Development and Deployment of an Intelligent Kite Aerial Photography Platform (iKAPP) for Site Surveying and Image Acquisition

Aerial photographs and images are used by a variety of industries, including farming, landscaping, surveying, and agriculture, as well as academic researchers including archaeologists and geologists. Aerial imagery can provide a valuable resource for analyzing sites of interest and gaining information about the structure, layout, and composition of large areas of land that would be unavailable otherwise. Current methods of acquiring aerial images rely on techniques such as satellite imagery,manned aircraft, or more recently unmanned aerial vehicles (UAVs) and micro-UAV technologies. These solutions, while accurate and reliable, have several drawbacks. Using satellite imagery or UAVs can prove to be very expensive, costing tens of thousands for images. They can also prove to be time-consuming and in some cases have constraints on use, such as no-fly zones. In this paper, we present an alternative low-cost, versatile solution to these methods, an intelligent kite aerial photography platform (iKAPP), for the purpose of acquiring aerial images and monitoring sites of interest.We show how this system provides flexibility in application, and we detail the system’s design, mechanical operation, and initial flight experiments for a low-cost, lightweight, intelligent platform capable of acquiring high-resolution images. Finally, we demonstrate the system by acquiring images of a local site, showing how the system functions and the quality of images it can capture. The application of the system and its capabilities in terms of capture rates, image quality, and limitations are also presented. The system offers several improvements over traditional KAP systems, including onboard “intelligent” processing and communications. The intelligent aspect of this system stems from the use of self-image stabilization of the camera, the advantage being that one is able to configure the system to capture large areas of a site automatically, and one can see the site acquisition in real time, all of which are not possible with previous methods of AP

N14C: A plant-soil nitrogen and carbon cycling model to simulate terrestrial ecosystem responses to atmospheric nitrogen deposition

The dynamic model N14C simulates changes in the plant–soil dynamics of nitrogen and carbon, brought about by the anthropogenic deposition of nitrogen. The model operates with four plant functional types; broadleaved and coniferous trees, herbs and dwarf shrubs. It simulates net primary production (NPP), C and N pools, leaching of dissolved organic carbon and nitrogen (DOC, DON) and inorganic nitrogen, denitrification, and the radiocarbon contents of organic matter, on an annual timestep. Soil organic matter (SOM) comprises three pools, undergoing first-order decomposition reactions with turnover rates ranging from c. 2 to c. 1000 years. Nitrogen immobilisation by SOM occurs if inorganic N remains after plant uptake, and leaching of inorganic N occurs if the immobilisation demand is met. SOM accumulates in the deeper soil by transport and sorption of DOM. Element soil pools accumulate with N inputs by fixation from 12,000 years ago until 1800, when anthropogenic N deposition begins. We describe the parameterisation of N14C with data from 42 published plot studies carried out in northern Europe, plus more general information on N deposition trends, soil radiocarbon, N fixation and denitrification. A general set of 12 parameters describing litter fractionation, N immobilisation, growing season length, DOC and DON leaching, denitrification and NH4 retention was derived by fitting the field data. This provided fair agreements between observations and simulations, which were appreciably improved by moderate (±20%) adjustments of the parameters for specific sites. The parameterised model gives reasonable blind predictions of ecosystem C and N variables from only temperature, precipitation, N deposition, and vegetation type. The results suggest an approximate doubling of NPP due to N deposition, although the majority of the sites remain N-limited. For a given N deposition, leaching rates of inorganic N at conifer and shrub sites exceed those at broadleaf and herb sites

Manual extraction of bedrock lineaments from high-resolution LiDAR data: methodological bias and human perception

Manual extraction of topographic features from Light Detection and Ranging (LiDAR) images is a quick, cost effective and powerful tool to produce lineament maps of fractured basement areas. This commonly used technique, however, suffers from several biases. In this contribution, we present the influence of (1) scale, (2) illumination azimuth and (3) operator, which significantly affect results of remote sensing expressed as number, orientation and length of the mapped lineaments. Six operators (N1\u2013N6) with differing experience in remote sensing and different Earth sciences backgrounds mapped the same LiDAR DEM of a fractured bedrock terrain located in western Norway at three different scales (1:20.000, 1:10.000, 1:5.000) and illuminated from three different azimuths (045\ub0, 180\ub0, 315\ub0). The 54 lineament maps show considerable output variability depending on the three factors: (1) at larger scales, both the number and the orientation variability of picked lineaments increase, whereas the line lengths generally decrease. (2) Linear features oriented perpendicular to the source of illumination are preferentially enhanced. (3) Inter-operator result reproducibility is generally poor. Operators have different perceptions of what is a lineament. Ironically, this is particularly obvious for the results of the \u201cmost experienced\u201d operators, seemingly reflecting a stronger conceptual bias of what lineaments are and an operational bias on how they should be mapped. Based on these results, we suggest guidelines aimed to improve the reliability of remote sensing lineament interpretations