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.     

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.     

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.     

Landsat MSS images of the clean‐up of Lake Chicot

As a result of the 1991 Persian Gulf war, between mid‐January and June 1991, the Persian Gulf was contaminated with an estimated 4 to 6 million barrels of crude oil, released directly into the Gulf from refinement facilities, transhipment terminals, and moored tankers along the coast of Kuwait, and precipitated from oil fire smoke plumes. To assess the environmental impact of the oil, an international team of marine scientists representing 14 nations was assembled under the auspices of the United Nations International Oceanic Commission and the Regional Organization for Protection of the Marine Environment to conduct detailed surveys of the Persian Gulf, the Strait of Hormuz, and the Gulf of Oman, including hydrographic, chemical, and biological measurements. To supplement the field surveys and to serve as an aid in data interpretation, astronauts aboard the Space Shuttle Atlantis photographed water features and coastal habitats in the Persian Gulf during mission STS‐45 (24 March to 02 April 1992). The astronauts collected 111 hand‐held, color photographs of the Gulf (72 70 mm photographs and 39 5‐inch photographs) from an altitude of 296 km (160 n.mi.). The photographs reveal distributions in water turbidity associated with outflow from the Shatt‐al‐Arab and water circulation along the entire coast of Iran and the Strait of Hormuz, coastal wetlands and shallow‐water habitats, and sticks appearing in the sunglint pattern, which appear to be oil.     

Multi‐temporal Mapping of Burned Forest over Canada Using Satellite‐based Change Metrics

Abstract

A procedure for continental‐scale mapping of burned boreal forest at 10‐day intervals was developed for application to coarse resolution satellite imagery. The basis of the technique is a multiple logistic regression model parameterized using 1998 SPOT‐4 VEGETATION clear‐sky composites and training sites selected across Canada. Predictor features consisted of multi‐temporal change metrics based on reflectance and two vegetation indices, which were normalized to the trajectory of background vegetation to account for phenological variation. Spatial‐contextual tests applied to the logistic model output were developed to remove noise and increase the sensitivity of detection. The procedure was applied over Canada for the 1998‐2000 fire seasons and validated using fire surveys and burned area statistics from forest fire management agencies. The area of falsely mapped burns was found to be small (3.5% commission error over Canada), and most burns larger than 10 km2 were accurately detected and mapped (R² = 0.90, P<0.005, n = 91 for burns in two provinces). Canada‐wide satellite burned area was similar, but consistently smaller by comparison to statistics compiled by the Canadian Interagency Forest Fire Centre (by 17% in 1998, 16% in 1999, and 3% in 2000).     

Assessment of crop stress conditions using low altitude aerial color‐infrared photography and computer image processing

Stress conditions on various crops caused by moisture deficiency, nutrient deficiency and plant diseases were studied using low altitude aerial color‐infrared (CIR) photography. A remotely controlled aircraft, carrying a color video camera and a 35 mm reflex camera, flying at altitudes of 50 to 100 m, was used for obtaining aerial photography. The CIR transparencies recorded for different crops were scanned into an AT 386 desktop computer and processed by MIPS software using on screen interpretation to classify crop stress conditions. In a rootrot‐affected lemon orchard diseased trees could be identified and differentiated from healthy trees. Soybean plots under moisture stress were classified into high, moderate and low stress levels. These stress levels related well with infrared (IR) thermal measurements of the crop canopy. In a wheat field experiment that was fertilized at different levels of phosphorus (P), areas with P deficiency and sufficient P could be classified on the processed CIR image. Soil P and wheat yield correlated well with the classified CIR image. Results indicate that low altitude CIR aerial photography obtained from remotely controlled aircraft and image processing computers can be a relative inexpensive tool to assess crop stress conditions.     

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.     

Predicting the Impact of Coastal Development on Water Quality Using Remote Sensing and GIS‐assisted Hydrologic Modeling Techniques

Abstract

A decline in water quality in the Okatie River, a coastal estuary located in Beaufort County, SC, has resulted in the closure of several shellfish beds. Continuing urban development within the watershed has altered land cover conditions and may be contributing to the recent decline in water quality. Remote sensing and geographic information system (GIS) technology, coupled with a water quality model were used to spatially model stormwater runoff to understand the relationship between recent changes in land cover and watershed runoff characteristics. High spatial resolution imagery acquired in 1994 and 1996 spatially documented pre‐ and post‐development land cover conditions within the watershed. The water quality model Agricultural Nonpoint Source Pollution (AGNPS) evaluated land characteristics such as soil type, topography, and land cover to simulate surface water flow and sediment transport over past and current land cover conditions. Results of the model were used to locate net increases of fresh water discharge and to suggest best management practices (BMP).     

Simulation Study of a Robust Algorithm for Soil Moisture and Surface Roughness Estimation Using L‐Band Radar Backscatter

Abstract

Various inversion algorithms have been developed to obtain estimates of soil moisture and surface roughness parameters from multifrequency, multiangle, and multipolarization radar reflectances. Since the penetration depth for radar signals increases with wavelength, an inversion algorithm using widely separated frequencies does not yield comparable probing depths. Furthermore, existing algorithms assume a linear relationship between the radar backscatter coefficient (in dB) and soil parameters, such as the volumetric soil moisture, soil surface roughness and surface slope. This assumption is valid only over a narrow range of soil parameters, thereby restricting its operational use under realistic conditions. Our research specifically explored the use of inversion algorithms based on L‐Band radar reflectances at 1 GHz and 2 GHz frequencies in order to retain relatively consistent probing depths. In order to extend the range of applicability, a non‐linear exponential‐type relationship was developed between radar reflectance at a specified frequency, polarization and incidence angle combination, and soil parameters of interest, viz., soil moisture, surface roughness, and surface slope. An over‐constrained inversion algorithm using a six‐parameter combination was found to yield relatively accurate estimates of soil parameters over a wide range of soil conditions even in the presence of system error.     

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.     

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.     

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.     

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.     

Transformation of amplitudes and frequencies of precession and nutation of the earth’s rotation vector to amplitudes and frequencies of diurnal polar motion

The temporal change of the rotation vector of a rotating body is, in the first order, identical in a space-fixed system and in a body-fixed system. Therefore, if the motion of the rotation axis of the earth relative to a space-fixed system is given as a function of time, it should be possible to compute its motion relative to an earth-fixed system, and vice versa. This paper presents such a transformation. Two models of motion of the rotation axis in the space-fixed system are considered: one consisting only of a regular (i.e., strictly conical) precession and one extended by circular nutation components, which are superimposed upon the regular precession. The Euler angles describing the orientation of the earth-fixed system with respect to the space-fixed system are derived by an analytical solution of the kinematical Eulerian differential equations. In the first case (precession only), this is directly possible, and in the second case (precession and nutation), a solution is achieved by a perturbation approach, where the result of the first case serves as an approximation and nutation is regarded as a small perturbation, which is treated in a linearized form. The transformation by means of these Euler angles shows that the rotation axis performs in the earth-fixed system retrograde conical revolutions with small amplitudes, namely one revolution with a period of one sidereal day corresponding to precession and one revolution with a period which is slightly smaller or larger than one sidereal day corresponding to each (prograde or retrograde) circular nutation component. The peculiar feature of the derivation presented here is the analytical solution of the Eulerian differential equations.     

Multi‐Temporal Study and Detailed Photogeological Mapping of the Armero Debris Flow (Colombia), Using Landsat TM 5 Images

Abstract

On November the 13^th of 1985, the City of Armero (Colombia) was destroyed by debris flows generated by a reactivation of the Nevado del Ruiz Volcano. The flows ocurred in at least three principal pulses, as was observed by the disater's survivors. Landsat TM 5 data processing was carried out in subscenes taken before and after the lahar sedimentation.

False color composites were generated and combined with the geological information available in order to visualize the magnitude of the catastrophe and the flow characteristics. Taking advantage of Landsat TM 5 images with high spectral resolution, a detailed photogeological mapping of the three principal pulses of the debris flows was carried out. Landsat TM 5 proved to be a powerful complementary source of information for hazard assesment of these catastrophic debris flows. The images were used in addition to ground‐based information, and were an easy way to help ordinary people and decision makers understand such hazardous volcanic situations.     

Determination of carrying capacity for Sheep‐breeding in Southern Greenland by means of Satellite mapping

Arctic and sub‐arctic areas can be regarded as very less favored in consideration to agricultural production. In the southern Greenland sheep‐breeding is crucial for the self‐reliance economy. The article deals with a mapping project that was carried out in order to assess the carrying capacity. The natural conditions are briefly described as background for the mapping which by means of NDVI and Principal Component Analysis of Landsat MSS data has proven very powerful. The aspects of growing season, climate and landscape variation are discussed.

The investigation was carried out at Institute for Geography, Socio‐Economic Analysis and Computer Science, Roskilde University Centre, Denmark.     

Satellite monitoring of urbanization and environmental impacts—A comparison of Stockholm and Shanghai

This study investigates urbanization and its potential environmental consequences in Shanghai and Stockholm metropolitan areas over two decades. Changes in land use/land cover are estimated from support vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix features. Landscape metrics are used to investigate changes in landscape composition and configuration and to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbanization is calculated by urbanization indices and the resulting impacts on the environment are quantified by ecosystem services. Growth of urban areas and urban green spaces occurred at the expense of cropland in both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120% in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover change was a 12% urban expansion that mostly replaced agricultural areas. From the landscape metrics results, it appears that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural/agricultural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted in ecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to the decrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very little. Total urban growth in Shanghai was 1768 km² and 100 km² in Stockholm. The developed methodology is considered a straight-forward low-cost globally applicable approach to quantitatively and qualitatively evaluate urban growth patterns that could help to address spatial, economic and ecological questions in urban and regional planning.