Remote sensing of atmospheric biogenic volatile organic compounds (BVOCs) via satellite-based formaldehyde vertical column assessments

Kefauver S.C., Filella I., Penuelas J. (2014) Remote sensing of atmospheric biogenic volatile organic compounds (BVOCs) via satellite-based formaldehyde vertical column assessments. International Journal of Remote Sensing. 35: 7519-7542.
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Doi: 10.1080/01431161.2014.968690

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Global vegetation is intrinsically linked to atmospheric chemistry and climate, and better understanding vegetation–atmosphere interactions can allow scientists to not only predict future change patterns, but also to suggest future policies and adaptations to mediate vegetation feedbacks with atmospheric chemistry and climate. Improving global and regional estimates of biogenic volatile organic compound (BVOCs) emissions is of great interest for their biological and environmental effects and possible positive and negative feedbacks related to climate change and other vectors of global change. Multiple studies indicate that BVOCs are on the rise, and with near 20 years of global remote sensing of formaldehyde (HCHO), the immediate and dominant BVOC atmospheric oxidation product, the accurate and quantitative linkage of BVOCs with plant ecology, atmospheric chemistry, and climate change is of increasing relevance. The remote sensing of BVOCs, via HCHO in a three step process, suffers from an additive modelling error, but improvements in each of the steps have reduced this error by over a multiplication factor improvement compared to estimates without remote sensing. Differential optical absorption spectroscopy (DOAS) measurement of the HCHO slant columns from spectral absorption properties has been adapted to include the correction of numerous spectral artefacts and intricately refined for each of a series of sensors of increasing spectral and spatial resolution. Conversion of HCHO slant to HCHO vertical columns using air mass factors (AMFs) has been improved with the launch of new sensors and the incorporation of radiative transfer and chemical transport models (CTM). The critical process of linking HCHO to BVOC emissions and filtering non-biogenic emissions to explicitly quantify biogenic emissions has also greatly improved. This critical last step in down-scaling from global satellite coverage to local biogenic emissions now benefits from the increasing precision and near-explicitness of available CTMs as well as the increasing availability of global remote-sensing data sets needed to proportionally assign the HCHO column to different related biogenic (global plant functional type and land cover classifications), atmospheric (dust, aerosols, clouds, other trace gases), climate (temperature, wind, precipitation), and anthropogenic (fire, biomass burning) factors.

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Using Pinus uncinata to monitor tropospheric ozone in the Pyrenees

Kefauver S.C., Penuelas J., Ribas A., Diaz-De-Quijano M., Ustin S. (2014) Using Pinus uncinata to monitor tropospheric ozone in the Pyrenees. Ecological Indicators. 36: 262-271.
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Doi: 10.1016/j.ecolind.2013.07.024

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Field metrics were investigated using the conifer species Pinus uncinata for the biomonitoring of tropospheric ozone in the Pyrenees of Catalonia, Spain. The Ozone Injury Index (OII) was investigated piecewise for improvement as a biomonitoring field metric for using sensitive conifer species to monitor tropospheric ozone across variable environmental conditions. The OII employs a weighted average of visual chlorotic mottling (VI), needle whorl retention (RET), needle length (LGT), and crown death (CD). Of note, VI includes subcomponents VI-Amount (% of symptomatic needles) and VI-Severity (% of chlorotic mottling on symptomatic needles) and RET includes the FWHORL subcomponent (average fraction of needles retained per whorl). All components and subcomponents of the OII correlated better to multiple year ozone exposure compared to single year ozone exposure measurements. VI-Severity and FWHORL modeled over half the variability of the three year average of ambient ozone concentrations (P < 0.0001, R2 = 0.53, RMSE = 2.73). Combining the biomonitoring metrics with GIS models related to landscape-scale variability in plant water relations resulted in considerable improvement in the ozone exposure model explanatory power (P < 0.0001, R2 = 0.90, RMSE = 1.35) including the parameters VI-Amount, VI-Severity, elevation, slope and topographic curvature. © 2013 Elsevier Ltd. All rights reserved.

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The synergy of the 0.05° (∼5km) AVHRR long-term data record (LTDR) and landsat TM archive to map large fires in the North American boreal region from 1984 to 1998

Moreno-Ruiz J.A., Garcia-Lazaro J.R., Riano D., Kefauver S.C. (2014) The synergy of the 0.05° (∼5km) AVHRR long-term data record (LTDR) and landsat TM archive to map large fires in the North American boreal region from 1984 to 1998. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7: 1157-1166.
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Doi: 10.1109/JSTARS.2013.2292853

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A Bayesian network classifier-based algorithm was applied to map the burned area (BA) in the North American boreal region using the 0.05\circ (\sim5\nbsp\hbox{km} ) Advanced Very High Resolution Radiometer (AVHRR) Long-Term Data Record (LTDR) data version 3 time series. The results showed an overall good agreement compared to reference maps (slope = 0.62;\ {R2} = 0.75 ). The study site was divided into six sub-regions, where south-western Canada performed the worst (slope = 0.25;\ {R2} = 0.47 ). The algorithm achieved good results as long as a year with high fire incidence was employed to train the Bayesian network, and the vegetation response to fire remained consistent across the region. Years with higher fire activity and larger fires, which were easier to detect at the LTDR spatial scale, matched the reference maps better. The LTDR postfire signal remained detectable for 6-9 years, extending opportunities to map the full fire extent with Landsat Thematic Mapper (TM). For fires larger than 1000\nbsp\hbox{km}2 , Landsat TM mapped 99%, whereas LTDR caught 69% of the reference BA reported. Landsat TM took four satellite overpasses (2 months) to map these large fires, and in some cases even until the following year, but LTDR detected them within days. Thus, results suggest that LTDR could be used to trigger the search for fires and then map their perimeter with Landsat TM. This study demonstrates an LTDR BA algorithm that could be extrapolated to other boreal regions using a similar methodology, although reference fire perimeters would be needed to train the Bayesian classifier and its thresholds. © 2008-2012 IEEE.

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Human migration, protected areas, and conservation outreach in Tanzania

Salerno J.D., Mulder M.B., Kefauver S.C. (2014) Human migration, protected areas, and conservation outreach in Tanzania. Conservation Biology. 28: 841-850.
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Doi: 10.1111/cobi.12237

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A recent discussion debates the extent of human in-migration around protected areas (PAs) in the tropics. One proposed argument is that rural migrants move to bordering areas to access conservation outreach benefits. A counter proposal maintains that PAs have largely negative effects on local populations and that outreach initiatives even if successful present insufficient benefits to drive in-migration. Using data from Tanzania, we examined merits of statistical tests and spatial methods used previously to evaluate migration near PAs and applied hierarchical modeling with appropriate controls for demographic and geographic factors to advance the debate. Areas bordering national parks in Tanzania did not have elevated rates of in-migration. Low baseline population density and high vegetation productivity with low interannual variation rather than conservation outreach explained observed migration patterns. More generally we argue that to produce results of conservation policy significance, analyses must be conducted at appropriate scales, and we caution against use of demographic data without appropriate controls when drawing conclusions about migration dynamics. © 2014 Society for Conservation Biology.

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