Joseph Scepan

The use of remote sensing and landscape metrics to describe structures and changes in urban land uses

Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, the determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial form—landscape metrics—to describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed ‘landscape metric signatures’ for the land-use categories. The analysis shows the importance of the spatial measurements as second-order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern.

An information systems approach to the preservation of biological diversity

Abstract Although biological diversity has emerged in the 1980s as a major scientific and political issue, efforts at scientific assessment have been hampered by the lack of cohesive sets of data. We describe, in concept, a comprehensive national diversity information system, using geographical information system (GIS) techniques to organize existing data and improve spatial aspects of the assessment. One potential GIS analysis, to identify gaps in the network of nature reserves for California, is discussed in greater detail. By employing an information systems approach, available data can be used more effectively and better management strategies can be formulated.

Crop area monitoring within an advanced agricultural information system

Abstract This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification pr...

Improving crop type determination using satellite imagery: A study for the Regione del Veneto, Italy

Abstract The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands....

Thematic validation of global land cover data sets-procedures and interpretation methods

The International Geosphere-Biosphere Programme DISCover Global Land Cover Data Set (v 1.0) was assembled to provide a high spatial resolution data set for global studies of climate, biogeochemical cycles, atmospheric chemistry, water, energy, vegetation, and ecosystems. DISCover was compiled at a resolution of 1 kilometer using data from the National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Following production of DISCover, a study was performed to verify its thematic accuracy and provide statistically valid accuracy estimates on an area-weighted and by-class basis. Landsat Thematic Mapper and SPOT satellite image data were used to verify 379 primary core samples selected from DISCover 1.0 using a stratified random sampling procedure. The goal was to verify a minimum of 25 samples per DISCover class; this was accomplished for 13 of the 15 verified classes. Three regional expert image interpreters independently verified each sample. A majority decision rule was used to determine sample accuracy. For the 15 DISCover classes validated, the average class accuracy was 59.4% with accuracies for the 15 verified DISCover classes ranging between 40.0% and 100%. The area-weighted accuracy of the data set was determined to be 66.9%. Considering only samples for which a majority of interpreters agreed were classified in error (with majority agreement on the correct class) as well as those samples interpreted by consensus as correct, the average class accuracy of the data set was calculated to be 73.5%. This paper describes procedures utilized to validate the thematic accuracy of DISCover. Data set sampling design and core sample interpretation procedures are summarized. Interpretation procedures and interpreter confidence (recorded for each sample by each participant during the validation) is analyzed by category and interpretation region. This DISCover 1.0 validation demonstrates that Landsat TM and SPOT imagery can be efficiently used to validate high-resolution global land cover products. This study demonstrates that the 15 validated IGBP land cover classes were not equally interpretable on the TM and SPOT imagery. The utility of and confidence that may be placed in this technique depends upon the land cover classification scheme used or a subset of categories.

Accuracy analysis and validation of global 1 km land cover data sets

An approach for analyzing and validating thematic and positional accuracy of high resolution global land cover data set is described. Portions of the International Geosphere/Biosphere Programmes (IGBP) 1 Kilometer Land Cover Data Set are used as a test bed to evaluate procedures for accuracy assessment and validation. The 1 km Land Cover Data Set contains 17 land cover classes ranging from natural vegetation to developed land and non-vegetated classes. This study focuses on developing statistical map accuracy procedures for data set validation through a continental scale cross validation exercise using the 1 km land cover data set. The objective of this exercise is to assess the potential of the IGBP fast track validation procedures. These include implementation of an integrated overall map accuracy analysis as well as a detailed category specific thematic accuracy assessment for both a North American and African study site. Specific goals of this study are to: (1) determine whether different classification techniques employed for Africa and North America significantly affect the classification of the 1 km Land Cover Data Set; (2) determine whether systematic differences are introduced into the datasets by the separate processing flows; (3) examine and attempt to explain any non-systematic differences which may be identified, and; (4) derive a statistical estimate of these differences between the two datasets for each of the 17 Land Cover classes-or as many of the classes as are adequately represented.

The imperative for proactive cooperative national mapping strategies in the new millennium

There is no comprehensive global mapping program in existence today, despite the efforts of those involved in the International Map of the World project begun in the late 1800s. While the IMW project was finally declared ended in the 1980s, there are now several current projects that will help fill some of the gaps in the mapping needs of a variety of users, including the Global Map project. In addition to a brief review of this project, an example of the application of Global Map data to local phenomena is given.

Principles used in compiling and editing relief features of soviet bathymetric maps as applied to the Western Indian Ocean

Abstract Some principles used in compiling and editing Soviet bathymetric charts are presented. We particularly focus on mapping relief of the ocean floor. We consider various morphologic and morphometric features of the ocean floor and describe how these features are represented on bathymetric maps. Morphologic features are those that are identified from source data but cannot be measured on the original map. Morphometric features are those for which quantitative data (i.e., absolute and relative height, slope gradient, extent) can be derived from source maps. For this analysis, we examine the Western Indian Ocean, and five major ocean‐bottom relief features are identified and described for this area: the continental shelf, the continental slope, hollows, local ridges, and midocean ridges. The choice of bathymetric contour interval (h) is the most important factor in accurate editing of ocean floor relief and we describe nine important physical parameters that must be evaluated for identifying proper con...

Remote sensing for detection of oil in the marine environment: An evaluation of the United States coast guard ‘Aireye’ surveillance system

Abstract In the United States, the U.S. Coast Guard has a primary responsibility for monitoring petrochemical pollution of U.S. coastal waters and has developed the ‘Aireye’ airborne surveillance system to assist in this task. Carried aboard a Dessault Falcon fanjet aircraft, Aireye is a multisensor, real-time, all weather day/night remote sensing system used to detect sea-surface oil pollutants and identify source vessels. The system includes a thermal infrared/ultraviolet line scanner, real aperature side-looking radar and an active-gated television. The Aireye system was field evaluated in a series of test flights over a portion the Santa Barbara Channel, California, during September and October, 1984, and May, 1985. This paper describes the Aireye system and presents the results of these tests. In addition, some general oil pollution detection concepts are summarized and problems in remote detection of sea-surface oil are discussed.