IKONOS Imagery Research Articles

Mapping Avian Habitat Suitability Using Linear and Non-Linear Techniques in the Case of Wetland Landscapes

Habitat quality is crucial for wildlife management that impacts the conservation of sensitive landscapes such as wetlands. With advancements in GIS, habitat modelling now effectively predicts species occurrences and habitat suitability. This study aims to model and map habitat suitability for case bird species of Kentish plover in Tuzla Lagoon using multiple techniques. Kentish plover nesting data were collected from 293 nests, and reproductive success measures such as lay date, egg volume, and nest fate were analysed. Spatial habitat modelling techniques, including regression, co-kriging, artificial neural networks, and decision trees, were used with IKONOS imagery and ground data. The overall prediction accuracies were poor for lay date across all techniques, with the decision tree being the most accurate, while egg volume was best predicted by co-kriging, egg success by linear regression, and nest fate by both binomial logistic regression and ANN with 75% accuracy.

The area and distribution changes of mangrove forests on the coast of Gresik Regency, East Java, 2012-2023

Abstract Environmental dynamics in coastal areas have resulted in changes in the area and distribution of mangrove forests in Gresik Regency. Mangroves have ecological and economic functions for living things. This study aims to analyse changes in the area and distribution of mangrove forests in Gresik Regency in 2012 and 2023. The data source is IKONOS imagery from Google Earth in 2012 and 2023, the coastal location of Gresik Regency, East Java Province. Interpretation and digitisation were done to determine the area and distribution of mangrove forests, and maps of the mangrove forests were produced in 2012 and 2023. Then, the two maps were overlayed to determine their area and distribution changes. Based on image interpretation and analysis, in 2012, the area with the largest mangrove forest was Ujung Pangkah District (802.75 hectares) or 63.21% of the total mangrove forest area of Gresik Regency. The largest mangrove forest in 2023 is still in Ujung Pangkah District, with an area of 1261.50 hectares or 64.26%. For ten years (from 2012 to 2023), Gresik Regency’s mangrove forests increased by 590.41 hectares, the most significant increase in Ujung Pangkah District (increased by 458; 76 hectares).

COMPARISON OF DIFFERENT PAN-SHARPENING METHODS APPLIED TO IKONOS IMAGERY

COMPARISON OF DIFFERENT PAN-SHARPENING METHODS APPLIED TO IKONOS IMAGERY

Hedgerow object detection in very high-resolution satellite images using convolutional neural networks

Hedgerows are one of the few remaining natural landscape features within European agricultural areas. To facilitate hedgerow monitoring, cost-effective and accurate mapping of hedgerows across large spatial scales is required. Current methods used for automatic hedgerow detection are overly complicated and generalize poorly to larger areas. We examine the application of transfer learning using two neural networks (Mask R-CNN and DeepLab v3+) for hedgerow mapping in south-eastern Germany using IKONOS imagery. We demonstrate the potential of such networks for hedgerow monitoring by investigating performances across varying input image bands, seasonal imagery, and image augmentation strategies. Both networks successfully detected hedgerows across a large spatial scale (562 km2), with DeepLab v3+ (75% F1-score) outperforming Mask R-CNN. Differences between band combinations were minimal, implying hedgerow detection could be achieved using RGB sensors. Results suggested that using all available training images across seasons is preferred and should have the same model generalizing effects as data augmentation. Experiments with varying data augmentations found augmentations effecting object geometries to greatly increase performance for both networks while results using augmentations modifying pixel spectral values showed concerning effects. Overall, our study finds that transfer learning in neural networks offers a simplified approach that outperforms previously established methods.

Superpixel-based imaging for residential area detection of high spatial resolution remote sensing imagery

The precise and efficient location of residential areas using high spatial resolution remote sensing imagery is a popular research area in the field of Earth observation. Most of the existing approaches are supervised or semisupervised and use data training. Among the unsupervised approaches, corner density-based mapping using kernel density estimate has been widely employed to predict the presence of built-up areas. However, it is computationally time-consuming and the statistical threshold segmentation makes it difficult to obtain a stable and accurate output. To overcome this deficiency, a new two-stage object-oriented residential area extraction scheme was designed. First, a set of corners was extracted using the Gabor filter bank with structural tensor analysis to indicate candidate buildings. Then, instead of pixel units, our method takes superpixel-based image partitions as the primary calculation elements, and an object-oriented weighted sparse spatial voting technique was proposed to accelerate the generation of a residential area presence index. It was demonstrated that the superpixel-based voting strategy was not only efficient in accelerating the calculation process, but it also reduced the false negative rate in the final detection result. Second, a graph-cut method was employed to address the residential area segmentation by integrating a density map as a prior cue that preserves the boundary accuracy better than traditional statistical threshold methods. The effectiveness of the proposed method was evaluated using a series of experiments on the sets of high-resolution Google Earth, IKONOS, and GaoFen-2 (GF2) satellite imagery. The results showed that the proposed approach outperforms the existing algorithms in terms of computational speed and accuracy.

Diagnostically counting palm date trees in Al-Ahssa Governorate of Saudi Arabia: an integrated GIS and remote sensing processing of IKONOS imagery

Sustainability of date palm trees based economy envisages regular monitoring of spatio-temporal variations of these trees over time. But, field based counting is a very old style of managing trees which is labour intensive, costly and time consuming while modern geo-spatial technologies provides a fast, cheaper and accurate mechanism. In this regard, the current study attempts to formulate a repeatable and robust approach utilising high spatial resolution remote sensing data integrated with image processing technique and GIS functionalities in automatically extracting palm trees. The study was carried out over Al-Ahssa region in Saudi Arabia. Difficulties posed by dark and bright canopy situation is resolved in this study based on appropriate kernel (based on multi-scale analysis) along with focal minimum and focal maximum functions, and individual tree locations were identified correctly. Supervised classification technique was utilised to differentiate trees from non-tree areas. The approach has successfully extracted date palm tree locations with an overall accuracy of 93% with an omission error of 5% and commission error of 4%.

Combining Ikonos and Bathymetric LiDAR Data to Improve Reef Habitat Mapping in the Florida Keys

Mapping reef habitat at a detailed level is a challenge when using single-source remote sensing data such as Ikonos imagery. In this study, Ikonos and bathymetric LiDAR data were combined to improve reef habitat mapping in the Florida Keys, USA. An object-based ensemble approach was applied to integrate two remote sensing data sources for generating accurate and informative reef maps of geomorphological structure types and detailed habitats. LiDAR can significantly increase reef classification by providing important bathymetry, reef habitat elevation, and intensity information. A synergy of Ikonos imagery and all LiDAR-derived features could effectively classify reef structure (3-class) with an overall accuracy (OA) of 93% and Kappa value of 0.8, and map detailed reef habitats (10-class) with an OA of 79% and Kappa value of 0.7. Ensemble analysis of three machine learning outputs did not significantly improve the classification but generated a complementary uncertainty map to identify regions with a robust classification and areas difficult to map. Fusion of Ikonos and bathymetry LiDAR is a promising alternative to traditional in-situ and manual interpretation methods for detailed reef habitat mapping.

Building Damage Detection of the 2004 Nagapattinam, India, Tsunami Using the Texture and Spectral Features from IKONOS Images

In this paper, we implemented and evaluated the proposed method for building detection and classification using high-resolution imagery (IKONOS) data. Nagapattinam province of Tamil Nadu, which was strongly attacked by the 2004 Indian Ocean tsunami, is selected as the demonstration site. IKONOS sets acquired on June 10, 2003, (pre-tsunami)–December 29, 2004, (post-tsunami) are used in this study. This approach follows the standard scheme of image analysis: building detection, feature extraction and selection, and classification. At the first level, we studied the damaged buildings from imagery data set. In the second level, 28 texture features and 3 spectral features are extracted, and then 10 best features are selected for next stage by using the statistical dependency feature selection method. In the classification, the proposed method feature mean ratio is used to classify the damaged buildings. The proposed method has been applied on an IKONOS imagery in Nagapattinam city, and visual validation demonstrates that the proposed method provides promising results. The results proved that the proposed work has produced a higher accuracy assessment (96.4%) when compared to earlier works.

IMPLEMENTASI METODE MARR-HILDERTH OPERATOR UNTUK MENDETEKSI TEPI CITRA IKONOS

Ikonos imagery is a satellite image that has high spatial resolution with an accuracy of one meter pixels for panchromatic and four meters to multispektral. Ikonos imagery is often used to map the process, view, measure and memoniotring areas of work/activities on the Earth. Ikonos image of Government also often use it for things like national security evaluation against the occurrence of the disaster, city planning, mineral exploration and mine planning monitoring of agriculture, and others. Image digital imaging results over long distances using satellite is often there are disturbances in the form of light distortion, noise or other distractions that cause the object on the image less obvious or obscure. This discussion on the research being done is knowing the process of detection on image by calculating the difference between two dots are bertetanggan, and is in the process of smoothing and thresholding on image ikonos. The methods used in this research is a method of Marr-Hilderth. In addition, a process that is done on this research is conducting a testing method against Marr-Hilderth, which can be implemented to fix the blurry objects on images ikonos. The results of this research is to generate image ikonos with display clear object with the menerapakan method of the Marr-Hilderth and tested using the matlab application version 7.8 (r2009a).Keywords: Ikonos image, image processing, method of Marr-Hilderth, Matlab 7.8

INFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE

Abstract. High resolution satellite images started with IKONOS imagery. After the launch of the very high resolution IKONOS in the 1990s, a new generation of commercial Earth-imaging satellites have pioneered a new era of space imaging for observations of Earth. The IKONOS satellite image has an important place sampling range with 1 m GSD. In the subsequent Quickbird satellite image, the GSD is down to 62 cm and the sensitivity is even higher. Advancements in the geometric resolution of space images have improved the conditions for generations of large-scale topographic maps. With using WorldView-1, WorldView-2, and GeoEye-1, images can now be captured from space with a 0.5 m ground sampling distance (GSD). The Worldview-4 display with the highest technology and resolution is being used in various application areas. WorldView-4 (formerly GeoEye-2), launched in November 2017, provides a second sensor which is capable of delivering imagery at 30 cm resolution, the highest level of detail commercially available from satellite. WorldView-4 greatly expands the 30 cm collection capabilities and archive growth in today’s imagery environment. Geometric accuracy and information content are the most significant components of mapping from space images. By using economical, rapid and periodic acquisition, and corresponding ground resolution, these satellites have established an alternative to aerial photos and have been widely used for various applications such as object extraction, change detection, topographic map production, and development of Geographic Information Systems (GIS). The utility of VHR images is dependent on their geometric accuracy and information content. Related with the study, the generally required production scale of 0.05 to 0.1 mm GSD in the map scale has been confirmed. This corresponds to a topographic map scale of 1 : 10,000 respectively 1 : 5000 for 1 m and 0.5 m GSD images. In this study, images from IKONOS, QuickBird, WorldView-1, Worldview-2 and WorldView-4 have been used for topographic mapping. For this reason, İstanbul and Zonguldak test fields are an important area for applications of the high resolution imageries. The details which can be identified in the space images dominantly depends upon the ground resolution, available as ground sampling distance (GSD). In this study, high resolution imageries have been tested depending on the GSD and corresponding to the map scales for updating GIS database.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Extracting Terrain Categories from Multi-Source Satellite Imagery

Geoscience Australia has ben conducting a series of national risk assessments for a range of natural hazards such as severe winds. The impact of severe wind varies considerably between equivalent structures located at different sites due to local roughness of the upwind terrain, shielding provided by upwind structures and topographic factors. Terrain surface roughness information is a critical spatial input to generate wind multipliers. It is generally the first spatial field to be evaluated, as it is utilised in both the generation of the terrain and topographic wind multiplier. Landsat imagery was employed to generate a terrain surface roughness product for six major metropolitan areas across Australia. It was necessary to investigate the applicability of multi-sensor approaches to generate a regional or national terrain surface roughness map based on the Australian-New Zealand wind loading standard. This paper presents a methodology to derive terrain surface roughness from various multi-source satellite images. MODIS, Landsat and IKONOS imagery were acquired during 12 September – 26 November 2002 covering a significant portion of New South Wales, Australia. An object-based image segmentation and classification technique was tested for seven bands of MODIS, six bands of Landsat Thematic Mapper, and four bands of IKONOS. Eleven terrain categories were identified using this technique which achieved classification accuracies of 79 percent and 93 percent over metropolitan Sydney and rural or urban areas respectively. It was revealed that the object-based image classification enhances the quality of the terrain product compared to traditional spectral-based maximum likelihood classification methods. To further improve the derivation of terrain roughness classification results, an integrated textural-spectral analysis merged Synthetic Aperture Radar and optical datasets. A comparison with results derived from textural-spectral classification showed considerable improvement over the results from earlier classification techniques.

Assessment of LiDAR and Spectral Techniques for High-Resolution Mapping of Sporadic Permafrost on the Yukon-Kuskokwim Delta, Alaska

Western Alaska’s Yukon-Kuskokwim Delta (YKD) spans nearly 67,200 km2 and is among the largest and most productive coastal wetland ecosystems in the pan-Arctic. Permafrost currently forms extensive elevated plateaus on abandoned floodplain deposits of the outer delta, but is vulnerable to disturbance from rising air temperatures, inland storm surges, and salt-kill of vegetation. As pan-Arctic air and ground temperatures rise, accurate baseline maps of permafrost extent are critical for a variety of applications including long-term monitoring, understanding the scale and pace of permafrost degradation processes, and estimating resultant greenhouse gas dynamics. This study assesses novel, high-resolution techniques to map permafrost distribution using LiDAR and IKONOS imagery, in tandem with field-based parameterization and validation. With LiDAR, use of a simple elevation threshold provided a permafrost map with 94.9% overall accuracy; this approach was possible due to the extremely flat coastal plain of the YKD. The addition of high spatial-resolution IKONOS satellite data yielded similar results, but did not increase model performance. The methods and the results of this study enhance high-resolution permafrost mapping efforts in tundra regions in general and deltaic landscapes in particular, and provide a baseline for remote monitoring of permafrost distribution on the YKD.

Vegetation Development in a Tidal Marsh Restoration Project during a Historic Drought: A Remote Sensing Approach

Tidal wetland restoration efforts can be challenging to monitor in the field due to unstable local conditions and poor site access. However, understanding how restored systems evolve over time is essential for future management of their ecological benefits, many of which are related to vegetation dynamics. Physical attributes such as elevation and distance to channel play crucial roles in governing vegetation expansion in developing tidal wetlands. However, in Mediterranean ecosystems, drought years, wet years and their resulting influence on salinity levels may also play a crucial role in determining the trajectory of restoration projects, but the influence of weather variability on restoration outcomes is not well understood. Here, we use object-based image analysis (OBIA) and change analysis of the high-resolution IKONOS and WorldView-2 satellite imagery to explore whether mean annual rates of change from mudflat to vegetation are lower during drought years with higher salinity (2011-2015) compared to years with lower salinity (2009-2011) at a developing restoration site in California’s San Francisco Bay. We found that vegetation increased at a mean rate of 1979 m2/year during California’s historic drought, 10.4 times slower than the rate of 20580 m2/year between 2009 and 2011 when the state was not in drought. Vegetation was significantly concentrated in areas in closer to channel edges, where salinity stress is ameliorated, and the magnitude of the effect increased in the 2015 image. In our image analysis, we found that different distributions of water, mud and algae between years led to different segmentation settings for each set of images, highlighting the need for more robust and reproducible OBIA strategies in complex wetlands. Our results demonstrate that adaptive monitoring efforts in variable climates should take into account the influence of weather on tidal wetland ecosystems, and that high-resolution remote sensing can be an effective means of assessing these dynamics.

Development of Cattle farming as a Model For Sustainable Rural Development Goal using Spatial Approach (Case in Southern Parts of Merapi Volcano Slope Sleman Regency of Yogyakarta)

This study aims to build a model of beef cattle farming development using spatial approach in rural areas of the southern slopes of Merapi Volcano Sleman Regency of Yogyakarta after eruption 2010. Samples were taken Glagaharjo sub-district (Cangkringan district) as impacted area and Wonokerto (Turi district) as unimpacted areas. Survey method were used to land evaluation analysis and Geographic Information System (GIS) software for spatial analysis. Materials were used RBI map in 1:25 000 scale, map of land use, landform, slope, and IKONOS imagery (2015. Analysis potential of land capability for forage cattle using the production unity in kg of TDN per AU. The research result showed that based on the land capability and suitability class maps, both villages had the potential of land carrying capacity of cattle feed of 2604.84 AU in Glagaharjo and 2162.26 AU in Wonokerto. However, further research to explore the potential of agricultural land use to develop smallholder model of beef cattle farming in both villages was still open to the researchers.

Specific Peak Discharge of Two Catchments Covered by Teak Forest with Different Area Percentages

In watershed area, forest has important roles in relation with peak discharge. This research was conducted to study the impacts of teak forest on peak discharge. On-screen digitizing of IKONOS imagery was done to classify the land cover of the study area. Kejalen and Gagakan catchments covered by old teak forests by 74% and 53% respectively, were chosen as the study area. These catchments are located in Blora Regency. Automatic streamflow recorder was set at the outlet of each catchment and subsequently, peak discharges were examined from the recorded data. During the observation, there were 36 evidences of specific peak discharge. The results showed that a trend of lower peak discharges occurred in Kejalen catchment which has the higher percentage of teak forest area in compared to Gagakan catchment with lower percentage of teak forest area, except when extreme rainfalls happened. At rainfall of 163 mm/day, specific peak discharge in Kejalen was higher than in Gagakan catchment. Although there is a relationship between specific peak discharge and the percentage of forest cover area, the increase of specific peak discharge is not only affected by forest cover, but also affected by daily rainfall, antecedent soil moisture, and rainfall intensity. Coefficients of determination between specific peak discharge and daily rainfall are 0.64 and 0.61 for Kejalen and Gagakan catchments, respectively.

Seeing trees from space: above-ground biomass estimates of intact and degraded montane rainforests from high-resolution optical imagery

Abstract: Accurately quantifying the above-ground carbon stock of tropical rainforest trees is the core component of “Reduction of Emissions from Deforestation and Forest Degradation-plus” (REDD+) projects and is important for evaluating the effects of anthropogenic global change. We used high-resolution optical imagery (IKONOS-2) to identify individual tree crowns in intact and degraded rainforests in the mountains of Northern Borneo, comparing our results with 50 ground-based plots dispersed in intact and degraded forests, within which all stems > 10 cm in diameter were measured and identified to species or genus. We used the dimensions of tree crowns detected in the imagery to estimate above-ground biomasses (AGBs) of individual trees and plots. To this purpose, preprocessed IKONOS imagery was segmented using a watershed algorithm; stem diameter values were then estimated from the cross-sectional crown areas of these trees using regression relationships obtained from ground-based measurements. Finally, we calculated the biomass of each tree (AGBT, in kg), and the AGB of plots by summation (AGBP, in Mg ha-1). Remotely sensed estimates of mean AGBT were similar to ground-based estimates in intact and degraded forests, even though small trees could not be detected from space-borne sensors. The intact and degraded forests not only had different AGB but were also dissimilar in biodiversity. A tree-centric approach to carbon mapping based on high-resolution optical imagery, could be a cheap alternative to airborne laser-scanning.

The structural inventory of a small complex impact crater: Jebel Waqf as Suwwan, Jordan

AbstractThe investigation of terrestrial impact structures is crucial to gain an in‐depth understanding of impact cratering processes in the solar system. Here, we use the impact structure Jebel Waqf as Suwwan, Jordan, as a representative for crater formation into a layered sedimentary target with contrasting rheology. The complex crater is moderately eroded (300–420 m) with an apparent diameter of 6.1 km and an original rim fault diameter of 7 km. Based on extensive field work, IKONOS imagery, and geophysical surveying we present a novel geological map of the entire crater structure that provides the basis for structural analysis. Parametric scaling indicates that the structural uplift (250–350 m) and the depth of the ring syncline (<200 m) are anomalously low. The very shallow relief of the crater along with a NE vergence of the asymmetric central uplift and the enhanced deformations in the up‐range and down‐range sectors of the annular moat and crater rim suggest that the impact was most likely a very oblique one (~20°). One of the major consequences of the presence of the rheologically anisotropic target was that extensive strata buckling occurred during impact cratering both on the decameter as well as on the hundred‐meter scale. The crater rim is defined by a circumferential normal fault dipping mostly toward the crater. Footwall strata beneath the rim fault are bent‐up in the down‐range sector but appear unaffected in the up‐range sector. The hanging wall displays various synthetic and antithetic rotations in the down‐range sector but always shows antithetic block rotation in the up‐range sector. At greater depth reverse faulting or folding is indicated at the rim indicating that the rim fault was already formed during the excavation stage.

Glacial lake inventory and lake outburst potential in Uzbekistan

Climate change has been shown to increase the number of mountain lakes across various mountain ranges in the World. In Central Asia, and in particular on the territory of Uzbekistan, a detailed assessment of glacier lakes and their evolution over time is, however lacking. For this reason we created the first detailed inventory of mountain lakes of Uzbekistan based on recent (2002–2014) satellite observations using WorldView-2, SPOT5, and IKONOS imagery with a spatial resolution from 2 to 10m. This record was complemented with data from field studies of the last 50years. The previous data were mostly in the form of inventories of lakes, available in Soviet archives, and primarily included localized in-situ data. The inventory of mountain lakes presented here, by contrast, includes an overview of all lakes of the territory of Uzbekistan. Lakes were considered if they were located at altitudes above 1500m and if lakes had an area exceeding 100m2. As in other mountain regions of the World, the ongoing increase of air temperatures has led to an increase in lake number and area. Moreover, the frequency and overall number of lake outburst events have been on the rise as well. Therefore, we also present the first outburst assessment with an updated version of well-known approaches considering local climate features and event histories. As a result, out of the 242 lakes identified on the territory of Uzbekistan, 15% are considered prone to outburst, 10% of these lakes have been assigned low outburst potential and the remainder of the lakes have an average level of outburst potential. We conclude that the distribution of lakes by elevation shows a significant influence on lake area and hazard potential. No significant differences, by contrast, exist between the distribution of lake area, outburst potential, and lake location with respect to glaciers by regions.

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.