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The acquisition of bathymetric data in shallower waters is difficult to attain using traditional survey methods because the areas to investigate may not be accessible to hydrographic vessels, due to the risk of grounding. For this reason, the use of satellite detection of depth data (satellite-derived bathymetry, SDB) constitutes a particularly useful and also economically advantageous alternative. In fact, this approach based on analytical modelling of light penetration through the water column in different multispectral bands allows to cover a big area against relatively low investment in time and resources. Particularly, the empirical method named band ratio method (BRM) is based on the degrees of absorption at different bands. The accuracy of the SDB is not comparable with that of traditional surveys, but we can certainly improve it by choosing satellite images with high geometric resolution. This article aims to investigate BRM applied to high geometric resolution images, IKONOS-2, concerning the Bay of Pozzuoli (Italy), and improve the accuracy of results performing the determination of the relation between band ratio and depth. Two non-linear functions such as the exponential function and the 3rd degree polynomial (3DP) are proposed, instead of regression line, to approximate the relationship between the values of the reflectance ratios and the true depth values collected in measured points. Those are derived from an Electronic Navigational Chart produced by the Italian Hydrographic Office. The results demonstrate that the adopted approach allows to enhance the accuracy of the SDB, specifically, 3DP supplies the most performing bathymetric model derived by multispectral IKONOS-2 images. [ABSTRACT FROM AUTHOR]

On board the IKONOS satellite there are sensors operating in the panchromatic and multispectral range: the geometric resolution of the acquired images is higher in the first case (1 m) than in the second one (4 m); on the contrary, panchromatic images have lower spectral resolution than the latter. Pan-sharpening methods allow to reduce the pixel dimensions of the multispectral images to comply with the panchromatic resolution. In this way, it is possible to obtain enhanced detailed data in both geometric and spectral resolution. This work aims to compare the results obtained from the application of eight different pan-sharpening methods, which are totally carried out by using the raster calculator in QGIS: Multiplicative, Simple Mean, Brovey Transformation, Brovey Transformation Fast, Intensity Hue Saturation (IHS), IHS Fast, Gram-Schmidt, and Gram-Schmidt Fast. Each resulting dataset is compared with the original one to evaluate the performance of each method by the following quality indices: Correlation Coefficient (CC), Universal Image Quality Index (UIQI), Relative Average Spectral Error (RASE), Erreur Relative Global Adimensionnelle de Synthèse (ERGAS), Spatial Correlation Coefficient (SCC) and Spatial ERGAS (SERGAS); however, this is a difficult task because the quality of the fused image depends on the considered datasets. Finally, a comparison the various between methods is carried out. [ABSTRACT FROM AUTHOR]

The objective of this study is to estimate stand development stages (SDS) and stand crown closures (SCC) of forest using different classification methods (maximum likelihood, support vector machine: linear, polynomial, radial and sigmoid kernel functions and artificial neural network) based on satellite imagery of different resolution (Landsat 7 ETM+ and IKONOS). The results showed that SDS and SCC were estimated with Landsat 7 ETM+ image using the artificial neural network with a 0.83 and 0.78 kappa statistic value, and 92.57 and 89.77% overall accuracy assessments, respectively. On the other hand, SDS and SCC were predicted with IKONOS image using support vector machine (polynomial) method with a 0.94 and 0.88 kappa statistic value, and 95.95 and 91.17% overall accuracy assessments, respectively. Our results demonstrated that IKONOS satellite image and support vector machine (polynomial) method produced a better estimation of SDS and SCC as compared to Landsat 7 ETM+ and other supervised classification methods used in this study.

Featured Application: Satellite image processing for change detection, target recognition, classification, map application, visual image analysis, etc. Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods. [ABSTRACT FROM AUTHOR]

Pansharpening is a method applied for the generation of high-spatial-resolution multispectral (MS) images using panchromatic (PAN) and multi-spectral images. A common challenge in pansharpening is to reduce the spectral distortion caused by increasing the resolution. In this paper, we propose a method for reducing the spectral distortion based on the intensity-hue-saturation (IHS) method targeting satellite images. The IHS method improves the resolution of an RGB image by replacing the intensity of the low-resolution RGB image with that of the high-resolution PAN image. The spectral characteristics of the PAN and MS images are different, and this difference may cause spectral distortion in the pansharpened image. Although many solutions for reducing spectral distortion using a modeled spectrum have been proposed, the quality of the outcomes obtained by these approaches depends on the image dataset. In the proposed technique, we model a low-spatial-resolution PAN image according to a relative spectral response graph, and then the corrected intensity is calculated using the model and the observed dataset. Experiments were conducted on three IKONOS datasets, and the results were evaluated using some major quality metrics. This quantitative evaluation demonstrated the stability of the pansharpened images and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Together with the developments in satellite technology, it is considered that high resolution satellite data may be used as an alternative source of information to aerial photos in delineation of stand types. The study aims to reveal how detailed one could work to generate the map of stand types which form the basis of forest management plans using IKONOS satellite data. For this purpose, object based classification was applied to satellite image. Firstly, image segments which represent target objects were generated applying image segmentation algorithm to the satellite image. The image segments generated at three different levels according to different scale parameters and homogeneity criteria were classified according to standard nearest-neighbor approach. Classification accuracy was determined using both the stand maps of study area and ground control points. Overall accuracy was calculated as 58% (Kappa=0.54). Accordingly, it was understood that it was not possible to generate a stand map with sufficient accuracy from the IKONOS satellite image using automatic classification.

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

This paper investigates the positioning accuracy of image pairs achieved by integrating images from multiple satellites. High-resolution satellite images from IKONOS, QuickBird, and KOMPSAT-2 for Daejeon, Korea were combined to produce pairs of stereo images. From single-satellite stereo pairs to multiple-satellite image pairs, all available combinations were analyzed via a rational function model (RFM). The positioning accuracy of multiple-satellite pairs was compared to a typical single-satellite stereo pair. The results show that dual-satellite integration can be an effective alternative to single-satellite stereo imagery for horizontal position mapping, but is less accurate for vertical mapping. The integration of additional higher-resolution images can improve the overall accuracy of the existing two images, but, conversely, may result in lower accuracy when very weak convergence or bisector elevation (BIE) angles occur. This highlights that the use of higher resolution images may not ensure improved accuracy, as it can result in very weak geometry. The findings confirm that multiple-satellite images can replace or enhance typical stereo pairs, but also suggest the need for careful verification, including consideration of various geometric elements and image resolution. This paper reveals the potential, limitations, and important considerations for mapping applications using images from multiple satellites.

The objective of this study is to estimate stand development stages (SDS) and stand crown closures (SCC) of forest using different classification methods (maximum likelihood, support vector machine: linear, polynomial, radial and sigmoid kernel functions and artificial neural network) based on satellite imagery of different resolution (Landsat 7 ETM+ and IKONOS). The results showed that SDS and SCC were estimated with Landsat 7 ETM+ image using the artificial neural network with a 0.83 and 0.78 kappa statistic value, and 92.57 and 89.77% overall accuracy assessments, respectively. On the other hand, SDS and SCC were predicted with IKONOS image using support vector machine (polynomial) method with a 0.94 and 0.88 kappa statistic value, and 95.95 and 91.17% overall accuracy assessments, respectively. Our results demonstrated that IKONOS satellite image and support vector machine (polynomial) method produced a better estimation of SDS and SCC as compared to Landsat 7 ETM+ and other supervised classification methods used in this study. [ABSTRACT FROM AUTHOR]

The ability to classify asphalt surfaces is an important goal for the selection of suitable non-variant targets as pseudo-invariant targets during the calibration/validation of remotely-sensed images. In addition, the possibility to recognize different types of asphalt surfaces on the images can help optimize road network management. This paper presents a multi-resolution study to improve asphalt surface differentiation using field spectroradiometric data, laboratory analysis and remote sensing imagery. Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data and multispectral images, such as Quickbird and Ikonos, were used. From scatter plots obtained by field data using λ = 460 and 740 nm, referring to MIVIS Bands 2 and 16 and Quickbird and Ikonos Bands 1 and 4, pixels corresponding to asphalt covering were identified, and the slope of their interpolation lines, assumed as asphalt lines, was calculated. These slopes, used as threshold values in the Spectral Angle Mapper (SAM) classifier, obtained an overall accuracy of 95% for Ikonos, 98% for Quickbird and 93% for MIVIS. Laboratory investigations confirm the existence of the asphalt line also for new asphalts, too.

Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard’s airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R2 > 0.98 and root mean square errors (RMSEs) < 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R2 > 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R2 > 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available.

The ancient city of Byblos (Jbail, Lebanon) provides one of the primary examples of urban organization in the Mediterranean world. This multimillenary city bears an exceptional testimony to the beginnings of the Phoenician civilization. The site covers an area of 10 ha located along the coast. The present paper deals with the synthetic mapping of the archaeological site and its environment using remote sensing techniques. Very high-resolution imagery is vital for monitoring any archaeological site in its widest context. In the specific case of Byblos, the comparison of the Orbview 3, Ikonos 2 and Geo-Eye 1digital data indicates that a spatial resolution of about half-a-meter is necessary for archaeological purposes and particularly for mapping the walls and the buildings. The use of Corona archive acquired in 1970 allows mapping the archaeological site and its coastal environment before the Lebanese civil war. This multitemporal approach allows us to evaluate the sensitivity of the archaeological site to external factors, such as the coastal erosion, the cliff degradation, and the urban growth. It also helps to better plan the improvement of the site for touristic purposes. [ABSTRACT FROM AUTHOR]

This research was aimed to indentify land cover change in coastal area of Kendari Bay in period 2003 to 2009. The satellite imagery data (Ikonos and Quick Bird) collected in 2003 and 2009 were used in this research to obtain the land cover change. The method used in this research was comparing the classification of satellite imagery. Field survey was conducted using handheld GPS for ground truth. The result of this research showed us the land use change in period 2003-2009. Mangrove vegetation decreased 56.57 Ha and the fishpond also decreased 205.5 Ha. The primary forest decreased into 3.28 Ha in year 2009. The secondary forest also decreases 124.84 Ha. In the same time the urban area increased from 382.37 Ha in year 2003 to 674.37 Ha in 2009. The land use change also occured for the public space which increased from 6.49 Ha in 2003 to 18.46 Ha in 2009 or increased 11,97 Ha.

Georeferencing is a mandatory issue at many applications where the satellite images are used. Georeferencing is based on a transformation between images and objects coordinate systems, and parametric, semi parametric and non-parametric mathematical models are preferred for this issue. A semi-parametric model, i.e. sensor-based RFM (Rational Functional Model) suggested by the OGC (Open Geospatial Consortium) is subjected in this paper. Some theoretical background about the general equation, the estimation of coefficients, the distortions carried and their removing, the simplification of RFMs thanks to the special characteristics of coefficients and also the various adjustment models will be presented at first. Then, the georeferencing accuracies of a mono IKONOS panchromatic image covering Zonguldak test field having an undulating and mountainous topography will be presented using 22 GCPs via a computation package called GeoFigcon derived in Matlab environment by the author. If a bias compensation is applied, the accuracies are when 1st degree RFM is preferred.

As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes.

Rapid technological advances in the second part of the 20th century have brought about immense changes in data collection methods, especially in developed countries. In addition to standard terrestric data collection methods, forest data are increasingly being collected with remote sensing methods. The application of remote sensing reduces the amount of field work and offers the possibility of saving time and money.Satellite remote sensing is a very efficient method, while satellite images are a useful means for the surveillance and study of forest vegetation. Remote sensing is based on the prediction of the relationship between spectral reflectance and the observed variable. To interpret this kind of relationship, it is important to know the reflective characteristics of the target object (spectral features of vegetation, leaf, etc.), the size of area presentation (spatial resolution), and other factors such as topography, sun height, slope and orientation between the surveyed area and the survey sensor. A detailed and high quality analysis of satellite imagery, recognition and selection of certain data is highly dependent on image resolution, and especially on spatial resolution.Acquiring information on forests from satellite photographs of high spatial resolution has been the subject of a large number of research activities. One of the approaches involves classification based on pixel value, or regression analyses in which spectral records are used to predict classes or continuous variables of stand structure.The basic objective of this work is to explore the relationship between spectral reflectance recorded in the IKONOS satellite image of high spatial resolution and any individual stand parameter, as well as regression models for the evaluation of stand parameters.The main material for this work was provided by the IKONOS satellite image of high spatial resolution for the Spačva basin area. The survey was conducted on 18 October 2006. An IKONOS satellite image of the Spačva basin with an area of 132 km² was delivered in 5 spectral channels: PAN (1x1m), and 4 MS Bundle. In the area of Vinkovci Forest Administration, within the management units encompassed by satellite surveying (MU Vrbanjske Šume, Kragujna, Slavir and Otočke Šume), field measurements were conducted in management classes of pedunculate oak and narrow-leaved ash through all age classes (2nd to 7th age class), for the purpose of subsequent comparison of interpretations and assessments of structural elements. Based on field measurements and Management Plan data, a database with compartments and subcompartments was established within two management units: Vrbanjske Šume and Slavir, where the largest number, 504 compartments in all, belong to the management class of pedunculate oak. For each compartment, the mean value of spectral reflectance within four channels was read from the satellite image. These read values were associated to the database mentioned earlier.Descriptive statistics was performed for all the variables. In all the analyses, the significance level of 5% was considered statistically significant. Canonical correlation analysis was used to examine the relationship between a linear combination of channel value and a linear combination of field data value for each feature separately (number of trees, diameter at breast height, height, basal area and volume). Since there is no uniform solution, a linear combination should be selected which provides the best correlation among the monitored variables.The Pearson’s correlation was used to study the relationship between arithmetic means of spectral reflectance values by channels and variables. The results of the assessment in the satellite image were compared with field measurement data, and with the data taken from the Management Plan. These data were used to generate the relationships between spectral reflectance recorded in the satellite image and a particular stand parameter, and consequently, the regression models for the assessment of stand parameters. Two regression models were assessed for each stand parameter within the management class of pedunculate oak, separately for each age class. The first model was assessed by means of the stepwise procedure, wherein the independent variables were the values of all four channels. A combination of the channels that best explains the dependent variable is selected. The second regression model contained a combination of all the four channels together (Table 2-6).The best correlation between the observed variables and spectral values of individual channels was determined by means of canonical correlation.Table 1 shows that canonical correlation for all the observed features in the third age class is higher than 0.9. In the third age class, the first canonical solution for the combination with four channels with the combination of tree number and correlation of 0.95 explains (overlaps) 90% of the variability. The poorest correlation of 0.46 proved to be for breast diameter and for all four channels in the sixth age class, with variability overlap of 21.19%. In terms of canonical correlation for tree number by all age classes, good correlations are observed in the 2nd (0.91), 3rd (0.95), and 5th (0.91) age class, where 80-90% of variability is explained. In contrast, correlation in the 6th and 7th age class was from 0.75 and 0.65, with determination coefficients of R² = 56.4% and 42%. Very similar results were obtained for volume, where the poorest correlation was found in the 6th (0.69) and 7th (0.76) age class.The results of canonical correlation analysis allow us to conclude that the observed variables are in good correlation with all the four channels for all age classes, except for age class 4, which cannot be assessed due to low spatial participation of this age class in the sample.As for the possibility of stand parameter assessment in satellite images, the results of regression models for the third age class proved to be the best. These results relate to the number of pedunculate oak trees and the total number of trees, the total basal area and the total volume. In general, the results of regression model assessment for the third age class show that all the observed parameters (number of trees, breast diameter, height, basal area and volume) are very well assessed in models with all four channels and determination coefficients of 60% and 80%. Stand parameters of the second and fifth age class are also well assessed. It should be pointed out that, according to research results, there is no justification to assess stand parameters for all the observed variables in the sixth and seventh age class due to low values of determination coefficients.Since data based on spectral information from pixels were used to assess stand structural elements, a possible cause of poorer assessment results of stand parameters for the 6th and 7th age class may be attributed to the spatial resolution of the IKONOS satellite image. Namely, images of high spatial resolution frequently show isolated pixels (classified as a class) inserted into the area that represents the second class, which makes further analysis and application of the satellite image more difficult.

This study evaluates a methodology for using IKONOS stereo imagery to determine the height and position of buildings in dense residential areas. The method was tested on three selected sites in an area of 8.5 km long by 7 km wide and covered by two overlapping (97% overlap) IKONOS images. The images were oriented using rational function models in addition to ground control points. Buildings were identified using an algorithm that utilized the Digital Surface Model (DSM) extracted from the images in addition to the image spectral properties. A digital terrain model was used with the DSM created from the IKONOS stereo imagery to compute building heights. Positional accuracy and building heights were evaluated using corner coordinates extracted from topographic maps and surveyed building heights. The results showed that the average building detection percentage for the test area was 82.6% with an average missing factor of 0.16. When the image rational polynomial coefficients were used to build the image model, results showed a horizontal accuracy of 2.42 and 2.39 m Root Mean Square Error (RMSE) for the easting and northing coordinates, respectively. When ground control points were used, the results improved to the sub-meter level. Differences between building heights extracted from the image model and the corresponding heights obtained through traditional ground surveying had a RMSE of 1.05 m.

The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km2) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.

In this search, a colored image of French satellite SPOT, of Shirkat country in Salahadeen was used with resolution about (5m) for an area with dimensions (4km×8km), and a digital elevation model (DEM) with grids (20m×20m) and levels reach to (1m) resolution from the same satellite for the same country, with an image taken from the IKONOS satellite with (1m) resolution, in addition to master plan and administrative map for the same area. The IKONOS satellite image processed digitally, and intersected with the DEM, to create a three dimensional model for the area under study, with resolution about (1mm), by the use of AutoCAD and GIS programs, and making a comparison between the field survey and the images information, resulting extensive information about the levels in shirkat country streets and the surrounding buildings, and an attributive results about the agricultural or desert spaces around the country.

Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

This paper has as purpose present the methodology developed to produce an updated digital map base support for participatory management Mamirauá Reserve of Sustainable Development in the state of Amazonas, Braszil. Because this protected área is situated within an area of flooded forest, both the physical landscape and social organization often change, and the dynamic demand the systematic update of cartographic databases. This work has images of orbital sensors IKONOS II and LANDSAT 5 TM, interviews with users and collecting spatial data in the Mamirauá Reserve. This work obtained a cartographic base at 1:100.000 scale and a geodatabase compatible with the local references, with which is possible to generate thematic maps updated to support dialogue in the sustainable management programs of the Mamirauá Reserve and minimize conflicts with communities.

The problem of old silver fir stands decay has appeared in the lastdecades of the last century with symptoms such as defoliation, wood decay and parasites attacks. The problems in monitoring this phenomenon is that the defoliation and mistletoe attack appear on the crown top, triggering the coronation process - tree develop branches on their lower stem part in order to resist to the defoliation. Thisreaction makes the attack difficult to notice on aerial or satellite images, due to the reflection of light on the lower branches. The objective of the article is to find a research methodology for identifying and even mapping the mistletoe attack phenomenonat a tree or stand level. For a tree-level analysis, a comparison between the data obtained in a 1 ha sample plot and the data extracted from an IKONOS satellite image has been used. The simple spectral response is less correlated at tree level with the defoliation and mistletoe attacks. We found a very significant correlation in the infrared channel but with low correlation coefficients. Better results have been obtained in a stand-level analysis. In order to improve the separability, the methodologyfor extracting the remote sensing data did not resume only to the mean spectral response, but we also performed a variability and texture analysis. The obtained correlation coefficients were around 0.7, very significant for the data used in the research. The results obtained with the texture analysis are also related to the biometriccharacteristics of the stands, mistletoe attacks occurring in stands with a low canopy closure index and is usually accompanied by distorted crowns. The model of forest health analysis should also be tested in similar conditions in order to validate and apply on a large scale inventory.

Vegetation distribution maps from remote sensors play an important role in urban planning, environmental protecting and related policy making. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. However, NDVI is usually used to generate lower resolution vegetation maps, and particularly the threshold needs to be chosen manually for extracting required vegetation information. To tackle this threshold selection problem for IKONOS imagery, a fixed-threshold approach is developed in this work, which integrates with an extended Tasseled Cap transformation and a designed image fusion method to generate high-resolution (1-meter) vegetation maps. Our experimental results are promising and show it can generate more accurate and useful vegetation maps for IKONOS imagery.

Assessing the status of tropical dry forest habitats using remote sensing technologies is one of the research priorities for Neotropical forests. We developed a simple method for mapping vegetation and habitats in a tropical dry forest reserve, Mona Island, Puerto Rico, by integrating the Normalized Difference vegetation Index (NDvI) from Landsat, topographic information, and high-resolution Ikonos imagery. The method was practical for identifying vegetation types in areas with a great variety of plant communities and complex relief, and can be adapted to other dry forest habitats of the Caribbean Islands. NDvI was useful for identifying the distribution of forests, woodlands, and shrubland, providing a natural representation of the vegetation patterns on the island. The use of Ikonos imagery allowed increasing the number of land cover classes. As a result, sixteen land-cover types were mapped over the 5 500 ha area, with a kappa coefficient of accuracy equal to 79 %. This map is a central piece for modeling vertebrate species distribution and biodiversity patterns by the Puerto Rico Gap Analysis Project, and it is of great value for assisting research and management actions in the island. Rev. Biol. Trop. 56 (2): 625-639. Epub 2008 June 30.El estudio y evaluación de los bosques tropicales secos mediante herramientas de teledetección es una de las prioridades de investigación en los ambientes neotropicales. Desarrollamos una metodología simple para mapear la vegetación de la isla de Mona, Puerto Rico, mediante el uso del índice de vegetación normalizado (NDVI por sus siglas en inglés) de Landsat, información topográfica, e imágenes auxiliares de alta resolución Ikonos. La metodología fue útil para identificar las clases de vegetación en un área de gran variedad de comunidades vegetales y relieve complejo, y puede ser adaptada a otras regiones de bosque seco de las islas del Caribe. El NDVI permitió identificar la distribución de los bosques cerrados, abiertos, y arbustos, proveyendo una representación natural de los patrones de vegetación en la isla. Las imágenes de Ikonos permitieron incrementar el número de clases detectadas. Como resultado, mapeamos 16 clases de cobertura del terreno en las 5 500 hectáreas de la isla de Mona, con un coeficiente de concordancia kappa de un 79%. La información obtenida en este estudio será utilizada para modelar la distribución de los vertebrados terrestres y patrones de biodiversidad en la isla, como parte del proyecto Gap Análisis de Puerto Rico, y es de gran valor para asistir en las actividades de investigación y manejo en la isla.

In recent years, satellite remote sensing has become a new tool for estimation of forest condition. The paper deals with spruce timber growing stock and vegetation cover assessment employing IKONOS satellite data from a mountain forest area of Central Slovakia. Original digital data as well as enhanced digital images were used to estimate some forest variables. Image enhancement approaches employing topographic normalization, PCA analysis and different vegetation indices are a very important part of data processing. Apart from spectral characteristics, texture as an additional variable was utilized. In order to improve classification accuracy the knowledge of the vertical distribution of tree species also was incorporated into classifiers. Spectral signatures as auxiliary variables measured with the aid of training sets were utilized for the construction of spectral models for growing stock estimation. In spite of the fact that the standard error of these models is not very favourable as it varies about 30%, they offer initial information for application of different sampling designs for timber growing stock assessment, where the final precision is acceptable. Stepwise discriminant analysis was employed to choose appropriate sets for the classification of vegetation cover. Classification results show an assumed contribution of categorial knowledge for increasing the correctly classified pixel proportion and this improvement was on average about 10%. Likewise, the texture contributes to better resolution of some very near spectral classes.

Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales. [ABSTRACT FROM AUTHOR]

This study aims at providing a new method to efficiently analyze detailed urban ecological conditions at the example of Shanghai, one of the world's most densely populated megacities. The main objective is to develop a method to effectively analyze high-resolution optical satellite data for mapping of ecologically important urban space and to evaluate ecological changes through the emerging ecosystem service supply and demand concept. Two IKONOS and GeoEye-1 scenes were used to determine land use/land cover change in Shanghai's urban core from 2000 to 2009. After preprocessing, the images were segmented and classified into seven distinct urban land use/land cover classes through SVM. The classes were then transformed into ecosystem service supply and demand budgets for regulating, provisioning and cultural services, and ecological integrity based on ecosystem functions. Decreases in continuous urban fabric and industrial areas in the favor of urban green sites and high-rise areas with commercial/residential function could be observed resulting in an increase of at least 20% in service supply budgets. Main contributor to the change is the decrease in continuous urban fabric and industrial areas. The overall results and outcome of the study strengthen the suggested application of the proposed method for urban ecosystem service budget mapping with hitherto for that purpose unutilized high-resolution data. The insights and results from this study might further contribute to sustainable urban planning, prove common grounds for interurban comparisons, or aid in enhancing ecological intraurban functionality by analyzing the distribution of urban ecospace and lead to improved accessibility and proximity to ecosystem services in urban areas. [ABSTRACT FROM PUBLISHER]

Makowski, C.; Finkl, C.W., and Vollmer, H.M., 2017. Geoform and landform classification of continental shelves using geospatially integrated IKONOS satellite imagery. Geomorphological characterization of coastal environments along continental shelves depends on accurate interpretation of mesoscale lithic and clastic benthic geoforms and landforms. Using the Geospatially Integrated Seafloor Classification Scheme (G-ISCS), cognitive visual interpretations of seafloor geoforms and their associated landforms were conducted along a diverse segment of the southeast (SE) Florida continental shelf. GeoEye IKONOS-2 satellite imagery provided the remotely sensed visual medium on which interpretations were based. With ESRI ArcGIS® ArcMap software, classification maps were created from the cognitive interpretations to show spatial distribution results of geoform and landform features throughout the study area. Additionally, smaller-scale 'call-out figures' documented specific geomorphological associations among the classified units. Analysis attribute tables compared and contrasted the abundance ( i.e. number of classifying vector polygons) and calculated areas for each geoform and landform classified. It was determined that classification of geoform and landform benthic features along continental shelves can be achieved where water clarity conditions allow for the cognitive visual interpretation of such seafloor formations. Future studies may build upon the classification of continental shelf geoforms and landforms to integrate more transient biogeomorphological features of the marine environment ( e.g., sediment distribution, biological species identification, density of flora and fauna present), thus creating a more detailed and inclusive classification of a selected coastal region. [ABSTRACT FROM AUTHOR]

Co-registration of optical and range data is a crucial step before they could be merged. Trustworthy correspondence between both data is determined via image registration models. These models are mainly built with points; however, with the aid of automated extraction tools for linear objects, photogrammetrists have been developing transformation models with lines. In this study, we propose to co-register satellite images and light detection and ranging (LIDAR) digital elevation models (DEMs) with straight lines. We developed a line extraction algorithm that was capable of extracting straight lines in both data sets. These lines were then utilised to register the LIDAR DEM with several high-resolution satellite images. We were able to reach one-pixel accuracy through the parallel projection transformation model and the direct linear transformation (DLT) model when geometric constraints among the lines were enforced. Our findings are similar to those reported with points and highlight the leverage of linear features in registering satellite imageries and LIDAR data. [ABSTRACT FROM PUBLISHER]

Beach forests are important ecological zones in many coastal regions. Many are under increasing anthropogenic and natural stress. Beach forest changes and their causes were examined in three tsunami-impacted sites with different land-use/cover (LULC): Ban Nam Khem (BNK, fishing village), Khao Lak (KL, tourist destination), and Thai Mueang (TM, part of a national park). Vegetation surveys, GIS, and interpretation of time series IKONOS and THEOS imagery using supervised classification (ENVI 4.7) from 2003-2013 were performed. Six beach forest tree and shrub species were found in BNK and KL, dominated by Casuarina equisetifolia, while 24 tree/shrub species, dominated by Syzygium grande, were observed at TM. After the tsunami, beach forests were severely damaged in BNK (45%), KL (40%), and TM (23%). Recovery of beach forests in 8 years varied from BNK (58%), KL (39%) with low rates in KL and high rates in TM (62%). Substantial portions of the three sites were still characterized as beach forest in 2013, though the forests now included areas that had recovered from the tsunami damage and/or altered LULC (e.g., barren land to beach forests and vice versa). Anthropogenic factors represented 40% (BNK), 56% (KL), and 5% (TM) of the changes with urbanization being a leading cause in tourist areas (KL; 24%). The study highlights the need for improved understanding of beach vegetation, tsunami impacts, and LULC to provide sustainable management of beach forests in Thailand in three sites with different anthropogenic characteristics. [ABSTRACT FROM AUTHOR]

Las imágenes digitales en su formato original poseen distorsiones, entre ellas geométricas que se originan en el momento de su adquisición. Para corregirlas es necesario aplicar un proceso denominado georrectificación el cual requiere de puntos de control terrestre claramente identificables tanto en la imagen como en el terreno, permitiendo obtener un producto con la integridad geométrica de un mapa. La investigación se enfoca en determinar la potencialidad de las diversas modalidades observacionales GPS aplicadas en el proceso de georrectificación de imágenes de alta resolución espacial (IKONOS). En el estudio se evalúan los procedimientos estático rápido de una y dos frecuencias, cinemático y RTK.The digital image’s raw format contains geometric distortions originated at their adquisition. They required a georectification process, which needs some ground control point cleary recognized in both, the satellite image and the corresponding terrain position too. This allows transforming the image to map information data with the geometric integrity. The potential of several GPS procedures applicated in the georectification of high spatial resolution images (IKONOS) is show. The fast-static using one and two frequencies recivers, kinematic and RTK procedures are evaluated.