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Clinton N. Jenkins

Publications and source records attributed to Clinton N. Jenkins.

4 recordsLinked to original sources
USGS2014

Land cover mapping of Greater Mesoamerica using MODIS data

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: ( i ) qualitative assessment provided good insight in identifying and correcting gross errors; ( ii ) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest ( r 2  = 0.88), shrubland ( r 2  = 0.75), and cropland ( r 2  = 0.97) but weak positive association for grassland ( r 2  = 0.26); and ( iii ) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Canadian Journal of Remote Sensing↗
USGS2008

Protection of mammal diversity in Central America

Central America is exceptionally rich in biodiversity, but varies widely in the attention its countries devote to conservation. Protected areas, widely considered the cornerstone of conservation, were not always created with the intent of conserving that biodiversity. We assessed how well the protected-area system of Central America includes the region's mammal diversity. This first required a refinement of existing range maps to reduce their extensive errors of commission (i.e., predicted presences in places where species do not occur). For this refinement, we used the ecological limits of each species to identify and remove unsuitable areas from the range. We then compared these maps with the locations of protected areas to measure the habitat protected for each of the region's 250 endemic mammals. The species most vulnerable to extinction—those with small ranges—were largely outside protected areas. Nevertheless, the most strictly protected areas tended toward areas with many small-ranged species. To improve the protection coverage of mammal diversity in the region, we identified a set of priority sites that would best complement the existing protected areas. Protecting these new sites would require a relatively small increase in the total area protected, but could greatly enhance mammal conservation.

Conservation Biology↗
USGS2006

Integration of the stratigraphic aspects of very large sea-floor databases using information processing

Information-processing methods are described that integrate the stratigraphic aspects of large and diverse collections of sea-floor sample data. They efficiently convert common types of sea-floor data into database and GIS (geographical information system) tables, visual core logs, stratigraphic fence diagrams and sophisticated stratigraphic statistics. The input data are held in structured documents, essentially written core logs that are particularly efficient to create from raw input datasets. Techniques are described that permit efficient construction of regional databases consisting of hundreds of cores. The sedimentological observations in each core are located by their downhole depths (metres below sea floor - mbsf) and also by a verbal term that describes the sample 'situation' - a special fraction of the sediment or position in the core. The main processing creates a separate output event for each instance of top, bottom and situation, assigning top-base mbsf values from numeric or, where possible, from word-based relative locational information such as 'core catcher' in reference to sampler device, and recovery or penetration length. The processing outputs represent the sub-bottom as a sparse matrix of over 20 sediment properties of interest, such as grain size, porosity and colour. They can be plotted in a range of core-log programs including an in-built facility that better suits the requirements of sea-floor data. Finally, a suite of stratigraphic statistics are computed, including volumetric grades, overburdens, thicknesses and degrees of layering. ?? The Geological Society of London 2006.

Geological Society Special Publication↗