High Resolution Satellite Sensors Research Articles

Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development

Policy and decision making in the context of sustainable development requires rapid, effective and efficient access to and integration of appropriate current information from a wide range of sources and disciplines, including land cover dynamic information derived from remotely sensed data. The analysis of data from high spatial resolution satellite sensors has potential in land cover monitoring. In this paper, a post-classification method is used to detect land cover change from multi-temporal satellite data, and particular attention is given to the selection of an appropriate method for land cover classification. The use of a Geographic Information System (GIS) allows further spatial analysis of the data derived from remotely sensed images and analysis of the impact of land cover change on regional sustainable development. The satellite-derived data used in this study are Thematic Mapper (TM) data acquired by Landsat-5 on 14 May 1985, 20 May 1987, 26 April 1990 and 20 May 1993 in Ansan City, Korea. The results obtained show that land cover change in the west coastal zone of Korea has occurred in the past decade as a result of both natural forces and human activities, which has in turn impacted on the regional sustainable development. The results thus provide very useful information to local government for decision making and policy planning.

Terrain measurement using automated digital photogrammetry

Introduction Photogrammetry has traditionally provided a means of generating three-dimensional spatial data to represent terrain surfaces, which complements traditional ground-based surveying methods. Although techniques such as airborne laser scanning (Lohr 1998) and synthetic aperture radar (Hogg et al. 1993; Vencatasawamy et al. 1998) have developed, photogrammetry remains the primary method of generating topographic maps (Wolf 1983; Capes 1998). One important advantage of photogrammetry is the flexibility of scale that allows application to imagery acquired from ground, air and space. Indeed, a new generation of high (i.e. 1 m) resolution satellite sensors (Capes 1998) is likely to further increase the potential applications of photogrammetry. Despite many advantages, there have been several problems with the application of photogrammetry using traditional methods. Most significantly, there was the requirement to use an expensive and complex photogrammetric stereoplotter. This ensured that the measurement process was slow and generally required the skills of an experienced operator, particularly if results of the highest accuracy were to be obtained. Rapid developments in computing hardware and software have allowed the science of photogrammetry to develop rapidly during the last ten years (Gruen 1994; Atkinson 1996; Greve 1996). These developments have radically eased many of the problems and limitations associated with traditional analogue instrumentation. Use of a purely numerical or analytical solution provides flexibility, which assists in two important ways. Satellite imagery, oblique aerial photography and ground-based imagery can be used, in addition to the more traditional vertical aerial perspective. Similarly, imagery acquired using

Refugee Camp Mapping Using Very High Spatial Resolution Satellite Sensor Images

Abstract Short time‐intervals for complex response in unfamiliar areas cause refugee‐relief organisations to have a strong need for timely and up‐to‐date geographic information of the environment during humanitarian operations. The objective of this study is to provide an overview of relief organisations' need for detailed geographic information, and to assess the potential of the upcoming very high spatial resolution (VHSR) satellite sensors to provide this geographic information by mapping refugee camps and their environment on an operational basis. To demonstrate the use of VHSR satellite technology in relief operations, a pilot proof‐of‐concept study using a 1992 Russian KVR‐lOOO 2 m resolution panchromatic image of the six refugee camps in the Qala en Nahal settlement scheme in the Sudan was performed. The VHSR satellite sensor image was found to be useful for mapping refugee camp environmental parameters, such as land use, roads, rivers, and water sources, as well as camp infrastructure, including geographic positioning of camps, housing, and street network. The image also allowed for detailed camp area estimates. In addition, a statistically significant relationship between camp area and population was revealed for refugee camps included in this study. In operational use of VHSR satellite sensor data, relief agencies should be aware of the limitations of optical satellite images, in particular their reduced applicability during cloudy conditions.

Refugee Camp Mapping Using Very High Spatial Resolution Satellite Sensor Images

Short time‐intervals for complex response in unfamiliar areas cause refugee‐relief organisations to have a strong need for timely and up‐to‐date geographic information of the environment during humanitarian operations. The objective of this study is to provide an overview of relief organisations' need for detailed geographic information, and to assess the potential of the upcoming very high spatial resolution (VHSR) satellite sensors to provide this geographic information by mapping refugee camps and their environment on an operational basis. To demonstrate the use of VHSR satellite technology in relief operations, a pilot proof‐of‐concept study using a 1992 Russian KVR‐lOOO 2 m resolution panchromatic image of the six refugee camps in the Qala en Nahal settlement scheme in the Sudan was performed. The VHSR satellite sensor image was found to be useful for mapping refugee camp environmental parameters, such as land use, roads, rivers, and water sources, as well as camp infrastructure, including geographic positioning of camps, housing, and street network. The image also allowed for detailed camp area estimates. In addition, a statistically significant relationship between camp area and population was revealed for refugee camps included in this study. In operational use of VHSR satellite sensor data, relief agencies should be aware of the limitations of optical satellite images, in particular their reduced applicability during cloudy conditions.

Reducing structural clutter in land cover classifications of high spatial resolution remotely-sensed images for urban land use mapping

A new generation of very high spatial resolution (1–5 m) satellite sensors is due to be launched within the next five years. Among other things, images acquired by these sensors offer considerable potential for the derivation of information on urban land use. It has been suggested that this can be achieved via a two-stage process involving (i) a standard (per-pixel) multispectral classification algorithm to identify the principal land cover parcels present in the observed scene and (ii) the application of structural pattern-recognition techniques to infer land use from the morphological properties of these parcels and the spatial relations that exist between them. It is implicit in this approach that the initial classification is of sufficient accuracy to allow land use to be inferred from these structural properties and relations. This assumption is investigated using airborne multispectral image data with a nominal spatial resolution of 2 m. It is shown that these data allow many features of interest in urban areas to be identified and delineated, but that they contain a significant amount of unwanted spatial detail (or ‘scene noise’). The latter results in structural ‘clutter’ in the initial land cover classification, which limits the potential to infer land use in the second stage of the data-processing chain. To address this issue, a simple, region-based, reflexive-mapping procedure is developed. This operates at the parcel (cf. pixel) level. The procedure is successful at removing much of the structural clutter, and performs well in comparison with traditional majority filtering; however, the inference of urban land use from the resulting data remains problematic.

Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps

Detailed geographic information is a key factor in decision making processes during refugee relief operations. The forthcoming commercial very high spatial resolution (VHSR) satellite sensors will be capable of acquiring multispectral (MS) images at spatial resolutions of 1m (panchromatic) and 4m (multispectral) of refugee camps and their environment. This work demonstrates how refugee camp environment, area and population can be analysed using a VHSR MS satellite sensor image from the Russian KVR-1000 sensor. This image, with a spatial resolution of 3.3m, was used to study Thailand's Site 2 refugee camps, which were established to accommodate Khmer refugees on the Thai-Kampuchean border. At the time of image acquisition, the total population of Site 2's five refugee camps was close to 143000. The VHSR MS image was found to be suitable for mapping the refugee camp environment and area. A statistically significant linear relationship between camp area and population was determined. Accordingly, the study suggests that VHSR MS images in general may be useful for refugee camp planning and management and points toward the utilization of forthcoming commercial VHSR MS satellite sensor images in humanitarian relief operations.

Distributed mapping of snow and glaciers for improved runoff modelling

Runoff in glacierized alpine basins results from both seasonal snow cover and glacier melt. In this paper we present a case study for an improved runoff modelling of the basin Massa-Blatten, 196 km 2 , 1447-4191 m a.s.l. Using high resolution satellite sensors, it is possible to separately map snow cover and glacier areas. From the satellite data we derive depletion curves of snow covered areas in six elevation zones and determine when the glacier ice becomes exposed. We compute melt depths based on experimental measurements with regard to snow and ice. With the use of the snowmelt runoff model SRM-ETH, the following input components to runoff were determined: seasonal snow cover, including new snow falling on the snow covered areas; new snow falling on the snow-free area; rain; and glacier-ice. In order to evaluate the effect of the refined snow and glacier mapping on detailed melt computations, we compute the runoff resulting from i) the conventional runoff simulation based on integral snow and glacier areas, and ii) and advanced simulation taking into account the individual contributions from snow cover and glacier melt. We achieve a significant improvement of the accuracy of the runoff simulation.

Distributed mapping of snow and glaciers for improved runoff modelling

Runoff in glacierized alpine basins results from both seasonal snow cover and glacier melt. In this paper we present a case study for an improved runoff modelling of the basin Massa-Blatten, 196 km2, 1447–4191 m a.s.l. Using high resolution satellite sensors, it is possible to separately map snow cover and glacier areas. From the satellite data we derive depletion curves of snow covered areas in six elevation zones and determine when the glacier ice becomes exposed. We compute melt depths based on experimental measurements with regard to snow and ice. With the use of the snowmelt runoff model SRM-ETH, the following input components to runoff were determined: seasonal snow cover, including new snow falling on the snow covered areas; new snow falling on the snow-free area; rain; and glacier-ice. In order to evaluate the effect of the refined snow and glacier mapping on detailed melt computations, we compute the runoff resulting from i) the conventional runoff simulation based on integral snow and glacier areas, and ii) and advanced simulation taking into account the individual contributions from snow cover and glacier melt. We achieve a significant improvement of the accuracy of the runoff simulation. Copyright © 1999 John Wiley & Sons, Ltd.

Evaluation of MK-4 multispectral satellite photography in land cover classification of eastern Ontario

Russian MK-4 multispectral satellite photography has been investigated for potential in land cover classification. Thematic maps were generated using maximum likelihood, neural network and context classifiers. Classifications of the raw spectral data, of spectral transforms, and of combined spectral/textural data were evaluated. Low point-based class accuracies resulted for land cover types exhibiting high spatial variability at the given pixel spacing of 7.5m, while more spatially homogeneous cover types were well classified. Several issues arose which need to be addressed for effective future use of high-resolution satellite sensors in regional land cover mapping. They include the need for further research in techniques for classification and accuracy assessment which are sensitive to the spatial variance of such high resolution imagery, and optimization of class attribute definitions.

Correction of satellite imagery over mountainous terrain

A method for the radiometric correction of satellite imagery over mountainous terrain has been developed to remove atmospheric and topographic effects. The algorithm accounts for horizontally varying atmospheric conditions and also includes the height dependence of the atmospheric radiance and transmittance functions to simulate the simplified properties of a three-dimensional atmosphere. A database has been compiled that contains the results of radiative transfer calculations (atmospheric transmittance, path radiance, direct and diffuse solar flux) for a wide range of weather conditions. A digital elevation model is used to obtain information about surface elevation, slope, and orientation. Based on the Lambertian assumption the surface reflectance in rugged terrain is calculated for the specified atmospheric conditions. Regions with extreme illumination geometries sensitive to BRDF effects can be optionally processed separately. The method is restricted to high spatial resolution satellite sensors with a small swath angle such as the Landsat thematic mapper and Systeme pour l'Observation de la Terre high resolution visible, since some simplifying assumptions were made to reduce the required image processing time.

Remote sensing and disease control: past, present and future

Remote sensing is the process of gathering information about an environment from a distance (CRACKNELL & HAYES, 1991). This article focuses on the insight that has been gained, and might further arise, from observations of the land surface by satellite-borne sensors. The potential for application of remote sensing techniques to epidemiological problems has long been argued (CLINE, 1970) and the present status of such application reviewed (HAY et al., in press). It is not intended to precis these observations here, but to draw attention to what can be achieved with existing satellite sensor data. Some of the major advances in spatial, temporal and spectral resolution of the satellite-born sensors that will be launched into orbit by the millennium are also highlighted. The resulting opportunities for research and the management and control of tropical diseases are then discussed. This article complements that of OPENSHAW (1996), which was concerned with the role of geographic information systems (GIS) in the management and control of tropical diseases, since specific examples of the use of remotely sensed data in a GIS framework are presented. Existing satellite-borne sensors, for which data are available to the scientific research community, range from those in low altitude orbits with sensors that record high spatial resolution data but with an infrequent repeat time (the time it takes for a satellite sensor to record data from the same location on the Earth), to those in high altitude orbits with correspondingly low spatial resolutions but more frequent repeat times. The potential of these contrasting categories of sensors to provide information of use to epidemiology has been reviewed by HAY et al. (1996). This article presents recent examples of the application of high and low spatial resolution satellite sensor data to the control of malaria and trypanosomiasis, respectively. The distribution of Anopheles albimanus has been investigated using Landsat-Thematic Mapper (TM) data for the tropical wetlands of Chiapas, Mexico, where malaria is endemic (POPE et al., 1994). Landsat is a low orbit satellite whose TM sensor records data in 6 spectral channels at 30x30 m spatial resolution, and one at 120x120 m, with a repeat time of 16 d. Scenes from both the dry and wet season were acquired for the Chiapas area and subjected to an unsupervised classification which groups areas in the image by their spectral similarity; the resulting regions were assigned to land cover types determined from colour infrared aerial photographs and field inspection of 30 test sites. These sites were independently sampled to determine mosquito density and information was collected about environmental variables affecting water and vegetation characteristics. These data were then used to group the sites into 16 habitat types using cluster analysis, and correlations were investigated between the habitat types and land cover units. The land cover units were subsequently ranked as having high, medium or low mosquito production potential. Incorporating this information into a

Correction of atmospheric and topographic effects for high spatial resolution satellite imagery

A method for the combined correction of atmospheric and topographic effects has been developed. It accounts for horizontally varying atmospheric conditions and also includes the height dependence of the atmospheric radiance and transmittance functions to simulate the simplified properties of a threedimensional atmosphere. A Digital Elevation Model (DEM) is used to obtain information about surface elevation, slope, and orientation. Based on the Lambertian assumption the surface reflectance in rugged terrain is calculated. The method is restricted to high spatial resolution satellite sensors like Landsat TM and SPOT HRV, since some simplifying assumptions are being made to reduce the required image processing time. The possibilities and limitations of the method are critically discussed.

Mapping forest‐cover disturbances in Papua New Guinea with AVHRR data

Abstract. Land management and land‐use planning in a forested country such as Papua New Guinea, which is subject to various anthropogenic pressures, requires an accurate mapping of forest‐cover disturbances. The central hypothesis of this study was that remote sensing indicators of forest‐cover conditions can be used to measure and map the impact of long‐term forest‐cover disturbances. This was tested with single year NOAA's Advanced Very‐High Resolution Radiometer sensor (AVHRR) data at 1.1 km resolution. First, an ordinal scale of forest‐cover disturbance was defined from field observations, a set of thematic maps and high spatial resolution satellite sensor data. Secondly, we analysed the relationship between the forest‐cover disturbance scale and several biophysical indicators derived from AVHRR data at two seasons. Thirdly, a statistical analysis identified the optimal combination of biophysical indicators and observation dates to discriminate between the forest disturbance levels defined previously. A forest‐cover disturbance map was then produced over part of Papua New Guinea. Finally, a regionalization of the study area in terms of aggregate degree of disturbance was produced and the spatial patterns of forest disturbances were interpreted for each region in terms of broad processes of forest‐cover change. The overall accuracy of the forest‐cover disturbance map was 79%. Nine regions, homogeneous in regard to the distribution and spatial pattern of disturbance categories, were identified.

A spatially adaptive fast atmospheric correction algorithm

Abstract An atmospheric correction algorithm for high spatial resolution satellite sensors like Landsat Thematic Mapper (TM) has been developed. The algorithm works with a catalogue of atmospheric correction functions stored in look-up tables. The catalogue consists of a broad range of atmospheric conditions (different altitude profiles of pressure, air temperature, and humidity; several aerosol types; ground elevations from 0-1 km above sea level; solar zenith angles ranging from 0°-70°). The catalogue covers visibilities (surface meteorological range) from 5-40 km, values can be extrapolated down to 4 km and up to 80km. The 1994 edition of the catalogue was compiled using the MODTRAN-2 and the SENSAT-5 codes. The algorithm consists of an interactive and an automatic part. The interactive phase serves for the definition of a reference target (dense dark vegetation or water) as well as haze and cloud. The reflectance of the reference target in a single spectral band (dark vegetation: TM band 3; water: TM ...

Scene locations and overpass dates for Landsat and SPOT sensors

Abstract It is frequently necessary to obtain information about scene locations and potential data acquisitions for the high resolution satellite sensors. This Letter provides formulas and basic data to permit users to compute locations, dates and times of possible coverage for past and future observations from Landsats 1-5 and SPOT.