ISPRS Int. J. Geo-Inf.2014, 3(1), 297-325; doi:10.3390/ijgi3010297 - published online 6 March 2014 Show/Hide Abstract
Abstract: Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping.
ISPRS Int. J. Geo-Inf.2014, 3(1), 274-296; doi:10.3390/ijgi3010274 - published online 4 March 2014 Show/Hide Abstract
Abstract: Greenhouse commercial horticulture in Kenya started more than two decades ago and has evolved to be a significant sector to the national economy. So far no studies have explored the spatial patterns and dynamics of the area under greenhouse cultivation. Google Earth archives alongside data from various portals provided an opportunity to study those farms’ spatial distribution. The roles of selected topo-edaphic, infrastructure and demographics factors that might influence current location within sub-watersheds in central highlands of Kenya are also examined. Results reveal a non-uniform spread with two high clusters; one in the semi-arid sub-watersheds 3AB shared by Kajiado and Machakos districts and the other is in sub-humid sub-watersheds 3BA shared by Kiambu and Nairobi districts. Multivariate linear regression analysis reveals four statistically significant parameters; population density (p < 0.01), number of dams (p < 0.01), average rainfall (p < 0.01) and average slope (p < 0.05) in predicting the number of greenhouse farms. Soil attributes are not significantly related with greenhouse farming in this study. Findings indicate that greenhouse commercial horticulture is heterogeneous, and rapidly expanding beyond the central highlands towards marginal semi-arid zones in Kenya. These findings are applicable in policy and decision making processes that aid the horticulture sector’s progress in a sustainable manner.
ISPRS Int. J. Geo-Inf.2014, 3(1), 254-273; doi:10.3390/ijgi3010254 - published online 4 March 2014 Show/Hide Abstract
Abstract: The Nagagamisis Central Plateau (located in Northern Ontario, Canada) is an area of distinct natural and cultural significance. The importance of this land was officially recognized in 1957 through the establishment of the Nagagamisis Provincial Park Reserve. The park has experienced significant expansion since its inception and is currently under development as one of Ontario Parks ‘Signature Sites’. Since the 1980s, timber harvest activity has led to widespread forest disturbance just outside of the park boundaries. This research is focused on the detection of stand level forest disturbances associated with timber harvest occurring near Nagagamisis Provincial Park. The image time-series data selected for this project were Landsat TM and ETM+; spanning a twenty-five year period from 1984 to 2009. The Tasselled Cap Transformation and Normalized Difference Moisture Index were derived for use in unsupervised image classification to determine the land cover for each image in the time-series. Image band differencing and raster arithmetic were performed to create maps illustrating the size and spatial distribution of stand level forest disturbances between image dates. A total area of 1649 km2 or 26.1% of the study area experienced stand level disturbance during the analysis period.
ISPRS Int. J. Geo-Inf.2014, 3(1), 236-253; doi:10.3390/ijgi3010236 - published online 28 February 2014 Show/Hide Abstract
Abstract: Governmental and commercial lists of food retailers are often used to measure food environments and foodscapes for health and nutritional research. Information about the validity of such secondary food source data is relevant to understanding the potential and limitations of its application. This study assesses the validity of two government lists of food retailer locations and types by comparing them to direct field observations, including an assessment of whether pre-classification of the directories can reduce the need for field observation. Lists of food retailers were obtained from the Central Business Register (CVR) and the Smiley directory. For each directory, the positive prediction value (PPV) and sensitivity were calculated as measures of completeness and thematic accuracy, respectively. Standard deviation was calculated as a measure of geographic accuracy. The effect of the pre-classification was measured through the calculation of PPV, sensitivity and negative prediction value (NPV). The application of either CVR or Smiley as a measure of the food environment would result in a misrepresentation. The pre-classification based on the food retailer names was found to be a valid method for identifying approximately 80% of the food retailers and limiting the need for field observation.
ISPRS Int. J. Geo-Inf.2014, 3(1), 209-232; doi:10.3390/ijgi3010209 - published online 25 February 2014 Show/Hide Abstract
Abstract: Local bioenergy will play a crucial role in national and regional sustainable energy strategies. Effective siting and feedstock procurement strategies are critical to the development and implementation of bioenergy systems. This paper aims to improve spatial decision-support in this domain by shifting focus from homogenous (forestry or agricultural) regions toward heterogeneous regions—i.e., areas with a presence of both forestry and agricultural activities; in this case, eastern Ontario, Canada. Multiple land-cover and resource map series are integrated in order to produce a spatially distributed GIS-based model of resource availability. These data are soft-linked with spreadsheet-based linear models in order to estimate and compare the quantity and supply-cost of the full range of non-food bioenergy feedstock available to a prospective developer, and to assess the merits of a mixed feedstock stream relative to a homogenous feedstock stream. The method is applied to estimate bioenergy production potentials and biomass supply-cost curves for a number of cities in the study region. Comparisons of biomass catchment areas; supply-cost curves; resource density maps; and resource flow charts demonstrate considerable strategic and operational advantages to locating a facility within the region’s “transition zone” between forestry and agricultural activities. Existing and emerging bioenergy technologies that are feedstock agnostic and therefore capable of accepting a mixed-feedstock stream are reviewed with emphasis on “intermediates” such as wood pellets; biogas; and bio-oils, as well as bio-industrial clusters.