COMPARATIVE EVALUATION OF LAND COVER DATA SOURCES FOR EROSION PREDICTION1

ABSTRACT: A fundamental problem in protecting surface drinking water supplies is the identification of sites highly susceptible to soil erosion and other forms of nonpoint source (NPS) pollution. The New York City Department of Environmental Protection is trying to identify erodible sites as part of a program aimed at avoiding costly filtration. New York City's 2,000 square mile watershed system is well suited for analysis with geographic information systems (GIS); an increasingly important tool to determine the spatial distribution of sensitive NPS pollution areas. This study used a GIS to compare three land cover sources for input into the Modified Universal Soil Loss Equation (MUSLE), a model estimating soil loss from rangeland and forests, for a tributary watershed within New York City's water supply system. Sources included both conventional data (aerial photography) and Landsat data (MSS and TM images). Although land cover classifications varied significantly across these sources, location-specific and aggregate watershed predictions of the MUSLE were very similar. We conclude that using Landsat TM imagery with a hybrid classification algorithm provides a rapid, objective means of developing large area land cover databases for use in the MUSLE, thus presenting an attractive alternative to photo interpretation.     

Implications of Spatial Data Variations for Protected Areas Management: An Example from East Africa

Geographic information systems and remote sensing technologies have become an important tool for visualizing conservation management and developing solutions to problems associated with conservation. When multiple organizations separately develop spatial data representations of protected areas, implicit error arises due to variation between data sets. We used boundary data produced by three conservation organizations (International Union for the Conservation of Nature, World Resource Institute, and Uganda Wildlife Authority), for seven Ugandan parks, to study variation in the size represented and the location of boundaries. We found variation in the extent of overlapping total area encompassed by the three data sources, ranging from miniscule (0.4 %) differences to quite large ones (9.0 %). To underscore how protected area boundary discrepancies may have implications to protected area management, we used a landcover classification, defining crop, shrub, forest, savanna, and grassland. The total area in the different landcover classes varied most in smaller protected areas (those less than 329 km²), with forest and cropland area estimates varying up to 65 %. The discrepancies introduced by boundary errors could, in this hypothetical case, generate erroneous findings and could have a significant impact on conservation, such as local-scale management for encroachment and larger-scale assessments of deforestation.     

Definition Procedures Have Little Effect on Performance of Environmental Classifications of Streams and Rivers

Mapped environmental classifications are defined using various procedures, but there has been little evaluation of the differences in their ability to discriminate variation in independent ecological characteristics. We tested the performance of environmental classifications of the streams and rivers of France that had been defined from the same environmental data using geographic regionalization and numerical classification of individual river valley segments. Test data comprised invertebrate assemblages, water chemistry, and hydrological indexes obtained from sites throughout France. Classification performance was measured by analysis of similarity (ANOSIM). Geometric regions defined by a regular grid and without regard to environmental variables and a posteriori classifications based on clustering the test datasets defined lower and upper bounds of performance for a given number of classes. Differences in classification performances were generally small. The ANOSIM statistics for the a posteriori classifications were around twice that of all environmental classifications, including geometrically defined regions. The hydro-ecoregions performed slightly better for the invertebrate data and the network classification performed slightly better for the chemistry and hydrological data. Our results indicate that environmental classifications that are defined using different procedures can be comparable in terms of their ability to discriminate variation of ecological characteristics and that alleged differences in performance arising from different classification procedures can be small relative to unexplained variation. We conclude that definition procedures might have little effect on the performance of large-scale environmental classifications and decisions over which procedures to use should be based primarily on pragmatic considerations.     

Synthetic Analysis for Extracting Information on Soil Salinity Using Remote Sensing and GIS: A Case Study of Yanggao Basin in China

/ This paper reports the experience of extracting information on the salinity of soil and offers a method of synthetic analysis. The experimental areas for analysis are located in Yanggao Basin, Shanxi Province, China. The types of soil are mainly meadow soil and salinized meadow soil. The method of synthetic analysis of salinity uses a geographic information system (GIS) as a tool, building a basic saltwater analysis model of saline soil and adjusting the result with expert experience after computer processing. The method of feature extraction has been used for remotely sensed data. An optimum combination of features has been determined and, after comparing several combinations in the Yanggao region, an improved result has been obtained after Kauth-Thomas (K-T) transformation. For precise quantitative analysis of the salinization, not only Thematic Mapper (TM) remote sensing data, but also two forms of non-remote-sensing data are needed: depth of groundwater and mineralization rate of groundwater according to the theory of genesis of soil. For the analysis of synthetic compounded multisources, a generalized Bayes classification is used after overlay, matching, and related coefficients have been determined. On the premise that various information sources are independent, global membership functions with probability are used to combine various pieces of information in order to apply them directly to the pixels and classifications of soil salinity. The experiment indicates that this analytical method is sound because of the increased speed of processing and its simplicity and improved precision of classification of salinity. Finally, it is necessary to examine and adjust the factors using expert intelligence. The experiment shows that synthetic analysis using the geographic information system can raise the precision of quantitative analysis of salinity, which has advantages for environmental monitoring and management.KEY WORDS: Salinity; Remote sensing; Thematic Mapper; Geographic information system; Classification     

Classifying hazardous events

Classification is an information management technique that helps achieve the goals of scientific and applied research by simplifying and ordering complex and varied observations. This paper is intended as a guide to the use and application of classifications of hazardous events. Proposed classifications are reviewed and evaluated in terms of their purposes, methods, supportive evidence and the scientific and applied goals of classification. The review is structured according to the metaphor of the natural life history of a hazard that distinguishes three stages of human-environment interaction: (a) hazard causes, (b) physical and psychosocial characteristics of hazards, and (c) individual and aggregate responses to hazards. The life history perspective has the advantage of being multidimensional and transactional. The structure highlights the relationship between proposed classifications, identifies needed areas of conceptual and empirical development, and provides a general guideline for the selection and use of classifications for hazard management.     

A comparison of different biogeographical classifications of Europe,Great Britain and Spain

Various biogeographical and bioclimatic classifications of a number of regions, countries and continents have been created to meet different objectives. A policy maker might ask the question 'why is there no single accepted classification and how do the different classifications compare with one another?' In order to answer these two questions three classifications created by different methods for Great Britain and two for Spain are compared using the Kappa statistic. All of the classifications were created from data on cellular grids with a set window size. Further non-statistical comparisons are made with other classifications. The biogeographic classifications studied in this paper produced three different types of zone: those that were always identified whatever the method; those that were broadly similar but where the boundaries differed; and those that were unique to a particular classification. These different types of zone are likely to exist for any comparison between classifications of a particular region. The extent of the geographic window from which data were obtained had a major effect on the classification of grid cells at the edges of the window. For example, the few grid cells in the south of England, with characteristics of continental Europe, are not detected if data from Great Britain alone are used for the classification. We conclude that the data window should always be larger than the area for which the classification is being made. The objective Kappa statistic, although useful, was not capable of discerning similarities and dissimilarities that appear obvious to the subjective human eye. Although the details of the classifications differed there were broad similarities between the classifications and these differences reflect important divisions along major environmental axes that have been inferred by earlier biogeographers. As the divisions are real there is a sound basis for their use in future land use or environmental policy.     

A Water Quality Model for Regional Stream Assessment and Conservation Strategy Development

Non-point-source (NPS) pollution remains the primary source of stream impairment in the United States. Many problems such as eutrophication, sedimentation, and hypoxia are linked with NPS pollution which reduces the water quality for aquatic and terrestrial organisms. Increasingly, NPS pollution models have been used for landscape-scale pollution assessment and conservation strategy development. Our modeling approach functions at a scale between simple landscape-level assessments and complex, data-intensive modeling by providing a rapid, landscape-scale geographic information system (GIS) model with minimal data requirements and widespread applicability. Our model relies on curve numbers, literature-derived pollution concentrations, and land status to evaluate total phosphorus (TP), total nitrogen (TN), and suspended solids (SS) at the reach scale. Model testing in the Chesapeake Bay watershed indicated that predicted distributions of water quality classes were realistic at the reach scale, but precise estimates of pollution concentrations at the local scale can have errors. Application of our model in the tributary watersheds along Lake Ontario suggested that it is useful to managers in watershed planning by rapidly providing important information about NPS pollution conditions in areas where large data gaps exist, comparisons among stream reaches across numerous watersheds are required, or regional assessments are sought.     

Governing Change: Land-Use Change and the Prevention of Nonpoint Source Pollution in the North Coastal Basin of California

Many rural areas in the United States and throughout much of the postindustrial world are undergoing significant ecological, socioeconomic, and political transformations. The migration of urban and suburban dwellers into rural areas has led to the subdivision of large tracts of land into smaller parcels, which can complicate efforts to govern human–environmental problems. Non-point source (NPS) pollution from private rural lands is a particularly pressing human–environmental challenge that may be aggravated by changing land tenure. In this article, I report on a study of the governance and management of sediment (a common NPS pollutant) in the North Coastal basin of California, a region undergoing a transition from traditional extractive and agricultural land uses to rural residential and other alternative land uses. I focus on the differences in the governance and management across private timber, ranch, residential, vacation, and other lands in the region. I find that (1) the stringency and strength of sediment regulations differ by land use, (2) nonregulatory programs tend to target working landscapes, and (3) rural residential landowners have less knowledge of sediment control and report using fewer sediment-control techniques than landowners using their land for timber production or ranching. I conclude with an exploration of the consequences of these differences on an evolving rural landscape.     

Identification and mapping of submerged plants in a shallow lake using Quickbird satellite data

Turkey is a country rich in lakes and wetlands--monitoring of all these will require advances in technology such as remote sensing. In this study, the aquatic plants of the large and shallow Lake Mogan, located in Central Anatolia were identified and mapped using high spatial resolution Quickbird imagery. As Lake Mogan is an important bird area the assessment of submerged plant species is of great value for ecosystem conservation and management. Quickbird multispectral image acquired on August 6, 2005 was geometrically corrected and a water mask was used based on strong absorption of Near Infrared (NIR) wavelengths by calm, clear and deep water. The water mask was applied using band reflectance values for a specific pixel satisfying the conditions of band decreasing property (Green>Red>NIR) and NIR相似文献   

Assessing Watershed-Scale, Long-Term Hydrologic Impacts of Land-Use Change Using a GIS-NPS Model

Land-use change, dominated by an increase in urban/impervious areas, has a significant impact on water resources. This includes impacts on nonpoint source (NPS) pollution, which is the leading cause of degraded water quality in the United States. Traditional hydrologic models focus on estimating peak discharges and NPS pollution from high-magnitude, episodic storms and successfully address short-term, local-scale surface water management issues. However, runoff from small, low-frequency storms dominates long-term hydrologic impacts, and existing hydrologic models are usually of limited use in assessing the long-term impacts of land-use change. A long-term hydrologic impact assessment (L-THIA) model has been developed using the curve number (CN) method. Long-term climatic records are used in combination with soils and land-use information to calculate average annual runoff and NPS pollution at a watershed scale. The model is linked to a geographic information system (GIS) for convenient generation and management of model input and output data, and advanced visualization of model results. The L-THIA/NPS GIS model was applied to the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana, USA. Historical land-use scenarios for 1973, 1984, and 1991 were analyzed to track land-use change in the watershed and to assess impacts on annual average runoff and NPS pollution from the watershed and its five subbasins. For the entire watershed between 1973 and 1991, an 18% increase in urban or impervious areas resulted in an estimated 80% increase in annual average runoff volume and estimated increases of more than 50% in annual average loads for lead, copper, and zinc. Estimated nutrient (nitrogen and phosphorus) loads decreased by 15% mainly because of loss of agricultural areas. The L-THIA/NPS GIS model is a powerful tool for identifying environmentally sensitive areas in terms of NPS pollution potential and for evaluating alternative land use scenarios for NPS pollution management.     

Biophysical‐Regulatory Classification and Profiling of Streams Across Management Units and Ecoregions1

Caruso, Brian S. and Joshua Haynes, 2011. Biophysical‐Regulatory Classification and Profiling of Streams Across Management Units and Ecoregions. Journal of the American Water Resources Association (JAWRA) 00(0):1‐22. DOI: 10.1111/j.1752‐1688.2010.00522.x Abstract: Aquatic resources management in the United States (U.S.) under Clean Water Act Section 404 has become more complex after recent Supreme Court decisions and U.S. Army Corps of Engineers and Environmental Protection Agency (USEPA) guidance. Many intermittent/ephemeral and headwater streams may not be jurisdictional if they lack a significant nexus with navigable waters. Streams in semiarid USEPA Region 8 were classified based on hydrologic permanence and stream order using National Hydrography Dataset (NHD) Plus and GIS to provide information across broad spatial scales to aid with jurisdictional determinations (JDs). Four classes were developed for profiling across management units and ecoregions. Based on medium‐resolution NHDPlus data, intermittent streams comprise >¾, and first order streams constitute >½ of the total stream length in Region 8. Mountain states and ecoregions have the largest percentage of perennial first order streams, whereas the Dakotas, plains, and desert ecoregions have the greatest percentages of intermittent first order and intermittent higher order streams. In the Upper Colorado River Basin, >50% of reaches are intermittent first order, and 9% are perennial first order. NHDPlus data can significantly underestimate the length of headwater and intermittent streams, but can still be a valuable tool to help develop stream classes and for regional JD planning and analysis. Refinement of the stream classes using high resolution NHD data and other key catchment parameters can improve their utility for JDs.     

Detection of Coastal Saline Land Uses with Multi-Temporal Landsat Images in Shangyu City,China

Many coastal regions in China are confronted with pressing problems of scarce land resources and heavy population. Over the past 30 years, considerable parts of coastal tidelands have been enclosed and reclaimed for agricultural land uses. To assess, plan, and implement large-scale reclamation programs, up-to-date and reliable information concerning the nature, areal extent, and physical and chemical characteristics of coastal saline lands is essential. This paper reports a remote sensing approach to detecting coastal saline land uses in Shangyu City, China, by using multi-temporal Landsat images. First, with the aid of resolution-sharpened Landsat-7 ETM+ images and their enhanced linear features, a visual interpretation is applied to extract individual dikes. Based on time series images and local government records, a spatial zoning procedure is then used to define six sub-zones with different historical years of reclamation. It shows that a total of 15,668 ha of coastal saline lands were enclosed and reclaimed from 1969 to 1996. Second, a modified land-use classification system for the study area is prescribed, and both unsupervised and supervised classifiers are performed for land-use classifications of grouped sub-zones. Information obtained from the spatial zoning, Tasseled Cap transformation and Normalized Difference Vegetation Index, is also utilized to facilitate the supervised classification process. Finally, a detailed land-use map is produced, with an overall classification accuracy of 77.8%. Results show that dominant agricultural land uses of sub-zones are changed with historical reclamation years, from saline lands with wildgrass (very recently reclaimed) to aqua-farm ponds, to cotton fields, and to paddy fields and orchards (very early reclaimed). This transform process is primarily affected by soil salinities, and according to a soil survey an electrical conductivity of saturation extract decreased from 7.3 ds/m in the saline land reclaimed in 1996 to below 2 ds/m in the land reclaimed before 1969. The study concludes that multi-temporal remotely sensed images are important and effective data sources for monitoring the rapid changes of coastal land uses.     

An Assessment of Road Impacts on Wildlife Populations in U.S. National Parks

Current United States National Park Service (NPS) management is challenged to balance visitor use with the environmental and social consequences of automobile use. Wildlife populations in national parks are increasingly vulnerable to road impacts. Other than isolated reports on the incidence of road-related mortality, there is little knowledge of how roads might affect wildlife populations throughout the national park system. Researchers at the Western Transportation Institute synthesized information obtained from a system-wide survey of resource managers to assess the magnitude of their concerns on the impacts of roads on park wildlife. The results characterize current conditions and help identify wildlife-transportation conflicts. A total of 196 national park management units (NPS units) were contacted and 106 responded to our questionnaire. Park resource managers responded that over half of the NPS units’ existing transportation systems were at or above capacity, with traffic volumes currently high or very high in one quarter of them and traffic expected to increase in the majority of units. Data is not generally collected systematically on road-related mortality to wildlife, yet nearly half of the respondents believed road-caused mortality significantly affected wildlife populations. Over one-half believed habitat fragmentation was affecting wildlife populations. Despite these expressed concerns, only 36% of the NPS units used some form of mitigation method to reduce road impacts on wildlife. Nearly half of the respondents expect that these impacts would only worsen in the next five years. Our results underscore the importance for a more systematic approach to address wildlife-roadway conflicts for a situation that is expected to increase in the next five to ten years.     

Implications of Land Use/Land Cover Change in the Buffer Zone of a National Park in the Tropical Andes

The impacts of land use and land cover (LULC) change in buffer zones surrounding protected ecological reserves have important implications for the management and conservation of these protected areas. This study examines the spatial and temporal patterns of LULC change along the boundary of Rio Abiseo National Park in the Northern Peruvian Andes. Landscape change within four ecological zones was evaluated based on trends expected to occur between 1987 and 2001. Landsat TM and ETM imagery were used to produce LULC classification maps for both years using a hybrid supervised/unsupervised approach. LULC changes were measured using landscape metrics and from-to change maps created by post-classification change detection. Contrary to expectations, tropical upper wet montane forest increased despite being threatened by human-induced fires and cattle grazing of the highland grasslands inside the park. Within the park’s buffer zone, tropical moist forest remnants were fragmented into more numerous and smaller patches between 1987 and 2001; this was in part due to conversion into agricultural land. The methods used in this study provide an effective way to monitor LULC change detection and support the management of protected areas and their surrounding environments.     

Landscape-level ecological regions: Linking state-level ecoregion frameworks with stream habitat classifications

Regionalization is a form of spatial classification, where boundaries are drawn around areas that are relatively homogeneous in landscape characteristics. The process of delineating ecological regions, or ecoregions, includes the analysis of ecosystem structure. To date, ecoregions have been developed at national and state scales for research and resource management. Stream classification is another method to order the variability of aquatic habitats that spans spatial scales from microhabitat to valley segment. In this study, landscape-level ecoregions are developed for the upper Grande Ronde River basin in northeastern Oregon, 3000 sq km in area. The ecoregion framework presented here is proposed to bridge the gap between stream habitat and state-level ecoregion classifications. Classification at this scale is meant to address issues of management at local scales: to aid in sampling design, in extrapolation of the results of site-specific studies, and in the development of best management practices that are more predictive of ecosystem response than current methods.     

Valuation of National Park System Visitation: The Efficient Use of Count Data Models, Meta-Analysis, and Secondary Visitor Survey Data

The National Park Service (NPS) currently manages a large and diverse system of park units nationwide which received an estimated 279 million recreational visits in 2011. This article uses park visitor data collected by the NPS Visitor Services Project to estimate a consistent set of count data travel cost models of park visitor willingness to pay (WTP). Models were estimated using 58 different park unit survey datasets. WTP estimates for these 58 park surveys were used within a meta-regression analysis model to predict average and total WTP for NPS recreational visitation system-wide. Estimated WTP per NPS visit in 2011 averaged $102 system-wide, and ranged across park units from $67 to $288. Total 2011 visitor WTP for the NPS system is estimated at $28.5 billion with a 95% confidence interval of $19.7–$43.1 billion. The estimation of a meta-regression model using consistently collected data and identical specification of visitor WTP models greatly reduces problems common to meta-regression models, including sample selection bias, primary data heterogeneity, and heteroskedasticity, as well as some aspects of panel effects. The article provides the first estimate of total annual NPS visitor WTP within the literature directly based on NPS visitor survey data.     

Tourism in the high north: Management challenges and recreation opportunity spectrum planning in Svalbard,Norway

The Arctic is subject to considerable pressure from a rapidly expanding world tourism industry and increasing demands for oil and gas resources. The archipelago of Svalbard in the Norwegian high arctic contains the most accessible islands in the entire Arctic, but despite extensive use of the resources, they still contain some of the last true wilderness lands in Europe. The growth in tourism with visitors from all parts of the world combined with coal mining and oil and gas exploration call for a coordinated environmental policy. A research-based management plan for tourism and recreation has now been developed. Some management problems were encountered in designing a recreation opportunity spectrum plan for this extremely vulnerable environment. Although Svalbard in many ways is a unique area, the challenges to management are comparable to those found in regions further south.     

Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method--the RIparian Classification Algorithm (RICAL)--to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future.     

WATERSHED CLASSIFICATION USING CANONICAL CORRESPONDENCE ANALYSIS AND CLUSTERING TECHNIQUES: A CAUTIONARY NOTE1

ABSTRACT: Watershed classification using multivariate techniques requires the incorporation of continuous datasets representing controlling environmental variables. Often, out of convenience and availability rather than importance to the structure of the system being modeled, the environmental data used originate from a variety of sources and scales. To demonstrate the importance of appropriate environmental data selection, classifications of six‐digit hydrologic units (1:24,000) across selected geographic areas within the Interior Columbia River Basin were produced. Canonical correspondence analysis was used to select and test environmental variables important in predicting Rosgen stream types and valley bottom classes. Then, hierarchical agglomerative clustering was used to group (classify) watersheds based on these variables. Statistically significant results were derived from the use of organized classification data with presumed predictive relationships to watershed properties, and a random distribution of environmental variables from the same datasets provided similar results. The results contained herein demonstrate that these analysis techniques do not necessarily select meaningful variables from a broad spectrum of data and that significant results are easily generated from randomly associated data. It is suggested that classifications produced using these multivariate techniques, especially when using multi‐scale data or data of unknown significance, are subject to invalid inferences and should be used with caution.     

Remote Sensing of Landscape-Level Coastal Environmental Indicators

Advances in technology and decreases in cost are making remote sensing (RS) and geographic information systems (GIS) practical and attractive for use in coastal resource management. They are also allowing researchers and managers to take a broader view of ecological patterns and processes. Landscape-level environmental indicators that can be detected by Landsat Thematic Mapper (TM) and other remote sensors are available to provide quantitative estimates of coastal and estuarine habitat conditions and trends. Such indicators include watershed land cover, riparian buffers, shoreline and wetland changes, among others. With the launch of Landsat 7, the cost of TM imagery has dropped by nearly a factor of 10, decreasing the cost of monitoring large coastal areas and estuaries. New satellites, carrying sensors with much finer spatial (1-5 m) and spectral (200 narrow bands) resolutions are being launched, providing a capability to more accurately detect changes in coastal habitat and wetland health. Advances in the application of GIS help incorporate ancillary data layers to improve the accuracy of satellite land-cover classification. When these techniques for generating, organizing, storing, and analyzing spatial information are combined with mathematical models, coastal planners and managers have a means for assessing the impacts of alternative management practices.