Resolution Satellite Images Research Articles

Estimating Crop Transpiration of Soybean under Different Irrigation Treatments Using Thermal Infrared Remote Sensing Imagery

Temporal and spatial resolution of satellite images are coarse and cannot provide the real-time, meter-scale resolution monitoring required in many applications, such as precision agriculture. Since high resolution thermal infrared data provide one means to observe canopy temperature variance, we developed an algorithm (three-temperature model, 3T) to estimate transpiration rate at meter-scale pixels and detected transpiration variation for soybean under different upper irrigation limits: No irrigation, 35% of field capacity (FC), 55% of FC, and 75% of FC, denoted as W0, W1, W2, and W3, respectively. The spatial patterns of the transpiration rate indicated that heterogeneity is common in farmland. Transpiration rates in the wet treatment (i.e., W3) were consistently higher than that in the dry treatment (i.e., W0). Transpiration rates reached peak values at around 12:30–14:30 and most of values showed that W3 > W2 > W1 > W0, with 0.91 mm/h, 0.89 mm/h, 0.79 mm/h, and 0.62 mm/h during the reproductive period, respectively. In general, the transpiration rate of soybean increased with increasing irrigation quantities. With a higher irrigation total, soil water content increased gradually, and then the transpiration rate increased. Although land surface temperature decreased by only 8.57 K (Kelvin), 6.33 K, and 5.47 K, respectively, the transpiration rate increased by 78%, 60%, and 40%, respectively, for the W3, W2, and W1 treatment compared with the W0 treatment. The magnitude of transpiration change is greater than that of canopy temperature, but both parameters are strongly interrelated with each other through a non-linear correlation. Heterogeneity of canopy leaf temperature and transpiration is mainly due to physical and biological interactions. Understanding transpiration rate and canopy temperature heterogeneity under different irrigation treatments can not only help in scheduling irrigation, but also in enhancing water utilization efficiency in irrigated agriculture. The real-time monitoring of crop transpiration at meter-scale is of great importance for large irrigation systems, especially for precision irrigation, and will have great application prospects in the near future.

Analogue-based colorization of remote sensing images using textural information

Satellite images are richer than ever before. For example, new Landsat-8 images with their 11 bands carry much more information than older generations of satellites. These differences in spectral representation imply a major difficulty for assessing long-term land surface changes. The easiest solution is to reduce the information of the most recent product, for example by only keeping a subset of the Landsat-8 bands that matches old imagery. To avoid such loss of information, we propose a new method based on multiband spatial pattern matching. We are focusing on increasing the spectral resolution of archive satellite images to the same level of spectral resolution and coverage as modern imagery. Our method uses analogous scenes taken from modern satellites, which have conceptually the same role as the training images used in multiple-point geostatistics simulation. The spectral characteristics of the training image are then transferred to a target archive image, where new synthetic spectral bands are generated. A spatial pattern matching procedure is used to control this transfer, resulting in preservation of spatial and spectral coherence in the results. We illustrate the methodology on Landsat 8 and Corona imagery. The proposed method was benchmarked against other state-of-the-art colorization techniques, and it shows globally better results.

Estimation of Linkages between Biometric Indexes of Forests and Pattern of Canopy Spaces on Super-High-Resolution Satellite Images

This paper presents the results of studying a promising area related to the remote assessment of canopy spaces in forests by thresholding methods of image segmentation. The study is conducted based on the example of mixed forests in the Losiny Ostrov National Park. The proposed methodological approach to assessing the pattern of forest canopy on super-high (detailed) resolution satellite images is based on an analysis of light and shaded plots of canopy spaces using image-thresholding algorithms. The pixel count for different brightness thresholds give enough information to estimate a range of biometric indexes, including volume density and average age and height of stands from statistical relationships. The accuracy of estimates is assessed for prescribed deviations and verified against the norms of estimation of corresponding taxation data. We have found a statistical relationship of forest-canopy morphology indicators with brightness thresholds for shaded plots of canopy spaces and stemwood phytomass in forest ecosystems. Thus, super-high-resolution images may be considered an information basis for estimating the biometric parameters of stands, morphological indicators of forest canopy, and the productivity of forest ecosystems.

The Use of NDVI in Digital Mapping of the Content of Available Lithium in the Arable Horizon of Soils in Southwestern Siberia

We have determined the informational value of the Normalized Difference Vegetation Index (NDVI) for predictive mapping of the content of available lithium in the arable horizon of soils of different slope positions: the first (280–310 m) and the second (240–280 m) altitudinal levels. The NDVI is not informative for the diagnostics or mapping of the content of available lithium in soils of small drainage valleys, the width of which is smaller than the resolution of the satellite image (30 m). In the regression model, the NDVI explains 28% of the variation in the content of available lithium in soils. Based on this model, a predictive map of the content of available lithium in soils has been compiled. Data on the spatial distribution pattern of the NDVI calculated based on a Landsat 8 satellite image (resolution of 30 m) were used as an indicator and the cartographical basis for digital mapping. The accuracy of the prediction of the content of available lithium in soils is good (MAPE is 16.9%). It has been revealed that the NDVI values and the content of available lithium in soils of the first altitudinal level are higher than in the second. The differences between NDVI in the drainage valley and on the first altitudinal level are not insignificant.

Identification of the Composition of Transport Streams for Remote Satellite Monitoring

The necessary condition for the successful conduct of project, construction and management activities in the field of road and transport infrastructure is the availability of information about the intensity and composition of TA. To determine the transport demand in urban street networks from the perspective of resource cost minimization, it is most expedient to use the method of remote satellite monitoring, which was tested in previous studies by the authors. The method is based on deciphering the satellite image by visual determination of density and subsequent determination of the intensity of motion through the functional dependence of the theory of transport flows. In the development of the method, the article investigates the possibility of identifying the composition of transport flows on the basis of resources provided by map service providers through information and telecommunication networks of General access. Under existing classifications identified visual, outline dimensional and constructive identity of the signs of motor vehicles of the different groups. The developed requirements for the spatial resolution of satellite images based on the analysis of its correlation dependencies and the scale of the image, allowed to determine the boundary values at which it is possible to recognize vehicles by composition. The estimation of accuracy of results of remote identification showed statistical reliability of the received values for streets and roads with a regular mode of movement.

GF‐2 Satellite 1m/4m Camera Design and In‐Orbit Commissioning

GF-2 (Gaofen-2) is the second high resolution imaging satellite of China high resolution earth observation system (CHEOS) and it is the first civilian high resolution imager with Ground sample distance (GSD) under 1m in China. The two 1m/4m cameras represent a breakthrough in comparison with the previous GF-1 camera. Recent advances in small relative aperture optics system design, vibration reduction, and high accuracy thermal control make it possible for this camera work perfect with high resolution, lihgtweight and high image quality performance. The in-orbit commissioning demonstrates that the performance of the cameras meets all the design requirements. The components of 1m/4m camera, the novel techniques adopted and in-orbit commissioning results are illustrated.

Edge Dependent Chinese Restaurant Process for Very High Resolution (VHR) Satellite Image Over-Segmentation

Image over-segmentation aims to partition an image into spatially adjacent and spectrally homogeneous regions. It could reduce the complexity of image representation and enhance the efficiency of subsequent image processing. Previously, many methods for image over-segmentation have been proposed, but almost of them need to assign model parameters in advance, e.g., the number of segments. In this paper, a nonparametric clustering model is employed to the over-segmentation of Very High Resolution (VHR) satellite images, in which the number of segments can automatically be inferred from the observed data. The proposed model is called the Edge Dependent Chinese restaurant process (EDCRP), which extends the distance dependent Chinese restaurant process to make full use of local image structure information, i.e., edges. Experimental results show that the presented methods outperform state of the art methods for image over-segmentation in terms of both metrics based direct evaluation and classification based indirect evaluation.

Influencing Factors and Outcomes of Health Information Seeking Behavior According to Platform: Focusing on Socioeconomic Factors, Digital Media Literacy, Social Support, and Life Satisfaction

This study presents an approach to synthesize hyperspectral images of lower resolution at a higher resolution using the high resolution images acquired from a sensor of commercial satellites. The proposed method was applied to the reconstruction of EO-1 Hyperion images using the images acquired from IKONOS sensor. Based on the FitPAN-Mod pansharpening technique (Lee, 2008b), the hyperspectral images of 30m resolution were reconstructed at 1m resolution of IKONOS panchromatic image. In this study, the synthesized hyperspectral images of 50 bands, whose wavelengths range in the wavelength of panchromatic sensor, were generated from the three stages of high resolution reconstruction using FitPAN-Mod. The experimental results show that the proposed method effectively integrates the spatial detail of the panchromatic modality as well as the spectral detail of the hyperspectral one into the synthesized image. It indicates the proposed method has a potential as a technique to produce alternative images for the images that would have been observed from a hyperspectral sensor at the high resolution of commercial satellite images.

Comparison of sensible heat flux measured by large aperture scintillometer and eddy covariance in a seasonally-inundated wetland

The eddy covariance (EC) technique has been widely used to measure ecosystem surface energy fluxes. However, with a relatively small representative area (footprint), errors are often introduced when upscaling EC data based on variables derived from large-footprint satellite products. Furthermore, EC measurements often fail to close the energy balance in ecosystems such as wetlands. As an alternative to EC, large aperture scintillometers (LAS) have been used to measure sensible heat fluxes (H) over representative areas comparable to the resolution of large-scale satellite images. However, because LAS measurements are based on semi-empirical simulations, their accuracy needs further evaluation across ecosystems. Changes in hydrology on the land surface significantly alter measurement conditions as well as the processes of energy transfer, and thus, may affect the accuracy of energy measurements from LAS. To address this issue, we compared the LAS-measured H (HLAS) to EC-derived H (HEC) in a seasonally inundated wetland within Everglades National Park. Overall, the HLAS was 7.1 ± 0.3 (SE) % greater than HEC, and the ratio of HLAS to HEC remained similar between the wet season, when the ecosystem was inundated, and the dry season. However, inundation, as a thermal buffer in the system, reduced the magnitude of H in the wet season and resulted in a smaller difference of seasonal H budget estimates between LAS and EC measurements. Because of the homogenous land surface at our site, the discrepancies between HLAS and HEC could not be explained by footprint mismatch. Compared to the energy balance closure calculated with HEC, the closure calculated with HLAS was improved by 6% during the dry season but remained similar during the wet season. Overall, we conclude that LAS can be a reliable approach to measure H in wetland ecosystems and its measurements were stable with seasonal hydrological changes.

High‐frequency attitude jitter correction for the Gaofen‐9 satellite

AbstractWith the gradually improving resolution of earth observation satellite images, attitude determination accuracy has become a key factor influencing image geometric positioning accuracy. However, high‐frequency attitude jitters caused by the instability of the satellite platform lead to image distortions which cannot be corrected by the low‐frequency star sensor and gyro attitude measurement. Therefore, this paper proposes a high‐frequency attitude jitter correction method based on micro‐vibration accelerometer measurement. Three images from the GaoFen‐9 (GF‐9) satellite over Lijiang in China were selected to verify the method. The correction effect was assessed in terms of road straightness and image relative registration error. The results show that the proposed method can effectively eliminate image distortions caused by high‐frequency jitters, providing a feasible way to process images from small agile satellite platforms.

Single image dehazing for visible remote sensing based on tagged haze thickness maps

ABSTRACTHaze degrades the quality of optical remote sensing data and reduces the accuracy of interpretation and classification. In this paper, we present an empirical single image-based haze removal method. Our work relies on an additive haze model, which describes at-satellite acquired radiance as the sum of globally constant path radiance, surface reflected radiance and spatially varying haze contribution. First, we search a tagged haze thickness map in band blue, green, red and near-infrared. Then a proportional strategy is adopted to infer haze thickness map of selected starting band from tagged haze thickness map and gain haze thickness map of other spectral bands from the starting band. Finally, haze is removed through subtracting haze thickness map from each band. The method is applicable for medium- and high- resolution satellite images and can handle scenes affected by different haze depths. Dehazed results are in good quality with improved visibility, enhanced image information and highly spectral consistency. The algorithm is simple enough and requires no human intervention, making it possible for use of non-experts and implementation of automatic batch processing.

Short-time analysis of shoreline based on RapidEye satellite images in the terminal area of Pecém Port, Ceará, Brazil

ABSTRACTCoastal environments are highly dynamic and sensitive to interference and variations caused by the numerous natural and anthropogenic agents. The northern coast of Northeastern Brazil has undergone intense erosion in recent years. However, the construction of the Pecém Port modified the beach and shoreline features differently from the adjacent areas. This article describes using remote-sensing images integrated with a Geographic Information System to evaluate the sedimentary balance and morphological changes observed in the sandy beach area that was affected by the construction of Pecém Port. Two methods were applied to the RapidEye images to quantify the short-time changes that occurred on the coast. The Digital Shoreline Analysis System method showed that the beach width increased west to the port, which was calculated by the Change Polygon Approach determined by intersecting and uniting polygons to estimate the difference between the areas over time. The short-time analysis results showed changing coastal morphology, demonstrating that the anthropic interventions in the region are transforming significantly the natural elements that make up the region landscape. Between 2011 and 2014, the investigated beach stretch of approximately 3 km suffered an accretion process of more than 102,000 m2 over 3 years. The high spatial resolution of satellite images, digital processing imaging techniques and geostatistical methods were effective in this study, allowing understanding the recent changes in the area.

Satellite Image Resolution Enhancement based on Dual-Domain Filtering

Satellite images place vital role in agriculture, Disaster mitigation and geosciences applications. Satellite images include both spatial and temporal resolution, in that spatial resolution is influence the accuracy of ground objects. The main idea of this work is to enhance the resolution of satellite images. In this work, a dual domain filtering based approach is introduced for resolution enhancement (RE). Initially, the source image is subdivided into approximation and detail coefficients by Stationary Wavelet transform (SWT). The detail coefficients are interpolated based on bi-linear interpolation. The interpolated detail coefficients are applied to Non-Local Means (NLM) filter to minimize the artifacts produced by SWT. The filtered detail and approximation coefficients of source image are fed to ISWT to attain high resolution (HR) image. The proposed system is superior to other existing strategies like DWT-NLM and DWT-SWT.

Hybridization of SLIC and Extra Tree for Object Based Image Analysis in Extracting Shoreline from Medium Resolution Satellite Images

Observation satellites orbiting the Earth provide important surveillance information that helps in identifying various types of land cover. As such, the resolution of satellite images is critical to ensure high accuracy in classifying land cover types. Clearly, high-resolution images are desirable, but such images are prohibitively expensive. Hence, the use of medium-resolution satellite images seems more economical and practical. Several techniques have been developed to monitor the conditions of land covers across the world, such as aerial photography, ground survey, and remote sensing. Among the three techniques, remote sensing is the best, given its ability to monitor vast geographical areas more accurately and much faster compared to those of the other two techniques. In recent decades, many countries have been relying on remote sensing to monitor the conditions of coastal areas by extracting shorelines from satellite images. To date, several pixel-based methods have been proposed for the extraction of shorelines, but most of these methods are fraught with problems. Thus, the object-based approach is proposed using a combination of segmentation algorithms, namely Felzenswalb, Quickshift, and SLIC, together with 15 machine learning classifiers, to classify segmented images of Langkawi Island. The performance of the segmentation algorithms and machine learning classifiers were assessed in terms of segmentation time and overall accuracy in four experimental settings comprising of three different parameters. The research findings showed that the proposed hybridization of SLIC segmentation and Extra Tree classifier was the most efficient and accurate technique compared to other combinations of techniques in extracting the shoreline of the study area. Specifically, SLIC was faster than Felzenswalb and Quickshift by as much as 37 times and 500 times, respectively. Together with Extra Tree classifier, SLIC managed to achieve 100% overall accuracy in the object-based classification in three out of four settings compared to the rest of the techniques tested in the study.

Numerical investigation of debris materials prior to debris flow hazards using satellite images

The volume of debris flows occurred in mountainous areas is mainly affected by the volume of debris materials deposited at the valley bottom. Quantitative evaluation of debris materials prior to debris flow hazards is important to predict and prevent hazards. At midnight on 7th August 2010, two catastrophic debris flows were triggered by the torrential rain from two valleys in the northern part of Zhouqu City, NW China, resulting in 1765 fatalities and huge economic losses. In the present study, a depth-integrated particle method is adopted to simulate the debris materials, based on 2.5 m resolution satellite images. In the simulation scheme, the materials are modeled as dry granular solids, and they travel down from the slopes and are deposited at the valley bottom. The spatial distributions of the debris materials are investigated in terms of location, volume and thickness. Simulation results show good agreement with post-disaster satellite images and field observation data. Additionally, the effect of the spatial distributions of the debris materials on subsequent debris flows is also evaluated. It is found that the spatial distributions of the debris materials strongly influence affected area, runout distance and flow discharge. This study might be useful in hazard assessments prior to debris flow hazards by investigating diverse scenarios in which the debris materials are unknown.

Improve the Spatial Resolution of Multispectral satellite Image using Different Image Sharpening Techniques

The process of combining the significant information from a series of images into a single image called image sharpening or image fusing, where the resultant fused image will be having more spatial and spectral information than any of the input images. in this research two images of the same place in different spatial resolution have been used the first one was panchromatic and the second image was multispectral with spatial resolution 0.5m and 2 m respectively. These images were captured by world view-2 sensor. This research resent four pan sharpening methods like (HSV, Brovey (color normalizes) , Gram shmidt and PCA)these methods were used to combine the adopted images to get multispectral image with high spatial resolution. Many criteria such as MSE, RMSE, PSNR, CC, ERGAS and RASE have been used to evaluate the quality of the result images.

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

Lake Ice Detection in Low-Resolution Optical Satellite Images

Abstract. Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

Super Resolution of Satellite Images Using Wavelet Based Edge Extraction through Sparse Representation

Super Resolution of Satellite Images Using Wavelet Based Edge Extraction through Sparse Representation

A count of palm trees from satellite image

In this research the number of palm trees was calculated from the satellite image programmatically, taking advantage of the accuracy of the spatial resolution of satellite image, the abilities of software recognition, and characteristics of the palm tree, which give it a systematic top view can be distinguished from the satellite image and the manner of cultivation and vertical growth and stability form for long periods of time. While other trees are irregular in shape mostly because of their twisted branches. Palm trees consist of a long stem, a large head, and a large flare that is almost circular and consists of large tufts. The palms have large self-shadows other than ordinary leaves. The large shadows and the circular shape of the upper view give it a special feature that we could use to design a program that distinguishes the shape of the palm without all the trees. Then it counts the number of palms in any field shown in the satellite image. This method is useful in counting the number of palm trees for commercial, agricultural or environmental purposes. It is also can be applied to high-resolution satellite imagery such as QuickBird because the resolution of the images is 0.6 meters. Less accurate images such as the 10-meter SPOT do not show the interior shadows of the top view of the palm enough, nor the accurate satellites (5 meters), while the interior shadows appear in high-resolution images only (0.6 meters) or below. It can also be applied to aerial images of less capacity because they are more accurate of course. Satellite images can be obtained free from Google Earth explorer, which can be downloaded free from the Google website. It connects the user to a global database of high-resolution images for all regions of the world.