Do looks matter? A case study on extensive green roofs using discrete choice experiments

Extensive green roofs are a promising type of urban green that can play an important role in climate proofing and ultimately in the sustainability of our cities. Despite their increasingly widespread application and the growing scientific interest in extensive green roofs, their aesthetics have received limited scientific attention. Furthermore, several functional issues occur, as weedy species can colonize the roof, and extreme roof conditions can lead to gaps in the vegetation. Apart from altering the function of a green roof, we also expect these issues to influence the perception of extensive green roofs, possibly affecting their acceptance and application. We therefore assessed the preferences of a self-selected convenience sample of 155 Flemish respondents for visual aspects using a discrete choice experiment. This approach, combined with current knowledge on the psychological aspects of green roof visuals, allowed us to quantify extensive green roof preferences. Our results indicate that vegetation gaps and weedy species, together with a diverse vegetation have a considerable impact on green roof perception. Gaps were the single most important attribute, indicated by a relative importance of ca. 53%, with cost coming in at a close second at ca. 46%. Overall, this study explores the applicability of a stated preference technique to assess an often overlooked aspect of extensive green roofs. It thereby provides a foundation for further research aimed at generating practical recommendations for green roof construction and maintenance

Setting Farmers Free: Righting the Unintended Anomaly of UCC Section 9-312(2)

status: publishe

Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium

The growing trend toward urbanisation and the increasingly frequent occurrence of extreme weather events emphasise the need for further monitoring and understanding of weather in cities. In order to gain information on these intra-urban weather patterns, dense high-quality atmospheric measurements are needed. Crowdsourced weather stations (CWSs) could be a promising solution to realise such monitoring networks in a cost-efficient way. However, due to their nontraditional measuring equipment and installation settings, the quality of datasets from these networks remains an issue. This paper presents crowdsourced data from the “Leuven.cool” network, a citizen science network of around 100 low-cost weather stations (Fine Offset WH2600) distributed across Leuven, Belgium (50∘52′ N, 4∘42′ E). The dataset is accompanied by a newly developed station-specific temperature quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for inter-station (between-station) and intra-station (station-specific) temperature biases by means of a random forest approach. The QC method is evaluated using data from four WH2600 stations installed next to official weather stations belonging to the Royal Meteorological Institute of Belgium (RMI). A positive temperature bias with a strong relation to the incoming solar radiation was found between the CWS data and the official data. The QC method is able to reduce this bias from 0.15 ± 0.56 to 0.00 ± 0.28 K. After evaluation, the QC method is applied to the data of the Leuven.cool network, making it a very suitable dataset to study local weather phenomena, such as the urban heat island (UHI) effect, in detail. (https://doi.org/10.48804/SSRN3F, Beele et al., 2022).</p

A Comparison of Nonlinear Mixing Models for Vegetated Areas Using Simulated and Real Hyperspectral Data

International audienceAbstract--Spectral unmixing (SU) is a crucial processing step when analyzing hyperspectral data. In such analysis, most of the work in the literature relies on the widely acknowledged linear mixing model to describe the observed pixels. Unfortunately, this model has been shown to be of limited interest for specific scenes, in particular when acquired over vegetated areas. Consequently, in the past few years, several nonlinear mixing models have been introduced to take nonlinear effects into account while performing SU. These models have been proposed empirically, however, without any thorough validation. In this paper, the authors take advantage of two sets of real and physical-based simulated data to validate the accuracy of various nonlinear models in vegetated areas. These physics-based models, and their corresponding unmixing algorithms, are evaluated with respect to their ability of fitting the measured spectra and providing an accurate estimation of the abundance coefficients, considered as the spatial distribution of the materials in each pixel

Nonlinear unmixing of vegetated areas: a model comparison based on simulated and real hyperspectral data

International audienceWhen analyzing remote sensing hyperspectral images, numerous works dealing with spectral unmixing assume the pixels result from linear combinations of the endmember signatures. However, this assumption cannot be fulfilled, in particular when considering images acquired over vegetated areas. As a consequence, several nonlinear mixing models have been recently derived to take various nonlinear effects into account when unmixing hyperspectral data. Unfortunately, these models have been empirically proposed and without thorough validation. This paper attempts to fill this gap by taking advantage of two sets of real and physical-based simulated data. Theaccuracy of various linear and nonlinear models and the corresponding unmixing algorithms is evaluated with respect to their ability of fitting the sensed pixels and of providing accurate estimates of the abundances

Global-scale characterization of turning points in arid and semi-arid ecosystem functioning

Aim: Changes in dryland ecosystem functioning are threatening the well‐being of human populations worldwide, and land degradation, exacerbated by climate change, contributes to biodiversity loss and puts pressures on sustainable livelihoods. Here, abrupt changes in ecosystem functioning [so‐called turning points (TPs)] were detected using time series of Earth observation data. Hotspot areas of high TP occurrence were identified, observed changes characterized and insights gained on potential drivers for these changes.Location: Arid and semi‐arid regions.Time period: 1982–2015.Methods: We used a time series segmentation technique (breaks for additive season and trend) to detect breakpoints in rain‐use efficiency as a means of analysing changes in ecosystem functioning. A new typology to characterize the detected changes was proposed and evaluated, at regional to local scales, for a set of case studies. Ancillary data on population and drought were used to provide insights on potential drivers of TP occurrence.Results: Turning points in ecosystem functioning were found in 13.6% (c. 2.1 × 106 km2) of global drylands. Turning point hotspots were primarily observed in North America, the Sahel, Central Asia and Australia. In North America, the majority of TPs (62.6%) were characterized by a decreasing trend in ecosystem functioning, whereas for the other regions, a positive reversal in ecosystem functioning was prevalent. Further analysis showed that: (a) both climatic and anthropogenic pressure influenced the occurrence of TPs in North America; (b) Sahelian grasslands were primarily characterized by drought‐induced TPs; and (c) high anthropogenic pressure coincided with the occurrence of TPs in Asia and Australia.Main conclusions: By developing a new typology targeting the categorization of abrupt and gradual changes in ecosystem functioning, we detected and characterized TPs in global drylands. This TP characterization is a first crucial step towards understanding the drivers of change and supporting better decision‐making for ecosystem conservation and management in drylands

The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

Opportunistically collected species occurrence data are often used for species distribution models (SDMs) when high-quality data collected through standardized recording protocols are unavailable. While opportunistic data are abundant, uncertainty is usually high, e.g. due to observer effects or a lack of metadata. To increase data quality and improve model performance, we filtered species records based on record attributes that provide information on the observation process or post-entry data validation. Data filtering does not only increase the quality of species records, it simultaneously reduces sample size, a trade-off that remains relatively unexplored. By controlling for sample size in a dataset of 255 species, we were able to explore the combined impact of data quality and sample size on model performance. We applied three data quality filters based on observers' activity, the validation status of a record in the database and the detail of a submitted record, and analyzed changes in AUC, Sensitivity and Specificity using Maxent with and without filtering. The impact of stringent filtering on model performance depended on (1) the quality of the filtered data: records validated as correct and more detailed records lead to higher model performance, (2) the proportional reduction in sample size caused by filtering and the remaining absolute sample size: filters causing small reductions that lead to sample sizes of more than 100 presences generally benefitted model performance and (3) the taxonomic group: plant and dragonfly models benefitted more from data quality filtering compared to bird and butterfly models. Our results also indicate that recommendations for quality filtering depend on the goal of the study, e.g. increasing Sensitivity and/or Specificity. Further research must identify what drives species' sensitivity to data quality. Nonetheless, our study confirms that large quantities of volunteer generated and opportunistically collected data can make a valuable contribution to ecological research and species conservation

Remote sensing for the observation of senescence in Conference pear trees

Leaf senescence in trees is the phenological stage during which nutrient resorption happens. In this process, part of the nutrients is transferred to the perennial organs of the plant, contributing to tree vitality and, in pome fruit trees, to flowering intensity the following year. Another share of the nutrients remains inside leaf litter and enters the agroecosystem’s nutrient cycles. The timing and duration of leaf senescence influences the ratio between the two parts of nutrients and thus influences nutrient cycles in the agroecosystem. Among innovative techniques to investigate these processes, satellite remote sensing has proved a valid tool in natural ecosystems. The same cannot be said about fruit orchards, because of the image quality of the satellites active before Sentinel-2, often deemed insufficient for agricultural studies. The features of Sentinel-2, instead, offer new possibilities for monitoring phenology in agricultural environments. This research aims to study senescence in Conference pear trees, in three regions of Flanders (Belgium). One cloud-free Sentinel-2 image, acquired in the middle of the senescence period, was analysed, by means of different spectral indices. Ground data was collected through a network of 34 webcams with an RGB camera. A visual analysis was performed, to determine the beginning of senescence (the moment in which the first yellow/red leaves appear in the canopy) and the end of senescence (the moment in which the entire canopy turns yellow/red). Webcam data showed that leaf (dis)colouration started between September and October, during a one-month time span. Full discolouration of the canopy, occurring at the end of November, was instead more synchronous. Moreover, some trees only turned yellow, while others showed red leaves, probably a stress indicator. Sentinel-2 data revealed that spectral indices correlate well with the date of the beginning of senescence, thus suggesting that it would possible to map it. These results already offer evidence that monitoring variability in the dynamics of senescence is possible from satellite remote sensing. Current focus is on the link between canopy colour, as it appears in the webcam imagery, and satellite data

Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

"© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.This work was supported in part by the Belgian Science Policy Office in the frame of the Stereo II program (Hypermix project-SR/00/141), in part by the project Chameleon of the Flemish Agency for Innovation by Science and Technology (IWT), and in part by the Spanish Ministry of Science and Education (MEC) for the projects AGL2012-40053-C03-01 and CONSOLIDER RIDECO (CSD2006-67). The European Facility for Airborne Research EUFAR (www.eufar.net) funded the flight campaign (Transnational Access Project 'Hyper-Stress'). The work of D. S. Intrigliolo was supported by the Spanish Ministry of Economy and Competitiveness program "Ramon y Cajal."Delalieux, S.; Zarco-Tejada, PJ.; Tits, L.; Jiménez Bello, MÁ.; Intrigliolo Molina, DS.; Somers, B. (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2571-2582. https://doi.org/10.1109/JSTARS.2014.2330352S257125827