Zeitschriftenartikel: „Multitemporal processing“

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Sabrina, Rahma Nafila Fitri, und Sudaryatno Sudaryatno. „MULTITEMPORAL ANALYSIS FOR TROPHIC STATE MAPPING IN BATUR LAKE AT BALI PROVINCE BASED ON HIGH-RESOLUTION PLANETSCOPE IMAGERY“. International Journal of Remote Sensing and Earth Sciences (IJReSES) 17, Nr. 2 (24.03.2021): 149. http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3381.

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Remote sensing data for analyzing and evaluating trophic state ecosystem problems seen in Batur Lake isan approach that is suitable for water parameters that cannot be observed terrestrially. As the multitemporal spatial data used in this study were extensive, it was necessary to consider the effectiveness and efficiency of the processing and analysis, therefore R Studio was used as a data processing tool. Theresearch aims to(1) map the trophic state of Batur Lake multitemporally usingPlanetScope Imagery;(2) assess the accuracy of the trophic state model and applyitto anothertemporal data as a SpatialBigData;and (3) understand the trophic state impacton the water quality of Batur Lake based on physical factors andthelake’s chemical concentration (sulfur concentration). Theresearch showsthatthetrophic state of Batur Lake isin good condition,with an ultraoligotrophic state as the majority class,based on the mean Trophic State Index (TSI) value of9.49. The standard errorsof each trophic state parameter were0.010 for total phosphor, 0.609 for chlorophyll-a, and 0.225 for Secchi Disk Transparency (SDT). The multitemporal model demonstratesthat the correlation between the increase oftrophic state and mass fish death cases in Batur Lake is existent.

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Shu, Chang, und Lihui Sun. „Automatic target recognition method for multitemporal remote sensing image“. Open Physics 18, Nr. 1 (05.06.2020): 170–81. http://dx.doi.org/10.1515/phys-2020-0015.

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AbstractThe traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.

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Esmaeili, Mostafa, Mahdi Motagh und Andy Hooper. „Application of Dual-Polarimetry SAR Images in Multitemporal InSAR Processing“. IEEE Geoscience and Remote Sensing Letters 14, Nr. 9 (September 2017): 1489–93. http://dx.doi.org/10.1109/lgrs.2017.2717846.

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Pavelka, Karel, Paulina Raeva und Karel Pavelka. „Evaluating the Performance of Airborne and Ground Sensors for Applications in Precision Agriculture: Enhancing the Postprocessing State-of-the-Art Algorithm“. Sensors 22, Nr. 19 (10.10.2022): 7693. http://dx.doi.org/10.3390/s22197693.

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The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time.

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Ma, Xiaoshuang, und Penghai Wu. „Multitemporal SAR Image Despeckling Based on a Scattering Covariance Matrix of Image Patch“. Sensors 19, Nr. 14 (11.07.2019): 3057. http://dx.doi.org/10.3390/s19143057.

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This paper presents a despeckling method for multitemporal images acquired by synthetic aperture radar (SAR) sensors. The proposed method uses a scattering covariance matrix of each image patch as the basic processing unit, which can exploit both the amplitude information of each pixel and the phase difference between any two pixels in a patch. The proposed filtering framework consists of four main steps: (1) a prefiltering result of each image is obtained by a nonlocal weighted average using only the information of the corresponding time phase; (2) an adaptively temporal linear filter is employed to further suppress the speckle; (3) the final output of each patch is obtained by a guided filter using both the original speckled data and the filtering result of step 3; and (4) an aggregation step is used to tackle the multiple estimations problem for each pixel. The despeckling experiments conducted on both simulated and real multitemporal SAR datasets reveal the pleasing performance of the proposed method in both suppressing speckle and retaining details, when compared with both advanced single-temporal and multitemporal SAR despeckling techniques.

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Sefrin, Oliver, Felix M. Riese und Sina Keller. „Deep Learning for Land Cover Change Detection“. Remote Sensing 13, Nr. 1 (28.12.2020): 78. http://dx.doi.org/10.3390/rs13010078.

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Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available.

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Kim, Taeheon, und Youkyung Han. „Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction“. Remote Sensing 13, Nr. 19 (06.10.2021): 3990. http://dx.doi.org/10.3390/rs13193990.

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Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images owing to external environmental factors, and these errors cause various inaccuracies, thereby hindering the effective use of multitemporal VHR satellite images. Such errors can be minimized by applying preprocessing methods such as image registration and relative radiometric normalization (RRN). However, as the data used in image registration and RRN differ, data consistency and computational efficiency are impaired, particularly when processing large amounts of data, such as a large volume of multitemporal VHR satellite images. To resolve these issues, we proposed an integrated preprocessing method by extracting pseudo-invariant features (PIFs), used for RRN, based on the conjugate points (CPs) extracted for image registration. To this end, the image registration was performed using CPs extracted using the speeded-up robust feature algorithm. Then, PIFs were extracted based on the CPs by removing vegetation areas followed by application of the region growing algorithm. Experiments were conducted on two sites constructed under different acquisition conditions to confirm the robustness of the proposed method. Various analyses based on visual and quantitative evaluation of the experimental results were performed from geometric and radiometric perspectives. The results evidence the successful integration of the image registration and RRN preprocessing steps by achieving a reasonable and stable performance.

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Liu, Bin, Zhengyu Yang, Jiaqing Wu und Jie Gu. „OLAP analysis of user energy consumption based on multitemporal distribution characteristics“. Journal of Physics: Conference Series 2290, Nr. 1 (01.06.2022): 012045. http://dx.doi.org/10.1088/1742-6596/2290/1/012045.

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Abstract With the development of databases, online transaction processing (OLTP) can no longer meet the needs of end users for database query and analysis, and the simple query of large databases by SQL can not meet the requirements of end user analysis. Therefore, online analytical processing (OLAP) is proposed. concept. On the one hand, we explained the basic knowledge of OLAP, including OLAP multidimensional data concept, multidimensional data structure, multidimensional data analysis, characteristics, etc. On the other hand, we established an OLAP analysis model of the multi-temporal and spatial distribution characteristics of user energy consumption, which is refined provide support for the analysis of user energy consumption behavior.

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Amitrano, Donato, Raffaella Guida und Giuseppe Ruello. „Multitemporal SAR RGB Processing for Sentinel-1 GRD Products: Methodology and Applications“. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, Nr. 5 (Mai 2019): 1497–507. http://dx.doi.org/10.1109/jstars.2019.2904035.

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Dubois, C., F. Stoffner, A. C. Kalia, M. Sandner, M. Labiadh und M. Mimouni. „COPERNICUS SENTINEL-2 DATA FOR THE DETERMINATION OF GROUNDWATER WITHDRAWAL IN THE MAGHREB REGION“. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1 (26.09.2018): 37–44. http://dx.doi.org/10.5194/isprs-annals-iv-1-37-2018.

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Abstract. Agriculture plays an important role in the economy of the Maghreb region. Most of the water needed for irrigation comes from pumping of the aquifers. A controlled pumping of the groundwater resources does not exist yet, thus, estimating the total water consumption for agricultural use only with in situ data is nearly impossible. In order to overcome this lack of information, Copernicus data are used for determining the groundwater withdrawal through agriculture in the Maghreb region. This paper presents an approach for estimating and monitoring crop water requirements in Tunisia based on multitemporal Sentinel-2 data. Using this multitemporal information, a thorough analysis of the different culture types over time is possible, from which a set of additional multitemporal features is deduced for crop type classification. In this paper, the contribution of those features is analyzed, showing a classification accuracy enhanced by 10% with the multitemporal features. Furthermore, relying on existing methods and FAO standards for the estimation of crop water needs, the methodology aims to estimate the specific crop water consumption. The results of the water estimates are validated against delimited areas where estimates of the water consumption are available from the authorities. Finally, as the study is conducted within the framework of an international technical cooperation, the methodology aims to be reproducible and sustainable for local authorities. The particularity of the results presented here is that they are achieved through automatic processing and using exclusively Open Source solutions, deployable on simple workstations.

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Zhang, Kaiyu, Xikai Fu, Xiaolei Lv und Jili Yuan. „Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR“. Remote Sensing 13, Nr. 3 (29.01.2021): 471. http://dx.doi.org/10.3390/rs13030471.

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Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more change information from time-series synthetic aperture radar (SAR) images, such as the change frequency and the change moments. This paper proposes a novel multitemporal building change detection framework that can generate change frequency map (CFM) and change moment maps (CMMs) from multitemporal SAR images. We first give definitions of CFM and CMMs. Then we generate change feature using four proposed generators. After that, a new cosegmentation method combining raw images and change feature is proposed to divide time-series images into changed and unchanged areas separately. Secondly, the proposed cosegmentation and the morphological building index (MBI) are combined to extract changed building objects. Then, the logical conjunction between the cosegmentation results and the binarized MBI is performed to recognize every moment of change. In the post-processing step, we use fragment removal to increase accuracy. Finally, we propose a novel accuracy assessment index for CFM. We call this index average change difference (ACD). Compared to the traditional multitemporal change detection methods, our method outperforms other approaches in terms of both qualitative results and quantitative indices of ACD using two TerraSAR-X datasets. The experiments show that the proposed method is effective in generating CFM and CMMs.

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Droin, Ariane, Wolfgang Sulzer und Matthias Wecht. „The generation of a swimming pool cadastre for Graz (1945–2015)“. Journal for Geography 11, Nr. 2 (31.12.2016): 71–80. http://dx.doi.org/10.18690/rg.11.2.3979.

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This paper deals with the generation of a swimming pool cadastre for Graz by means of multitemporal analyses of aerial photographs. Twelve temporal steps between 1944/45 and 2015 are analysed partly by digital image processing and visual mapping. The result shows an enormous increase of private swimming pool between the 1990 (600) and 2015 (5600). The distribution of swimming pools and their different types shows specific patterns, which can be geographically interpreted by social settlement structures.

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Taraben, Jakob, und Guido Morgenthal. „Integration and Comparison Methods for Multitemporal Image-Based 2D Annotations in Linked 3D Building Documentation“. Remote Sensing 14, Nr. 9 (09.05.2022): 2286. http://dx.doi.org/10.3390/rs14092286.

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Data acquisition systems and methods to capture high-resolution images or reconstruct 3D point clouds of existing structures are an effective way to document their as-is condition. These methods enable a detailed analysis of building surfaces, providing precise 3D representations. However, for the condition assessment and documentation, damages are mainly annotated in 2D representations, such as images, orthophotos, or technical drawings, which do not allow for the application of a 3D workflow or automated comparisons of multitemporal datasets. In the available software for building heritage data management and analysis, a wide range of annotation and evaluation functions are available, but they also lack integrated post-processing methods and systematic workflows. The article presents novel methods developed to facilitate such automated 3D workflows and validates them on a small historic church building in Thuringia, Germany. Post-processing steps using photogrammetric 3D reconstruction data along with imagery were implemented, which show the possibilities of integrating 2D annotations into 3D documentations. Further, the application of voxel-based methods on the dataset enables the evaluation of geometrical changes of multitemporal annotations in different states and the assignment to elements of scans or building models. The proposed workflow also highlights the potential of these methods for condition assessment and planning of restoration work, as well as the possibility to represent the analysis results in standardised building model formats.

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Parker, J. Anthony, Robert V. Kenyon und Lwarence R. Young. „Measurement of Torsion from Multitemporal Images of the Eye Using Digital Signal Processing Techniques“. IEEE Transactions on Biomedical Engineering BME-32, Nr. 1 (Januar 1985): 28–36. http://dx.doi.org/10.1109/tbme.1985.325613.

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Coppin, P. R., und M. E. Bauer. „Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features“. IEEE Transactions on Geoscience and Remote Sensing 32, Nr. 4 (Juli 1994): 918–27. http://dx.doi.org/10.1109/36.298020.

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Lin, Yang, Hu Yang, Wu Weihong, Sheng Yehua und Jia Xin. „Processing of Multitemporal 3D Point Cloud Data for Use in Reconstructing Historical Geographic Scenarios“. Sensors and Materials 34, Nr. 12 (21.12.2022): 4551. http://dx.doi.org/10.18494/sam4128.

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Jung, Minyoung, und Jinha Jung. „A Scalable Method to Improve Large-Scale Lidar Topographic Differencing Results“. Remote Sensing 15, Nr. 17 (31.08.2023): 4289. http://dx.doi.org/10.3390/rs15174289.

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Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP).

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Kandylakis, Z., und K. Karantzalos. „PRECISION VITICULTURE FROM MULTITEMPORAL, MULTISPECTRAL VERY HIGH RESOLUTION SATELLITE DATA“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (23.06.2016): 919–25. http://dx.doi.org/10.5194/isprs-archives-xli-b8-919-2016.

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In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.

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Kandylakis, Z., und K. Karantzalos. „PRECISION VITICULTURE FROM MULTITEMPORAL, MULTISPECTRAL VERY HIGH RESOLUTION SATELLITE DATA“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (23.06.2016): 919–25. http://dx.doi.org/10.5194/isprsarchives-xli-b8-919-2016.

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In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.

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Scott, Chelsea, Minh Phan, Viswanath Nandigam, Christopher Crosby und J. Ramon Arrowsmith. „Measuring change at Earth’s surface: On-demand vertical and three-dimensional topographic differencing implemented in OpenTopography“. Geosphere 17, Nr. 4 (14.05.2021): 1318–32. http://dx.doi.org/10.1130/ges02259.1.

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Abstract Topographic differencing measures landscape change by comparing multitemporal high-resolution topography data sets. Here, we focused on two types of topographic differencing: (1) Vertical differencing is the subtraction of digital elevation models (DEMs) that span an event of interest. (2) Three-dimensional (3-D) differencing measures surface change by registering point clouds with a rigid deformation. We recently released topographic differencing in OpenTopography where users perform on-demand vertical and 3-D differencing via an online interface. OpenTopography is a U.S. National Science Foundation–funded facility that provides access to topographic data and processing tools. While topographic differencing has been applied in numerous research studies, the lack of standardization, particularly of 3-D differencing, requires the customization of processing for individual data sets and hinders the community’s ability to efficiently perform differencing on the growing archive of topography data. Our paper focuses on streamlined techniques with which to efficiently difference data sets with varying spatial resolution and sensor type (i.e., optical vs. light detection and ranging [lidar]) and over variable landscapes. To optimize on-demand differencing, we considered algorithm choice and displacement resolution. The optimal resolution is controlled by point density, landscape characteristics (e.g., leaf-on vs. leaf-off), and data set quality. We provide processing options derived from metadata that allow users to produce optimal high-quality results, while experienced users can fine tune the parameters to suit their needs. We anticipate that the differencing tool will expand access to this state-of-the-art technology, will be a valuable educational tool, and will serve as a template for differencing the growing number of multitemporal topography data sets.

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Zhang, Yi, Yuanxi Li, Xingmin Meng, Wangcai Liu, Aijie Wang, Yiwen Liang, Xiaojun Su, Runqiang Zeng und Xu Chen. „Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry“. Remote Sensing 15, Nr. 20 (13.10.2023): 4951. http://dx.doi.org/10.3390/rs15204951.

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Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions.

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Sunarmodo, Wismu, und Anis Kamilah Hayati. „CLOUD IDENTIFICATION FROM MULTITEMPORAL LANDSAT-8 USING K-MEANS CLUSTERING“. International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, Nr. 2 (30.04.2020): 157. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3285.

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In the processing and analysis of remote-sensing data, cloud that interferes with earth-surface data is still a challenge. Many methods have already been developed to identify cloud, and these can be classified into two categories: single-date and multi-date identification. Most of these methods also utilize the thresholding method which itself can be divided into two categories: local thresholding and global thresholding. Local thresholding works locally and is different for each pixel, while global thresholding works similarly for every pixel. To determine the global threshold, two approaches are commonly used: fixed value as threshold and adapted threshold. In this paper, we propose a cloud-identification method with an adapted threshold using K-means clustering. Each related multitemporal pixel is processed using K-means clustering to find the threshold. The threshold is then used to distinguish clouds from non-clouds. By using the L8 Biome cloud-cover assessment as a reference, the proposed method results in Kappa coefficient of above 0.9. Furthermore, the proposed method has lower levels of false negatives and omission errors than the FMask method.

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Candido, C. G., A. C. Blanco, N. J. B. Borlongan und R. M. de la Cruz. „MULTISOURCE AND MULTITEMPORAL LAND COVER MAPPING OF GREATER LUZON ISLAND USING GOOGLE EARTH ENGINE“. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W8-2023 (24.04.2024): 93–99. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w8-2023-93-2024.

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Abstract. A variety of research endeavors and practical applications necessitate the use of land cover maps. These maps are valuable for tasks such as change detection, forest monitoring, urban expansion monitoring, natural resource mapping, catering to diverse user requirements. While satellite sensors offer essential data for comprehending spatial and temporal variations in land cover, relying on a single satellite system can be limiting, especially considering the potential hindrance of cloud cover in the case of optical sensors. To enhance temporal frequency, it becomes essential to utilize multiple satellite systems, albeit requiring harmonization to ensure consistent outcomes. This study presents a large-scale annual land cover mapping which utilizes harmonized Landsat-8 and Sentinel-2 satellite imagery, in conjunction with supplementary data, and a machine learning algorithm. In addition, the use of powerful computational processing platforms such as Google Earth Engine and Google Colaboratory is now a requirement to manage big geospatial data as well as to run different algorithms for processing and analysis.

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Di Giacomo, Giacomo, und Giuseppe Scardozzi. „Multitemporal High-Resolution Satellite Images for the Study and Monitoring of an Ancient Mesopotamian City and its Surrounding Landscape: The Case of Ur“. International Journal of Geophysics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/716296.

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The paper concerns the use of multitemporal high-resolution satellite images for the study of the ancient city of Ur, in southern Mesopotamia, inaccessible to scholars from 2003. The acquired dataset is composed by two Gambit KH-7 (1966) and one Corona KH-4B (1968) declassified spy space photos and by few images taken by the recent satellites for civilian use QuickBird-2 (2002, 2004, 2007), Ikonos-2 (2008), and WorldView-1 (2008). The processing of all these images and the integration with ASTER and SRTM DEMs allowed the acquisition of new data about the topographical layout of the city and its monuments and ancient roads; the georeferencing of all archaeological remains and traces visible on the images allowed the upgrade of the archaeological map of Ur. The research also provided important data concerning the reconstruction of the surrounding landscape, where a lot of traces of old channels and riverbeds of the Euphrates were identified in areas much modified and altered during the last decades by urbanization and agricultural works. Moreover, the multitemporal images allowed the monitoring of the conservation of the archaeological area, particularly before and after second Gulf War.

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Slesinski, Jakub, Damian Wierzbicki und Michal Kedzierski. „Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence“. Sensors 23, Nr. 10 (19.05.2023): 4922. http://dx.doi.org/10.3390/s23104922.

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Constant monitoring of airports and aviation bases has become one of the priorities in today’s strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.

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Liu, Sicong, Francesca Bovolo, Lorenzo Bruzzone, Xiaohua Tong und Qian Du. „Editorial Foreword to the Special Issue on Recent Advances in Multitemporal Remote-Sensing Data Processing“. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (2022): 776–78. http://dx.doi.org/10.1109/jstars.2022.3140594.

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Fornaro, Gianfranco, Simona Verde, Diego Reale und Antonio Pauciullo. „CAESAR: An Approach Based on Covariance Matrix Decomposition to Improve Multibaseline–Multitemporal Interferometric SAR Processing“. IEEE Transactions on Geoscience and Remote Sensing 53, Nr. 4 (April 2015): 2050–65. http://dx.doi.org/10.1109/tgrs.2014.2352853.

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Bektas, F., und C. Goksel. „Remote sensing and GIS integration for land cover analysis, a case study: Bozcaada Island“. Water Science and Technology 51, Nr. 11 (01.06.2005): 239–44. http://dx.doi.org/10.2166/wst.2005.0411.

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In this study, remote sensing and geographic information system (GIS) techniques were used in order to accomplish land cover change of Bozcaada Island, Turkey, by using multitemporal Landsat Thematic Mapper data. Digital image processing techniques were conducted for the processes of image enhancement, manipulation, registration and classification for land cover change analysis. The land cover changes between two different dates were visualized and analyzed by using Geographic Information System techniques. The results showed that remotely sensed data and GIS are effective and powerful tools for carrying out changes on land cover of the island and monitoring of its impact on the environment.

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Takahashi Miyoshi, Gabriela, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Marcus Vinícius Antunes de Moraes und Eija Honkavaara. „Evaluation of Hyperspectral Multitemporal Information to Improve Tree Species Identification in the Highly Diverse Atlantic Forest“. Remote Sensing 12, Nr. 2 (10.01.2020): 244. http://dx.doi.org/10.3390/rs12020244.

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The monitoring of forest resources is crucial for their sustainable management, and tree species identification is one of the fundamental tasks in this process. Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests.

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Panuju, Dyah R., David J. Paull und Amy L. Griffin. „Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics“. Remote Sensing 12, Nr. 11 (01.06.2020): 1781. http://dx.doi.org/10.3390/rs12111781.

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Satellite images provide an accurate, continuous, and synoptic view of seamless global extent. Within the fields of remote sensing and image processing, land surface change detection (CD) has been amongst the most discussed topics. This article reviews advances in bitemporal and multitemporal two-dimensional CD with a focus on multispectral images. In addition, it reviews some CD techniques used for synthetic aperture radar (SAR). The importance of data selection and preprocessing for CD provides a starting point for the discussion. CD techniques are, then, grouped based on the change analysis products they can generate to assist users in identifying suitable procedures for their applications. The discussion allows users to estimate the resources needed for analysis and interpretation, while selecting the most suitable technique for generating the desired information such as binary changes, direction or magnitude of changes, “from-to” information of changes, probability of changes, temporal pattern, and prediction of changes. The review shows that essential and innovative improvements are being made in analytical processes for multispectral images. Advantages, limitations, challenges, and opportunities are identified for understanding the context of improvements, and this will guide the future development of bitemporal and multitemporal CD methods and techniques for understanding land cover dynamics.

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Lopes, Danilo de Sousa, Rodrigo Affonso de Albuquerque Nóbrega und Diego Rodrigues Macedo. „Towards a Robust Approach for Multitemporal Landcover Dataset: 3 Decades of Landcover Changes in Piauí, Brazil“. Revista Brasileira de Cartografia 74, Nr. 1 (02.02.2022): 197–213. http://dx.doi.org/10.14393/rbcv74n1-62751.

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Mapping the changes of land use and cover through the classification of satellite images is one of the essential sources to investigate and monitor the Earth´s surface. When performed in a multitemporal perspective, this approach requires specific procedures to match the images used. Considering that parkland/grassland savanna patches could increase over time due to different uses, this research aims to present a suitable method for processing Landsat-like images to investigate land cover dynamics. The study area covers seven municipalities in the Brazilian state of Piauí, Cerrado biome, which has been substantially affected by deforestation due to extensive agricultural projects in the past decades. The semi-automatic satellite imaging georegistration and the object-oriented classification of Landsat 5 and 8 satellites over the last 30 years in decadal periods are among the methodological procedures used. Findings demonstrate the semi-automatic image registration process as an effective method for the geometric correction of Landsat scenes, and the object-based classification procedures are appropriate for multitemporal studies allowing comparative metrics of landcover class changes by period. Regarding the remaining natural landcover within the study area, the results showed a substantial decrease of woodland savanna patches from 73% in 1986 to 43% in 2016, while agricultural fields increased from 4% to 25% in 30 years.

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Markelin, L., E. Honkavaara, R. Näsi, N. Viljanen, T. Rosnell, T. Hakala, M. Vastaranta, T. Koivisto und M. Holopainen. „RADIOMETRIC CORRECTION OF MULTITEMPORAL HYPERSPECTRAL UAS IMAGE MOSAICS OF SEEDLING STANDS“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (19.10.2017): 113–18. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-113-2017.

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Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5&thinsp;% to 25&thinsp;%. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

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Trevisiol, Francesca, Ester Barbieri und Gabriele Bitelli. „Multitemporal Thermal Imagery Acquisition and Data Processing on Historical Masonry: Experimental Application on a Case Study“. Sustainability 14, Nr. 17 (24.08.2022): 10559. http://dx.doi.org/10.3390/su141710559.

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The recent improvement of infrared image quality has increased the use of thermography as a non-destructive diagnostic technique. Amongst other applications, thermography can be used to monitor historic buildings. The present work was carried out within the framework of the Horizon 2020 European project SHELTER, which aims to create a management plan for cultural heritage subject to environmental and anthropogenic risk. Among the chosen case studies is the Santa Croce Complex in Ravenna (Italy), which is exposed to different hazards, including flooding. The church has a peculiar architecture that develops below the street level, so the internal walls are affected by the deterioration caused by rising humidity. In such a case of advanced degradation, passive thermography cannot be used to its full potential. For this reason, an innovative methodology involving active thermography was first developed and validated with laboratory tests. Secondly, we conducted its first application to a real case study. With this purpose, an active thermography survey with forced ventilation was carried out to enhance different stages of material degradation by means of automatic classification of multitemporal data. These experiments have resulted in a method using an active thermal survey in a high moisture content environment to detect masonry degradation.

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Lombardo, P., und T. M. Pellizzeri. „Maximum likelihood signal processing techniques to detect a step pattern of change in multitemporal SAR images“. IEEE Transactions on Geoscience and Remote Sensing 40, Nr. 4 (April 2002): 853–70. http://dx.doi.org/10.1109/tgrs.2002.1006363.

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Bovolo, F., G. Camps-Valls und L. Bruzzone. „A support vector domain method for change detection in multitemporal images“. Pattern Recognition Letters 31, Nr. 10 (Juli 2010): 1148–54. http://dx.doi.org/10.1016/j.patrec.2009.07.002.

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Matahelemual, Godfried Junio Sebastian, Agung Budi Harto und Tri Muji Susantoro. „Oil Spill Detection using Sentinel-1 Multitemporal Data in Offshore Karawang“. Scientific Contributions Oil and Gas 43, Nr. 2 (31.08.2020): 69–79. http://dx.doi.org/10.29017/scog.43.2.522.

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Oil spill is a serious problem that could lead to economic and ecological losses, both in the short and long term. On July 12, 2019, there occurred an oil leakage around YYA-1 oil platform of Pertamina Hulu Energi Offshore North West Java (PHE ONWJ), located off the northern coast of Karawang, Java Sea. This incident has caused the death of fishes and marine animals, damage to coral reefs, mangroves, and seagrass beds, and several health problems of coastal communities. Therefore, it is necessary to map and monitor oil spills, so that actions can be taken to prevent the spread of oil spills. This study aims to map the distribution of oil spills in Karawang sea using multitemporal Sentinel-1 data from July to September 2019. The detection is carried out using the adaptive thresholding algorithm combined with manual interpretation. The result shows that the oil spills spread around Karawang sea from YYA-1 platform to Sedari Village and there are oil spills spreading from the Central Plant F/S platform. The oil spills tend to shift westward from July to September 2019. This shifting is supposed to be influenced by current and wave factors that were dominant moving westward at that time. Based on data processing, it was found that the oil spill area from July to September was respectively 24.79 km2, 20.05 km2, and 27.12 km2.

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Karaca, Ali Can, Ozan Kara und Mehmet Kemal Güllü. „MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks“. Journal of Visual Communication and Image Representation 81 (November 2021): 103385. http://dx.doi.org/10.1016/j.jvcir.2021.103385.

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Sona, Giovanna, Daniele Passoni, Livio Pinto, Diana Pagliari, Daniele Masseroni, Bianca Ortuani und Arianna Facchi. „UAV MULTISPECTRAL SURVEY TO MAP SOIL AND CROP FOR PRECISION FARMING APPLICATIONS“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (06.06.2016): 1023–29. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1023-2016.

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New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB) and false color (NIR-RG) images, were used. The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM) of bare soil and crop, and multispectral orthophotos. To overcome some difficulties in the automatic searching of matching points for the block adjustment of the crop image, also the scientific software developed by Politecnico of Milan was used to enhance images orientation. Surveys and image processing are described, as well as results about classification of multispectral-multitemporal orthophotos and soil indices.

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Sona, Giovanna, Daniele Passoni, Livio Pinto, Diana Pagliari, Daniele Masseroni, Bianca Ortuani und Arianna Facchi. „UAV MULTISPECTRAL SURVEY TO MAP SOIL AND CROP FOR PRECISION FARMING APPLICATIONS“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (06.06.2016): 1023–29. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1023-2016.

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New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB) and false color (NIR-RG) images, were used. The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM) of bare soil and crop, and multispectral orthophotos. To overcome some difficulties in the automatic searching of matching points for the block adjustment of the crop image, also the scientific software developed by Politecnico of Milan was used to enhance images orientation. Surveys and image processing are described, as well as results about classification of multispectral-multitemporal orthophotos and soil indices.

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Latella, Melissa, Arjen Luijendijk, Antonio M. Moreno-Rodenas und Carlo Camporeale. „Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas“. Remote Sensing 13, Nr. 22 (16.11.2021): 4613. http://dx.doi.org/10.3390/rs13224613.

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In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses.

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Rodrigues, F. A. A., J. S. Nobre, R. Vigélis, V. Liesenberg, R. C. P. Marques und F. N. S. Medeiros. „A FAST APPROACH FOR THE LOG-CUMULANTS METHOD APPLIED TO INTENSITY SAR IMAGE PROCESSING“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (06.11.2020): 499–503. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-499-2020.

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Abstract. Synthetic aperture radar (SAR) image processing and analysis rely on statistical modeling and parameter estimation of the probability density functions that characterize data. The method of log-cumulants (MoLC) is a reliable alternative for parameter estimation of SAR data models and image processing. However, numerical methods are usually applied to estimate parameters using MoLC, and it may lead to a high computational cost. Thus, MoLC may be unsuitable for real-time SAR imagery applications such as change detection and marine search and rescue, for example. Our paper introduces a fast approach to overcome this limitation of MoLC, focusing on parameter estimation of single-channel SAR data modeled by the G0I distribution. Experiments with simulated and real SAR data demonstrate that our approach performs faster than MoLC, while the precision of the estimation is comparable with that of the original MoLC. We tested the fast approach with multitemporal data and applied the arithmetic-geometric distance to real SAR images for change detection on the ocean. The experiments showed that the fast MoLC outperformed the original estimation method with regard to the computational time.

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Xu, Lu, Hong Zhang, Chao Wang, Bo Zhang und Meng Liu. „Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data“. Remote Sensing 11, Nr. 1 (29.12.2018): 53. http://dx.doi.org/10.3390/rs11010053.

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With the increasing temporal resolution of space-borne SAR, large amounts of intensity data are now available for continues land observations. Previous researches proved the effectiveness of multitemporal SAR in land classification, but the characterizations of temporal information were still inadequate. In this paper, we proposed a crop classification scheme, which made full use of multitemporal SAR backscattering responses. In this method, the temporal intensity models were established by the K-means clustering method. The intensity vectors were treated as input features, and the mean intensity vectors of cluster centers were regarded as the temporal models. The temporal models summarized the backscatter evolutions of crops and were utilized as the criterion for crop discrimination. The spectral similarity value (SSV) measure was introduced from hyperspectral image processing for temporal model matching. The unlabeled pixel was assigned to the class to which the temporal model with the highest similarity belonged. Two sets of Sentinel-1 SAR time-series data were used to illustrate the effectiveness of the proposed method. The comparison between SSV and other measures demonstrated the superiority of SSV in temporal model matching. Compared with the decision tree (DT) and naive Bayes (NB) classifiers, the proposed method achieved the best overall accuracies in both VH and VV bands. For most crops, it either obtained the best accuracies or achieved comparable accuracies to the best ones, which illustrated the effectiveness of the proposed method.

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Martino, A., F. Gerla und C. Balletti. „MULTI-SCALE AND MULTI-SENSOR APPROACHES FOR THE PROTECTION OF CULTURAL NATURAL HERITAGE: THE ISLAND OF SANTO SPIRITO IN VENICE“. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-2-2023 (26.06.2023): 1027–34. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-2-2023-1027-2023.

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Abstract. The study of Cultural Natural Heritage (CNH) requires the development of multi-disciplinary and multi-scale methodologies for data recording, representation, and correlation from various platforms such as terrestrial, aerial and satellite sensors. The heterogeneity of geo-databases currently available demands on-site validation and time monitoring to control the phenomena related to climate change that inevitably affect the Cultural Heritage (CH). The pressures stressing the territorial dimension due to climatic changes lead to the decrease of essential resources and burden on the CH. To overcome the lack of information needed at various territorial scales, it becomes necessary to construct detailed and dynamic cognitive frameworks. This paper establishes a multitemporal information framework regarding the case study area, the Island of Santo Spirito in Venice, using several geomatic techniques to investigate the island's ecological significance and constructed heritage. The suggested methodology uses the integration of multitemporal data resulting from the processing of satellite images provided by the Copernicus satellites (Sentinel-2) and data from geomatic documentation techniques. Two separate methods were used in the survey operations: a Terrestrial Laser Scanning (TLS) and aerial photogrammetry from Uncrewed Aerial Vehicles (UAV) survey. The integration of satellite, aerial, and terrestrial data has allowed a complete knowledge of the necessary parameters for the monitoring of the CH of the area. In order to manage conservative policy from a preventive perspective and to recreate and digitally visualize missing historical phases, programmed monitoring is a crucial instrument.

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Papoutsis, Ioannis, Charalampos Kontoes, Stavroula Alatza, Alexis Apostolakis und Constantinos Loupasakis. „InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data“. Remote Sensing 12, Nr. 19 (01.10.2020): 3207. http://dx.doi.org/10.3390/rs12193207.

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Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

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Karantzalos, K., A. Karmas und A. Tzotsos. „Monitoring crop growth and key agronomic parameters through multitemporal observations and time series analysis from remote sensing big data“. Advances in Animal Biosciences 8, Nr. 2 (01.06.2017): 394–99. http://dx.doi.org/10.1017/s2040470017001261.

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In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state.

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Simarmata, Nirmawana, Ketut Wikantika, Trika Agnestasia Tarigan, Muhammad Aldyansyah und Rizki Kurnia Tohir. „Utilization of multitemporal imagery for analysis of changes in mangrove cover by Using Cloud Computing Method in the East Coast Region of Lampung Province“. IOP Conference Series: Earth and Environmental Science 830, Nr. 1 (01.09.2021): 012013. http://dx.doi.org/10.1088/1755-1315/830/1/012013.

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Abstract The East Coast of Lampung Province has extraordinary potential, unique potential such as visual appeal. In addition, the coast also has the potential as a residential area, fishery cultivation, ponds, agriculture, ports, tourism and so on. However, behind its potential, the East Coast region of Lampung Province, especially the coastal area, is prone to destructive activities around the sea, the cause of the damage can be influenced by natural factors which include wave and tidal action. and human activities such as converting mangrove land to ponds. The purpose of this study was to analyze changes in mangrove area cover using multitemporal imagery from 1991 to 2019. The research data used were Landsat imagery and Sentinel imagery. Identification of mangrove areas can be done through interpretation of remote sensing technology. Changes in mangrove land cover can be detected using multitemporal imagery. It is early to see how widespread the changes have been in a certain time frame. The use of remote sensing technology is one method that is widely used to map and determine the condition of an area using the classification method. Classification is designed to derive thematic information by classifying phenomena based on criteria. Satellite image processing is carried out using the image classification method with cloud computing-based software, namely Google Earth Engine (GEE). The sampling points for classification are evenly distributed in the mangrove area. Apart from mangrove objects, samples were also taken from water objects, urban areas, vegetation, ponds and coastlines. This aims to distinguish mangrove objects from other objects. Based on the results of image processing, the accuracy test obtained on the land cover map is above 85% because the image resolution used is a medium resolution image. The results of the field survey indicated that there was a change in mangrove cover to become ponds which resulted in a reduction in the area of mangrove cover.

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Simarmata, Nirmawana, Ketut Wikantika, Trika Agnestasia Tarigan, Muhammad Aldyansyah und Rizki Kurnia Tohir. „Utilization of multitemporal imagery for analysis of changes in mangrove cover by Using Cloud Computing Method in the East Coast Region of Lampung Province“. IOP Conference Series: Earth and Environmental Science 830, Nr. 1 (01.09.2021): 012013. http://dx.doi.org/10.1088/1755-1315/830/1/012013.

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Abstract The East Coast of Lampung Province has extraordinary potential, unique potential such as visual appeal. In addition, the coast also has the potential as a residential area, fishery cultivation, ponds, agriculture, ports, tourism and so on. However, behind its potential, the East Coast region of Lampung Province, especially the coastal area, is prone to destructive activities around the sea, the cause of the damage can be influenced by natural factors which include wave and tidal action. and human activities such as converting mangrove land to ponds. The purpose of this study was to analyze changes in mangrove area cover using multitemporal imagery from 1991 to 2019. The research data used were Landsat imagery and Sentinel imagery. Identification of mangrove areas can be done through interpretation of remote sensing technology. Changes in mangrove land cover can be detected using multitemporal imagery. It is early to see how widespread the changes have been in a certain time frame. The use of remote sensing technology is one method that is widely used to map and determine the condition of an area using the classification method. Classification is designed to derive thematic information by classifying phenomena based on criteria. Satellite image processing is carried out using the image classification method with cloud computing-based software, namely Google Earth Engine (GEE). The sampling points for classification are evenly distributed in the mangrove area. Apart from mangrove objects, samples were also taken from water objects, urban areas, vegetation, ponds and coastlines. This aims to distinguish mangrove objects from other objects. Based on the results of image processing, the accuracy test obtained on the land cover map is above 85% because the image resolution used is a medium resolution image. The results of the field survey indicated that there was a change in mangrove cover to become ponds which resulted in a reduction in the area of mangrove cover.

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Feitosa, Raul Queiroz, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lúcio Abelha Mota und Bruno Feijó. „Modeling alternatives for fuzzy Markov chain-based classification of multitemporal remote sensing data“. Pattern Recognition Letters 32, Nr. 7 (Mai 2011): 927–40. http://dx.doi.org/10.1016/j.patrec.2010.09.024.

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Hamuna, Baigo, und Rosye H. R. Tanjung. „Deteksi Perubahan Luasan Mangrove Teluk Youtefa Kota Jayapura Menggunakan Citra Landsat Multitemporal“. Majalah Geografi Indonesia 32, Nr. 2 (30.09.2018): 115. http://dx.doi.org/10.22146/mgi.33755.

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Kondisi mangrove di kawasan Teluk Youtefa, baik dari aspek kualitas maupun kuantitasnya terus mengalami penurunan dari tahun ke tahun. Penelitian ini dilakukan untuk mengetahui seberapa besar perubahan luasan mangrove yang terjadi di kawasan Teluk Youtefa, Kota Jayapura dari tahun 1994 sampai tahun 2017 dengan menggunakan citra satelit Landsat 5 TM dan Landsat 8 OLI. Pengamatan kondisi mangrove di lapangan dilakukan dengan menggunakan GPS dan pengolahan citra menggunakan algoritma NDVI dengan klasifikasi supervised. Tumpang susun peta hasil interpretasi citra satelit untuk mengetahui sebaran dan perubahan luasan kawasan mangrove. Hasil penelitian menunjukan bahwa luasan mangrove pada tahun 1994 sebesar 392,45 ha dan luasan mangrove pada tahun 2017 mengalami penurunan menjadi 233,12 ha. Perubahan luasan mangrove dalam kurun waktu 23 tahun sebesar 159,34 ha atau sebesar 40,59%. Perubahan kawasan mangrove pada umumnya disebabkan oleh faktor antropogenik seperti penebangan, perubahan fungsi kawasan mangrove menjadi jalan, jembatan, pemukiman dan perubahan secara alami. ABSTRACTThe condition of mangrove in Youtefa Bay both qualitatively and quantitatively has decreased from year to year. This research was conducted to determine how much of the change occurring mangrove area in Youtefa Bay, Jayapura City from 1994 to 2017 by using Landsat 5 TM images and Landsat 8 OLI. Monitoring of mangrove condition in the field used GPS, and processing of images used NDVI algorithm with supervised classification. The map was overlaying satellite imagery interpretation to determine the distribution and changes of mangrove area. The result of research showed that the mangrove area in 1994 was about 392.45 hectares, mangrove area in 2017 have decreased becoming was 233.12 hectares. Changing of mangrove area for 23 years was about 159.34 hectares or 40.59%. Changes in mangrove were generally caused by anthropogenic factors such as logging, changes over the function of mangroves into the road, bridge, settlement, and change naturally.

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Dias, Rodrigo Zolini, und Gustavo Macedo de Mello Baptista. „Wetland nutrient retention and multitemporal growth – Case study of Riacho Fundo’s Wetland“. Acta Limnologica Brasiliensia 27, Nr. 3 (September 2015): 254–64. http://dx.doi.org/10.1590/s2179-975x0114.

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Abstract Aim: The aim of this research was to evaluate the nutrient retention of Riacho Fundo’s wetland in controlling Lake Paranoá (Brasilia, Brazil) eutrophication analyzing its ability to retain nitrogen and phosphorus. Furthermore, the article aimed at verifying the multitemporal growth rate of Riacho Fundo’s wetland. Methods Five sampling points were distributed along the wetland, from its beginning to its outflow into Lake Paranoá. Twenty-five field campaigns during periods of drought and rain from November 2011 to October 2012 with intervals of fifteen days were accomplished. The parameters total nitrogen and total phosphorus were analyzed and for comparison between the sampling points a non-parametric statistical analysis was performed and the efficiency of retention of these parameters between input and outputs of the wetland was calculated. For the analysis of multitemporal pace of growth in the area of wetland, water area and was estimated by processed satellite images of the years 1973, 1985, 1995, 2005 and 2011. Results The general behavior of the analyzed nutrients was decay between the entry point and exit point of showing an overall average retention of nitrogen 36.66% and 33.95% overall average total phosphorus. By image processing was possible to estimate that the surface of the lake was lost 0.5273 km2 or 84.07% of the initial area in the range of 38 years. Conclusion Despite having lost water area, Lake Paranoá gained a natural filter that retains nutrients that could be being invested in it and so can cause eutrophication of its waters. Thus, the wetland provides an ecological service of water treatment and preservation of aquatic and terrestrial Lake Paranoá life and the wetland should be considered as an importanttheme in Lake Paranoá management.

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