Joseph F. Knight

Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach

Currently available land cover data sets for large geographic regions are produced on an intermittent basis and are often dated. Ideally, annually updated data would be available to support environmental status and trends assessments and ecosystem process modeling. This research examined the potential for vegetation phenology-based land cover classification over the 52,000 km2 Albemarle-Pamlico estuarine system (APES) that could be performed annually. Traditional hyperspectral image classification techniques were applied using MODIS-NDVI 250 m 16-day composite data over calendar year 2001 to support the multi-temporal image analysis approach. A reference database was developed using archival aerial photography that provided detailed mixed pixel cover-type data for 31,322 sampling sites corresponding to MODIS 250 m pixels. Accuracy estimates for the classification indicated that the overall accuracy of the classification ranged from 73% for very heterogeneous pixels to 89% when only homogeneous pixels were examined. These accuracies are comparable to similar classifications using much higher spatial resolution data, which indicates that there is significant value added to relatively coarse resolution data though the addition of multi-temporal observations.

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource

The integration of optical, topographic, and radar data for wetland mapping in northern Minnesota

Accurate and current wetland maps are critical tools for water resources management, however, many existing wetland maps were created by manual interpretation of one aerial image for each area of interest. As such, these maps do not inherently contain information about the intra- and interannual hydrologic cycles of wetlands, which is important for effective wetland mapping. In this paper, several sources of remotely sensed data will be integrated and evaluated for their suitability to map wetlands in a forested region of northern Minnesota. These data include: aerial photographs from two different times of a growing season, National Elevation Dataset and topographical derivatives such as slope and curvature, and multitemporal satellite-based synthetic aperture radar (SAR) imagery and polarimetric decompositions. We identified the variables that are most important to accurately classify wetland from upland areas and discriminate between wetland types for a forested region of northern Minnesota using the decision-tree classifier randomForest. The classifier was able to differentiate wetland from upland and water with 75% accuracy using optical, topographic, and SAR data combined, compared with 72% using optical and topographical data alone. Classifying wetland type proved to be more challenging; however, the results were significantly improved over the original National Wetland Inventory classification of only 49% compared with 63% using optical, topographic, and SAR data combined. This paper illustrates that integration of remotely sensed data from multiple sensor platforms and over multiple periods during a growing season improved wetland mapping and wetland type classification in northern Minnesota.

Cryptococcal Genotype Influences Immunologic Response and Human Clinical Outcome after Meningitis

ABSTRACT In sub-Saharan Africa, cryptococcal meningitis (CM) continues to be a predominant cause of AIDS-related mortality. Understanding virulence and improving clinical treatments remain important. To characterize the role of the fungal strain genotype in clinical disease, we analyzed 140 Cryptococcus isolates from 111 Ugandans with AIDS and CM. Isolates consisted of 107 nonredundant Cryptococcus neoformans var. grubii strains and 8 C. neoformans var. grubii/neoformans hybrid strains. Multilocus sequence typing (MLST) was used to characterize genotypes, yielding 15 sequence types and 4 clonal clusters. The largest clonal cluster consisted of 74 isolates. The results of Burst and phylogenetic analysis suggested that the C. neoformans var. grubii strains could be separated into three nonredundant evolutionary groups (Burst group 1 to group 3). Patient mortality was differentially associated with the different evolutionary groups (P = 0.04), with the highest mortality observed among Burst group 1, Burst group 2, and hybrid strains. Compared to Burst group 3 strains, Burst group 1 strains were associated with higher mortality (P = 0.02), exhibited increased capsule shedding (P = 0.02), and elicited a more pronounced Th2 response during ex vivo cytokine release assays with strain-specific capsule stimulation (P = 0.02). The results of these analyses suggest that cryptococcal strain variation can be an important determinant of human immune responses and mortality. IMPORTANCE Cryptococcus neoformans is a common life-threatening human fungal pathogen that is responsible for an estimated 1 million cases of meningitis in HIV-infected patients annually. Virulence factors that are important in human disease have been identified, yet the impacts of the fungal strain genotype on virulence and outcomes of human infection remain poorly understood. Using an analysis of strain variation based on in vitro assays and clinical data from Ugandans living with AIDS and cryptococcal infection, we report that strain genotype predicts the type of immune response and mortality risk. These studies suggest that knowledge of the strain genotype during human infections could be used to predict disease outcomes and lead to improved treatment approaches aimed at targeting the specific combination of pathogen virulence and host response. Cryptococcus neoformans is a common life-threatening human fungal pathogen that is responsible for an estimated 1 million cases of meningitis in HIV-infected patients annually. Virulence factors that are important in human disease have been identified, yet the impacts of the fungal strain genotype on virulence and outcomes of human infection remain poorly understood. Using an analysis of strain variation based on in vitro assays and clinical data from Ugandans living with AIDS and cryptococcal infection, we report that strain genotype predicts the type of immune response and mortality risk. These studies suggest that knowledge of the strain genotype during human infections could be used to predict disease outcomes and lead to improved treatment approaches aimed at targeting the specific combination of pathogen virulence and host response.

An experimental assessment of minimum mapping unit size

Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU) to characterize a particular landscape theme of interest. The choice of an MMU that is appropriate for the projected use of a classification is an important consideration. The objective of this experiment was to determine the effect of MMU on a LC classification of the Neuse River Basin (NRB) in North Carolina. The results of this work indicate that MMU size had a significant effect on accuracy estimates only when the MMU was changed by relatively large amounts. Typically, an MMU is selected as close as possible to the original data resolution so as to reduce the loss of specificity introduced in the resampling process. Since only large MMU changes resulted in significant differences in the accuracy estimates, an analyst may have the flexibility to select from a range of MMUs that are appropriate for a given application.

Mapping Impervious Cover Using Multi-Temporal MODIS NDVI Data

Mapping impervious surfaces over regional or continental scale study areas with high spatial resolution imagery is difficult due to the cost and time involved in processing the large number of images required. This study investigated the benefits of using the coarse spatial resolution, high temporal resolution MODIS sensor to produce impervious surface maps. MODIS NDVI data for multiple years were analyzed with two multi-temporal image analysis methods: the Sequential Maximum Angle Convex Cone and Linear Spectral Unmixing. Impervious surface maps were generated and compared with a set of reference data and a Landsat-derived impervious cover map. The mapping accuracies for the algorithms used were generally good, particularly for the LSU approach, which was able to identify areas with 50-60% impervious cover at 77% accuracy and areas with a cumulative impervious cover of 50% or greater at 80% accuracy. The methods presented in this paper have potential for mapping impervious cover over large areas where the use of higher spatial resolution data is impracticable.

The effects of data selection and thematic detail on the accuracy of high spatial resolution wetland classifications

Accurate wetland maps are of critical importance for preserving the ecosystem functions provided by these valuable landscape elements. Though extensive research into wetland mapping methods using remotely sensed data exists, questions remain as to the effects of data type and classifi cation scheme on classifi cation accuracy when high spatial resolution data are used. The goal of this research was to examine the effects on wetland mapping accuracy of varying input datasets and thematic detail in two physiographically different study areas using a decision tree classifi er. The results indicate that: topographic data and derivatives signifi cantly increase mapping accuracy over optical imagery alone, the source of the elevation data and the type of topographic derivatives used were not major factors, the inclusion of radar and leaf-off imagery did not improve mapping accuracy, and increasing thematic detail resulted in signifi cantly lower mapping accuracies i.e., particularly in more diverse wetland areas.

Wetland mapping in the Upper Midwest United States: an object-based approach integrating lidar and imagery data: An object-based approach integrating lidar and imagery data

This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer’s and user’s accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods.

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary, USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible–Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R 2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R 2 = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite‐based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively. **Current address: University of Minnesota, Department of Forest Resources, St Paul, MN 55108, USA.The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R2=0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite-based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.

Application of MODIS Imagery for Intra-Annual Water Clarity Assessment of Minnesota Lakes

Monitoring of water clarity trends is necessary for water resource managers. Remote sensing based methods are well suited for monitoring clarity in water bodies such as the inland lakes in Minnesota, United States. This study evaluated the potential of using imagery from NASA’s MODIS sensor to study intra-annual variations in lake clarity. MODIS reflectance images from six dates throughout the 2006 growing season were used with field collected Secchi disk transparency data to estimate water clarity in large lakes throughout Minnesota. The results of this research indicate the following: water clarity estimates derived from MODIS imagery are largely similar to those derived from lower temporal resolution sensors such as Landsat, robust water clarity estimates can be derived using MODIS for many dates throughout a growing season (R2 values between 0.32 and 0.71), and the relatively low spatial resolution of MODIS restricts its applicability to a subset of the largest inland lakes (>160 ha, or 400 acres). This study suggests that water clarity maps developed with MODIS imagery and bathymetry data may be useful tools for resource managers concerned with intra- and inter-annual variations in large inland lakes.