, and changes in these measures over time. The local convex hull method recently developed for home range delineation to provide these measures was compared with that of the widely applied parametric kernel density estimation and with the commonly used tile method. The assessment used location records from 14 years of aerial surveys on four mammalian herbivores selected because of their distinct distribution patterns. Impala showed an almost continuous distribution with few gaps, wildebeest a wide distribution with regional concentrations, waterbuck a linear distribution along rivers, and sable antelope a widespread but patchy distribution. The kernel method tended to extend ranges beyond observed records, obscuring gaps within distributions. With parametric kernel approaches, bandwidth obtained via Least Squares Cross Validation techniques was not optimal when the local abundance was widely disparate, as was the case for wildebeest. The LoCoH method most effectively revealed meaningful gaps. The LoCoH method is advantageous for precisely mapping the distributions of conspicuous species for which the absence of records indicates true gaps in occurrence.