Modeling and classifying variable width riparian zones utilizing digital elevation models, flood height data, digital soil data and national wetlands inventory : a new approach for riparian zone delineation

Riparian zones are dynamic, transitional ecosystems between aquatic and terrestrial ecosystems with well defined vegetation and soil characteristics. Development of an all-encompassing definition for riparian ecotones, because of their high variability, is challenging. However, there are two primary factors that all riparian ecotones are dependent on: the watercourse and its associated floodplain. Previous approaches to riparian boundary delineation have utilized fixed width buffers, but this methodology has proven to be inadequate as it only takes the watercourse into consideration and ignores critical geomorphology, associated vegetation and soil characteristics. Our approach offers advantages over other previously used methods by utilizing: the geospatial modeling capabilities of ArcMap GIS; a better sampling technique along the water course that can distinguish the 50-year flood plain, which is the optimal hydrologic descriptor of riparian ecotones; the Soil Survey Database (SSURGO) and National Wetland Inventory (NWI) databases to distinguish contiguous areas beyond the 50-year plain; and land use/cover characteristics associated with the delineated riparian zones. The model utilizes spatial data readily available from Federal and State agencies and geospatial clearinghouses. An accuracy assessment was performed to assess the impact of varying the 50-year flood height, changing the DEM spatial resolution (1, 3, 5 and 10m), and positional inaccuracies with the National Hydrography Dataset (NHD) streams layer on the boundary placement of the delineated variable width riparian ecotones area. The result of this study is a robust and automated GIS based model attached to ESRI ArcMap software to delineate and classify variable-width riparian ecotones.

Assessing visual disturbance conditions on the Custer National Forest

Assessment of soil disturbance on the Custer National Forest was conducted during two summers to determine if the U.S. Forest Service Forest Soil Disturbance Monitoring Protocol (FSDMP) was able to distinguish post-harvest soil conditions in a chronological sequence of sites harvested using different ground-based logging systems. Results from the first year of sampling suggested that the FSDMP point sampling method may not be sensitive enough to measure post-harvest disturbance in stands with low levels of disturbance. Therefore, a revised random transect method was used during the second sampling season to determine the actual extent of soil disturbance in these cutting units. Using combined data collected from both summers I detected statistically significant differences (p < 0.05) in fine fraction bulk density measurements between FSDMP disturbance classes across all sites. Disturbance class 3 (most severe) had the highest reported bulk density, which suggest that the FSDMP visual class estimates are defined adequately allowing for correlations to be made between visual disturbance and actual soil physical characteristics. Forest site productivity can be defined by its ability to retain carbon and convert it to above- and belowground biomass. However, forest management activities that alter basic site characteristics have the potential to alter productivity. Soil compaction is one critical management impact that is important to understand; compaction has been shown to impede the root growth potential of plants, reduce water infiltration rates increasing erosion potential, and alter plant available water and nutrients, depending on soil texture. A new method to assess ground cover, erosion, and other soil disturbances was recently published by the U.S. Forest Service, as the Forest Soil Disturbance Protocol (FSDMP). The FSDMP allows soil scientists to visually assign a disturbance class estimate (0 – none, 1, 2, 3 – severe) from field measures of consistently defined soil disturbance indicators (erosion, fire, rutting, compaction, and platy/massive/puddled structure) in small circular (15 cm) plots to compare soil quality properties pre- and post- harvest condition. Using this protocol we were able to determine that ground-based timber harvesting activities occurring on the Custer National Forest are not reaching the 15% maximum threshold for detrimental soil disturbance outlined by the Region 1 Soil Quality Standards.

Will Whole-Tree Harvest of Jack Pine (Pinus banksiana) deplete soil nutrients in low-productivity sand soils?

In 2009 and 2010 a study was conducted on the Hiawatha National Forest (HNF) to determine if whole-tree harvest (WTH) of jack pine would deplete the soil nutrients in the very coarse-textured Rubicon soil. WTH is restricted on Rubicon sand in order to preserve the soil fertility, but the increasing construction of biomass-fueled power plants is expected to increase the demand for forest biomass. The specific objectives of this study were to estimate biomass and nutrient content of above- and below-ground tree components in mature jack pine (Pinus banksiana) stands growing on a coarse-textured, low-productivity soil, determine pools of total C and N and exchangeable soil cations in Rubicon sand, and to compare the possible impacts of conventional stem-only harvest (CH) and WTH on soil nutrient pools and the implications for productivity of subsequent rotations. Four even-aged jack pine stands on Rubicon soil were studied. Allometric equations were used to estimate above-ground biomass and nutrients, and soil samples from each stand were taken for physical and chemical analysis. Results indicate that WTH will result in cation deficits in all stands, with exceptionally large Ca deficits occurring in two stands. Where a deficit does not occur, the cation surplus is small and, chemical weathering and atmospheric deposition is not anticipated to replace the removed cations. CH will result in a surplus of cations, and will likely not result in productivity declines during the first rotation. However even under CH, the surplus is small, and chemical weathering and atmospheric deposition will not supply enough cations for the second rotation.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Community managed rural water supply systems in the Dominican Republic : assessment of sustainability of systems built by the National Institute of Potable Water and Peace Corps, Dominican Republic

In the Dominican Republic economic growth in the past twenty years has not yielded sufficient improvement in access to drinking water services, especially in rural areas where 1.5 million people do not have access to an improved water source (WHO, 2006). Worldwide, strategic development planning in the rural water sector has focused on participatory processes and the use of demand filters to ensure that service levels match community commitment to post-project operation and maintenance. However studies have concluded that an alarmingly high percentage of drinking water systems (20-50%) do not provide service at the design levels and/or fail altogether (up to 90%): BNWP (2009), Annis (2006), and Reents (2003). World Bank, USAID, NGOs, and private consultants have invested significant resources in an effort to determine what components make up an “enabling environment” for sustainable community management of rural water systems (RWS). Research has identified an array of critical factors, internal and external to the community, which affect long term sustainability of water services. Different frameworks have been proposed in order to better understand the linkages between individual factors and sustainability of service. This research proposes a Sustainability Analysis Tool to evaluate the sustainability of RWS, adapted from previous relevant work in the field to reflect the realities in the Dominican Republic. It can be used as a diagnostic tool for government entities and development organizations to characterize the needs of specific communities and identify weaknesses in existing training regimes or support mechanisms. The framework utilizes eight indicators in three categories (Organization/Management, Financial Administration, and Technical Service). Nineteen independent variables are measured resulting in a score of sustainability likely (SL), possible (SP), or unlikely (SU) for each of the eight indicators. Thresholds are based upon benchmarks from the DR and around the world, primary data collected during the research, and the author’s 32 months of field experience. A final sustainability score is calculated using weighting factors for each indicator, derived from Lockwood (2003). The framework was tested using a statistically representative geographically stratified random sample of 61 water systems built in the DR by initiatives of the National Institute of Potable Water (INAPA) and Peace Corps. The results concluded that 23% of sample systems are likely to be sustainable in the long term, 59% are possibly sustainable, and for 18% it is unlikely that the community will be able to overcome any significant challenge. Communities that were scored as unlikely sustainable perform poorly in participation, financial durability, and governance while the highest scores were for system function and repair service. The Sustainability Analysis Tool results are verified by INAPA and PC reports, evaluations, and database information, as well as, field observations and primary data collected during the surveys. Future research will analyze the nature and magnitude of relationships between key factors and the sustainability score defined by the tool. Factors include: gender participation, legal status of water committees, plumber/operator remuneration, demand responsiveness, post construction support methodologies, and project design criteria.

Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.