Sensors for Digital Transformation in Smart Forestry.

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation-the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions.

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline-no AI can do this. Consequently, human-centered AI (HCAI) is a combination of "artificial intelligence" and "natural intelligence" to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.

Environmental impact assessment of wood ash utilization in forest road construction and maintenance--A field study.

The ever increasing use of wood material as fuel for green energy production requires innovative, environmentally safe strategies for recycling of the remaining wood ash. Utilizing wood ash in forest road construction and maintenance to improve mechanical stability has been suggested as a feasible recycling option. To investigate the environmental impact of wood ash application in forest road maintenance, a two-year field experiment was conducted at two Austrian forest sites (Kobernausserwald (KO) (soil pH 5.5) and Weyregg (WE) (pH 7.7)) differing in their soil chemical properties. Two different ashes, one produced by grate incineration (GA) and the other by fluidized bed incineration in a mixture with 15 vol% burnt lime (FBA), were incorporated in repeated road sections at a 15:85% (V/V) ash-to-soil rate. Leaching waters from the road body were collected and analyzed for 32 environmentally relevant parameters over two years. Upon termination of the experiment, sub-road soil samples were collected and analyzed for ash-related changes in soil chemistry. Even though a larger number of parameters was affected by the ash application at the alkaline site (WE), we observed the most pronounced initial increases of pH as well as Al, As, Fe, Mn, Ni, Co, Cu, Mo, and NO2(−) concentrations in leachates beneath GA-treated road bodies at Kobernausserwald due to the lower soil buffer capacity at this site. Despite the observed effects our results indicate that, when specific requirements are met (i.e. appropriate ash quality, sufficient soil buffer capacity below the road body, and single time-point ash incorporation within several decades), wood ash application in forest road construction is generally environmentally acceptable.