Interband cascade lasers with room temperature threshold current densities below 100 A/cm(2)

Interband Cascade Lasers (ICLs) with threshold current densities below 100 A/cm(2) in pulsed operation at room temperature are presented. The laser structure comprises 10 active stages of 41 nm length, each stage containing a W-quantum well active region for emission in the spectral region around 3.6 mu m. A comparison of devices with 6 and 10 stages shows that the latter have a reduced threshold due to an increased optical confinement factor, very competitive threshold power densities of 428 W cm(-2) despite an increased threshold voltage and large differential slope efficiencies of 1390 mW/A. For a narrow ridge device, continuous wave operation is observed up to 65 degrees C.Publisher PDFPeer reviewe

Assessing the Vertical Accuracy of Arkansas Five-Meter Digital Elevation Model for Different Physiographic Regions

Digital Elevation Models (DEMs) represent the elevation of the earth’s surface. Scientists and decision makers have used DEMs to address questions relating to the earth’s landscape. This study assessed the vertical accuracy of Arkansas 5-meter raster DEM dataset produced in 2006 photogrammetrically, for three physiographic regions that represented a variation of elevations. The vertical accuracy of the DEM datasets was assessed by comparing their elevations to elevations collected using a surveying carrier phase Global Position System (GPS). To make comparisons between physiographic regions, paired t-tests using absolute elevation value difference and elevation difference along with the Absolute Mean Range Value (AMRV) was also computed. The results of the study revealed that 5-meter DEM is statistically different from the true elevation for the state with a mean absolute difference elevation error of 2.90 meters. The mean absolute elevation error for the Boston Mountains, the Ouachita Mountains, and the Mississippi Alluvial Plain physiographic regions are 4.98, 2.81, and 1.06 meters, respectively. The absolute mean range value (AMRV) revealed that in the Mississippi Alluvial Plain, the DEM might be problematic, since there is more error fluctuation (AMRV = 12.421%) across a smaller distribution of true elevation values compared to 1.283% for the Boston Mountains and 1.271% for the Ouachita Mountains physiographic regions

Integrating GIS and Remote Sensing with Ecosystem Research

In the Phase II Ecosystem Management Research Program in the Ouachita and Ozark National Forests, an interdisciplinary group of scientists are evaluating the effects and trade-offs of partial cutting methods in a replicated stand level study. Information from approximately 2,000 plots is being collected by more than fifty researchers during this five-year project with plans to continue data collection long term. To evaluate the effects of different management strategies and their interactions with forest resources, data must be brought into a common format and made available to all researchers. To this end, a data support system was developed which utilizes Geographic Information System (GIS), Global Positioning Systems (GPS) and remote sensing technologies. Aerial photography, along with digitized layers of stand and greenbelt boundaries, roads and streams, and GPSed silvicultural plot locations form a framework to which data from diverse research areas can be linked. Researchers can not only share information resources, but can graphically visualize and query both spatial and attribute data to reflect forest ecosystem changes under various management strategies. The methodology used to develop and configure this large, relational database into an easily accessible form usable in an interactive GIS program could be transferable to other areas of natural resource management

Historical Forest Landscape Changes in the Buffalo River Sub-Basin in Arkansas

Forested areas in the United States have been altered since the time of European settlement. For this reason, research interests have increased in comparing present day vegetation with that of the preEuroamerican era to see what changes, if any, have occurred in some of our more outstanding natural areas. Such studies have been conducted in other parts of the United States but limited research has been done in Arkansas. The General Land Office (GLO) surveys of Arkansas were originally conducted between approximately 1815 and 1850 shortly after Arkansas was acquired from France by means of the Louisiana Purchase and provides the only systematic on-ground survey in Arkansas that predates most formal botanical investigations. The GLO surveys used witness trees to define the location of section corners and lines. Descriptions of witness trees included tree species and diameter along with distance and direction to the section corner or line. This historical GLO data was compared to United States Forest Service (USFS) Forest Inventory and Analysis (FIA) data, which represent present vegetation conditions for 62 townships in the Buffalo River Sub-basin. Comparisons indicated that eastern red cedar (Juniperus virginiana) increased from 0.7% to 7.8% of the total forest species in the sub-basin, hickory (Carya spp.) increased from 8.2% to 14.3%, while oak (Quercus spp.) species decreased from 43.0% to 30.1%. Based on this study it appears that postEuroamerican settlement fire suppression and agricultural practices in addition to other human activities has caused vegetation changes in this area

Integrating Supervised and Unsupervised Classification Methods to Develop a More Accurate Land Cover Classification

The classification and mapping of land cover provides fundamental information about the characteristics, activities, and status of specific areas on the earth\u27s surface. The quality of the final classification is critical in providing accurate information for ecologists and resource managers in decision-making and for developing a landscape-level understanding of an ecosystem. A land cover classification was developed for 5 research watersheds in Garland and Saline counties in Arkansas using 2002 LANDSAT7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery. The supervised classification was based upon 146 training areas identified from reference data and then applied to the imagery using the maximum likelihood classification algorithm. The unsupervised classification used an Iterative Self-Organizing Data Analysis Techniques (ISODATA) algorithm to classify the imagery into 300 spectral classes which then were identified from reference data. Data from 171 field locations were used to assess the accuracy of the final classifications using an error matrix. The supervised classification had an overall accuracy of 74.85% compared to 40.94% for the unsupervised classification. However, the dense canopy pine plantation class, which comprises 10.69% of the total area of the watersheds (1,216.69 ha), was more accurately classified in the unsupervised classification (64.29%) than the supervised classification (43.86%). The unsupervised classification of dense canopy pine plantation was incorporated into the supervised classification to produce a final integrated classification with an improved overall accuracy of 76.61%. We found that, where greater accuracy is desired, both classification methods should be used and the results integrated to utilize each method\u27s strengths

Land-Use/Land-Cover Characterization Using an Object-Based Classifier for the Buffalo River Sub-Basin in North-Central Arkansas

Sensors for remote sensing have improved enormously over the past few years and now deliver high resolution multispectral data on an operational basis. Most Land-use/Land-cover (LULC) classifications of high spatial resolution imagery, however, still rely on basic image processing concepts (i.e., image classification using single pixel-based classifiers) developed in the 1970s. This study developed the methodology using an object-based classifier to characterize the LULC for the Buffalo River sub-basin and surrounding areas with a 0.81- hectare (2-acre) minimum mapping unit (MMU). Base imagery for the 11-county classification was orthorectified color-infrared aerial photographs taken from 2000 to 2002 with a one-meter spatial resolution. The object-based classification was conducted using Feature Analyst® , Imagine® , and ArcGIS® software. Feature Analyst® employs hierarchical machine learning techniques to extract the feature class information from the imagery using both spectral and inherent spatial relationships of objects. The methodology developed for the 7-class classification involved both automated and manual interpretation of objects. The overall accuracy of this LULC classification method, which identified more than 146,000 features, was 87.8% for the Buffalo River sub basin and surrounding areas

Methodology for Integrating Aerial Photography and LANDSAT TM Imagery for Inventory of Forest Land Cover

Forest cover for 7.25 million acres (2.93 million hectares) in southeastern Georgia was characterized for the years 1988 and 1994 with the intent of assessing the efficacy of remote sensing procedures for broad scale forest inventory. Landsat-5 Thematic Mapper digital satellite scenes of seven spectral bands were obtained for winter and summer of each year and were analyzed two separate 14-band multi-temporal images. Images were geo-referenced to the universal transverse mercator (UTM) coordinate system prior to classification. Spectral classification with the 1SOCLUSTER algorithm produced 250 categories. Color infrared aerial photographs were mapped to the digital imagery and were used to convert spectral categories to land cover features. For this study, land features of interest were limited to water, marsh, pine forest, hardwood forest, mixed pine/hardwood forest, urban, and where distinguishable, clearcut and agriculture. Accuracy assessment techniques indicated very good consistency

Modeling Slope in a Geographic Information System

Geographic Information Systems (GIS) offer a cost-effective way to analyze and inventory land and environmental resources. There are many attributes that can be displayed and analyzed in GIS. One of these attributes is slope, which can be calculated from a digital elevation model (DEM). Slope is an important factor in a variety of models used in land analysis as well as land use and management. There are several different mathematical computational algorithms used to calculate slope within a GIS. Eight different slope calculation methods were investigated in this study. These methods were used to calculate slope using 10-m, 30-m, and 100-m DEMs. There were two phases of analysis in this study. The first phase was a cell-by-cell comparison of the eight slope algorithms for all three DEMs to obtain an understanding of differences between the calculated slope methods. The second phase was to determine the method that calculated the most accurate slope from a 10- m, 30-m, and 100-m DEM, by comparing calculated slope to actual slope value. All methods underestimated slope for the 100-m DEM with a mean slope difference ranging from 9.28% to 11.085%. For the 30-meter DEMs all the slope methods underestimated slope, with a mean slope difference range from 0.21% to 4.18%. The 10-meter DEM mean slope difference ranged from -2.63% to 1.82% for the cell slope methods. For all methods, steeper slopes, greater than approximately 40%, were underestimated when slope was calculated from a DEM

Assessing the Spatial Accuracy of Applanix DSS\u3csup\u3eTM\u3c/sup\u3e Model-301 Sensor Stereo Imagery using a Survey GPS Ground Control Network

During the past decade, Geographic Information Systems (GISs) have become widely used in many disciplines and that has created demands for accurate high-resolution digital data, especially digital imagery. Photogrammetry has emerged as one of the most important disciplines employed in the collection of spatially related information for use in GIS databases, especially for terrestrial landscapes. This study assessed the horizontal and vertical accuracy of the Applanix Digital Sensor System (DSS ) 301 orthophotographs. The study area was located on the University of Arkansas at Monticello campus and included 950 acres. To assess the spatial accuracy of the DSS, 56 Ground Control Points (GCPs) were collected prior to image acquisition using Trimble Surveying grade 4700 Global Positioning Systems (GPS). The 28 stereo aerial photographs used to create the orthorectified mosaic were taken with the DSS™ 30I, with approximately a 15.24 cm pixel spatial resolution. The average horizontal Root Mean Square Error (RMSE) for the DSS\u27 mosaic was 0.212m using the GPS-aided Inertial Measurement Unit (IMU) and 0.194 m from the mosaic created using one GCP per photo with the IMU. The vertical RMSE was 0.371 m for the 2-meter DEM created from stereo imagery using only the IMU

Comparison of Pixel-based versus Object-based Land Use/Land Cover Classification Methodologies

Land Use/Land Cover (LULC) classification data have proven to be valuable assets for various governmental agencies, park managers, and natural resource managers. Traditional pixel-based classification methods have difficulty with high resolution imagery, resulting in a “salt and pepper” appearance. Newer object-based methods may prove to be more accurate. This study compared an object based classification procedure utilizing Feature Analyst© software with a traditional pixel-based methodology (supervised classification) when applied to medium-spatial resolution satellite imagery merged with high-spatial resolution aerial imagery. This study utilized two multi-spectral SPOT-5 satellite images, leaf-on and leaf-off, merged with a color infrared aerial image. Because of correlation between some of the bands of the merged image, Principal Component Analysis (PCA) was used to reduce redundancy in the data. Field data was collected in the study area to serve as a reference for the accuracy assessment. A training set was produced by selecting and identifying specific LULC class-types using 1-foot high-spatial resolution aerial imagery. This training set was used by both of the classification methods (supervised and object-based) to identify the various cover types within the study area. An accuracy assessment was performed on each image utilizing error matrices, the Kappa coefficient, and a two-tailed Z-test. Results indicate that the overall accuracy of the object-based classification was 82.0%, while the pixel-based classification was 66.9%. A Kappa analysis and a two tailed Z test were calculated. These values indicated a significant difference in the overall accuracies of the classifications