Assessing the Impact of a Geospatial Data Collection App on Student Engagement in Environmental Education

A critical component of environmental education is to ensure student understanding and use of available technologies to better experience and analyze spatially distributed features of the environment. Combining mobile technologies with geographic information systems in field data collection may provide a unique opportunity for students to feel engaged in what they are learning and take ownership of their learning process. We customized an open access data collection application using Collector for ArcGIS and investigated its impacts on student engagement and perception of the incorporation of technology within an environmental science curriculum. Analyses of pre- and post-surveys indicate that the inclusion of geospatial technologies as a part of environmental curricula allows students to take the lead on their own research, view field data interactively as opposed to looking at a database in hindsight and analyze multiscale data as it is presented during field data collection. The findings of this study are consistent with previous studies, suggesting a strong association between the inclusion of geospatial technologies as a part of curricula and student engagement

Mt. Apo Natural Park in the Southern Philippines Using Terrestrial LiDAR System

Extraction of plot-level field measurements entails a rigid and time-consuming task. Fine resolution remote sensing technology offers an objective and consistent method for estimation of forest vertical structures. We explored the development of algorithms for estimating above ground biomass (AGB) at the plot level using terrestrial LiDAR system (TLS). This research follows IPCC Tier 2 approach, by combining field and remote sensing data, in estimating forest carbon stocks. Permanent plots (30 × 30 m diameter) were established inside Mt. Apo Natural Park. Forest inventory was conducted in July 2013, recording tree heights and stem diameters for all hardwood species with diameter at breast height (DBH) ≥ 5 cm in three management zones: multiple use, strict protection, and restoration. Quadratic mean stem diameter was employed for large DBH intervals for deriving midpoint biomass. Three tropical allometric equations were used to derive referenced biomass values. Regressions results showed satisfactory modeling fit in relating plot-level AGB to DBH class size: 80%–89%. Mean tree heights from field and TLS data were related showing R2 = 88%. TLS variables derived include percentile heights and normalized height bins at 5-m intervals. The generalized linear model is a more robust model for percentile heights, while stepwise regression showed a better regression performance for normalized height bins. Strict protection zone contained the highest carbon storage. This study demonstrated the significant TLS-derived metrics to assess plot-level biomass. TLS scanning is also the first work to be done in this ASEAN Natural Heritage Park, which is constrained with local insurgency problems. Biomass in plot-level can be used to extrapolate to landscape-level using available multispectral or radar imagery

Monitoring Vegetation Dynamics and Carbon Stock Density in Miombo Woodlands

Background The United Nation’s Program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to reduce the 20% contribution to global emissions of greenhouse gases from the forest sector, offering a financial value of the carbon stored in forests as an incentive for local communities. The pre-requisite for the setup of a participatory REDD + Program is the monitoring, reporting and verification (MRV) of baseline carbon stocks and their changes over time. In this study, we investigated miombo woodland’s dynamics in terms of composition, structure and biomass over a 4-year period (2005–2009), and the Carbon Stock Density (CSD) for the year 2009. The study was conducted in the Niassa National Reserve (NNR) in northern Mozambique, which is the 14th largest protected area in the world. Results Mean tree density distributed across 79 species increased slightly between 2005 and 2009, respectively, from 548 to 587 trees ha-1. Julbernardia globiflora (Benth.) was the most important species in this area [importance value index (IVI2005= 61 and IVI2009 = 54)]. The woodlands presented an inverted J-shaped diametric curve, with 69% of the individuals representing the young cohort. Woody biomass had a net increase of 3 Mg ha-1 with the highest growth observed in Dyplorhynchus condilocarpon (Müll.Arg.) Pichon (0.54 Mg ha-1). J. globiflora had a net decrease in biomass of 0.09 Mg ha-1. Total CSD density was estimated at ca. 67 MgC ha-1 ± 24.85 with soils (average 34.72 ± 17.93 MgC ha-1) and woody vegetation (average 29.8 MgC ha-1 ± 13.07) representing the major carbon pools. The results point to a relatively stable ecosystem, but they call for the need to refocus management activities. Conclusions The miombo woodlands in NNR are representative of the woodlands in the eco-region in terms of vegetation structure and composition. They experienced net increase in woody biomass, a considerable recruitment level and low mortality. According to our results, NNR may present good potential for carbon sequestration especially in soils and woody biomass, representing an important potential carbon sink. However, further investigations are needed in order to address the contribution of this area to MRV REDD + initiatives

Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha−1 (ranging from 11 to 95 Mg ha−1) and 28 MgC ha−1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha−1 for NSR, ranging from 0.6 to 200 Mg ha−1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha−1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB.info:eu-repo/semantics/publishedVersio

Quantification of the Ecological Resilience of Drylands Using Digital Remote Sensing

Drylands cover 41% of the terrestrial surface and support > 36% of the world's population. However, the magnitude of dryland degradation is unknown at regional and global spatial scales and at 15-30-yr temporal scales. Historical archives of > 30 yr of Landsat satellite imagery exist and allow local to global monitoring and assessment of a landscape's natural resources in response to climatic events and human activities. Vegetation indices (VIs), i.e., proxies of vegetation characteristics such as phytomass, can be derived from the spectral properties of Landsat imagery. A dynamical systems analysis method called mean-variance analysis can be used to describe and quantify dynamic regimes of VI response to disturbance using characteristics of ecological resilience, particularly amplitude and malleability, from a change detection perspective. Amplitude is the magnitude of response of a VI to a disturbance; malleability is the degree of recovery of a resource after a disturbance. Spatially aggregate and spatially explicit (image) differencing are methods whereby a VI image or statistic from one time period is subtracted from a VI image or statistic from another time period. To illustrate this method, we used a time series of Landsat imagery from 1972 to 1987 to measure the response of vegetation communities that are managed by subsistence agropastoral communities to the severe 1982-1984 El Niño-induced drought on the Bolivian Altiplano. We found that the entire landscape had decreased vegetation cover, increased variance (diagnostic of a regime shift), and thus, increased susceptibility to soil erosion during the drought. The wet meadow vegetation cover class had the lowest amplitude and thus the most resilience relative to other vegetation cover classes. This response identified the wet meadow as a key resource, as well as a harbinger of climate change for agropastoral communities in areas where drought is an endemic stressor

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p 10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group

The seeds of divergence: the economy of French North America, 1688 to 1760

Generally, Canada has been ignored in the literature on the colonial origins of divergence with most of the attention going to the United States. Late nineteenth century estimates of income per capita show that Canada was relatively poorer than the United States and that within Canada, the French and Catholic population of Quebec was considerably poorer. Was this gap long standing? Some evidence has been advanced for earlier periods, but it is quite limited and not well-suited for comparison with other societies. This thesis aims to contribute both to Canadian economic history and to comparative work on inequality across nations during the early modern period. With the use of novel prices and wages from Quebec—which was then the largest settlement in Canada and under French rule—a price index, a series of real wages and a measurement of Gross Domestic Product (GDP) are constructed. They are used to shed light both on the course of economic development until the French were defeated by the British in 1760 and on standards of living in that colony relative to the mother country, France, as well as the American colonies. The work is divided into three components. The first component relates to the construction of a price index. The absence of such an index has been a thorn in the side of Canadian historians as it has limited the ability of historians to obtain real values of wages, output and living standards. This index shows that prices did not follow any trend and remained at a stable level. However, there were episodes of wide swings—mostly due to wars and the monetary experiment of playing card money. The creation of this index lays the foundation of the next component. The second component constructs a standardized real wage series in the form of welfare ratios (a consumption basket divided by nominal wage rate multiplied by length of work year) to compare Canada with France, England and Colonial America. Two measures are derived. The first relies on a “bare bones” definition of consumption with a large share of land-intensive goods. This measure indicates that Canada was poorer than England and Colonial America and not appreciably richer than France. However, this measure overestimates the relative position of Canada to the Old World because of the strong presence of land-intensive goods. A second measure is created using a “respectable” definition of consumption in which the basket includes a larger share of manufactured goods and capital-intensive goods. This second basket better reflects differences in living standards since the abundance of land in Canada (and Colonial America) made it easy to achieve bare subsistence, but the scarcity of capital and skilled labor made the consumption of luxuries and manufactured goods (clothing, lighting, imported goods) highly expensive. With this measure, the advantage of New France over France evaporates and turns slightly negative. In comparison with Britain and Colonial America, the gap widens appreciably. This element is the most important for future research. By showing a reversal because of a shift to a different type of basket, it shows that Old World and New World comparisons are very sensitive to how we measure the cost of living. Furthermore, there are no sustained improvements in living standards over the period regardless of the measure used. Gaps in living standards observed later in the nineteenth century existed as far back as the seventeenth century. In a wider American perspective that includes the Spanish colonies, Canada fares better. The third component computes a new series for Gross Domestic Product (GDP). This is to avoid problems associated with using real wages in the form of welfare ratios which assume a constant labor supply. This assumption is hard to defend in the case of Colonial Canada as there were many signs of increasing industriousness during the eighteenth and nineteenth centuries. The GDP series suggest no long-run trend in living standards (from 1688 to circa 1765). The long peace era of 1713 to 1740 was marked by modest economic growth which offset a steady decline that had started in 1688, but by 1760 (as a result of constant warfare) living standards had sunk below their 1688 levels. These developments are accompanied by observations that suggest that other indicators of living standard declined. The flat-lining of incomes is accompanied by substantial increases in the amount of time worked, rising mortality and rising infant mortality. In addition, comparisons of incomes with the American colonies confirm the results obtained with wages— Canada was considerably poorer. At the end, a long conclusion is provides an exploratory discussion of why Canada would have diverged early on. In structural terms, it is argued that the French colony was plagued by the problem of a small population which prohibited the existence of scale effects. In combination with the fact that it was dispersed throughout the territory, the small population of New France limited the scope for specialization and economies of scale. However, this problem was in part created, and in part aggravated, by institutional factors like seigneurial tenure. The colonial origins of French America’s divergence from the rest of North America are thus partly institutional