Jerry A. Griffith

GEOGRAPHIC TECHNIQUES AND RECENT APPLICATIONS OF REMOTE SENSING TO LANDSCAPE-WATER QUALITY STUDIES

This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can nowbe more easily performed on regional, multi-state scales. Thetraditional method of relating land use and land cover to waterquality has been extended to include landscape pattern and otherlandscape information derived from satellite data. Three itemsare focused on in this article: 1) the increasing recognition ofthe importance of larger-scale studies of regional water qualitythat require a landscape perspective; 2) the increasingimportance of remotely sensed data, such as the imagery-derivednormalized difference vegetation index (NDVI) and vegetationphenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape patternmetrics explained some of the variation in water quality notexplained by land use/cover. However, in some other studies, theNDVI metrics were even more highly correlated to certain waterquality parameters than either landscape pattern metrics or landuse/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, thisrecent body of work applying these landscape measures andsatellite-derived metrics to water quality analysis hasdemonstrated their potential usefulness in monitoring watershedconditions across large regions.

INTERRELATIONSHIPS AMONG LANDSCAPES, NDVI, AND STREAM WATER QUALITY IN THE U.S. CENTRAL PLAINS

During late spring through summer of 1994 and 1995, 290 randomly selected stream sites in Nebraska, Kansas, and Missouri were sampled once for several parameters including conductivity, turbidity, total phosphorus, nitrate-nitrite nitrogen, the index of biotic integrity, and a habitat index. Based on landscape data from watersheds that were delineated for each sampling location, interrelationships were examined between these water quality parameters and land use/land cover, the normalized difference vegetation index (NDVI), and vegetation phenological metrics derived from the NDVI. Statistically signif- icant relationships were found between NDVI values and the derived metrics with the stream condition parameters ( r values to 0.8, a5 0.05). The NDVI or vegetation pheno- logical metrics (VPMs) were more highly correlated to the selected stream condition pa- rameters than were the land use/land cover proportions. Knowledge of the general land use/land cover setting within the watersheds, however, was important for interpreting these relationships. The most common variables associated with the stream data were early spring NDVI values or VPMs associated with the date of onset of greenness. These results dem- onstrate the utility of NDVI and VPMs as broad-scale environmental indicators of watershed conditions.

Remote Sensing and Mapping of Tamarisk along the Colorado River, USA: A Comparative Use of Summer-Acquired Hyperion, Thematic Mapper and QuickBird Data

Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De Beque, Colorado, USA. As a result of highly correlated reflectance among the spectral bands provided by each sensor, relatively standard image analysis methods were employed. Multispectral data at high spatial resolution (QB, 2.5 m Ground Spatial Distance or GSD) proved more effective in tamarisk delineation than either multispectral (TM5) or hyperspectral (Hyperion) data at moderate spatial resolution (30 m GSD).

Seagrass Mapping in the Northern Gulf of Mexico using Airborne Hyperspectral Imagery: A Comparison of Classification Methods

Abstract Mapping areas of seagrass is important, in part because the extent of seagrass habitat can serve as a general indicator of coastal ecosystem health. While aerial photography and multispectral imagery have commonly been used to map seagrass, digital hyperspectral imagery is at the forefront of current mapping technology in many natural resource applications. In this study, HyMap hyperspectral imagery at 2.9-m resolution was used to map seagrass distribution off Horn Island, Mississippi, and estimate its areal coverage. Seagrass beds and sand-bottom classes were defined based on visual interpretation of the imagery coupled with field observations. Image spectra were sampled for each class in three water-depth zones determined by distance from shore. Supervised image classifications were performed using maximum likelihood, minimum distance to means, and spectral angle mapper methods to compare relative accuracies in mapping seagrass coverage. The maximum likelihood classification produced the highest overall accuracy of 83%. The spectral angle mapper yielded the lowest accuracy due to the predominant influence of water-column optical properties on the apparent spectral characteristics of seagrass and sand bottom. The ML classification indicated total seagrass coverage of 107 ha. This compared favorably with the results of a separate, independent study based on aerial photography acquired 1 day after the HyMap flyover. For tracking sea-grass coverage in the northern Gulf of Mexico, the mapping of individual seagrass patches at a spatial resolution of at least 3 m is recommended.

Detecting trends in landscape pattern metrics over a 20-year period using a sampling-based monitoring programme

Temporal trends in landscape pattern metrics describing texture, patch shape and patch size were evaluated in the US Middle Atlantic Coastal Plain Ecoregion. The landscape pattern metrics were calculated for a sample of land use/cover data obtained for four points in time from 1973-1992. The multiple sampling dates permit evaluation of trend, whereas availability of only two sampling dates allows only evaluation of change. Observed statistically significant trends in the landscape pattern metrics demonstrated that the sampling-based monitoring protocol was able to detect a trend toward a more fine-grained landscape in this ecoregion. This sampling and analysis protocol is being extended spatially to the remaining 83 ecoregions in the US and temporally to the year 2000 to provide a national and regional synthesis of the temporal and spatial dynamics of landscape pattern covering the period 1973-2000.

A multivariate analysis of biophysical parameters of tallgrass prairie among land management practices and years.

Six treatments of eastern Kansas tallgrass prairie – native prairie, hayed, mowed, grazed, burned and untreated – were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference VegetationIndex (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine whichvariables were contributing to any significant difference. Results showed a significant difference (p < 0.0005) among treatments in the composite of parameters during each of the months sampled. In most treatment types, there was asignificant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects ofdifferent land management practices but not to yearlychange in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.

The role of landscape pattern analysis in understanding concepts of land cover change

Landscape ecology and landscape pattern analysis are important components of national-scale programs to identify trends in land cover change because: 1) Statistics on changes in land cover proportions are not spatial. A change matrix derived from GIS provides useful information, but it does not show the spatial form of change in the landscape. Landscape pattern metrics reveal spatial pattern. 2) A growing body of literature has shown that a change in landscape pattern might indicate important changes in ecological functions: forest connectivity and species movements, number and size of farm patches, effects on water quality. Spatial pattern is important in structuring ecological communities and in maintaining existence of competitors. Spatial pattern may be determined by disturbance and may in turn, determine how disturbances propagate through the system. 3) Sometimes landscape pattern may not significantly change, even though land cover proportions do change. Or, vice-versa, sometimes landscape pattern can significantly change, even though land cover proportions don’t significantly change. 4) Landscape pattern is an inherent and important part of describing landscapes: based on the literature, one of the most important descriptive characteristics of a landscape is its texture. The objectives of this paper are to: 1) Explain the importance of the role of landscape ecology and landscape pattern analysis in land cover change studies; 2) Review the literature that specifically incorporates landscape ecology into land cover change studies; and 3) List the theoretical and technical issues involved and suggest solutions for them.

Malaria incidence in Nairobi, Kenya and dekadal trends in NDVI and climatic variables

The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R2 = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.

Regions of High Fish Diversity for Conservation Concern within the U.S. Central Plains

ABSTRACT Fish samples from 300 stream sites in Nebraska, Kansas, and Missouri were used to identify potential conservation areas based on (a) number of endemic species, (b) number of threatened and endangered species, c) Index of Biotic Integrity scores, and d) species/family richness. Three areas emerged as potential high-priority conservation areas—the Ozark plateau, south-central Kansas and the Nebraska Sand Hills. The techniques used here can highlight watersheds and streams of potential management concern.

Binary frontal polymerization: Velocity dependence on initial composition

Abstract Frontal polymerization is a mode of polymerization in which a localized zone of reaction propagates through the coupling of thermal diffusion and the Arrhenius dependence of the reaction rate. The dependence of the front propagation velocity on the initial composition has been determined in initially miscible binary systems of a free-radically cured diacrylate and an amine- or cationically cured epoxy resin. A minimum of the velocity as a function of the monomer mole fraction is observed if the two polymerizations occur independently. Excellent agreement with an analytical description was found with the diacrylate and an amine-cured epoxy but not for a diacrylate and a cationically cured one because of the effect of HCl impurities on the peroxide.