Using lidar remote sensing and support vector machines to classify fire disturbance legacies in a Florida oak scrub landscape

Background/Question/Methods

Ecologists have long emphasized the reciprocal interactions between spatial pattern and ecological processes in the creation of landscape mosaics. While an enormous amount of recent research has focused on the quantification of spatial patterns, efforts to infer process from pattern have been hindered by the presence of multi-scale, often confounding, drivers of pattern in many landscapes. At the mesoscale, Holling’s extended keystone hypothesis posits that spatially contagious disturbances such as fire are the dominant pattern-generating processes. To test this hypothesis, we used fire history data and discrete, small-footprint lidar remote sensing acquired over a 22 sq. km landscape of oak scrub in the Kennedy Space Center/Merritt Island National Wildlife Refuge area on the east-central coast of Florida. We binned the lidar return data into 1 m vertical height intervals for each 5 m x 5 m horizontal cell. Since community structure tends to recover by 7 years post-fire, we tested for significant differences between recently-burned (< 7 years) and unburned (≥ 7 years) patches with multivariate analysis of variance. To predict the burn status of each cell, we then used distribution-free, nonlinear support vector machine (SVM) classifiers, which have proven to be highly accurate for complex pattern recognition problems.

Results/Conclusions 

We detected statistically significant differences in vegetation structure between burned and unburned patches for all of the dominant land cover types (upland non-forested, upland forested, wetland hardwood forest, and non-forested wetlands) in the study area. Initially, we obtained a predicted error rate of approximately 34% from the SVM classifier; by averaging the binned lidar data over a moving window of increasing size, however, we achieved substantial reductions in the predicted error rate for the SVM classifier. The optimal window size of 100 m x 100 m yielded a predicted misclassification rate of approximately 3%, an order of magnitude lower than the error rate obtained on the same data using a logistic regression classifier. These results suggest that, as predicted by the extended keystone hypothesis, fire disturbance is a dominant pattern-generating process at the patch scale in this oak scrub landscape. Furthermore, these results indicate that it is possible to use vertical vegetation structure, as represented by the binned lidar data, to predict burn status with a high level of accuracy. While our study employed a simple binary classification scheme, future research will focus on using SVM regression techniques to predict burn status with finer-grained classes of time since fire

Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida

Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 x 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range

No good surprises: intending lecturers' preconceptions and initial experiences of further education

Current initiatives to promote lifelong learning and a broader inclusiveness in post-16 education have focused attention on further education (FE). The article examines the experiences and reactions of 41 intending lecturers studying full-time for a Postgraduate Certificate in Further Education and Training (PGCET), as they enter FE colleges on teaching practice and encounter FE students for the first time. It argues that the sector may have something to learn from the contrast between these intending lecturers' expectations and their subsequent experiences, and that attempts to address problems which are endemic within the current FE sector by initiatives to improve teacher competence, such as the Further Education National Training Organisation (FENTO)'s recently introduced FE teacher training standards, are inadequate and misdirected

Interpretation of particle number size distributions measured across an urban area during the FASTER campaign

Abstract. Particle number size distributions have been measured simultaneously by scanning mobility particle sizers (SMPSs) at five sites in central London for a 1 month campaign in January–February 2017. These measurements were accompanied by condensation particle counters (CPCs) to measure total particle number count at four of the sites and Aethalometersmeasuringblackcarbon(BC)atfivesites.The spatialdistributionandinter-relationshipsoftheparticlesize distribution and SMPS total number counts with CPC total number counts and black carbon measurements have been analysed in detail as well as variations in the size distributions. One site (Marylebone Road) was in a street canyon with heavy traffic, one site (Westminster University) was on a rooftop adjacent to the Marylebone Road sampler, and a further sampler was located at Regent’s University within a major park to the north of Marylebone Road. A fourth sampler was located nearby at 160m above ground level on the BT tower and a fifth sampler was located 4km to the west of the main sampling region at North Kensington. Consistent with earlier studies it was found that the mode in the size distribution had shifted to smaller sizes at the Regent’s University (park) site, the mean particle shrinkage rate being 0.04nms−1 with slightly lower values at low wind speeds and some larger values at higher wind speeds. There was evidence of complete evaporation of the semi-volatile nucleation mode under certain conditions at the elevated BT Tower site. While the SMPS total count and black carbon showed typical traffic-dominated diurnal profiles, the CPC count data typically peaked during night-time as did CPC/SMPS and CPC/BC ratios. This is thought to be due to the presence of high concentrations of small particles (2.5–15nm diameter) probably arising from condensational growth from traffic emissions during the cooler night-time conditions. Such behaviour was most marked at the Regent’s University and Westminster University sites and less so at Marylebone Road, while at the elevated BT Tower site the ratio of particle number(CPC) to black carbon peaked during the morning rush hour and not at night-time, unlike the other sites. An elevation in nucleation mode particles associated with winds from the west and WSW sector was concluded to result from emissions from London Heathrow Airport, despite a distance of 22km from the central London sites

Does implementation matter if comprehension is lacking? A qualitative investigation into perceptions of advance care planning in people with cancer

Purpose: While advance care planning holds promise, uptake is variable and it is unclear how well people engage with or comprehend advance care planning. The objective of this study was to explore how people with cancer comprehended Advance Care Plans and examine how accurately advance care planning documentation represented patient wishes. Methods: This study used a qualitative descriptive design. Data collection comprised interviews and an examination of participants’ existing advance care planning documentation. Participants included those who had any diagnosis of cancer with an advance care plan recorded: Refusal of Treatment Certificate; Statement of Choices; and/or Enduring Power of Attorney (Medical Treatment) at one cancer treatment centre. Results: Fourteen participants were involved in the study. Twelve participants were female (86%). The mean age was 77 (range: 61-91) and participants had completed their advance care planning documentation between 8 and 72 weeks prior to the interview (mean 33 weeks). Three themes were evident from the data: Incomplete advance care planning understanding and confidence; Limited congruence for attitude and documentation; Advance care planning can enable peace of mind. Complete advance care planning understanding was unusual; most participants demonstrated partial comprehension of their own advance care plan, and some indicated very limited understanding. Participants’ attitudes and their written document congruence was limited, but advance care planning was seen as helpful. Conclusions: This study highlighted advance care planning was not a completely accurate representation of patient wishes. There is opportunity to improve how patients comprehend their own advance care planning documentation

Differentiation of coarse-mode anthropogenic, marine and dust particles in the High Arctic islands of Svalbard

19 pages, 8 figures,1 table, 1 appendix.-- Data availability: The APS data can be accessed from https://doi.org/10.5281/zenodo.3961473 (Traversi et al., 2020). The absorption coefficient data are available upon request from Gilardoni et al. (2020). Data supporting this publication can be accessed upon request from the corresponding authorsUnderstanding aerosol–cloud–climate interactions in the Arctic is key to predicting the climate in this rapidly changing region. Whilst many studies have focused on submicrometer aerosol (diameter less than 1 µm), relatively little is known about the supermicrometer aerosol (diameter above 1 µm). Here, we present a cluster analysis of multiyear (2015–2019) aerodynamic volume size distributions, with diameter ranging from 0.5 to 20 µm, measured continuously at the Gruvebadet Observatory in the Svalbard archipelago. Together with aerosol chemical composition data from several online and offline measurements, we apportioned the occurrence of the coarse-mode aerosols during the study period (mainly from March to October) to anthropogenic (two sources, 27 %) and natural (three sources, 73 %) origins. Specifically, two clusters are related to Arctic haze with high levels of black carbon, sulfate and accumulation mode (0.1–1 µm) aerosol. The first cluster (9 %) is attributed to ammonium sulfate-rich Arctic haze particles, whereas the second one (18 %) is attributed to larger-mode aerosol mixed with sea salt. The three natural aerosol clusters were open-ocean sea spray aerosol (34 %), mineral dust (7 %) and an unidentified source of sea spray-related aerosol (32 %). The results suggest that sea-spray-related aerosol in polar regions may be more complex than previously thought due to short- and long-distance origins and mixtures with Arctic haze, biogenic and likely blowing snow aerosols. Studying supermicrometer natural aerosol in the Arctic is imperative for understanding the impacts of changing natural processes on Arctic aerosoThis research has been supported by the Natural Environment Research Council (grant no. NE/S00579X/1). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI)With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe

Evaluation of isoprene nitrate chemistry in detailed chemical mechanisms

Isoprene nitrates are important chemical species in the atmosphere which contribute to the chemical cycles that form ozone and secondary organic aerosol (SOA) with implications for climate and air quality. Accurate chemical mechanisms are important for the prediction of the atmospheric chemistry of species such as isoprene nitrates in chemical models. In recent years, studies into the chemistry of isoprene nitrates have resulted in the development of a range of mechanisms available for use in the simulation of atmospheric isoprene oxidation. This work uses a 0-D chemical box model to assess the ability of three chemically detailed mechanisms to predict the observed diurnal profiles of four groups of isoprene-derived nitrates in the summertime in the Chinese megacity of Beijing. An analysis of modelled C5H9NO5 isomers, including isoprene hydroperoxy nitrate (IPN) species, highlights the significant contribution of non-IPN species to the C5H9NO5 measurement, including the potentially large contribution of nitrooxy hydroxyepoxide (INHE). The changing isomer distribution of isoprene hydroxy nitrates (IHNs) derived from OH-initiated and NO3-initiated chemistry is discussed, as is the importance of up-To-date alkoxy radical chemistry for the accurate prediction of isoprene carbonyl nitrate (ICN) formation. All mechanisms under-predicted C4H7NO5 as predominately formed from the major isoprene oxidation products, methyl vinyl ketone (MVK) and methacrolein (MACR). This work explores the current capability of existing chemical mechanisms to accurately represent isoprene nitrate chemistry in urban areas significantly impacted by anthropogenic and biogenic chemical interactions. It suggests considerations to be taken when investigating isoprene nitrates in ambient scenarios, investigates the potential impact of varying isomer distributions on iodide chemical ionisation mass spectrometry (I-CIMS) calibrations, and makes some proposals for the future development of isoprene mechanisms

Measurements of particulate methanesulfonic acid above the remote Arctic Ocean using a high resolution aerosol mass spectrometer

Methanesulfonic acid (MSA) is an important product from the oxidation of dimethyl sulfide (DMS), and thus is often used as a tracer for marine biogenic sources and secondary organic aerosol. MSA also contributes to aerosol mass and potentially to the formation of cloud condensation nuclei and new particles. However, measurements of MSA at high temporal resolution in the remote Arctic are scarce, which limits our understanding of its formation, climate change impact and regional transport. Here, we applied a validated quantification method to determine the mass concentration of MSA and non-sea salt sulfate (nss-SO4) in PM2.5 in the marine boundary layer, using a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) during a research cruise to the Arctic and North Atlantic Ocean, between 55 ◦N and 68 ◦N (26th May to June 23, 2022). With this method, the concen�trations of MSA in the remote Arctic marine boundary layer were determined for the first time. Results show that the average MSA concentration was 0.025 ± 0.03 μg m− 3 , ranging from <0.01 to 0.32 μg m− 3 . The lowest MSA level was found towards the northern leg of the cruise (near Sisimut (67 ◦N)) with air masses from sea ice over the northern polar region, and the highest MSA concentrations were observed over the Atlantic open ocean. The diurnal cycles of gas MSA, particulate MSA and nss-SO4 peaked in the afternoon, about one hour later than that of peak of solar radiation, which suggests that photochemical process is an important mechanism for the conversion of DMS into MSA above the remote ocean. The mass ratio of MSA to nss-SO4 (MSA/nss-SO4) presents a tem�perature dependence, which indicates that the addition branching pathway favors MSA formation, while thermal decay of intermediate radicals could be a possible pathway for sulfate formation. Finally, we found that the MSA/ nss-SO4 ratio is around 0.22-0.25 in the remote northern marine atmosphere