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Abstract:Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.Keywords: UAV; drone-based remote sensing; geometric image resolution; multi-view reconstruction; reconstruction efficiency; reconstruction quality; structure from motion; precision forestry

In conjunction with the World Resources Institute, GLAD has developed a new weekly alert system: deforestation is detected by satellites with each new Landsat image, and users can subscribe for email updates. The freely available alert system is already operating for Congo, Uganda, Indonesia, Peru, and Brazil. The researchers hope to have the system operating for Cambodia and the rest of the tropics in 2017.

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The qualitative analysis of medical (e.g. histopathological or radiological) images is time-consuming and subject to inter- and intra-reader variations. This often negatively affects the prediction of the clinical outcome.

At CIA Lab, we are developing image analysis systems for computer-assisted analysis and interpretation of medical images to help medical professionals (e.g. pathologists, radiologists). Our goal is to develop computational tools to extract quantitative features useful for more objective and accurate detection, diagnosis, and prognosis. Frequently, we need high performance computational infrastructures to efficiently process large medical images and associate them with other sources of data.

The Forest History Society's Alvin J. Huss Archives maintains a rich collection of over 40,000 photos, slides, negatives, plates, and films documenting the history of human interaction with the environment. Indexed by subject, the main collection covers a wide array of topics while focusing on the history of forests, the forest products industry, the U.S. Forest Service, and lumbering and sawmilling practices. The majority of the collection consists of black-and-white prints taken in the United States from the 1920s to the 1960s.

Several auxiliary image collections created by companies or individuals are also available for study at the Forest History Society. Examples include photograph albums compiled by forestry school students on field trips; collections amassed by foresters or forest products companies in the course of conducting everyday operations; and images used in the publication of forest industry magazines. Examples include collections such as Champion International Corporation, Puget Sound Pulp and Timber Company, the Red River Lumber Company, and the Forest Industries Magazine Photographs.

Authorized use of images from the FHS collections is allowed for most non-commercial uses; however, permission from the Society is required prior to publication or display. Photographic use fees may apply.

You may also view a number of photo galleries containing images from the database that represent selected subject areas in our photograph collection. A small portion of our collection is also highlighted on Flickr.

Colors are an important element in forest photography. The way you adjust the colors will depend on what you want to communicate with your photo, so it is quite subjective. With forest photography, increasing the intensity of the colors might work quite well. To do that, you can increase the vibrance and/or the saturation by moving its sliders to the right.

The Forest Preserves of Cook County uses blind judging to select the contest winners. This means judges do not know the names of the photographers when deciding winning images. The Forest Preserves of Cook County shall determine winner eligibility in its sole and absolute discretion. All decisions made by the judging panel are final.

A NASA-led team of scientists has developed the first-ever method for detecting the presence of different types of underground forest fungi from space, information that may help researchers predict how climate change will alter forest habitats.

Because the two types of fungi are expected to respond differently to a changing climate, knowing where each type predominates may help scientists predict where forests will thrive in the future and where they will falter.

Mycorrhizal fungi (white/yellow) trading nutrients for carbon with tree roots (brown). Credit: Indiana University.Creating maps of forests and their fungi has traditionally relied on various methods of counting individual tree species, an approach that cannot be done at large scales. In a new study published in the journal Global Change Biology, a team led by Joshua Fisher of NASA's Jet Propulsion Laboratory, Pasadena, California, and UCLA found a way to detect this hidden network using satellite images.

Having identified the timing sequences related to each type of fungus, the researchers developed and tested a statistical model to predict the areas of fungus domination in any particular Landsat image from canopy changes alone. They found they could predict the fungus association correctly in 77 percent of the images. They went on to produce landscape-wide maps of fungi associations, uncovering intriguing patterns in forests that will be studied in greater depth in the future.

The typical CVD parameters available for CNT forest synthesis include catalyst composition, catalyst thickness, buffer layer composition, buffer layer thickness, substrate temperature, gas temperature, catalyst conditioning, hydrocarbon gas composition, carrier gas composition, synthesis pressure, and synthesis time, among others. Each of these parameters can influence the resulting diameter distribution, areal density, growth rate, and CNT catalyst lifetime. For example, increasing the porosity of an alumina buffer layer (controlled by deposition methodology) was shown to drastically increase the growth rate, lifetime, and density of CNTs when using an Fe catalyst thin film37. Likewise, trace amounts of carbon can increase CNT number density by reducing oxidized catalyst nanoparticles38,39. Because of the cost and time required to explore the available synthesis parameter space, researchers typically operate within a small parameter range that has produced acceptable CNT growth in the past. In fact, time and cost constraints are disincentives to thoroughly explore the vast parameter space. For the 11 synthesis parameters mentioned above, a full combinatorial experimental campaign consisting of just three quantized levels per parameter, without replicates, would consist of 177,147 experiments. At a relatively aggressive pace of five experiments per day, this experimental campaign would require almost a 100 years to complete. Characterization of the resulting CNT forests would require additional time that may exceed the time required for synthesis.

Nested box plots in Fig. 4 display the stiffness and buckling load representative of all CNT forests. Axes are plotted using a logarithmic scale to show the power-law scaling that exists between forest attributes and the corresponding mechanical response. Within each plot, the mean value is plotted as a solid dot, the shaded box represents the upper and lower quartile of the data, while the vertical whiskers represent 1.5 times the interquartile range. The dual x-axes are sorted by nesting CNT linear density and CNT outer radius in ascending order. Note that the inner CNT radius is 70% that of the outer radius in all simulations. The various population growth rate coefficients of variation are plotted by color within the box plots. Based on the box plots, the buckling load and stiffness are positively correlated with all CNT attributes, with a ranked sensitivity (from smallest to greatest) of population growth rate variation, diameter, and linear density. The variation of stiffness and buckling load spans >3 orders of magnitude for the combination of parameters varied in the current study.

The predicted buckling load and stiffness resulting from the regression, plotted relative to their ground-truth values, may be found in Fig. S1. Note that the linear regression model using the known values of physical attributes (without using image properties) provides a single prediction for each of the 63 classes. A statistical linear regression model produced an R2 value of 0.94 for both buckling load and forest stiffness, indicating that buckling load and CNT forest stiffness was predicted relatively well conditioned upon knowledge of the CNT physical attributes. The linear regression model had a root-mean-squared error (RMSE) of 0.22 and 0.20 for stiffness and buckling load, respectively, for the same data. While this linear regression approach is straightforward when using synthetic simulation because all CNT forest simulation parameters are prescribed, for real-world experiments the determination of CNT diameter, density, and growth rate variation is time consuming, cost prohibitive, and may be unrealistic to obtain with physical experiments for even modest parametric studies. These difficulties motivate the physics-based simulation and image-based prediction capabilities afforded by the DL techniques. e24fc04721