Sunil Narumalani

Benthic Habitat Mapping in Tropical Marine Environments Using QuickBird Multispectral Data

The objective of this research focused on the utility of QuickBird multispectral data for identifying and classifying tropical-marine benthic habitats after applying atmospheric and water-column corrections for an area around Roatan Island, Honduras. Atmospheric (Rayleigh and aerosol path radiance) and water column corrections (water depth and water column attenuation) were applied to the imagery, making it an effective method for mapping benthic habitats. Water depth for each pixel was calculated based on a linear model by regressing transformed radiance over known homogenous benthos against measured depths. Water column correction was achieved by deriving absorption and backscattering coefficients for each band of the image using a 50 � 50 window of clear water pixels. Corrections for water path radiance and water column attenuation of the bottom reflected radiance were made for the entire scene, allowing the bottom albedo to be determined for shallow coastal areas. An image of the bottom (i.e., an albedo image), minus the water column, was produced. Albedos were ≤8 percent for seagrass benthos, approximately 8 to 18 percent for coral areas, and ≥18 percent for sand dominated areas. An unsupervised classification algorithm was applied to the bottom albedo image, generating a classified map of benthic habitats. Accuracy assessment based on 383 reference points revealed an overall accuracy of 81 percent, with an overall Kappa value of 0.774.

A review of wetlands remote sensing and defining new considerations

Significant progress has been made in using remote sensing as a means of acquiring information about wetlands. This research provides a brief review of selected previous works, which address the issues of wetland identification, classification, biomass measurement, and change detection. Suggested new research emphases include compiling basic spectral‐reflectance characteristics for individual wetland species by means of close‐range instrumentation, analyzing canopies architectures to facilitate species identification, and assessing the impact on composite spectral signatures of wet soils and variable depths of standing water beneath emergent canopies. These research foci are justifiable when considered in the context of environmental change / variability and the production of trace gases.

Application of remote sensing and geographic information systems to the delineation and analysis of riparian buffer zones

Abstract Non-point source pollution has a significant impact on the quality of water resources. Studies have revealed that agricultural activities are often major contributors to non-point source pollution of aquatic environments. A common means of reducing the threat of non-point source pollution is through the establishment of riparian vegetation strips (or buffers) along those areas of stream channels that would be most susceptible to the threat. Remote sensing and geographic information systems (GIS) offer a means by which ‘critical’ areas can be identified, so that subsequent action toward the establishment of riparian zones can be taken. This research focuses on the development and analysis of riparian buffer zones for a portion of the Iowa River basin. Landsat Thematic Mapper (TM) data were used to characterize the land cover for the study area. An updated hydrology data layer was developed by integrating the United States Geological Survey (USGS) Digital Line Graph (DLG) data base with the TM-derived classification of surface water bodies. Spatial distance search tools were applied to develop the buffer zones around all surface hydrologic features. The buffer zones were integrated with the remotely sensed classification data to identify ‘critical’ areas for the establishment of riparian vegetation strips. Results indicated that while most of the main channel of the Iowa River was protected by natural vegetation, more than 44% (or 1008 ha) of the area along its tributaries lack any protective cover from non-point source pollution. As these ‘critical’ areas are adjacent to agricultural fields it is important that water resources management strategies focus on the establishment of riparian zones in order to minimize the impact of non-point source pollution.

Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

Abstract Geospatial technologies are increasingly important tools used to assess the spatial distributions and predict the spread of invasive species. The objective of our research was to quantify and map four dominant invasive plant species, including saltcedar, Russian olive, Canada thistle, and musk thistle, along the flood plain of the North Platte River corridor within a 1-mile (1.6-km) buffer. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from visible to near infrared), we evaluated an image processing technique known as spectral angle mapping for mapping the invasive species distribution. A minimum noise fraction algorithm was used to remove the inherent noise and redundancy within the dataset during the classification. The classification algorithm applied on the AISA image revealed five categories of invasive species distribution including (1) saltcedar; (2) Russian olive; and a mix of (3) Canada and musk thistle, (4) Canada/musk thistle and reed canary grass, or (5) Canada/musk thistle, saltcedar, and reed canary grass. Validation procedures confirmed an overall map accuracy of 74%. Saltcedar and Russian olive classes showed producer and user accuracies of greater than 90%, whereas the mixed categories revealed accuracy values of between 35 and 74%. The immediate benefit of this research has been to provide information on the spatial distribution of invasive species to land managers for implementation of management programs. In addition, these data can be used to establish a baseline of the species distributions for future monitoring and control efforts. Nomenclature: Canada thistle, Cirsium arvense L. Scop.; musk thistle, Carduus nutans L.; reed canary grass, Phalaris arundinacea L.; Russian olive, Elaeagnus angustifolia L.; saltcedar, Tamarix sp. Lour

High-resolution ocean color remote sensing of benthic habitats: a case study at the Roatan island, Honduras

Natural resource managers clamor for detailed reef habitat maps for monitoring smaller scale disturbances in reef communities. Coastal ocean color remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before. The objective of this research was to develop an accurate benthic habitat map for an area off the northwest coast of Roatan Island, Honduras, using high-resolution multispectral IKONOS data. Atmospheric (Rayleigh and aerosol path radiance) and water column corrections (water depth and water column attenuation) were applied to the imagery, making it a robust method for mapping benthic habitats. Water depth for each pixel was calculated based on a site-specific polynomial model. A mechanistic radiative transfer approach was developed that removed the confound effect of the water column (absorption and scattering) from the imagery to retrieve an estimate of the bottom reflectance (albedo). Albedos were /spl les/ 12% for seagrass benthos, 12% to 24% for coral areas, and /spl ges/ 24% for sand-dominated areas. The retrieved bottom albedos were then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment.

A GIS-based Spatial Pattern Analysis Model for eco-region mapping and characterization

Growing concerns about global climate change, biodiversity maintenance, natural resources conservation, and long-term ecosystem sustainability have been responsible for the transformation of traditional single resource management approaches into integrated ecosystem management models. Eco-regions are large ecosystems of regional extent that contain smaller ecosystems of similar response potential and resource production capabilities. They can be used as a geographical framework for organizing and reporting resource information, setting bioecological recovery criteria, extrapolating site-level management, and monitoring global change. The objective of this research is to develop a quantitative, multivariate regionalization model that is capable of delineating eco-regions at multiple levels from remotely sensed information and other environmental and natural resources spatial data. The Spatial Pattern Analysis Model developed in this study uses a region-growing algorithm to generate spatially contiguous regions from primitive polygonal land units. The algorithm merges the most similar pair of neighbouring units at each iteration, based on satisfying certain similarity criteria until all units are grouped into one. This model was utilized to develop an eco-region map of Nebraska with three hierarchical levels. In the mapping process, the STATSGO data set was used to build the primitive map units. Environmental parameters included in the model were multi-temporal AVHRR data, soil rooting depth, organic matter content, available water capacity, and long-term annual averages of water balance and growing degree day totals. Development of the model provides a new and useful approach to eco-region mapping for resource managers and researchers. The method is automated and efficient, reduces the judgement biases and uncertainty of manual analyses, and can be replicated for other regions or for the regionalization of other themes.

Predicting Potential Occurrence and Spread of Invasive Plant Species along the North Platte River, Nebraska

Abstract Riparian habitats are important components of an ecosystem; however, their hydrology combined with anthropogenic effects facilitates the establishment and spread of invasive plant species. We used a maximum-entropy predictive habitat model, MAXENT, to predict the distributions of five invasive plant species (Canada thistle, musk thistle, Russian olive, phragmites, and saltcedar) along the North Platte River in Nebraska. Projections for each species were highly accurate. Elevation and distance from river were most important variables for each species. Saltcedar and phragmites appear to have restricted distributions in the study area, whereas Russian olive and thistle species were broadly distributed. Results from this study hold promise for the development of proactive management approaches to identify and control areas of high abundance and prevent further spread of invasive plants along the North Platte River. Nomenclature: Canada thistle, Cirsium arvense (L.) Scop; common reed, Phragmites australis (Cav.) Trin. ex Steud; musk thistle, Carduus nutans L.; saltcedar, Tamarix sp. L.; Russian olive, Elaeagnus angustifolia L.

Assessment of Vegetation Response to Drought in Nebraska Using Terra-MODIS Land Surface Temperature and Normalized Difference Vegetation Index

Eight-day composite Terra-MODIS cumulative LST and NDVI timeseries data were used to analyze the responses of crop and grassland cover types to drought in Nebraska. Four hundred ninety 1 km pixels that included irrigated and non-irrigated corn and soybeans and three grassland cover types were selected across the state of Nebraska. Statistical analyses revealed that the majority of the land cover pixels experienced significantly higher daytime and nighttime LSTs and lower NDVI during the drought-year growing season (p < 0.01). Among the land cover types analyzed, grassland experienced the highest increase in daytime LST and decrease in NDVI.

A Comparative Evaluation of ISODATA and Spectral Angle Mapping for the Detection of Saltcedar Using Airborne Hyperspectral Imagery

Abstract Nonnative plant species often cause adverse ecological and environmental impacts on the indigenous species of an area. Remote sensing methods have had mixed successes in providing spatial information on the distribution characteristics of specific vegetation species. Such research has been limited to broad‐band satellite based sensor systems whose spatial and spectral capabilities may not be adequate. Our research focuses on using hyperspectral data and innovative image processing techniques for mapping specific invasive species based on their spectral characteristics. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from Visible to Near Infrared (VNIR)). This research evaluated two methods of processing hyperspectral imagery including the Iterative Self‐Organizing Data (ISODATA) algorithm and Spectral Angle Mapping (SAM) for detecting saltcedar (Tamarix sp.) in Lake Meredith Recreational Area, Texas. A Minimum Noise Fraction (MNF) algorithm was used to remove the inherent noise and redundancy within the dataset during the SAM classification. Validation procedures revealed higher accuracies for the SAM method (83%) when compared to ISODATA (76%) in identifying saItcedar. The immediate benefit of this research has been to provide improved information on the spatial extent and density of saltcedar to land managers for the effective implementation of management programs to control this invasive plant.

Bathymetric Mapping Using IKONOS Multispectral Data

The objective of this research was to develop an accurate bathymetric map for an area around Roatan Island, Honduras using high-resolution multispectral IKONOS data based on a variation of a linear regression model. Linear regression models estimate water depths by regressing brightness values over known benthos (albeit non-homogeneous) and known depths. However, we contend that if mixed bottom types are used, the regression coefficients deteriorate because the variability in brightness values from a heterogeneous bottom has a deleterious effect on the correlation coefficient. By selecting uniform bottom types, this variability can be reduced and a strong correlation between depth and brightness value can be established, thus improving the accuracy of estimated depths. Three uniform bottom types (seagrass, coral, and sand) were selected, and the transformed brightness values derived from principal components analysis for each bottom type were regressed against known depths. The most statistically significant coefficient (r 2 = 0.909 for seagrass benthos) was then used in the depth estimation algorithm and a bathymetric map was derived. A comparative evaluation between estimated and actual depths was performed and the bathymetric map was found to be within a standard error of 0.648 m. Consequently, our results suggest that accurate depth estimates can be derived by using transformed input brightness values over homogeneous bottom types from IKONOS multispectral imagery.