Statewide groundwater‐vulnerability assessment in nebraska using the drastic/GIS model

The paper summarizes a technique for implementing the “DRASTIC” groundwater‐vulnerability model within the context of an automated raster‐based geographic information system. Discussion focuses on a methodological development and a statewide project completed recently in Nebraska. The final products, a comprehensive flow chart illustrating procedures and a map of calculated potential pollution hazard, are presented. The methodology can be executed successfully with minimal training and experience. Areas of Nebraska considered vulnerable to groundwater pollution are identified.     

Multi‐temporal Mapping of Burned Forest over Canada Using Satellite‐based Change Metrics

Abstract

A procedure for continental‐scale mapping of burned boreal forest at 10‐day intervals was developed for application to coarse resolution satellite imagery. The basis of the technique is a multiple logistic regression model parameterized using 1998 SPOT‐4 VEGETATION clear‐sky composites and training sites selected across Canada. Predictor features consisted of multi‐temporal change metrics based on reflectance and two vegetation indices, which were normalized to the trajectory of background vegetation to account for phenological variation. Spatial‐contextual tests applied to the logistic model output were developed to remove noise and increase the sensitivity of detection. The procedure was applied over Canada for the 1998‐2000 fire seasons and validated using fire surveys and burned area statistics from forest fire management agencies. The area of falsely mapped burns was found to be small (3.5% commission error over Canada), and most burns larger than 10 km2 were accurately detected and mapped (R² = 0.90, P<0.005, n = 91 for burns in two provinces). Canada‐wide satellite burned area was similar, but consistently smaller by comparison to statistics compiled by the Canadian Interagency Forest Fire Centre (by 17% in 1998, 16% in 1999, and 3% in 2000).     

Mapping spiny aster infestations with QuickBird imagery

QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.     

Catchment storm runoff modelling using the geomorphologic instantaneous unit hydrograph

A lumped empirical model, the Geomorphologic Instantaneous Unit Hydrograph (GIUH) rainfall-runoff model, is developed for the Can Le catchment in the upstream region of the Sai Gon river Basin (Vietnam). This model can serve to simulate catchment runoff into the Dau Tieng Reservoir and can be used as a flood forecasting tool for the ungauged Can Le catchment. The GIUH couples geomorphology and hydrology quantitatively. The obtained Unit Hydrograph is based on Horton's morphometric parameters; bifurcation, length and area ratios. A new functionality within the ILWIS GIS-RS package, namely ‘DEM-hydro processing’, is applied to effectively process a Digital Elevation Model to extract these ratios from the drainage network. To supplement the limited field data available, various satellites images have been used such as ASTER, SRTM (Shuttle Radar Topography Mission) and METEOSAT 5. A short field campaign to collect missing ground data was executed between September and October 2005. The data collected included discharge (and stage – discharge curve), meteorological data, soil, land use information that are used for paramerisation, calibration and validation of the GIUH. The model was successfully applied for the Can Le catchment. Using the Horton's morphometric parameters derived from the DEM with estimated overland and stream flow velocities, the model is easy-to-use.     

Assessing the Effect of Data Imports on the Completeness of OpenStreetMap – A United States Case Study

The assessment of OpenStreetMap (OSM) data quality has become an interdisciplinary research area over the recent years. The question of whether the OSM road network should be updated through periodic data imports from public domain data, or whether the currency of OSM data should rather rely on more traditional data collection efforts by active contributors, has led to perpetual debates within the OSM community. A US Census TIGER/Line 2005 import into OSM was accomplished in early 2008, which generated a road network foundation for the active community members in the US. In this study we perform a longitudinal analysis of road data for the US by comparing the development of OSM and TIGER/Line data since the initial TIGER/Line import. The analysis is performed for the 50 US states and the District of Columbia, and 70 Urbanized Areas. In almost all tested states and Urbanized Areas, OSM misses roads for motorized traffic when compared with TIGER/Line street data, while significant contributions could be observed in pedestrian related network data in OSM compared with corresponding TIGER/Line data. We conclude that the quality of OSM road data could be improved through new OSM editor tools allowing contributors to trace current TIGER/Line data.     

Field Measurements of Plant Emissivity Spectra: An Experimental Study on Remote Sensing of Vegetation in the Thermal Infrared Region

Spectral features of plant species in the visible to SWIR (0.4–2.5 μm) region have been studied extensively, but scanty attention has been given to plant thermal infrared (TIR: 4–14 μm) properties. This paper presents preliminary results of a study that was conducted first time in India to measure radiance and emissivity properties of eight plant species in TIR spectral region in the field conditions using a FTIR (Fourier Transform Infrared) field spectroradiometer working in 4–14 μm at an agriculture experimental farm. Several spectral features in the emissivity spectra of plant species were observed that are probably related to the leaf chemical constituents, such as cellulose and xylan (hemicellulose) and structural aspects of leaf surface like abundance of trichomes and texture. Observations and results from the field measurements were supported by the laboratory measurements like biochemical analysis. These preliminary field emissivity measurements of leaves in TIR show that there is useful spectral information that may be detectable by field-based instrument. More detailed field and laboratory measurements are underway to explore this research theme.     

Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

This study aims to illustrate how remotely sensed oceanic variables and fishing operations data can be used to predict suitable habitat of fishery resources in Geographic Information System. We used sea surface height anomaly (SSHa), sea surface temperature (SST), chlorophyll concentration (CC), photosynthetically active radiation (PAR) and fishing depth as predictor variables. Fishery data of Indian squid (Loligo spp.) and catfish (Tachysurus spp.) for study period (1998–2004) were segregated randomly to create training and validation. Catch was normalized into Catch per unit Effort (kg h^?1). Generalized additive modelling was performed on training data and then tested on validation data. Suitable ranges of SST, CC, SSHa and PAR for different species distributions were derived and integrated to predict their spatial distributions. Results indicated good match between predicted and actual catch. Monthly probability maps of predicted habitat areas coincide with high catch of the particular month for the study period.     

High Resolution Köppen‐Geiger Classifications of Paleoclimate Simulations

The development and application of an algorithm to compute Köppen‐Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid‐Holocene (6k yBP) and the Pre‐Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach.