Deepak R. Mishra

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.

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.

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.

A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach

The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. We present a novel spectral band ratio algorithm using 700 and 600 nm that is much less sensitive to the presence of chlorophyll.

Bio-Optical Inversion in Highly Turbid and Cyanobacteria-Dominated Waters

Phytoplankton pigment absorption data from algal-bloom-dominated waters are highly desirable to better understand the primary productivity and carbon uptake by algal biomass in a regional scale. However, retrieving phytoplankton pigment absorption coefficients, in turbid and hypereutrophic waters, from above-surface remote sensing reflectance (R_rs) is often challenging because of the optical complexity of the water body. In this paper, a quasi-analytical algorithm has been parameterized using in situ data to retrieve inherent optical properties from R_rs(λ) in highly turbid productive aquaculture ponds, where the phytoplankton absorption coefficient (3.44-37.67 m^-1 ) contributes 54 % of the total absorption at 443 nm (4.99-47.21 m^-1). The model was validated using an independent data set by comparing the model-derived optical parameters with in situ measured values. The absolute percentage error (assuming no error in the in situ measurements) of the estimated total absorption coefficient at( λ) varied from 15.22 % to 24.13 % within 413-665 nm, and the overall average error was 19.87 %. Maximum and minimum errors occurred at 443 and 665 nm, respectively. Similarly, the percentage error for the phytoplankton absorption coefficient a_φ(λ) varied from 15.9 % to 41.27 % within the 413-665-nm range, and the average error was 27.24 %. The spectral shape of modeled a_φ(λ) matched very well (R² = 0.97) with the measured a_φ(λ). A supplementary method was also developed to retrieve first-order estimates of colored detrital matter absorption coefficients a_CDM( λ) from subsurface remote sensing reflectance r_rs( λ) using an empirical approach. Results reveal that the retrieval accuracy of a_φ(λ) improved after incorporating the first-order estimates of a_CDM(λ) in the algorithm.

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms

We present a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC3 calibration and validation were acquired from highly turbid productive catfish aquaculture ponds. Since the characteristic PC absorption feature at 620 nm is contaminated with residual chlorophyll-a (Chl-a) absorption, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating Chl-a absorption at 620 nm–665 nm enables PC3 to compensate for the confounding effect of Chl-a at the PC absorption band and considerably increases the accuracy of the PC prediction algorithm. In the current dataset, PC3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μg L−1). Therefore, introduction of PC3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms.

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.