David M. Banach

Agricultural Fires in European Russia, Belarus, and Lithuania and Their Impact on Air Quality, 2002–2012

This chapter describes the first research to quantify air pollution emissions at a moderate to coarse scale from agricultural burning in Belarus, Lithuania, and European Russia using MODIS and Landsat-based estimates of fire, land-cover and land-use. Agricultural burning in Belarus, Lithuania, and European Russia showed a strong and consistent seasonal geographic pattern from 2002 to 2012, with the majority of fires occurring from March to June and a smaller peak in July and August. Over this 11-year period, there was a decrease in both cropland and pasture burning throughout the region. For Smolensk Oblast, a Russian administrative region with comparable agro-environmental conditions to Belarus and Lithuania, a detailed analysis of Landsat-based burned area estimations for croplands, pastures and field data collected in summer 2014 showed that the agricultural burning area can be up to 10 times larger than the 1 km MODIS active fire estimates. Using the annual MODIS and Landsat-based burned area estimations, we identified 25 carbon, particulate matter, volatile organic carbon (VOCs), and harmful air pollutants (HAPs) emissions for all agricultural burning, including both croplands and pastures. In general, European Russia is the main source of agricultural burning emissions. Lithuania and Belarus have relatively minor contributions. Indeed, emissions from certain agricultural burning air pollutants in European Russia are so large that they are equivalent to 5 % of emissions from all sectors (industry, energy, transportation, all sources of fire) in Lithuania and likely in other neighboring Eastern European countries.

Using Multitemporal Imagery to Improve Mapping and Inventory of Forested Roads in Michigan’s Upper Peninsula

Although a road network provides transportation for people and goods, several negative ecological impacts stem from a road network. Significant effects of concern for rural roads include an increase in the number of road–stream crossings, which can potentially cause fish passage problems, in-stream habitat impacts, increased stream sedimentation, and an increase in potentially affected wetlands through hydrologic alteration. Although several studies have examined and quantified the effects that roads have on terrestrial and aquatic ecosystems, many studies may have been made on the basis of incomplete road network data sets. An updated and improved road network data set is needed before researchers can draw inferences that quantify the total ecological impact that a road network has on an ecosystem. The purpose of this work was to develop an updated and improved regional road network data layer through interpretation of existing multitemporal aerial imagery, with the resulting geographic information system layer being applied in ecological analyses and planning. Results from two watersheds in Michigan’s Upper Peninsula indicated that each watershed experienced an approximate increase of 200% in total road mileage when compared with the results from the original road network data set. Individual hydrological unit code level-12 subwatersheds within each watershed experienced an increase ranging from 16% to 2,700%. The number of road–stream crossings for each watershed also increased by at least 100%. The updated road network data set also reported the location and total area of wetlands that have potential hydrological impacts, ranging from 70,000 ha to 121,000 ha for each watershed.

Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

Abstract Predictions of chlorophyll a (Chl-a) in lentic waterbodies (lakes and reservoirs) are valuable to researchers and resource managers alike but have been rarely conducted at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of Chl-ain waterbodies at global and regional scales, we first developed a database of 227 globally distributed waterbodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and selected models that most parsimoniously related Chl-ato predictor variables for all 227 waterbodies and for a subset of 51 within the Laurentian Great Lakes region. Our best global model contained 3 hydrogeomorphic variables (waterbody area, shoreline development index, and watershed to waterbody area ratio) and a climate variable (mean temperature in the warmest quarter) that explained about 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (watershed area), the same climate variable, and a nutrient variable (percent of watershed area cover by waterbodies) that explained 58% of variation in Chl-a. Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-athan a global approach and that nearly a third of global variation in Chl-amay be explained using hydrogeomorphic and climate variables.