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Species identification is one of the key steps in the management and conservation planning of many forest ecosystems. We introduce Deep BarkID, a portable tree identification system that detects tree species from bark images. Existing bark identification systems rely heavily on massive computing power access, which may be scarce in many locations. Our approach is deployed as a smartphone application that does not require any connection to a database. Its intended use is in a forest, where internet connection is often unavailable. The tree bark identification is expressed as a bark image classification task, and it is implemented as a convolutional neural network (CNN). This research focuses on developing light-weight CNN models through knowledge distillation. Overall, we achieved 96.12% accuracy for tree species classification tasks for ten common tree species in Indiana, USA. We also captured and prepared thousands of bark images—a dataset that we call Indiana Bark Dataset—and we make it available at https://github.com/wufanyou/DBID. [ABSTRACT FROM AUTHOR]

In order to advance the scientific understanding of carbon exchange with the land surface, build an effective carbon monitoring system, and contribute to quantitatively based U.S. climate change policy interests, fine spatial and temporal quantification of fossil fuel CO2 emissions, the primary greenhouse gas, is essential. Called the "Hestia Project", this research effort is the first to use bottom-up methods to quantify all fossil fuel CO2 emissions down to the scale of individual buildings, road segments, and industrial/electricity production facilities on an hourly basis for an entire urban landscape. Here, we describe the methods used to quantify the on-site fossil fuel CO2 emissions across the city of Indianapolis, IN. This effort combines a series of data sets and simulation tools such as a building energy simulation model, traffic data, power production reporting, and local air pollution reporting. The system is general enough to be applied to any large U.S. city and holds tremendous potential as a key component of a carbon-monitoring system in addition to enabling efficient greenhouse gas mitigation and planning. We compare the natural gas component of our fossil fuel CO2 emissions estimate to consumption data provided by the local gas utility. At the zip code level, we achieve a bias-adjusted Pearson r correlation value of 0.92 (p < 0.001). [ABSTRACT FROM AUTHOR]