Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska

The emergence of a new generation of remote sensing and geopositioning technologies, as well as increased capabilities in image processing, computing, and inferential techniques, have enabled the development and implementation of increasingly efficient and cost-effective multilevel sampling designs for forest inventory. In this paper, we (i) describe the conceptual basis of multilevel sampling, (ii) provide a detailed review of several previously implemented multilevel inventory designs, (iii) describe several important technical considerations that can influence the efficiency of a multilevel sampling design, and (iv) demonstrate the application of a modern multilevel sampling approach for estimating the forest biomass resources in a remote area of interior Alaska. This approach utilized a combination of ground plots, lidar strip sampling, satellite imagery (multispectral and radar), and classified land cover information. The variability in the total biomass estimate was assessed using a bootstrapping approach. The results indicated only marginal improvement in the precision of the total biomass estimate when the lidar sample was post-stratified using the classified land cover layer (reduction in relative standard error from 7.3% to 7.0%), whereas there was a substantial improvement in the precision when the estimate was based on the biomass map derived via nearest-neighbor imputation (reduction in relative standard error from 7.3% to 5.1%).This is the publisher’s final pdf. The published article is copyrighted by the Canadian Aeronautics and Space Institute and can be found at: http://www.casi.ca/cdn-journal-of-remote-sensing

Taking Stock of Circumboreal Forest Carbon With Ground Measurements, Airborne and Spaceborne LiDAR

The boreal forest accounts for one-third of global forests, but remains largely inaccessible to ground-based measurements and monitoring. It contains large quantities of carbon in its vegetation and soils, and research suggests that it will be subject to increasingly severe climate-driven disturbance. We employ a suite of ground-, airborne- and space-based measurement techniques to derive the first satellite LiDAR-based estimates of aboveground carbon for the entire circumboreal forest biome. Incorporating these inventory techniques with uncertainty analysis, we estimate total aboveground carbon of 38 +/- 3.1 Pg. This boreal forest carbon is mostly concentrated from 50 to 55degN in eastern Canada and from 55 to 60degN in eastern Eurasia. Both of these regions are expected to warm >3 C by 2100, and monitoring the effects of warming on these stocks is important to understanding its future carbon balance. Our maps establish a baseline for future quantification of circumboreal carbon and the described technique should provide a robust method for future monitoring of the spatial and temporal changes of the aboveground carbon content

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

This article presents a novel approach to the acquisition, processing, and analytics of industrial food production by employing state-of-the-art artificial intelligence (AI) at the edge. Intelligent Industrial Internet of Things (IIoT) devices are used to gather relevant production parameters of industrial equipment and motors, such as vibration, temperature and current using built-in and external sensors. Machine learning (ML) is applied to measurements of the key parameters of motors and equipment. It runs on edge devices that aggregate sensor data using Bluetooth, LoRaWAN, and Wi-Fi communication protocols. ML is embedded across the edge continuum, powering IIoT devices with anomaly detectors, classifiers, predictors, and neural networks. The ML workflows are automated, allowing them to be easily integrated with more complex production flows for predictive maintenance (PdM). The approach proposes a decentralized ML solution for industrial applications, reducing bandwidth consumption and latency while increasing privacy and data security. The system allows for the continuous monitoring of parameters and is designed to identify potential breakdown situations and alert users to prevent damage, reduce maintenance costs and increase productivity.publishedVersio