Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Estimating the Creation and Removal Date of Fracking Ponds Using Trend Analysis of Landsat Imagery

Hydraulic fracturing, or fracking, is a process of introducing liquid at high pressure to create fractures in shale rock formations, thus releasing natural gas. Flowback and produced water from fracking operations is typically stored in temporary open-air earthen impoundments, or frack ponds. Unfortunately, in the United States there is no public record of the location of impoundments, or the dates that impoundments are created or removed. In this study we use a dataset of drilling-related impoundments in Pennsylvania identified through the FrackFinder project led by SkyTruth, an environmental non-profit. For each impoundment location, we compiled all low cloud Landsat imagery from 2000 to 2016 and created a monthly time series for three bands: red, near-infrared (NIR), and the Normalized Difference Vegetation Index (NDVI). We identified the approximate date of creation and removal of impoundments from sudden breaks in the time series. To verify our method, we compared the results to date ranges derived from photointerpretation of all available historical imagery on Google Earth for a subset of impoundments. Based on our analysis, we found that the number of impoundments built annually increased rapidly from 2006 to 2010, and then slowed from 2010 to 2013. Since newer impoundments tend to be larger, however, the total impoundment area has continued to increase. The methods described in this study would be appropriate for finding the creation and removal date of a variety of industrial land use changes at known locations

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios

Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available