XS Images Research Articles

Agricultural Field Detection from Satellite Imagery Using the Combined Otsu’s Thresholding Algorithm and Marker-Controlled Watershed-Based Transform

An accurate detection of agricultural fields is often needed for agricultural-related applications, such as subsidies monitoring, field-based crop yield estimation and agricultural statistics extraction. High-resolution space images have become the fundamental source to extract agricultural field boundaries. Manual boundary delineation is not practical. In this study, we present an approach to detect agricultural fields from satellite images on the basis of agricultural field blocks. An agricultural field block consists of one or more fields that are owned by the farmers. The approach combines the Otsu’s thresholding algorithm and marker-controlled watershed (MCW)-based segmentation. First, the well-separated field segments within a field block being considered are detected through recursive processing of the Otsu’s thresholding algorithm. Then, these distinct field segments are used to generate a marker image, and further extraction of individual fields is carried out through a marker-controlled watershed (MCW)-based segmentation. The approach was tested using 10-m resolution Satellite Pour l’Observation de la Terre (SPOT)-5 multi-spectral (XS) image, 4-m resolution IKONOS XS image, 2.40-m resolution QuickBird XS image, and 0.60-m resolution QuickBird pan-sharpened (PS) image. The results were evaluated using the reference field boundary dataset. The achieved overall accuracies were 89.7, 83.2, 81.0, and 77.4% for the IKONOS XS, QuickBird XS, SPOT-5 XS, and QuickBird PS images, respectively. The results are promising and indicate that the approach can be used for the extraction of agricultural fields from space imagery.

Pansharpening using data-centric optimization approach

ABSTRACT Earth’s observation satellites provide simultaneously both multispectral (XS) and panchromatic (pan) images but XS image has a lower spatial resolution when compared to pan image. Pansharpening is a pixel-level fusion technique resulting in a high-resolution multispectral image in terms of both spatial and spectral resolution. The problem lies in maintaining the spectral characteristics of each channel of the XS image when pan image is used to estimate the high spatial XS image. Many techniques have been proposed to address the problem. A popular method involves a sensor-based approach where correlation among the XS channels and correlation between the pan and spectral channels are incorporated. In this paper, we take a wholesome approach based on the reflectance data irrespective of the sensor physics. A linear regression model is formulated between the XS channel and the panchromatic data. We formulate an optimization problem in terms of Lagrange multiplier to maximise the spectral consistency of the fused data with respect to the original XS data, and to minimise the error in variance between the reference data and the computed data. We validate and compare our method with IHS and Brovey methods based on evaluation metrics such as Chi-square test and the R 2 test. The implementation is done and presented using IKONOS satellite data.

An improved cluster-based snake model for automatic agricultural field boundary extraction from high spatial resolution imagery

ABSTRACT Agricultural field boundary information is important and often required for the geosciences and the agricultural sector. In this paper, a novel method is developed to extract sub-boundaries within the permanent boundaries of agricultural land parcels from high-resolution optical satellite imagery using an improved cluster-based snake model. The method takes the advantage of the results of an automatic fuzzy c-means (FCM) clustering and edge detection to compute external forces for an improved gradient vector flow (GVF) snake model. The GVF snake algorithm is improved by using an automatic seeding model based on clustering results and image moment functions. To seed the improved GVF algorithm, an ellipse is automatically delineated for each cluster within agricultural parcel by utilizing image moment functions (in particular silhouette moments). The GVF snake model is then implemented for each seed, one seed at a time. Active contours tend to have curve shapes rather than straight lines due to their structure that consists of several connected nodes within each contour. Therefore, the final accurate results are obtained after performing a three-stage line simplification operation. The experiments of the method were conducted on 20 test fields in a study area located near to the town of Karacabey, Turkey, using the 4-m resolution IKONOS multispectral (xs) image, the 2.44-m resolution QuickBird xs image, and the 0.61-m resolution QuickBird pan-sharpened (PS) image. Experimental results demonstrate that using both the clustering and edge detection results as external forces for the improved GVF snake model increases the accuracy of the results. In addition, the developed method showed a fairly good performance in extracting sub-boundaries for the fields comprising crops with an inherent high inner heterogeneity, such as rice and corn. The method can potentially be applied in the extraction of within-field sub-boundaries from high-resolution satellite imagery in agricultural areas.

Land use and land suitability assessment within the context of spatial planning regulation

This paper presents the results of study aimed at assessing land suitability at a regional scale, and examining spatial matching between suitability classes and spatial planning regulations for Mamminasata, one of the strategic regions in Indonesia. We focussed on assessing areas for producing food crop, in Gowa Regency, using integrated techniques of ground surveys and geographic information systems (GIS) technology. We used available soil and climate information to generate land suitability for food crops. Ground survey was carried out to verify several soil data, and SPOT XS images (resolution 20 meters) were employed to map land use in the area of interest. A set of spatial planning data bases developed by local government was used to understand planned land use types. Spatial matching procedures within GIS were performed to see the best fit between suitability classes and planned land use according to spatial planning regulation of Mamminasata region. The same procedure was also performed between present and planned land use types. The results show that some suitable areas for food crops have been allocated for residential development. Similarly, areas that should be protected according to the spatial planning scheme, in fact, have been converted or practiced for long as, intensive agriculture and residential. The results of this study may give insights into the future anticipation of land use development in this region.

Comparison of Virtual Dental Implant Planning Using the Full Cross-Sectional and Transaxial Capabilities of Cone Beam Computed Tomography vs Reformatted Panoramic Imaging and 3D Modeling.

To compare the choice and placement of virtual dental implants in the posterior edentulous bounded regions using the full cross-sectional and transaxial capabilities of cone beam computed tomography (CBCT) vs reformatted panoramic images and three-dimensional (3D) virtual models. Fifty-two cases with posterior bounded edentulous regions (61 dental implant sites) were identified from a retrospective audit of 4,014 radiographic volumes. Two image sets were created from selected CBCT data: (1) a combination of reformatted panoramic imaging and a 3D model (PIref/3D), and (2) the full 3D power in CBCT image volume analyses (XS). One virtual implant was placed by consensus of three prosthodontists in each image set: PIref/3D and XS. The choice of implant length and the perceived need for ridge augmentation were recorded for implant placement in both test situations. All the virtual implant placements from both PIref/3D and XS image sets were inspected retrospectively using virtual 3D models, and the number of exposed threads on both the buccal and lingual/palatal aspects of the virtual dental implant was evaluated. The chi-square and paired t tests were used with the level of significance set at α = .05. Shorter implants were chosen more often using XS than PIref/3D (P = .001). Fewer threads were exposed when placed with XS than with PIref/3D (P = .001). The use of XS reduced the perceived need for ridge augmentation compared with PIref/3D (P = .001). The use of the full 3D power of CBCT (including cross-sectional images in all three orthagonal planes and transaxially) provides supplemental information that significantly changes the choice of virtual implant length and vertical position of the implant, and reduces the frequency of perceived need for ridge augmentation before implant placement.

Land cover classification of Finnish Lapland using decision tree classification algorithm

Land cover of Finnish Lapland was classified to 16 land cover classes using optical IRS LISS, Spot XS and MODIS satellite images, ancillary GIS data and decision tree classifier. The aim of this study was to test decision tree classifier for land cover classification and study the effects of its parameters to classification result. In the best case, the overall accuracy was about 68% for all 16 classes when individual images were classified. The overall accuracy was only about 45% when whole mosaic was classified. It seems that the most problematic classes are those with vegetation but which are not forest.

Ar4-50 Model, the Extractor of Spectral Values Into Remote Sensing Image Data-based Land Use Class

Th is study attempted to develop a n extraction model of spectral values ​​of land objects into land use/land cover classes o n remote sensing image in the provision of land database f or planning , evaluation , and monitoring in agriculture and forestry . This study employed an Isodata method and Knowledge -Based Systems ( KBS) using the Landsat 7 ETM + image in the coverage area of ​​117,799.06 ha , and the SPOT 5 XS image in the coverage area of ​​113,241.37 ha in Palu , Sigi and Donggala . The study found two image models labelled as AR4 - 50 and SBP - AR4 - 50. The s eparability image AR4 - 50 model has an average capability for separating land object pixels which are statistically 1811.98 to 1972.08 ( moderate -good ), with the class accuracy of land use/land cover using the image homogeneity model of SBP - AR4 - 50, which is totally ( confusion matrix ) 72.15 % -87.17 %, the accuracy level of land map generator for agricultural land/forestry is in good - excellent category on the Landsat 7 ETM+ and SPOT 5 XS images . Keywords : Image, Class , Land U se , Model , Separability , Homogeneity . Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Times New Roman; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi;}

AR4-50 MODEL, THE EXTRACTOR OF SPECTRAL VALUES INTO REMOTE SENSING IMAGE DATA-BASED LAND USE CLASS

Th is study attempted to develop a n extraction model of spectral values ​​of land objects into land use/land cover classes o n remote sensing image in the provision of land database f or planning , evaluation , and monitoring in agriculture and forestry . This study employed an Isodata method and Knowledge -Based Systems ( KBS) using the Landsat 7 ETM + image in the coverage area of ​​117,799.06   ha , and the SPOT 5 XS image in the coverage area of ​​113,241.37 ha in Palu , Sigi and Donggala . The study found two image models labelled as AR4 - 50 and SBP - AR4 - 50. The s eparability image AR4 - 50 model has an average capability for separating land object pixels which are statistically 1811.98 to 1972.08 ( moderate -good ), with the class accuracy of land use/land cover using the image homogeneity model of SBP - AR4 - 50, which is totally ( confusion matrix ) 72.15 % -87.17 %, the accuracy level of land map generator for agricultural land/forestry is in good - excellent category on the Landsat 7 ETM+ and SPOT 5 XS images . Keywords : Image, Class , Land U se , Model , Separability , Homogeneity . Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping

This study presents an approach for the automatic extraction of dynamic sub-boundaries within existing agricultural fields from remote sensing imagery using perceptual grouping. We define sub-boundaries as boundaries, where a change in crop type takes a place within the fixed geometry of an agricultural field. To perform field-based processing and analysis operations, the field boundary data and satellite imagery are integrated. The edge pixels are detected using the Canny edge detector. The edge pixels are then analyzed to find the connected edge chains and from these chains the line segments are detected using the graph-based vectorization method. The spurious line segments are eliminated through a line simplification process. The perceptual grouping of the line segments is employed for detecting sub-boundaries and constructing sub-fields within the fixed geometry of agricultural fields. Our strategy for perceptual grouping involves the Gestalt laws of proximity, continuation, symmetry and closure. The processing and analysis operations are carried out on field-by-field basis. For each field, the geometries of sub-boundaries are determined through analyzing the line segments that fall within the field and the extracted sub-boundaries are integrated with the fixed geometry of the field.The experimental validation of the approach was carried out on the SPOT4 multispectral (XS) and SPOT5 XS images that cover an agricultural area located in the north-west section of Turkey. The overall matching percentages between the reference data and the automatically extracted sub-boundaries were computed to be 82.6% and 76.2% for the SPOT5 and SPOT4 images respectively. The higher matching percentage of the SPOT5 image is due to the fact that some of the boundaries present in the SPOT5 image were not detected in the coarser resolution SPOT4 image. For the SPOT5 image, of the total 292 fields processed, 177 showed a total agreement between the detected segments and the reference data. For the SPOT4 image, 154 fields showed a total agreement between the detected segments and the reference data.

Spatial Based Assessment of Land Suitability and Availability for Maize (<i>Zea mays</i> L.) Development in Maros Region, South Sulawesi, Indonesia

Recently, South Sulawesi Provincial government has launched the “gong” program with the main objective to optimize all the resources (land, infrastructures, and farmers) in agriculture areas for maize production in the province. This study is aimed at identifying the suitability and availability of land areas for maize development in Maros Regionthe regency having the most extensive agriculture production in South Sulawesi province. This study employed land evaluation method in geographic information system (GIS) based on the FAO Framework for Land Evaluation. Land availability was assessed from overlaying information on land use (obtained from available land use map and SPOT XS image interpretation) and suitability classes based on the FAO Framework, as well as administration boundary map. The results indicated that the S1 (highly suitable) class comprises a total area of approximately 34,468 ha, or about 24% from the total area. The limiting factors for S2 (moderately suitable) and S3 (marginally suitable) classes are slope and nutrient availability, but with the advanced management efforts (moderately input) such sub-classes can actually promote S3 class to S2 level. It was also found that from a total of 144,085 ha of the study area, potential maize development area (for extensification) covers approximately 24,716 ha (or 35.6%). Tanralili, Bantimurung, and Simbang sub-districts cover the largest suitable area, where no significant limiting factors exist. Surprisingly, potential development area for maize in Camba, Mallawa, and Tompobulu sub-districts denotes minus values. This implies the facts that maize cultivation is still practiced on the land that is ecologically not suitable, where steep slope is the dominant limiting factor.

Comparison of different fusion algorithms in urban and agricultural areas using sar (palsar and radarsat) and optical (spot) images

Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutions and polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusion algorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused images were performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as "urban" and "agricultural". It has been observed that some of the methods have enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers' spectral quality is far better than those of the other methods. HPF performs almost best in agricultural areas for both SAR images.

Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study

A comparison of agricultural crop maps from independent field-based classifications of the Satellite Pour l'Observation de la Terre (SPOT) 4 multispectral (XS), SPOT5 XS, IKONOS XS, QuickBird XS and QuickBird pan-sharpened (PS) images is presented. An agricultural area within the north-west section of Turkey was analysed for field-based crop identification. The SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird images were collected in similar climatic conditions during July and August 2004. The classification of each image was carried out separately on a per-field basis on all bands and the coincident bands that are green, red and near-infrared (NIR). To examine the effect of filtering on field-based classification, the images were each filtered using the 3 × 3, 5 × 5, 7 × 7 and 9 × 9 mean filter and the filtered bands were also classified on per-field basis. For the unfiltered images, IKONOS XS provided the highest overall accuracies of 88.9% and 88.1% for the all-bands and the coincident bands classifications, respectively. On average, IKONOS XS performed slightly better than QuickBird XS and QuickBird PS, while it outperformed SPOT4 XS and SPOT5 XS. The use of filtered images in field-based classification reduced the accuracies for SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird XS. The results of this study indicate that smoothing images prior to classification does not improve the accuracies for the field-based classification. On the contrary, the accuracies for the filtered QuickBird PS images indicated a slight improvement. On the whole, both IKONOS and QuickBird images produced quite promising results for field-based crop mapping, yielding overall accuracies above 83%.

Rainfall, topography and primary production relationships in a semiarid ecosystem

AbstractA series of 15 Satellite Pour l'Observation de la Terra XS images, which were acquired during a single growing season, were used to study the spatial and temporal relationships between topography, rainfall and aboveground net primary production (ANPP) of annuals in a semiarid environment. A digital elevation model was used to locate five physiographic units: interfluve, shoulder, backslope, foot slope, and channel, along the slope catena. ANPP values were then surrogated by the normalized difference vegetation index (NDVI). Four phenological phases were interpreted from the seasonal NDVI profiles: germination, green‐up, drying, and senescence. A significant difference between NDVI values of all physiographic units was found, mainly during the green‐up phase. NDVI values at peak season were characterized as typical of semiarid ecosystems, except those of the footslope and the channel, which were found to have meaningfully high NDVI values, due to run‐off distribution and a thick soil profile. The seasonal NDVI integral was related positively and linearly to model‐derived water availability values for all physiographic units and the NDVI was correlated to multiple‐timescale rainfall data and the length of dry spells. The 1‐month rainfall data were found to have the highest correlation with the NDVI, indicating lag and cumulative effects of rainfall on production. This implies that the time required for the plants to use the water for production is around a month. It is concluded that studies of ecosystem functioning and capabilities in semiarid environments should consider not only mean annual rainfall amounts, but also the temporal rainfall distribution, mainly on a monthly scale, and the effect of physiographic units. Copyright © 2010 John Wiley & Sons, Ltd.

Estimation of tropical forest structure from SPOT-5 satellite images

Predictions of tropical forest structure at the landscape level still present relatively high levels of uncertainty. In this study we explore the capabilities of high-resolution Satellite Pour l'Observation de la Terre (SPOT)-5 XS images to estimate basal area, tree volume and tree biomass of a tropical rainforest region in Chiapas, Mexico. SPOT-5 satellite images and forest inventory data from 87 sites were used to establish a multiple linear regression model. The 87 0.1-ha plots covered a wide range of forest structures, including mature forest, with values from 74.7 to 607.1 t ha−1. Spectral bands, image transformations and texture variables were explored as independent variables of a multiple linear regression model. The R2s of the final models were 0.58 for basal area, 0.70 for canopy height, 0.73 for bole volume, and 0.71 for biomass. A leave-one-out cross-validation produced a root mean square. error (RMSE) of 5.02 m2 ha−1 (relative RMSE of 22.8%) for basal area; 3.22 m (16.1%) for canopy height; 69.08 m3 ha−1 (30.7%) for timber volume, and 59.3 t ha−1 (21.2%) for biomass. In particular, the texture variable ‘variance of near-infrared’ turned out to be an excellent predictor for forest structure variables.

Block-regression based fusion of optical and SAR imagery for feature enhancement

This paper focuses on the establishment of a pixel-level fusion framework for optical and synthetic aperture radar (SAR) images to combine these two types of remotely sensed imagery for feature enhancement. We have proposed a new fusion technique, namely block-based synthetic variable ratio (Block-SVR), which is a technique based on multiple linear regression of block regions to fuse optical and SAR imagery. In order to investigate the effectiveness of the method, the fusion results of a higher resolution airborne SAR image and a lower resolution multispectral image are presented. According to the fusion results, the fused images have enhanced certain features, namely the spatial and textural content and features that are invisible in multispectral images, while preserving colour characteristics. The spectral, spatial and textural effects of the presented algorithm were also evaluated mainly by visual and quantitative methods, and compared to those of intensity-hue-saturation (IHS), principal component analysis (PCA) and wavelet-based methods. During the implementation of the block-regression based technique there are at least two advantages. One is that the block-regression based technique drastically decreases the amount of computation, whereas regression of the whole scene image is almost impossible. The other, most important, advantage is that adjustment of regressed block size can result in different emphasis between preservation of spectral characteristics and enhancement of spatial and textural content. The larger the regression block, the more the spatial and textural details are enhanced. In contrast, the smaller the regression block, the more the spectral features are preserved. The assessments indicate that the block-regression based method is more flexible than others, because it can achieve a satisfactory trade-off between preservation of spectral characteristics and enhancement of spatial and textural content by selection of optimal block size with respect to visual interpretation and mapping. This paper also proposes a scheme for the fusion of SPOT5 panchromatic, XS images with airborne SAR images using the block-regression based technique.

Monitoring the lateral channel movements on the alluvial fan of Wadi Feiran drainage basin, South Sinai, Egypt using multi-temporal satellite imagery

Human activities triggered various lateral channel movements over the alluvial fan of Wadi Feiran drainage basin, south Sinai, Egypt. Landsat TM images dated 1986, 1996, and 2001, along with a SPOT 4 XS image dated 2006 were acquired to monitor the lateral channel movement on the alluvial fan of Wadi Feiran through the last two decades. The lateral channel movements have been initiated by the channel avulsion of the incised channel of the alluvial fan as a result of obstructing the flow by developing a man-made barrier from the accumulated flood debris in the early years of 1990(s). The man-made barrier had evolved into an elevated concrete road of 3.75 m height with two culverts underneath directly south the fan apex by March 2007. These human activities enforced the incised channel to avulse in a new location leaving the original path and the active depositional lobe of the fan abandoned. Under the influence of successive flash flood events through the last twenty years, channel aggradations occurred resulting in channel braiding and channel widening processes in the reaches of the new formed distributary channels. The channel widening processes occurred on the expense of the inactive fan lobes located within and south the reaches of the new distributary channels. Observing the southward lateral growth of the reaches of the new formed distributary channels implies the possibility of forming a new active depositional lobe that could be merged with the abandoned depositional lobe of the fan.

Remote sensing of Red Mediterranean soils: a case study in the viticultural southern Rhone Valley using SPOT satellite imagery

The aim of this work was to map Red Mediterranean soils, for which no previous mapping approach exists, using optical multispectral satellite remote sensing data. This case study explores the use of SPOT XS images over the viticultural Southern Rhone Valley, France, to map exposed vineyard soils. Field spectral measurements were used to distinguish Red Mediterranean soil surfaces during Spring 1999. A supervised maximum likelihood classification was applied to sparsely vegetated and unvegetated surfaces of two spring images from 1995 and 1997, drawing on the field training set and available soil data. Similar global spatial segmentation was obtained despite different soil surface states on these dates. Classification performances were higher than 84% in both images. Mean classification accuracies of Red Mediterranean soils at seven reference surfaces were 60% in 1995 and 70% in 1997. This suggests that the direct use of optical remote sensing data at medium resolution can be useful for mapping bare Red Mediterranean soils.

Integration of panchromatic and SAR features into multispectral SPOT images using the ‘à trous’ wavelet decomposition

A method is described for integrating panchromatic (P) and synthetic aperture radar (SAR) features into multispectral (XS) images using conjointly the modified Brovey transform (MBT) and the ‘à trous’ wavelet decomposition (ATDW). The MBT is based on the local modulation of each multispectral image by the ratio of the new and initial intensity components to produce new multispectral images directly. The ATWD allows extraction of features from P and SAR images, which are combined through a feature selection rule to integrate into the initial intensity component. For evaluating the effect of each feature selection on new XS images, experimental results are conducted on SPOT (XS, P) and Radarsat (SAR) images using both visual inspection and many refined statistical measures.

The Application of Decision Binary Trees to Assess the Usefulness of The Digital Terrain Model in Studying the Relationships Between Relief and Vegetation in The Polish High Tatra

Abstract The relationships between individual components of the natural environment have long been an object of research (Kostrowicki, Wójcik, 1972; Rączkowska, Kozłowska, 1994; Kozłowska, Rączkowska, 1996). This paper is an attempt to analyse the relationships between two geocomponents of the natural environment: relief and vegetation, from a perspective contrary to the one currently prevailing in the literature of the subject. This approach assumes that relief, with its dominant role as a component strongly affecting the formation of the remaining factors, can be indicative in character and as such can represent basic factors that help determine and anticipate the occurrences of certain plant communities as well as locations with no vegetation. Using geoinformation data along with the tools to process them, an attempt was made to assess the usefulness of the DTM (Digital Terrain Model) to identify selected plant communities, rock and water. The development of a model of the relationships between the relief and the vegetation is an attempt to capture the correspondence between the parameters characterising the relief, calculated using the DTM model and classes of objects, with the use of information obtained from an Ikonos XS image. This model was subsequently used to draw a map of the land cover for a part of the Gąsienicowa Valley in the High Tatra (Dolina Gąsienicowa). For the purpose of this exercise, a technique of data classification called DBT (Decision Binary Trees) was used.

Additive integration of SAR features into multispectral SPOT images by means of the à trous wavelet decomposition

This paper aims to describe a method for integrating the Synthetic Aperture Radar (SAR) features into multispectral (XS) images using conjointly the modified Brovey transform (MBT) and the à trous wavelet decomposition (ATWD). The MBT is based on the intensity-hue-saturation (IHS) fusion method, which allows modulating locally each XS image by the ratio of the new intensity component and the initial intensity component in order to produce the new XS-SAR images. The ATWD allows extracting features from the SAR image in order to integrate through an additive procedure into the initial intensity component. The resultant new intensity component is then used for producing new XS-SAR images. In order to control the amount of SAR features to be integrated into XS images, a normalized importance measure is derived from the wavelet coefficients for selecting the significant SAR features according an adjustable threshold. SPOT-XS and RADARSAT-SAR images covering a region of Vietnam are used to evaluate visually and statistically the effect of the proposed method, in particular other existing methods in terms of spectral preservation and amount of SAR features integrated into XS images.