High Resolution Optical Satellite Research Articles

Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution

The 3 August 2014 Ludian, Yunnan, China Mw 6.2 (Ms 6.5) earthquake triggered a large number of coseismic landslides. Based on pre- and post-quake high-resolution optical satellite images, this study established a new, complete and objective database of these landslides with field investigations. The updated inventory shows that this earthquake triggered at least 12 817 landslides with a total occupation area of 16.33 km2, covering a nearly circular area about 600 km2, which all exceed those in our previous work and other relevant studies. In addition, we used this database to examine the correlations of the landslides with topographic, geologic, and seismic factors. Results show that the landslides occurred mostly at places with slope gradients 10°–40°, showing an increase tendency with steeper slopes. Affected by the propagation direction of the earthquake rupture, the eastward-facing slopes are more prone to landsliding. The differences between the landslide susceptibility in different strata indicate that lithology is also an important controlling factor. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16g is obviously larger than those with PGA less than 0.16g. Meanwhile, the greater the distance from the epicenter, the lower the susceptibility of landslides is. This study suggests that when using satellite images to create coseismic landslide inventories, it should meet certain conditions, including high resolution, whole coverage, and timely data collection. The correct criteria of coseismic landslide inventorying also should be followed. Such inventories can provide a reliable basis for hazard assessment of earthquake-triggered landslides and other quantitative studies.

Sentinel-1 Change Detection Analysis for Cyclone Damage Assessment in Urban Environments

For disaster emergency response, timely information is critical and satellite data is a potential source for such information. High-resolution optical satellite images are often the most informative, but these are only available on cloud-free days. For some extreme weather disasters, like cyclones, access to cloud-free images is unlikely for days both before and after the main impact. In this situation, Synthetic Aperture Radar (SAR) data is a unique first source of information, as it works irrespective of weather and sunlight conditions. This paper shows, in the context of the cyclone Idai that hit Mozambique in March 2019, that Change Detection between pairs of SAR data is a perfect match with weather data, and therefore captures impact from the severe cyclone. For emergency operations, the filtering of Change Detections by external data on the location of houses prior to an event allows assessment of the impact on houses as opposed to impact on the surrounding natural environment. The free availability of SAR data from Sentinel-1, with further automated processing of it, means that this analysis is a cost-effective and quick potential first indication of cyclone destruction.

A Fire Hazard Assessment Using Sentinel Imagery; A Case Study Over Gippsland Australia

Accurate and the efficient rapid mapping of the fire-damaged areas are the most fundamental things for any places to retain from environmental loss. To support the fire management, make definite strategy and planning, and restore the vegetation, it is important to detect the area before and after the fire damages. Under climate change conditions, heat and drought may trigger tough fire regimes in terms of number and dimension of fires. To deliver the rapid information of the area damaged by the fires, Burned Area Index (BAI), Normalized Burned Ratio (NBR) and their versions are applied to map burned areas from high-resolution optical satellite data. The new MSI sensor aboard Sentinel-2 satellites records the more spectral information in the red edge spectral region making it more convenient to the development of new indices for the burned area mapping. Recently, Australia had confronted a devastating bushfire recorded in the history of the nation. In this project, NBR deployed to detect burned areas at around 10m-20m spatial resolution based on pre and post-fire Sentinel-2 images. A dNBR (differentiated Normalized Burned Ratio) was calculated while burn severity was mapped as purposed by United States Geological Survey (USGS). It observed that more than half of the East Gippsland region i.e. about 53% of the area affected by the wildfire while 38% remained unburned and 8.4% showed the regrowth.

Building Extraction in Multitemporal High-Resolution Remote Sensing Imagery Using a Multifeature LSTM Network

Inspired by recent promising developments by deep learning networks, this letter presents a novel approach based on a multifeature long short-term memory (LSTM) network with an optimal unit number for extracting buildings from multitemporal high-resolution optical satellite imagery. The algorithm first extracts specified multifeature data of buildings, including spectral, shape, texture, and indices from multitemporal high-resolution imagery. Next, a designed LSTM network architecture with an optimal unit number is trained using the multifeature data to extract buildings at the pixel level. The proposed network is compared with popular deep learning-based networks, including VGG, U-Net, and ResNet. Finally, postprocessing (e.g., morphological algorithm) is employed to optimize the classification results produced by deep learning. The approach is validated on a newly created data set using Gaofen-2 (GF-2) imagery that contains various buildings on a campus with different materials, shapes, sizes, heights, and directions. Experimental results indicate that the proposed approach outperforms current deep learning-based methods for building extraction and has a potential for practical applications.

Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.

Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1

Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m2 for TSX and 2000 m2 for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018.

Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

The Geometric Imaging Model for High-Resolution Optical Remote Sensing Satellites Considering Light Aberration and Atmospheric Refraction Errors

With advances in satellite maneuvering imaging capability, stereoscopic images with large roll and pitch angles can be captured to improve the efficiency of observations. At the same time, the influences of light aberration and atmospheric refraction on image positioning accuracy will be more significant. However, these errors are not accounted for in the traditional imaging and calibration model for optical agile satellites. In this study, the formation mechanisms of the aberration and atmospheric refraction errors in optical remote sensing satellite Earth observation imaging were analyzed quantitatively, and correction models were constructed. From this, the traditional geometric imaging model was refined by introducing a correction model for aberration and atmospheric refraction errors to create a more comprehensive geometric imaging model. The feasibility of an extended rational function model, based on the constructed more comprehensive geometric imaging model, was verified quantitatively.

Filtering DSM extraction from Worldview-3 images to DTM using open source software

Today, the Indonesia one map policy has encouraged for producing maps up to scale 1/5.000 – 1/10.000. Therefore, all efforts to accelerate large-scale map production are still needed. One of the challenges is for producing DEM (Digital Elevation Model). DEM data extraction from high-resolution optical satellite imagery is an efficient choice for the production of large-area DEM. This study uses Worldview 3 stereo ready images for DSM (Digital Surface Model) Extraction. Although the DSM data have a high resolution 30cm x 30cm, unfortunately, the DEM extraction techniques from optical images produce DSM (Digital Surface Model) data instead of DTM. While topographic maps generally use DTM data instead of DSM. So, the goal of this study is to use an appropriate procedures method in Open-Source (OS) software towards the generation of Bare-Earth data by filtering DSM. The OS software is SAGA-GIS that it is can be used for any point cloud data. The quality of DTM generated by SAGA-GIS is evaluated by visual interpretation from Worldview 3 orthoimage itself. A good filtering result will remove the non-terrain point. Some independent check point used to evaluate the error in elevation. The error in elevation still less than 2 pixels. Final product is a high resolution DTM which has 30cm × 30cm grid spacing.

Resolving time-varying attitude jitter of an optical remote sensing satellite based on a time-frequency analysis.

Attitude jitter is a crucial factor that limits the imaging quality and geo-positioning accuracy of high-resolution optical satellites, which has attracted significant research interests in recent years. However, few researchers have attempted to retrieve the dynamic characteristics and time-varying trends of a satellite attitude jitter. This paper presents a novel processing framework for detecting, estimating, and investigating time-varying attitude jitter in long strips based on a time-frequency analysis with the input from either an attitude sensor or an optical imaging sensor. Attitude angle signals containing attitude jitter information are detected from attitude data through generating the Euler angles relative to the orbit coordinate system, or from image data through high-accuracy dense matching between parallax observations, correction of integration time variation and frequency domain-based deconvolution. Variational mode decomposition is adopted to extract the separate band-limited periodic components, and Hilbert spectral analysis is integrated to estimate the instantaneous attributes for each time sample and the varying trends for the entire duration. Experiments with three sets of ZiYuan-3 long-strip datasets were carried out to test the novel processing framework of attitude jitter. The experimental results indicate that the processing framework could reveal the dynamic jitter characteristics, and the mutual validations of different data sources demonstrate the effectiveness of the proposed method.

Space Remote Sensing Dynamic Monitoring for Urban Complex

Urban complex is the basic unit of urban politics, economy and social life, and space remote sensing dynamic monitoring for urban complex is an efficient and accurate technical method. A space remote sensing dynamic monitoring system for urban complex is proposed and constructed from the aspects of application demand, technical method, remote sensing products, management release, etc. Using the system, the construction progress and surrounding water environment monitoring of the urban complex of Huoshenshan Hospital and Leishenshan Hospital are carried out. The construction progress of Huoshenshan Hospital and Leishenshan Hospital is monitored by high-resolution optical satellites such as Gaofen-2, Jilin-1 and Pleiades, which is identical with the construction progress in the news report and the officially published hospital area. The construction intensity of Huoshenshan Hospital is monitored by the high-resolution night light satellite Jilin-1, most areas of the construction plant are under high-intensity construction at night. The water environment around Huoshenshan Hospital and Leishenshan Hospital is monitored by the hyperspectral satellite Zhuhai-1, the construction process has no significant impact on the surrounding water environment. The surface stability of Huoshenshan Hospital and Leishenshan Hospital before construction is analyzed by the high-resolution radar satellite Sentinel-1A, there is no obvious surface subsidence in the construction area of Huoshenshan Hospital, and there are different degrees of surface subsidence signals in the surrounding areas of Leishenshan Hospital. In the critical period of fighting against the coronavirus disease 2019(COVID-19), it has met the public's attention needs and plays a certain role in stabilizing social mood. A retrospective monitoring of the collapse of Xinjia hotel in Quanzhou is carried out to provide data reference for accident liability analysis. The deformation of Xinjia hotel is mainly caused by the south wall, and the foundation settlement is not obvious. This result is consistent with the fact that the hotel collapses to the south, and the main reason for the collapse is the instability of the internal structure of the building, rather than the uneven ground settlement. The effectiveness of the system is verified.

An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images

Automatically monitoring newly constructed building areas (NCBAs) is essential for efficient land resource management and sustainable urban development, particularly in the rapidly urbanizing country of China. In this regard, time-series multi-view high-resolution optical satellite images can provide fine spatial details for clearly characterizing NCBAs, but this leads to great heterogeneity and complexity, owing to the high spectral variation, complicated imaging conditions, and different viewing angles. Moreover, to date, the vertical features and time-series information from these images have not been fully exploited for urban change detection. In this paper, our primary objective is to automatically detect the presence of NCBAs, and meanwhile, to investigate the feasibility of identifying their change timing using time-series multi-view ZY-3 high-resolution satellite images. To this aim, we propose an automatic change detection method consisting of three components: 1) firstly, we jointly use planar-vertical features to delineate the NCBAs; 2) object-based temporal correction is subsequently applied to improve the spatiotemporal consistency of the features; and 3) finally, a multi-temporal change detection model is used to simultaneously capture the NCBAs and the change timing. We applied the method on two urban fringe areas of Beijing (7 multi-temporal image sets) and Shanghai (7 multi-temporal image sets), respectively, which are cities that have been experiencing rapid urbanization. The experimental results confirmed the effectiveness of the proposed method. For both study areas, the F-score values reached nearly 90% in terms of NCBA detection, and with respect to the change timing, the overall accuracies with a one-year tolerance strategy reached around 92%. The joint use of the planar-vertical features and the inclusion of multi-temporal images make the proposed method a promising approach for automatically providing the spatiotemporal information of NCBAs in practical applications.

Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m2. The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.

Line segment fusion method for high-resolution optical satellite image

Line segment fusion method for high-resolution optical satellite image

The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China

To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time series data tend to be uneven and unique to specific frameworks. The objective of this study was to establish a more general framework to study LST changes caused by urban renewal using multi-source remote sensing data. Specifically, urban renewal areas during 2007–2017 were obtained by integrating Landsat and yearly Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and LST was retrieved from Landsat thermal infrared data using the generalized single-channel algorithm. Our results showed that urban renewal land (URL) area accounted for 1.88% of urban land area. Relative LST between URL and general urban land (GUL) of Liwan, Yuexiu, Haizhu, and Tianhe districts dropped by 0.88, 0.42, 0.43, and 0.10 K, respectively, whereas those of Baiyun, Huangpu, Panyu, and Luogang districts presented opposite characteristics, with a rise in the LST of 0.98, 1.03, 1.63, and 2.11 K, respectively. These results are attributable to population density, building density, and landscape pattern changes during the urban renewal process.

Mapping and Damage Assessment of “Royal” Burial Mounds in the Siberian Valley of the Kings

The Valley of the Kings in Tuva Republic, southern Siberia, is arguably one of the most important archaeological landscapes in the eastern Eurasian steppes. Nonetheless, little information exists about the spatial characteristics and preservation conditions of this burial ground consisting of large “royal” mounds. We map the large monuments of the Uyuk Valley’s northern river terrace and assess their state of preservation based on high-resolution optical satellite data. The burial site consists of several hundred mounds, over 150 of them with diameters of more than 25 m, the largest monuments are bigger than 100 m in diameter. This makes the Valley of the Kings in Tuva Republic one of the largest Early Iron Age burial sites in the Eurasian steppes. Unfortunately, around 92% of the large monuments are in bad condition, mostly due to looting.

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

Abstract The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel‐1 and Sentinel‐2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel‐1, +2.5% for Sentinel‐2 and +2% combining Sentinel‐1 and Sentinel‐2). Overall, combining Sentinel‐2 and Sentinel‐1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open‐source tools satellite imagery.

A PCA-PD fusion method for change detection in remote sensing multi temporal images

In remote sensing, for applications as environment monitoring, change detection based on image processing is one of the most important techniques. To reach high performance various techniques of fusion are exploited using a combination of multi-temporal, multispectral and panchromatic satellite images. A solution for handling such kind of images holds when using some simple statistical methods like the Percent Difference (PD) technique as well as the Principal Component Analysis (PCA) one. In this paper, an automatic change detection method issued from the two previous techniques is proposed and applied on multispectral and panchromatic images captured by a high resolution optical satellite. This approach is characterized by two aspects: the first one consists of the fusion of the different data and the second one performs the detection of the changes for the resulting images. The experimental results show the reasonable quantitative performance and the effectiveness of the proposed method for change detection, consisting of an automatic extraction of most of change information as well as the obtention of better results for most precision metrics consisting of an overall accuracy of up to 91% and a Kappa coefficient of up to 66%, comparing to those obtained using the simple PD and PCA techniques.

Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks

Convolution neural network (CNN) is an effective and popular deep learning method which automatically learns complicated non-linear mapping from original inputs to given labels or ground truth through a series of convolutional layers. This study focuses on detecting landslides from high-resolution optical satellite images using CNN-based methods, providing opportunities for recognizing latent landslides and updating large-scale landslide inventory with high accuracy and time efficiency. Considering the variety of landslides and complicated backgrounds, attention mechanisms originated from the human visual system are developed for boosting the CNN to extract more distinctive feature representations of landslides from backgrounds. As deep learning needs a large number of labeled data to train a learning model, we manually prepared a landslide dataset which is located in the Bijie city, China. In the dataset, 770 landslides, including rock falls, rock slides, and a few debris slides, were interpreted by geologists from the satellite images and digital elevation model (DEM) data and further checked by fieldwork. The landslide data was separated into a training set that trains the attention boosted CNN model and a testing set that evaluates the performance of the model with a ratio of 2:1. The experimental results showed that the best F1-score of landslide detection reached 96.62%. The results also proved that the performance of our spatial-channel attention mechanism was fairly over other recent attention mechanisms. Additionally, the effectiveness of predicting new potential landslides with high efficiency based on our dataset is demonstrated.

Atmospheric Refraction Calibration of Geometric Positioning for Optical Remote Sensing Satellite

Owing to the effects of atmospheric refraction, the path of light propagation is bent, making the three-point collinear principle inapplicable and influencing the geometric accuracy of high-resolution optical satellite geometric positioning. This letter presents a novel geometric positioning method with atmospheric refraction calibration for optical remote sensing satellites. The atmospheric ellipsoid model is established using the measured atmospheric parameters and accurately describes the shape and characteristics of the real atmosphere. With iterative processing of geometric positioning and atmospheric refraction calibration, the path of light propagation in atmospheric ellipsoids is calibrated, and the real coordinates of the ground object are positioned accurately. With the advantages of simplicity and independence of sensors, the rational function model with atmospheric refraction calibration is proposed to achieve geometric positioning with higher geometric accuracy. Experimental results demonstrate that the proposed model can calibrate atmospheric refraction error and improve the geometric accuracy of the optical imagery with a large view angle. Furthermore, compared with the refraction index using the measurement data, it is proven that atmospheric refraction calibration should be implemented according to the measured atmospheric parameters during imaging instead of using the empirical model.