High Resolution IKONOS Imagery Research Articles

A comparison of unsupervised segmentation parameter optimization approaches using moderate- and high-resolution imagery

Unsupervised segmentation optimization methods have been proposed to aid in selecting an “optimal” set of scale parameters quickly and objectively for object-based image analysis. The goal of this study was to qualitatively assess three unsupervised approaches using both moderate-resolution Landsat and high-resolution Ikonos imagery from two study sites with different landscape characteristics to demonstrate the continued need for analyst intervention during the segmentation process. The results demonstrate that these methods selected parameters that were optimal for the scene which varied with method, image type, and site complexity. Several takeaways from this exercise are as follows: (1) some methods do not work as intended, (2) single-scale unsupervised optimization procedures cannot be expected to properly segment all the features of interest in the image every time, and (3) many multi-scale approaches require subjectively chosen weights or thresholds or additional testing to determine those values that meet the objective. Visual inspection of segmentation results is still required in order to assess over and under-segmentation as no method can be expected to select the best parameters for land cover classifications every time. These approaches should instead be used to narrow down parameter values in order to save time.

Mapping Robinia pseudoacacia forest health in the Yellow River delta by using high-resolution IKONOS imagery and object-based image analysis

The Robinia pseudoacacia forest in the Yellow River delta of China has been planted since the 1970s, and a large area of dieback of the forest has occurred since the 1990s. To assess the condition of the R. pseudoacacia forest in three forest areas (i.e., Gudao, Machang, and Abandoned Yellow River) in the delta, we combined an estimation of scale parameters tool and geometry/topology assessment criteria to determine the optimal scale parameters, selected optimal predictive variables determined by stepwise discriminant analysis, and compared object-based image analysis (OBIA) and pixel-based approaches using IKONOS data. The experimental results showed that the optimal segmentation scale is 5 for both the Gudao and Machang forest areas, and 12 for the Abandoned Yellow River forest area. The results produced by the OBIA method were much better than those created by the pixel-based method. The overall accuracy of the OBIA method was 93.7% (versus 85.4% by the pixel-based) for Gudao, 89.0% (versus 72.7%) for Abandoned Yellow River, and 91.7% (versus 84.4%) for Machang. Our analysis results demonstrated that the OBIA method was an effective tool for rapidly mapping and assessing the health levels of forest.

A GIS-approach for determining permanent riparian protection areas in Mato Grosso, central Brazil

In this paper we propose a DEM-based GIS methodology for the identification and delimitation of riparian protection areas according to stream width. In Brazil, vegetation cover in buffer zones is monitored by mid-resolution satellite imagery, so our methodological approach includes procedures to ensure geometrical accuracy for multiple spatial layer overlay. For a study area in Mato Grosso, a sequence of DEM pre-processors was used to hydrologically correct a SRTM 90 m resolution digital elevation model. Then samples of river widths were obtained by measures using high-resolution IKONOS imagery and regressed against watershed contribution areas to define protection zone buffers. We propose a procedure to attribute the DEM-based width estimates derived from topographic maps and satellite imagery to the river networks. Validation using high-resolution IKONOS imagery indicates that the proposed methodology produces RMS errors of about 20 m on average for watercourses up to 600 m wide. This is a considerable improvement in protection area delimitation compared with current practice, particularly in headwater regions and along streams with widths of up to 200 m. Accuracy assessments indicate that environmental licensing procedures should include a tolerance of between 48% (<50 m) and 15% (200–600 m) of stream width, according to systematic errors included in the estimates.

Mapping urban forest tree species using IKONOS imagery: preliminary results

A stepwise masking system with high-resolution IKONOS imagery was developed to identify and map urban forest tree species/groups in the City of Tampa, Florida, USA. The eight species/groups consist of sand live oak (Quercus geminata), laurel oak (Quercus laurifolia), live oak (Quercus virginiana), magnolia (Magnolia grandiflora), pine (species group), palm (species group), camphor (Cinnamomum camphora), and red maple (Acer rubrum). The system was implemented with soil-adjusted vegetation index (SAVI) threshold, textural information after running a low-pass filter, and brightness threshold of NIR band to separate tree canopies from non-vegetated areas from other vegetation types (e.g., grass/lawn) and to separate the tree canopies into sunlit and shadow areas. A maximum likelihood classifier was used to identify and map forest type and species. After IKONOS imagery was preprocessed, a total of nine spectral features were generated, including four spectral bands, three hue-intensity-saturation indices, one SAVI, and one texture image. The identified and mapped results were examined with independent ground survey data. The experimental results indicate that when classifying all the eight tree species/ groups with the high-resolution IKONOS image data, the identifying accuracy was very low and could not satisfy a practical application level, and when merging the eight species/groups into four major species/groups, the average accuracy is still low (average accuracy = 73%, overall accuracy = 86%, and κ = 0.76 with sunlit test samples). Such a low accuracy of identifying and mapping the urban tree species/groups is attributable to low spatial resolution IKONOS image data relative to tree crown size, to complex and variable background spectrum impact on crown spectra, and to shadow/shaded impact. The preliminary results imply that to improve the tree species identification accuracy and achieve a practical application level in urban area, multi-temporal (multi-seasonal) or hyperspectral data image data should be considered for use in the future.

Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey

Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology.

The impact of land development regulation on residential tree cover: An empirical evaluation using high-resolution IKONOS imagery

Public sector policies regulating land development can have a major influence on the environmental impacts of urban development, yet few empirical studies have examined the impact of these policies. Our study attempted to address this gap by examining the relationship between a land development code associated with the protection of trees and the extent of urban tree cover on residential parcels. We developed an accurate very high resolution urban tree cover classification using IKONOS imagery, quantified parcel-specific tree cover and evaluated the relationship between year of construction, adoption of a tree ordinance and extent of canopy cover in Tampa, Florida, USA and in nearby areas lacking similar regulation. Statistical results revealed significantly greater tree cover on parcels with homes built after compared to before adoption of tree protection policies, despite a trend toward increased building cover. After controlling for the effects of spatial dependence and relevant physical and socio-demographic characteristics at the census block, multivariate regression results indicate the percentage of homes built after implementing the policy was a strong predictor of increased tree cover. These results and comparisons with nearby areas further suggest Tampa's 1974 tree protection regulations appears to have been effective at increasing tree cover, but other influential factors were also at work. This study demonstrates the potential for evaluating the impact of specific land development policies using remote sensing and other spatial data analysis techniques. In addition, we argue that scientific evidence of the effectiveness of past policy should be used a guide for the creation of future land development regulation.

Mapping tropical dry forest habitats integrating Landsat NDVI, Ikonos imagery, and topographic information in the Caribbean Island of Mona

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 5500 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.

Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators

The objective of this study was to directly detect impervious cover using new high-resolution IKONOS imagery in South Lake Tahoe, California, USA. The research presented was a pilot analysis to assess the ability of satellite imagery to derive accurate estimates of impervious cover, critical for assessing impacts to water quality, wildlife, and fish habitat. A combination of image processing methods based on principal component analysis and spatial morphological operators was developed for a 25 km2 urban area in the Lake Tahoe Basin. The methodology produced very accurate identification of both commercial and residential impervious cover in an area dominated by dense conifer canopy. Sub-canopy and sub-shadow surfaces were not only detectable, but also discernible with respect to the underlying substrate. An overall accuracy of 92.94% was obtained, with an even higher user accuracy of 95.83% based on 170 ground truth points. Impervious cover is a difficult feature to delineate accurately and efficiently using direct methods. For this application, spatial resolution proved a better operator than spectral resolution. Results from this analysis will be used to better understand the impacts of urban development on the ecology of the Lake Tahoe Basin.