Remote Sensing of Heat Fluxes Validation and Inter-Sensor Comparison

Instantaneous heat fluxes were modeled using data obtained from Landsat 5 TM (Thematic Mapper), Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Terra MODIS (Moderate Resolution Imaging Spectroradioineter) using the Surface Energy Balance Algorithm for Land (SEBAL) model for cloud-free days. The modeled results were compared with measurements of net radiation (both incoming and outgoing, shortwave and longwave), soil sensible and latent heat fluxes from two flux towers located in Brookings, SD, and Fort Peck, MT. Flux tower data consisted of 30 minute averages at every half an hour, and footprints of contributing movement of air within the period were estimated for each satellite overpass by taking into account the factors of observation height, atmospheric stability, and surface roughness, as well as wind speed and directions (Hsieh et al. 2000). It was found that footprints (considering 90% contributing areas) were normally larger than the size of one Landsat pixel (30 m) but smaller than that of one MODIS pixel (1 km). Therefore, for Landsat the data were averaged for pixels within the concurrent footprint, and for MODIS the data for the particular pixel covering the flux tower was used. The R values between the modeled and the observed net radiation (Rn) for Landsat and MODIS were found to be 0.70 and 0.66, respectively. Relatively, comparisons between modeled and observed values were better at Brookings than at Fort Peck for both sensors. This may be because the former site has a relatively flat topography and larger fetch than the latter, minimizing the possible effects of terrain heterogeneity on incoming and outgoing radiation modeling. Both satellites performed poorly in modeling soil heat flux (G0) . Our results show that SEBAL provides a better modeling of sensible heat flux (H) with Landsat (R2= 0.62) than with MODIS (R2 = 0.11), even though the MODIS performance for estimating latent heat flux (lambdaE) improved (R2 = 0.37). The improvement found in estimating latent heat flux is probably due to the fact that in SEBAL cold pixels are used to estimate air temperature and then also used in computation for both Rn and H. The uncertainties associated with this assumption cancelled out in deriving lambdaE. Overall, SEBAL performed better in modeling the heat fluxes when Landsat data were used. This may be due to the scaling issue, as the footprint areas were always significantly less than a single MODIS pixel. By simulating MODIS observations using Landsat, it was found that the R2 value for the aggregated Landsat pixels decreased from 0.62 to 0.25 with an increase of root mean square difference (RMSD) from 50.5 to 68.3 Wm\u272. This suggested that the poor performance of MODIS in estimating heat fluxes was due to heterogeneity of the surface within a field of view. In addition, sensitivity analyses of the model to input parameters suggested that the model is more sensitive to surface-to- air temperature difference than to surface roughness conditions. Appendix A lists symbols mentioned in this thesis

Sources of variation for indoor nitrogen dioxide in rural residences of Ethiopia

<p>Abstract</p> <p>Background</p> <p>Unprocessed biomass fuel is the primary source of indoor air pollution (IAP) in developing countries. The use of biomass fuel has been linked with acute respiratory infections. This study assesses sources of variations associated with the level of indoor nitrogen dioxide (NO₂).</p> <p>Materials and methods</p> <p>This study examines household factors affecting the level of indoor pollution by measuring NO₂. Repeated measurements of NO₂were made using a passive diffusive sampler. A <it>Saltzman </it>colorimetric method using a spectrometer calibrated at 540 nm was employed to analyze the mass of NO₂on the collection filter that was then subjected to a mass transfer equation to calculate the level of NO₂for the 24 hours of sampling duration. Structured questionnaire was used to collect data on fuel use characteristics. Data entry and cleaning was done in EPI INFO version 6.04, while data was analyzed using SPSS version 15.0. Analysis of variance, multiple linear regression and linear mixed model were used to isolate determining factors contributing to the variation of NO₂concentration.</p> <p>Results</p> <p>A total of 17,215 air samples were fully analyzed during the study period. Wood and crop were principal source of household energy. Biomass fuel characteristics were strongly related to indoor NO₂concentration in one-way analysis of variance. There was variation in repeated measurements of indoor NO₂over time. In a linear mixed model regression analysis, highland setting, wet season, cooking, use of fire events at least twice a day, frequency of cooked food items, and interaction between ecology and season were predictors of indoor NO₂concentration. The volume of the housing unit and the presence of kitchen showed little relevance in the level of NO₂concentration.</p> <p>Conclusion</p> <p>Agro-ecology, season, purpose of fire events, frequency of fire activities, frequency of cooking and physical conditions of housing are predictors of NO₂concentration. Improved kitchen conditions and ventilation are highly recommended.</p

The Influence of Region of Interest Heterogeneity on Classification Accuracy in Wetland Systems

Classifying and mapping natural systems such as wetlands using remote sensing frequently relies on data derived from regions of interest (ROIs), often acquired during field campaigns. ROIs tend to be heterogeneous in complex systems with a variety of land cover classes. However, traditional supervised image classification is predicated on pure single-class observations to train a classifier. This ultimately encourages end-users to create single-class ROIs, nudging ROIs away from field-based points or gerrymandering the ROI, which may produce ROIs unrepresentative of the landscape and potentially insert error into the classification. In this study, we explored WorldView-2 images and 228 field-based data points to define ROIs of varying heterogeneity levels in terms of class membership to classify and map 22 discrete classes in a large and complex wetland system. The goal was to include rather than avoid ROI heterogeneity and assess its impact on classification accuracy. Parametric and nonparametric classifiers were tested with ROI heterogeneity that varied from 7% to 100%. Heterogeneity was governed by ROI area, which we increased from the field-sampling frame of ~100 m2 nearly 19-fold to ~2124 m2. In general, overall accuracy (OA) tended downwards with increasing heterogeneity but stayed relatively high until extreme heterogeneity levels were reached. Moreover, the differences in OA were not statistically significant across several small-to-large heterogeneity levels. Per-class user&rsquo;s and producer&rsquo;s accuracies behaved similarly. Our findings suggest that ROI heterogeneity did not harm classification accuracy unless heterogeneity became extreme, and thus there are substantial practical advantages to accommodating heterogeneous ROIs in image classification. Rather than attempting to avoid ROI heterogeneity by gerrymandering, classification in wetland environments, as well as analyses of other complex environments, should embrace ROI heterogeneity