Sam Ottoy

Efficient management of soil and water resources is fundamental to sustainable agricultural production, particularly under increasing pressures from climate variability, water scarcity, and soil degradation

Thermal cameras on unoccupied aerial vehicles (UAVs) are increasingly being used in environmental and ecological research, including hydrology, wildfire detection and prediction, urban heat studies, precision agriculture, ecosystem functioning, wildlife monitoring and microclimate studies. Converting raw thermal signals to quantitative land surface temperature (LST) values requires careful application of correction procedures. However, these steps are often overlooked or ignored—either due to limited expertise in thermal remote sensing or because of the technical complexity involved. Neglecting corrections for atmospheric effects and surface emissivity can lead to discrepancies of up to 5°C in the resulting LST estimate. We introduce theRmalUAV, an R package that facilitates LST processing with two workflows: an orthomosaic‐based and an image‐based approach. The orthomosaic workflow applies a single function to the entire dataset, whereas the image‐based workflow can account for variations in environmental conditions during the flight that affect surface temperature. The package corrects for atmospheric effects, background temperature, spatial emissivity and weather fluctuations, incorporating a novel method to handle rapid illumination changes. The package currently supports 11 common thermal sensors. It also includes tools for data cleaning, co‐registration and reporting. We demonstrate both the importance of the workflow and its implementation using two distinct case studies to highlight its versatility. The main text presents a detailed example using the research‐grade TeAx ThermalCapture 2.0. A complementary example, featuring the more commercially oriented DJI Mavic 3T, is provided in the . For comprehensive guidance and tutorials, readers are directed to the package vignette and its companion website.

Accurate quantification of wildland fuel consumption is essential for effective fire management in Northern European heathland ecosystems, yet traditional assessment methods remain spatially limited and labour-intensive. This study combined multitemporal UAV LiDAR with SLIC superpixel-based classification to directly measure fuel consumption following a prescribed burn in a Belgian heathland. Pre- and post-fire LiDAR surveys were conducted to capture vegetation height changes. Superpixel segmentation successfully classified three vegetation types (grassland, heather and trees with understory vegetation) with 97.8% accuracy. Fuel consumption analysis revealed remarkable differences between vegetation types, with heather (mean ± SD: 0.165 ± 0.102 m) exhibiting the highest consumption compared to grass (0.089 ± 0.088 m) and tree understory vegetation (0.091 ± 0.068 m). Statistical analysis confirmed the significant differences between all vegetation types (p-value < 0.001). This methodology provides quantitative evidence for developing vegetation-specific burning protocols by demonstrating the critical importance of both pre- and post-fire remote sensing data. The approach demonstrates the effectiveness of UAV-based multitemporal LiDAR for precise fuel consumption assessment in heathland fire management.

This manuscript explores how incorporating temporal dimensions into built environment research can promote a more circular society, adding societal improvements to efficiency-driven measures closing waste or material cycles. The current circularity approaches in industrial environments mainly focus on short-term innovations reducing resource extraction and waste, overlooking long-term circularity potentials of natural resource management such as living soils as a basis for all life. This study addresses this gap by investigating, analyzing, and drawing interplays between regenerative soil cycles and business development cycles in two Flemish industry parks, Kortrijk-Noord and Haasrode. Using diachronic mapping, a qualitative design and action research tool, the study aims to generate a space–time composite of soil and business cycles, integrating archival research, interviews, and policy document reviews. This method visually captures interplays between geology, land valuation, and economic development, demonstrating that integrating soil and business cycles can suggest new pathways for site-specific circular practices on Flemish industry parks, which can inform site-specific project frameworks for circular built environments. As such, the research advocates a paradigm shift in industry park (re)development, from product and material innovation within a ‘time is money’ framework to an integrated ‘time is life’ approach, where time’s historical and social dimensions are part of circular landscape development.

Recent technical and jurisdictional advances, together with the availability of low-cost platforms, have facilitated the implementation of unmanned aerial vehicles (UAVs) in individual tree detection (ITD) applications. UAV-based photogrammetry or structure from motion is an example of such a low-cost technique, but requires detailed pre-flight planning in order to generate the desired 3D-products needed for ITD. In this study, we aimed to find the most optimal flight parameters (flight altitude and image overlap) and processing options (smoothing window size) for the detection of taxus trees in Belgium. Next, we tested the transferability of the developed marker-controlled segmentation algorithm by applying it to the delineation of olive trees in an orchard in Greece. We found that the processing parameters had a larger effect on the accuracy and precision of ITD than the flight parameters. In particular, a smoothing window of 3 × 3 pixels performed best (F-scores of 0.99) compared to no smoothing (F-scores between 0.88 and 0.90) or a window size of 5 (F-scores between 0.90 and 0.94). Furthermore, the results show that model transferability can still be a bottleneck as it does not capture management induced characteristics such as the typical crown shape of olive trees (F-scores between 0.55 and 0.61).

Since the early 2000s, there have been substantial efforts to transform the concept of ecosystem services into practice. Spatial assessment tools are being developed to evaluate the impact of spatial planning on a wide range of ecosystem services. However, the actual implementation in decision-making remains limited. To improve implementation, tools that are tailored to local conditions can provide accurate, meaningful information. Instead of a generic and widely-applicable tool, we developed a regional, spatially-explicit tool (ECOPLAN-SE) to analyse the impact of changes in land use on the delivery of 18 ecosystem services in Flanders (Belgium). The tool incorporates ecosystem services relevant to policy-makers and managers and makes use of detailed local data and knowledge. By providing an easy-to-use tool, including the required spatial geodatasets, time investment and the learning curve remain limited for the user. With this tool, constraints to implement ecosystem service assessments in local decision-making are drastically reduced. We believe that region-specific decision support systems, like ECOPLAN-SE, are indispensable intermediates between the conceptual ecosystem service frameworks and the practical implementation in planning processes.

Storage of soil organic carbon ( SOC ) is an essential function of ecosystems underpinning the delivery of multiple services to society. Regional SOC stock estimates often rely on data collected during land‐use‐specific inventory schemes with varying sampling depth and density. Using such data requires techniques that can deal with the associated heterogeneity. As the resulting SOC assessments are not calibrated for the local scale, they could suffer from oversimplification of landscape processes and heterogeneity. This might especially be the case for sandy regions where typical historical land use practices and soil development processes determine SOC storage. The aims of this study were (a) to combine four land‐use‐specific SOC stock assessments to estimate the total stock in Flanders, Belgium, and (b) to evaluate the applicability of this regional‐scale estimate at the local scale. We estimated the SOC stock in the upper 100 cm of the unsealed area in Flanders (887,745 ha) to be 111.67 Mt OC , or 12.6 ± 5.65 kg OC m ⁻² on average. In general, soils under (semi‐) natural land‐use types, for example forests, store on average more organic carbon than under agriculture. However, overall agricultural soils store the largest amounts of SOC due to their vast spatial extent. Zooming in on a sandy location study (13.55 km ² ) revealed the poor performance of the regional estimates, especially where Histosols occurred. Our findings show that a greater spatial sampling density is required when SOC stock estimates are needed to inform carbon‐aware land management rather than to provide for regional reporting.

Ecosystem services (ES) mapping is becoming mainstream in many sustainability assessments, but its impact on real world decision-making is still limited. Robustness, end-user relevance and transparency have been identified as key attributes needed for effective ES mapping. However, these requirements are not always met due to multiple challenges, referred to here as bottlenecks, that scientists, practitioners, policy makers and users from other public and private sectors encounter along the mapping process. A selection of commonly encountered ES mapping bottlenecks that relate to seven themes: i) map-maker map-user interaction; ii) nomenclature and ontologies; iii) skills and background; iv) data and maps availability; v) methods-selection; vi) technical difficulties; and vii) over-simplification of mapping process/output. The authors synthesise the variety of solutions already applied by map-makers and map-users to mitigate or cope with these bottlenecks and discuss the emerging trade-offs amongst different solutions. Tackling the bottlenecks described here is a crucial first step towards more effective ES mapping, which can in turn ensure the adequate impact of ES mapping in decision-making.