Stuart E. Marsh

Farm-scale evaluation of the impacts of transgenic cotton on biodiversity, pesticide use, and yield

Higher yields and reduced pesticide impacts are needed to mitigate the effects of agricultural intensification. A 2-year farm-scale evaluation of 81 commercial fields in Arizona show that use of transgenic Bacillus thuringiensis (Bt) cotton reduced insecticide use, whereas transgenic cotton with Bt protein and herbicide resistance (BtHr) did not affect herbicide use. Transgenic cotton had higher yield than nontransgenic cotton for any given number of insecticide applications. However, nontransgenic, Bt and BtHr cotton had similar yields overall, largely because higher insecticide use with nontransgenic cotton improved control of key pests. Unlike Bt and BtHr cotton, insecticides reduced the diversity of nontarget insects. Several other agronomic and ecological factors also affected biodiversity. Nevertheless, pairwise comparisons of diversity of nontarget insects in cotton fields with diversity in adjacent noncultivated sites revealed similar effects of cultivation of transgenic and nontransgenic cotton on biodiversity. The results indicate that impacts of agricultural intensification can be reduced when replacement of broad-spectrum insecticides by narrow-spectrum Bt crops does not reduce control of pests not affected by Bt crops.

A Landscape Approach for Detecting and Evaluating Change in a Semi-Arid Environment

Vegetation change in the American West has been a subject of concern throughout the twentieth century. Although many of the changes have been recorded qualitatively through the use of comparative photography and historical reports, little quantitative information has been available on the regional or watershed scale. It is currently possible to measure change over large areas and determine trends in ecological and hydrological condition using advanced space-based technologies. Specifically, this process is being tested in a community-based watershed in southeast Arizona and northeast Sonora, Mexico using a system of landscape pattern measurements derived from satellite remote sensing, spatial statistics, process modeling, and geographic information systems technology. These technologies provide the basis for developing landscape composition and pattern indicators as sensitive measures of large-scale environmental change and thus may provide an effective and economical method for evaluating watershed condition related to disturbance from human and natural stresses. The project utilizes the database from the North American Landscape Characterization (NALC) project which incorporates triplicate Landsat Multi-Spectral Scanner (MSS) imagery from the early 1970s, mid 1980s, and the 1990s. Landscape composition and pattern metrics have been generated from digital land cover maps derived from the NALC images and compared across a nearly 20-year period. Results about changes in land cover for the study period indicate that extensive, highly connected grassland and desertscrub areas are the most vulnerable ecosystems to fragmentation and actual loss due to encroachment of xerophytic mesquite woodland. In the study period, grasslands and desertscrub not only decreased in extent but also became more fragmented. That is, the number of grassland and desertscrub patches increased and their average patch sizes decreased. In stark contrast, the mesquite woodland patches increased in size, number, and connectivity. These changes have important impact for the hydrology of the region, since the energy and water balance characteristics for these cover types are significantly different. The process demonstrates a simple procedure to document changes and determine ecosystem vulnerabilities through the use of change detection and indicator development, especially in regard to traditional degradation processes that have occurred throughout the western rangelands involving changes of vegetative cover and acceleration of water and wind erosion.

Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands

The primary objective of this study was to assess the condition of a portion of Saudi Arabias rangelands and evaluate the effects of grazing by the animal herds of indigenous nomads over the last decade. Because of the desertic condition of these rangelands, changes in vegetation cover are more subtle than would be the case for other, less arid areas. Consequently, a new analytic methodology for the detection of desertification of arid and hyper-arid rangelands was developed specifically for this project. The conceptual framework for the analysis is the use of the coefficient of variation (COV) of the monthly Normalized Difference Vegetation Index (NDVI, maximum-value composite) as a measure of vegetative biomass change. A higher NDVI COV for a given pixel (excluding cases of changes in land use) represents a greater change in vegetation biomass in the ground area represented by that pixel. Linear regression was used to determine the trend in COV values for each pixel over the 12-year period for which data was available; pixels with a negative slope are considered to represent ground areas with decreasing amounts of vegetation. Results were validated by tests of statistical significance and by comparison of the theoretical results to vegetation change and land-cover data from the remote sensing systems and from reconnaissance flights over select areas. These desertification trend results were then combined with land-cover information to provide an assessment of desertification status.

Application of standardized principal component analysis to land-cover characterization using multitemporal AVHRR data

Abstract The concept of a vegetation vector has been developed to better visualize and characterize land-cover at regional scales. The vegetation vector is derived from long time-series multitemporal normalized difference vegetation index (NDVI) data sets from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) by means of principal component analysis (PCA). The vegetation vector can characterize vegetation cover based upon both the spatial variation of the magnitude of NDVI and the seasonal variation of NDVI. The PCA study showed that the area under analysis must exhibit a variety of dissimilar vegetation communities in terms of density and phenology to successfully derive these two factors. When the PCA was applied to the entire state of Arizona, these two factors were derived as the first two principal components (PCs). However, when the PCA was applied to subset areas extracted from the entire study area by overlaying a vegetation map compiled through bioclimatological and ecological studies, the first two PCs did not always represent these two factors. This indicated that these two factors were not always the major cause of variation in NDVI for some vegetation communities. In this study the vegetation vector was constructed utilizing the first two PCs derived from the entire study area. Analysis of histograms of the direction of the vegetation vector for each community found in the vegetation map could be used to characterize most of the communities in terms of photosynthetic activity and phenology. A profile of the histogram could be interpreted as characteristic of the community. In addition, the range exhibited by the histogram could be used as a measure of the homogeneity/heterogeneity of the community based upon photosynthetic activity and phenology. Graphical projection of the mean vegetation vectors could be used to visualize characteristics and relationships between communities. The position of the plot represents the mean characteristics of the community. The difference in the mean vegetation vector between communities represents the similarity/dissimilarity of the characteristic between communities. These techniques represent a simple means of visualizing many vegetation communities and should facilitate characterizing land cover at global scales.

Large-scale, spatially-explicit test of the refuge strategy for delaying insecticide resistance

The refuge strategy is used worldwide to delay the evolution of pest resistance to insecticides that are either sprayed or produced by transgenic Bacillus thuringiensis (Bt) crops. This strategy is based on the idea that refuges of host plants where pests are not exposed to an insecticide promote survival of susceptible pests. Despite widespread adoption of this approach, large-scale tests of the refuge strategy have been problematic. Here we tested the refuge strategy with 8 y of data on refuges and resistance to the insecticide pyriproxyfen in 84 populations of the sweetpotato whitefly (Bemisia tabaci) from cotton fields in central Arizona. We found that spatial variation in resistance to pyriproxyfen within each year was not affected by refuges of melons or alfalfa near cotton fields. However, resistance was negatively associated with the area of cotton refuges and positively associated with the area of cotton treated with pyriproxyfen. A statistical model based on the first 4 y of data, incorporating the spatial distribution of cotton treated and not treated with pyriproxyfen, adequately predicted the spatial variation in resistance observed in the last 4 y of the study, confirming that cotton refuges delayed resistance and treated cotton fields accelerated resistance. By providing a systematic assessment of the effectiveness of refuges and the scale of their effects, the spatially explicit approach applied here could be useful for testing and improving the refuge strategy in other crop–pest systems.

Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived phenological metrics (e.g., season start, peak greenness) can characterize specific vegetation communities across an elevation gradient, and to examine the interactions between climate and phenological metrics for each vegetation community. We found that representative vegetation types (11), varying between desert scrub, mesquite, grassland, mixed oak, juniper and pine, often had unique seasonal and interannual phenological trajectories and spatial patterns. Satellite derived land surface phenometrics (11) for each of the vegetation communities along the cline showed numerous distinct significant relationships in response to temperature (4) and precipitation (7) metrics. Satellite-derived sky island vegetation phenology can help assess and monitor vegetation dynamics and provide unique indicators of climate variability and patterns of change.

Evaluation of airborne video data for land-cover classification accuracy assessment in an isolated Brazilian forest

Abstract This research was designed to evaluate the operational utility of airborne bispectral video data for reconnaissance assessment of land-cover variability and to document the viability of the video data for classification accuracy assessment. There are distinct logistical advantages utilizing airborne video data in isolated and environmentally sensitive regions where there is limited preexisting aerial photography and poor infrastructure and where ground accessibility is difficult and expensive. In a study of land-cover characteristic in Mato Grosso, Brazil, video data provided important insights into the variability and transitions in land-cover that could be used to identify targets for field work and training areas for subsequent satellite image (Landsat TM) classifications. Comparisons of Landsat TM classification accuracy assessments derived from the airborne video and standard color photography revealed that comparable results can be achieved at a high statistical level of significance (0.01). These results demonstrated that the video data can provide information for accuracy assessments equivalent to more standard photographic point-sample data acquisition missions with the added benefit of easily acquiringfar more data. Results of the supervised Landsat TM classification coupled with a topographic model produced an overall accuracy of 68% with a Kappa coefficient of 0.60, based upon a priori classes identified through evaluation of the airborne data and a site visit.

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.

Assessment of environmental impacts of river basin development on the riverine forests of eastern Kenya using multi-temporal satellite data

The utility of Landsat MSS (Multispectral Scanner) and SPOT XS data in monitoring the impacts of river basin development on a riverine forest located in the lower Tana River Basin of eastern Kenya was evaluated. Land cover change maps derived from Landsat MSS indicated little change in total forest area between 1975 and 1984. Land cover change maps derived from SPOT XS data indicated a 27% decline in forest area between 1989 and 1996. Mean patch size and area-perimeter ratio of the closed riverine forest remained virtually unchanged whereas these parameters for the open forest class decreased by 31% and 4% respectively. In addition, the average extent of the open riverine forest from the river channel declined by about 200 m between 1989 and 1996. This decline was attributed to decreased extent of floods along the floodplain following construction of dams in the upper river basin, and increased exploitation of the forests for fuelwood, especially in the vicinity of the established Bura Irrigation and Settlement Project. The greater lateral movement observed in the location of the river channel for the 1975-1985 period, compared to the 1985-1996 period, was also attributed to construction of dams in the upper river basin.

Biophysical characterization and management effects on semiarid rangeland observed from Landsat ETM+ data

Semiarid rangelands are very sensitive to global climatic change; studies of their biophysical attributes are crucial to understanding the dynamics of rangeland ecosystems under human disturbance. In the Santa Rita Experimental Range, AZ, the vegetation has changed considerably, and there have been many management activities applied. This study calculates seven surface variables: the enhanced vegetation index, the normalized difference vegetation index (NDVI), surface albedos (total shortwave, visible, and near-infrared), leaf area index (LAI), and the fraction of photosynthetically active radiation (FPAR) absorbed by green vegetation from the Enhanced Thematic Mapper (ETM+) data. Comparison with the Moderate Resolution Imaging Spectroradiometer vegetation index and albedo products indicates they agree well with our estimates from ETM+, while their LAI and FPAR are larger than from ETM+. Human disturbance has significantly changed the cover types and biophysical conditions. Statistical tests indicate that surface albedos increased and FPAR decreased following tree-cutting disturbances. The recovery will require more than 67 years and is about 50% complete within 40 years at the higher elevation. Grass cover, vegetation indexes, albedos, and LAI recovered from cutting faster at the higher elevation. Woody plants, vegetation indexes, and LAI have recovered to their original characteristics after 65 years at the lower elevation. More studies are needed to examine the spectral characteristics of different ground components.