High-resolution Stereo Satellite Imagery Research Articles

Influence of plot and sample sizes on aboveground biomass estimations in plantation forests using very high resolution stereo satellite imagery

AbstractRegular biomass estimations for natural and plantation forests are important to support sustainable forestry and to calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has been amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we investigated the influence of field plot and sample sizes on the accuracy of random forest models trained with information derived from Pléiades very high resolution (VHR) stereo images applied to plantation forests in an arid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and 500 m2). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass estimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but constant number of samples. In the second experiment, we fixed the total area to be sampled to account for the additional effort to collect large field plots. Our results for the first experiment show that model performance metrics Spearman’s r, RMSErel and RMSEnor improve from 0.61, 0.70 and 0.36 at a sample size of 24–0.79, 0.51 and 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with a plot size of 100 m2 (most samples) with Spearman’s r = 0.77, RMSErel = 0.59 and RMSEnor = 0.15. Results from an analysis of variance type-II suggest that the overall most important factors to explain model performance metrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach accurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly contradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing data types (e.g. LiDAR).

Contrasting seismic risk for Santiago, Chile, from near-field and distant earthquake sources

Abstract. More than half of all the people in the world now live in dense urban centres. The rapid expansion of cities, particularly in low-income nations, has enabled the economic and social development of millions of people. However, many of these cities are located near active tectonic faults that have not produced an earthquake in recent memory, raising the risk of losing hard-earned progress through a devastating earthquake. In this paper we explore the possible impact that earthquakes can have on the city of Santiago in Chile from various potential near-field and distant earthquake sources. We use high-resolution stereo satellite imagery and imagery-derived digital elevation models to accurately map the trace of the San Ramón Fault, a recently recognised active fault located along the eastern margins of the city. We use scenario-based seismic-risk analysis to compare and contrast the estimated damage and losses to the city from several potential earthquake sources and one past event, comprising (i) rupture of the San Ramón Fault, (ii) a hypothesised buried shallow fault beneath the centre of the city, (iii) a deep intra-slab fault, and (iv) the 2010 Mw 8.8 Maule earthquake. We find that there is a strong magnitude–distance trade-off in terms of damage and losses to the city, with smaller magnitude earthquakes in the magnitude range of 6–7.5 on more local faults producing 9 to 17 times more damage to the city and estimated fatalities compared to the great magnitude 8+ earthquakes located offshore in the subduction zone. Our calculations for this part of Chile show that unreinforced-masonry structures are the most vulnerable to these types of earthquake shaking. We identify particularly vulnerable districts, such as Ñuñoa, Santiago, and Providencia, where targeted retrofitting campaigns would be most effective at reducing potential economic and human losses. Due to the potency of near-field earthquake sources demonstrated here, our work highlights the importance of also identifying and considering proximal minor active faults for cities in seismic zones globally in addition to the more major and distant large fault zones that are typically focussed on in the assessment of hazard.

Estimating biomass and carbon sequestration of plantations around industrial areas using very high resolution stereo satellite imagery

Abstract: Plantations established in highly-pollutant industrial areas have a crucial role to absorb greenhouse gases, particularly CO2. A thorough monitoring of their aboveground biomass and carbon balance is essential to ensure their beneficial effects. This can be operationally supported by using a combination of field and multispectral stereo remote sensing data to provide surface height information with high resolution and wide coverage. We estimated the fresh and dry aboveground biomass and the carbon sequestration from pairs of Pleiades satellite imagery of 25-year-old monoculture plantations of Pinus eldarica Medw., Cupressus arizonica Greene, Morus alba L. and Robinia pseudoacacia L., around the Mobarakeh Steel Complex near the megacity Isfahan. This complex is the largest-scale of its kind in semi-arid Iran. Tree heights were derived from a Canopy height model (CHM) at plantation management unit level. Parsimonious regression models were developed, and the accuracy was assessed by the coefficient of determination, bias and root mean square errors (RMSEs) at plot level. This resulted in R2 of total biomass, dry biomass, carbon sequestration, tree height and tree count of 0.90, 0.90, 0.91, 0.89, and 0.88, respectively. Moreover, mixed bias (with lowest value of -0.12 m for tree height) and NRMSE% (with lowest value of 5.93 % for tree carbon sequestration) values were obtained. The results demonstrated that pairs of stereo imageries can be effectively used for predicting forest biomass and carbon sequestration across semi-arid plantations, hence enabling a continuous monitoring of vegetation established around pollutant industrial areas.

Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as disaster monitoring and urban management. Digital Surface Models (DSMs) generated from stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting in rough building shape representations. To handle 3-D building model reconstruction using such low-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs together with orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satellite imagery. The algorithm consists of multiple steps including building boundary extraction and decomposition, image-based roof type classification, and initial roof parameter computation which are prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM (nDSM) and to select the best one, a parameter optimization method based on exhaustive search is used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building block are intersected to reconstruct the 3-D model of connecting roofs. All corresponding experiments are conducted on a dataset including four different areas of Munich city containing 208 buildings with different degrees of complexity. The results are evaluated both qualitatively and quantitatively. According to the results, the proposed approach can reliably reconstruct 3-D building models, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by limiting the number of primitive roof types and by performing automatic parameter initialization.

Assessment of DEM derived from very high-resolution stereo satellite imagery for geomorphometric analysis

ABSTRACTVery high-resolution satellite stereo images play an important role in cartographical and geomorphological applications, provided that all the processing steps follow strict procedures and the result of each step is carefully assessed. We outline a general process for assessing a reliable analysis of terrain morphometry starting from a GeoEye-1 stereo-pair acquired on an area with different morphological features. The key steps were critically analyzed to evaluate the uncertainty of the results. A number of maps of morphometric features were extracted from the digital elevation models in order to characterize a landslide; on the basis of the contour line and feature maps, we were able to accurately delimit the boundaries of the various landslide bodies.

A Comparison of the Performance of Bias-Corrected RSMs and RFMs for the Geo-Positioning of High-Resolution Satellite Stereo Imagery

High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D) geo-positioning of the bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs) with respect to the high-resolution QuickBird stereo images in three spaces (i.e., orbital space, image space and object space). The compared models include a bias-corrected RSM in the orbital space, a bias-corrected RSM and RFM in the image space, and a bias-corrected RSM and RFM in the object space. In the comparison, the RSMs and RFMs use the vendor-provided orbit data and Rational Polynomial Coefficients (RPCs), respectively. The experimental results indicated that, (1) these five bias-corrected models can provide a sub-pixel geo-positioning accuracy. With the zero-order polynomial correction model in the orbital space and a minimum of three Ground Control Points (GCPs), the accuracy based on RPCs better than 0.8 m in horizontal direction and 1.3 m in vertical direction. With an increase in the number of GCPs, or in the order of correction models, the regenerated orbital parameters achieve a slight improved positioning accuracy of 0.5 m in horizontal direction and 0.8 m in vertical direction with 25 GCPs, which indicates that the low-order correction model in the orbital space can accurately model the effects of ephemeris and attitude errors; (2) the performances of bias-corrected RSM and RFM in image space are rather similar. However, the bias-corrected RSM and RFM in image space achieve a better accuracy than the bias-corrected RSM and RFM in object space, with the same configuration of GCPs.

Detection of geometric change in railway curves caused by earthquakes, using high-resolution stereo satellite images

Rapid change detection of railway curves is crucial for the damage assessment of unexpected disasters such as earthquakes. Detection of the geometric change in railway curves involves the measurement of the shape change by comparing the pre- and post-seismic railway curves. This paper proposes an approach for change detection in railway curves by the use of a pre-seismic digital topographic map and post-seismic stereo IKONOS images. The proposed approach consists of four main components, they are as follows: (1) The geometric parameters of the pre-seismic railway curves are first estimated based on the digitized points from the digital topographic map by the use of least squares adjustment. (2) The feature points on the post-seismic railway curves are then extracted from the high-resolution stereo satellite imagery, and the ground coordinates of these points are further refined by the use of rational function polynomial coefficients bundle adjustment. (3) Five change detection models—the shift model, the scale and shift model, the affine model, the second-order polynomial model, and the similarity model—are introduced to describe the geometric change between the pre- and post-seismic railway curves. Based on this, an objective function is formulated to minimize the squared distances between the transformed points from the post-seismic points refined from the high-resolution stereo imagery, based on the change detection model and the pre-seismic railway curves. An empirical experiment in change detection in railway curves as a result of the 2008 Wenchuan earthquake in Dujiangyan City, China, was conducted. The experimental results showed the feasibility of the proposed geometric change detection method, they are as follows: (1) The root-mean-square error of the geometric parameter estimation of the pre-seismic railway curve was 0.068 m, which showed that the proposed least squares approach could recover the geometric curve of the pre-event railway with a centimeter-level accuracy by the use of the digital topographic map. (2) The result of the refinement of the ground control points extracted from the pre-seismic high-resolution satellite imagery by the use of the four bias compensation models showed that the second-order polynomial model achieved the highest geo-positioning accuracy of 0.788 and 0.701 m in the x- and y-directions. This result indicated that the second-order polynomial model was the best bias compensation model for improving the accuracy of ground points extracted from the post-event stereo image pairs. (3) A comparison of the five change detection models, in terms of displacement between the pre- and post-seismic railway curves, showed that the affine model obtained the highest accuracy of 0.318 m. This result showed that the geometric change in railway curves due to earthquakes could be detected with a decimeter-level accuracy by the use of the proposed change detection approach, based on a pre-seismic digital topographic map and post-seismic IKONOS stereo images.

DSM BASED ORIENTATION OF LARGE STEREO SATELLITE IMAGE BLOCKS

Abstract. High resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). A system for highly automated and operational DSM and orthoimage generation based on CARTOSAT-1 imagery is presented, with emphasis on fully automated georeferencing. The proposed system processes level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The RPC are derived from orbit and attitude information and have a much lower accuracy than the ground resolution of approximately 2.5 m. In order to use the images for orthorectification or DSM generation, an affine RPC correction is required. In this paper, GCP are automatically derived from lower resolution reference datasets (Landsat ETM+ Geocover and SRTM DSM). The traditional method of collecting the lateral position from a reference image and interpolating the corresponding height from the DEM ignores the higher lateral accuracy of the SRTM dataset. Our method avoids this drawback by using a RPC correction based on DSM alignment, resulting in improved geolocation of both DSM and ortho images. Scene based method and a bundle block adjustment based correction are developed and evaluated for a test site covering the nothern part of Italy, for which 405 Cartosat-1 Stereopairs are available. Both methods are tested against independent ground truth. Checks against this ground truth indicate a lateral error of 10 meters.

Modeling Method for the Prediction of the Caspian Sea Level Fluctuation Impacts on the Azerbaijan Coastal Zones Landscape Based on GIS

In this research was conducted the development of the up-to-date method of how to make modeling of the periodic Caspian Sea water level fluctuation impacts on the coastal areas. For the implementation of this research the stereo satellite imagery of Absheron Peninsula Shakh-Dili zone were processed by the following sequence of Geographical Information Systems and Geomatics processes: triangulation for the preparation of precise stereo model, automatic digital elevation model generation, interactive editing of digital elevation model, and orthorectification for the removal of satellite imagery relief distortions. As a result of the photogrammetric processing were prepared high-accurate digital elevation model and digital orthophoto map what was used for the Geographical Information Systems modeling of water flood and drop areas. As a result of this research it was calculated as the prediction what coastal areas of Absheron Peninsula Shakh-Dili zone will be flooded in cases of Caspian Sea water level increase to the levels of -26 and -27m. In addition the development of this method showed that the application of highresolution stereo satellite imagery prepared products was highly suitable for environmental management purposes of coastal zones of Caspian Sea.