Rule‐based Integration of Remotely‐sensed Data and GIS for Land Cover Mapping in NE Costa Rica

Abstract

A classification method was developed for mapping land cover in NE Costa Rica at a regional scale for spatial input to a biogeochemical model (CENTURY). To distinguish heterogeneous cover types, unsupervised classifications of Landsat Thematic Mapper data were combined with ancillary and derived data in an iterative process. Spectral classes corresponding to ground control types were segregated into a storage raster while ambiguous pixels were passed through a set of rules to the next stage of processing. Feature sets were used at each step to help sort spectral classes into land cover classes. The process enabled different feature sets to be used for different types while recognizing that spectral classification alone was not sufficient for separating cover types that were defined by heterogeneity. Spectral data included the TM reflective bands, principal components and the NDVI. Ancillary data included GIS coverages of swamp extents, banana plantation boundaries and river courses. Derived data included neighborhood variety and majority measures that captured texture. The final map depicts 18 land cover types and captures the general patterns found in the region. Some confusion still exists between closely related types such as pasture with different amounts of tree cover.     

The Utility of Day‐and‐Night Observation and Cloud‐Penetration Capability of ERS‐1 SAR Data for Detection of Wetlands

Abstract

The sensitivity of microwave sensors to changes in the complex dielectric constant of soil/water mix with change in water content has been used in several studies for soil moisture estimation and in the detection of wetlands. In the study, reported here, an attempt has been made to delineate various categories of wetlands, namely ‘bils’ (acquaculture ponds), lakes/ponds, creeks, and mangroves through visual interpretation of ERS‐1 SAR images acquired on 29th April, 1993 over part of Sundarban delta, abutting Bay of Bengal. An overall accuracy of 91.2% with respect to delineation of wetlands has been achieved. Further, the cloud penetration and day‐and‐night observation capability of ERS‐1 SAR, though well known, is also illustrated.     

A Robust Texture Analysis and Classification Approach for Urban Land‐Use and Land‐Cover Feature Discrimination

Abstract

Attempts to analyze urban features and to classify land use and land cover directly from high‐resolution satellite data with traditional computer classification techniques have proven to be inefficient for two primary reasons. First, urban landscapes are composed of complex features. Second, traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture plays an important role in image segmentation and object recognition, as well as in interpretation of images in a variety of applications. This study analyzes urban texture features in multi‐spectral image data. Recent developments in the very powerful mathematical theory of wavelet transforms have received overwhelming attention by image analysts. An evaluation of the ability of wavelet transform in urban feature extraction and classification was performed in this study, with six types of urban land cover features classified. The preliminary results of this research indicate that the accuracy of texture analysis in classifying urban features in fine resolution image data could be significantly improved with the use of wavelet transform approach.     

Monitoring Detailed Land‐Cover Changes in Shrubland Habitat Reserves Using Multi‐temporal IKONOS Data

Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.     

Geographic information system for semi‐detailed mapping of soils in a semi‐arid region

Abstract

The paper presents a GIS model for mapping soils in the semi arid region of Israel. The model is based on a priori knowledge of the soil generating factors in the study area, namely (1) the parent‐material (lithology) which determines the origin of the soil; and (2) the relief (including the drainage patents) which is responsible for erosion, deposition, and leaching processes along the catena. A special attempt was made to represent soil belts in small subwatersheds with different parent‐material. The width of these belts can be varied as a function of the local relief.

We believe that GIS techniques, in contrast to conventional survey and mapping methods, have the potential to overcome the manpower and fiscal restrictions which limit monitoring of large areas and areas which are for different reasons difficult to access. However, systematically generalizing a spatial model for a large area may introduce some errors, either due to local variations which were not taken into account, or regional variations.     

Combining Metric Aerial Photography and Near‐Infrared Videography to Define Within‐Field Soil Sampling Frameworks

Abstract

This paper investigates the combination of metric aerial photography and near‐infrared (NIR) videography data to improve the design of field‐survey sampling frameworks. Spatial data collection can contribute up to 80% of the cost of deploying a Geographic Information System (GIS) based Decision Support System (DSS). The use of remotely sensed information, field survey using differential Global Positioning System (dGPS) and geostatistical interpolation methods maximises data quality for a given rate of sampling.

Medium‐format colour aerial photography and NIR videography were orthorectified to the national map base and mosaiced using ERDAS Imagine. The green and red layers of the aerial photography were combined with the NIR videography to form a false‐colour composite image. Two sampling strategies were tested. The first stratified sampling on a per field basis, creating four points per hectare, randomly located within each field. The second strategy used the remotely sensed information to identify within‐field variability classes for each field, using red‐green difference or normalised difference vegetation index (NDVI) models. These variability classes were used as a sub‐stratification framework with each class sampled at the same rate of 4 per hectare. For both strategies the sample points were generated within ESRI ArcView and were located in the field using dGPS. Maps of stone content were created using geostatistical methods and validated against samples collected on a 100 metre grid. It was concluded that combining the two image sources to create a within‐field stratification framework improved the precision of the results obtained from field‐survey.     

Land Surface Emissivity and Temperature Determination Using NOAA‐AVHRR Data

Abstract

Studies on land surface processes using remote sensing data gains importance in the context of Geosphere Biosphere Programme. Present study addresses the applicability of split‐window method, in a tropical environment for mapping of surface temperature over heterogeneous surface from satellite data. The accuracy of the method is about +2.2°K, which is reasonable value taking into account the atmospheric attenuation in tropical environment. An attempt has been made to derive emissivity from normalized difference vegetation index (NDVI) by taking into account the fraction of vegetation cover of each pixel, which is determined by satellite data. The emissivity values estimated from satellite data found to be in reasonable agreement with an estimated error of less than 1%. The results of the study indicate the potential use of NDVI as a modulating parameter in the land surface temperature estimation from satellite data.     

An attempt to separate out climatic and man‐induced causes of range degradation in Botswana using Landsat MSS imagery

Changes in natural vegetation cover comprising the Kalahari rangeland were undertaken using Landsat MSS imagery over a period of above average rainfall (1972–1982) and a period of drought (1982–1986). This and ancillary data were collected to determine whether changes in the range were related primarily to rainfall events or to man‐induced effects. Data from different orbits were made compatible digitally. Dark area subtraction was a problem because deep shadow and water were lacking in the Kalahari landscape. Eleven land‐use/land cover classes were derived for the 1984 base year. Additional signatures had to be obtained for the later drought years because of extreme increases in brightness. Broadly the south‐eastern Kalahari was divided into an interior, relatively uninhabited homogeneous area and a more diverse area containing fossil valleys and pans. Changes in vegetation cover in the interior appeared to be more related to rainfall events than anthropogenic factors. Changes in the fossil valley vegetation cover appeared to be more related to rainfall events during the period of above average rainfall and more related to cattle and smallstock densities during the drought period.     

Multi‐sensor Image Maps from SPOT,ERS and JERS

Abstract

Image mapping using data from visible and infrared sensors has, as a major drawback, the frequent cloud cover experienced in many countries. This is one of the main reasons why topographic maps at 1:100,000 scale and larger are often outdated. The results of a study which investigated the possibilities of fusing up‐to‐date spaceborne microwave data with existing images from optical sensors for topographic map updating at a scale of 1:100, 000 are presented in this paper. A key issue researched was the influence of geometric distortions and corrections of remote sensing data on the results of pixel based digital image fusion. After having terrain‐geocoded and radiometrically enhanced imagery from the Landsat, SPOT, ERS‐1 and JERS‐1 satellites, the data were fused applying a variety of colour transformation techniques as well as statistical or arithmetic methods. Initially, the image fusion was implemented using images covering a test site in the north of The Netherlands in order to calibrate specified combinations and techniques in a rather flat area. With the experience gained, the remote sensing data acquired over the research site were processed. The research test site is located in a typical Developing Country in the humid Tropics, on the mountainous south‐west coast of Sumatra in Indonesia. The results of the various applied techniques and image combinations were evaluated with reference to their capability to overcome the cloud cover problem. New combinations of techniques and images were developed as result of an optimisation process. The research produced two prototypes of annotated 1:100,000 scale image maps containing fused, cloud‐free optical/microwave imagery.     

Detecting and Enumerating New Building Structures Utilizing Very‐High Resolution Imaged Data and Image Processing

Abstract

Information on the number and type of new building structures is required by urban and transportation planners and the real estate industry. The goal of this paper is to explore the potential of high resolution imagery for meeting public and private sector demands for information on new buildings. The value of 1 m, 5 m, and 10 m panchromatic and 1 m color scanned aerial photography images acquired in 1997 and 1998 for a study area within the City of San Diego, California is assessed for general change detection and building enumeration. Both semi‐automated and interactive change‐detection approaches are evaluated. We demonstrate that interactive, visual‐based approaches appear to be the most accurate (within 1% of actual count) and efficient approach for generating information on the number of new buildings associated with single family residential land use. More automated approaches to detecting and enumerating image microfeatures may be useful as enhancements for visual‐based assessments and may be practical in areas composed mostly of large buildings associated with commercial and industrial land use. The highest accuracy for automated approaches was an undercounting of 11% for residential buildings and overcounting of 20% for those associated with commercial and industrial land use.     

“Mission Geography” and the Use of Satellite Imagery in K‐12 Geographic Education — A NASA ‐ GENIP Partnership

Abstract

Mission Geography is a collaboration between the United States' National Aeronautic and Space Administration (NASA) and the American “Geography Education National Implementation Project” (GENIP), created to provide for and develop curriculum supplementary material for teachers of grades Kindergarten through High School (grade twelve). Mission Geography uses NASA imagery and scientific inquiries to teach about Earth. This paper provides an example of how geographic concepts concerning volcanoes and volcanic hazards can be taught at the American middle school level (grades 5‐8) using NASA resources     

Using Low‐Resolution Satellite Data to Quantify Terrestrial Carbon in Tropical Areas

Abstract

The ability to map and monitor terrestrial carbon is important in tropical regions where land conversion is intense and tropical moist forests store much of Earth's terrestrial carbon. The release of terrestrial carbon in the form of carbon dioxide could alter local, regional, and global weather, and enhance the greenhouse effect. This study analyzed the ability of coarse‐resolution Advanced Very High Resolution Radiometer (AVHRR) remote sensor data to quantify carbon stored in the Guaporé / Itenez River Basin in Bolivia and Brazil. This area was selected because of the amount of land conversion that has occurred there relative to other areas of the Amazon Basin. A supervised vegetation classification map was created with training sites acquired through fieldwork done in the area in summer 1998. Image pixels were classified as tropical moist forest, degraded tropical moist forest, cerrado, grasslands, degraded savanna, or bare ground. Estimated above and below‐ground carbon values of the different land cover types were applied to each class to calculate total carbon values. It was concluded that data such as AVHRR may be used to calculate the amount of carbon in terrestrial ecosystems in regional scale areas.     

Using Maximum Likelihood (ML) and Maximum A Prior Probability (MAP) in Iterative Self‐Organizing Data (ISODATA)

Abstract

With the availability of high‐resolution satellite imagery featuring as high as 1 meter resolution in the panchromatic mode and 3‐meter resolution in the multispectral mode, there is an interest by many new commercial and public service applications such as cellular telephones coverage area design, urban/land cover planning, and real estate marketing to extract features from images automatically. To that end, the demand for unsupervised classification techniques is growing. In this paper, the Maximum Likelihood (ML) and Maximum A prior Probability (MAP) algorithms are used as decision rules to find boundaries of classes computed by the Iterative Self‐Organizing Data (ISOADATA) algorithm. Different satellite images with different resolutions were used to experiment with these algorithms. The results of comparing and analyzing the algorithms revealed that MAP‐ISODATA performed better than ML‐ISODATA even when the same initial matrix was used. It was shown that there was no significant difference between ML‐ISODATA and MAP‐ISODATA in terms of accuracy. It was also realized that better results could be obtained if homogenous initialization strategies were used.     

Usefulness of the temporal analysis and the normalized difference in the study of rice by means of landsat‐5 TM images: Identification and inventory of rice fields

The multispectral and multitemporal analysis of the spectral response of rice has made it possible to determine at which point in the vegetative cycle of rice it is best to make an inventory, together with the usefulness of the normalized‐difference vegetation index for such an inventory. The results could be usefully included in any classification procedure of the TM image in order to make the inventory in a systematic way. In this case a supervised classification of the image has been made which assumes a Gaussian behaviour for each spectral class. The results obtained are, for the most part, consistent with those obtained by using traditional methods.     

Statewide groundwater‐vulnerability assessment in nebraska using the drastic/GIS model

The paper summarizes a technique for implementing the “DRASTIC” groundwater‐vulnerability model within the context of an automated raster‐based geographic information system. Discussion focuses on a methodological development and a statewide project completed recently in Nebraska. The final products, a comprehensive flow chart illustrating procedures and a map of calculated potential pollution hazard, are presented. The methodology can be executed successfully with minimal training and experience. Areas of Nebraska considered vulnerable to groundwater pollution are identified.     

Three‐dimensional displays for natural hazards analysis,using classified Landsat thematic mapper digital data and large‐scale digital elevation models

Abstract

Professional aerial photography missions are generally outside the reach of most students and faculty involved with teaching and research. Oblique aerial photography using handheld cameras for image acquisition from a light high‐wing aircraft offers an excellent learning experience for students in a first course in remote sensing and offers a useful research tool for graduate students and faculty engaged in environmental investigations. This paper is essentially a guide, covering all aspects of hand‐held camera aerial imaging and the subsequent processing needed to produce low‐obliques, stereograms, anaglyphs, and flight line mosaics. Scales, ground coverage distances, and stereogram and mosaic timing intervals, are included along with a section on the calculations used to produce these numbers. A list of additional resources concludes the paper.     

Evaluating the Relationships between Fire Induced Canopy Mortality and Pre‐fire Multispectral Patterns

Abstract

Statistical tools were used to evaluate the relationships between observed fire effects and characteristics identifiable in pre‐fire multispectral and terrain data. Random points were placed within field delimited polygons representing areas of high and low canopy mortality. Each point was then used to extract Landsat TM based pre‐fire spectral characteristics and DEM derived terrain characteristics. The values for these random points were subjected to a multivariate discriminant analysis to ascertain whether specific spectral bands, indices, terrain characteristics, or specific combinations of these, could be effectively associated with the observed fire effects. Data values for high and low mortality points were found to be significantly different for all the pre‐fire data sets. The normalized difference vegetation index (NDVI) and tasseled cap greenness values provided the highest magnitude of direct differentiation between high and low mortality points. Discriminant analysis revealed that NDVI had the highest correspondence to degree of future canopy mortality, while the combined effect of the pre‐fire spectral response provided a prediction of observed fire effects with 87% accuracy, and the addition of terrain data improved accuracy to 90%.     

Interpretation of crop growth patterns by means of NDVI‐time series in Zambia

A method to correlate crop production in Zambia to the yearly evolution of the Normalized Difference Vegetation Index (NDVI) is proposed. The method consists of the analysis of remote sensing data together with meteorological data and simulated crop production to obtain indicators of crop production. The accuracy of these indicators is assessed with statistical data.

The main objective was to assess whether the NDVI‐time series extracted from NOAA‐AVHRR‐images , having a pixel resolution of 73 km may give reliable information on crop production in Zambia where agricultural areas cover just 1% of the land area.

The mean NDVI‐value of several pixels, e.g. for one province or other administrative units, relates to the dominant type of vegetation in the area under consideration.

It is shown that the 7.3 km NDVI‐data give reliable indications on crop production in Zambia, when small areas (200–450 km² large ) are considered where agricultural land use is intensive. This implies that preliminary analysis is required to localize the agricultural areas. This has been done by means of high resolution satellite images i.e. LANDSAT‐MultiSpectral Scanner.

Consequently, the NDVI‐time series of the ‘agricultural ‘ pixels are used to calculate crop growth indicators which can be applied to assess the crop production.     

Analysis and Representation of Changes in Change Detection

In this paper,factors lead to changes ande what changes should be considered are firstly discussed.then changes to be represented by set theory is given.Twelve basic change types are described in detail.At last the paper points out that the change between geoobjects and pixels in images is not all corresponging and it causes the difficulty of accurate and robust change detection techniques.