Journal articles: 'Multitemporel'

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Octariady, J., A. Hikmat, E. Widyaningrum, R. Mayasari, and M. K. Fajari. "VERTICAL ACCURACY COMPARISON OF DIGITAL ELEVATION MODEL FROM LIDAR AND MULTITEMPORAL SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 419–23. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-419-2017.

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Digital elevation model serves to illustrate the appearance of the earth's surface. DEM can be produced from a wide variety of data sources including from radar data, LiDAR data, and stereo satellite imagery. Making the LiDAR DEM conducted using point cloud data from LiDAR sensor. Making a DEM from stereo satellite imagery can be done using same temporal or multitemporal stereo satellite imagery. How much the accuracy of DEM generated from multitemporal stereo stellite imagery and LiDAR data is not known with certainty. The study was conducted using LiDAR DEM data and multitemporal stereo satellite imagery DEM. Multitemporal stereo satellite imagery generated semi-automatically by using 3 scene stereo satellite imagery with acquisition 2013&ndash;2014. The high value given each of DEM serve as the basis for calculating high accuracy DEM respectively. The results showed the high value differences in the fraction of the meter between LiDAR DEM and multitemporal stereo satellite imagery DEM.

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Huang, Liang, Qiuzhi Peng, and Xueqin Yu. "Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering." Journal of Spectroscopy 2020 (March 23, 2020): 1–9. http://dx.doi.org/10.1155/2020/2725186.

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In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

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Zhang, Xiaokang, Wenzhong Shi, Zhiyong Lv, and Feifei Peng. "Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model." Remote Sensing 11, no. 23 (November 26, 2019): 2787. http://dx.doi.org/10.3390/rs11232787.

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This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.

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Fosbury, Adam M. "Estimation with Multitemporal Measurements." Journal of Guidance, Control, and Dynamics 33, no. 5 (September 2010): 1518–26. http://dx.doi.org/10.2514/1.47984.

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Zhu, Wei, Qian Du, and James E. Fowler. "Multitemporal Hyperspectral Image Compression." IEEE Geoscience and Remote Sensing Letters 8, no. 3 (May 2011): 416–20. http://dx.doi.org/10.1109/lgrs.2010.2081661.

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Oliveira Soares, Eduardo. "A MULTITEMPORAL VILA ITORORÓ." PIXO - Revista de Arquitetura, Cidade e Contemporaneidade 7, no. 24 (March 23, 2023): 140–51. http://dx.doi.org/10.15210/pixo.v7i24.3220.

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Vila Itororó foi inaugurada em 1922 e está localizada na cidade de São Paulo. O conjunto arquitetônico inicialmente era formado por um palacete, casas de aluguel e uma piscina. Desde 2013 abriga um Centro Cultural. A configuração arquitetônica atual revela a variedade de usos e de públicos ao longo do tempo; as mudanças urbanas da cidade e do bairro; as oscilações entre os perfis dos moradores; e as abordagens sobre como lidar com o patrimônio das cidades. Esses fatores ajudaram a forjar, ao longo de um século, um conjunto arquitetônico que mescla edificações já restauradas, obras em andamento e estruturas aparentemente abandonadas. O artigo apresenta uma narrativa textual e fotográfica a partir de uma visita realizada em 2022, registrando atributos e percepções de um patrimônio marcado pela atuação do tempo.

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Shu, Chang, and Lihui Sun. "Automatic target recognition method for multitemporal remote sensing image." Open Physics 18, no. 1 (June 5, 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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Gutierrez, Laura, Elías Haro, and Natalia Díaz. "Multitemporal Analysis of Potential Geographic Distribution of Lama Guanicoe." Revista Ciencia y Tecnología 20, no. 1 (March 8, 2024): 89–100. http://dx.doi.org/10.17268/rev.cyt.2024.01.07.

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The purpose of this research is focused on modeling the geographic distribution of Lama guanicoe in South America in two time periods, 2021 and 2070, using MaxEnt software to correlate the bioclimatic variables and calculate the change in the distribution area. Then, with this information, a comparative map of the current and future areas was made in QGIS. As conclusions we can see that in the region of Peru the change of distribution area is reduced, which is observable in the majority of the nations where the guanaco (Lama guanicoe) lives, which is currently considered Endangered, so according to our model it predicts that it will reduce the distribution area by 20%, and the temperature variables have a negative correlation with the area, which indicates that climate change will have a relationship with the Lama guanicoe. This information is necessary for all countries to take action in the conservation of Lama guanicoe by adopting strategies to reduce and prevent climate change, generating and updating their conservation plans.

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Ilteralp, Melike, Sema Ariman, and Erchan Aptoula. "A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images." Remote Sensing 14, no. 1 (December 22, 2021): 18. http://dx.doi.org/10.3390/rs14010018.

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This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.

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Cheng, Xinglu, Yonghua Sun, Wangkuan Zhang, Yihan Wang, Xuyue Cao, and Yanzhao Wang. "Application of Deep Learning in Multitemporal Remote Sensing Image Classification." Remote Sensing 15, no. 15 (August 3, 2023): 3859. http://dx.doi.org/10.3390/rs15153859.

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The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image classification can extract richer spatiotemporal features. However, this also presents the challenge of mining massive data features. In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. The combination of remote sensing classification and deep learning has become a trend and has developed rapidly in recent years. However, there is a lack of summary and discussion on the research status and trends in multitemporal images. This review retrieved and screened 170 papers and proposed a research framework for this field. It includes retrieval statistics from existing research, preparation of multitemporal datasets, sample acquisition, an overview of typical models, and a discussion of application status. Finally, this paper discusses current problems and puts forward prospects for the future from three directions: adaptability between deep learning models and multitemporal classification, prospects for high-resolution image applications, and large-scale monitoring and model generalization. The aim is to help readers quickly understand the research process and application status of this field.

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Shen, Hongda, Zhuocheng Jiang, and W. Pan. "Efficient Lossless Compression of Multitemporal Hyperspectral Image Data." Journal of Imaging 4, no. 12 (December 2, 2018): 142. http://dx.doi.org/10.3390/jimaging4120142.

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Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

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Small, Christopher. "Multitemporal analysis of urban reflectance." Remote Sensing of Environment 81, no. 2-3 (August 2002): 427–42. http://dx.doi.org/10.1016/s0034-4257(02)00019-6.

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Sigurdsson, Jakob, Magnus O. Ulfarsson, Johannes R. Sveinsson, and Jose M. Bioucas-Dias. "Sparse Distributed Multitemporal Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 55, no. 11 (November 2017): 6069–84. http://dx.doi.org/10.1109/tgrs.2017.2720539.

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Zheng, Yongjie, Sicong Liu, Qian Du, Hui Zhao, Xiaohua Tong, and Michele Dalponte. "A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 10691–704. http://dx.doi.org/10.1109/jstars.2021.3119942.

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Lefsky, M. A., W. B. Cohen, and T. A. Spies. "An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon." Canadian Journal of Forest Research 31, no. 1 (January 1, 2001): 78–87. http://dx.doi.org/10.1139/x00-142.

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This research evaluates the utility of several remote sensing data types for the purpose of mapping forest structure and related attributes at a regional scale. Several sensors were evaluated, including (i) single date Landsat Thematic Mapper (TM); (ii) multitemporal Landsat TM; (iii) Airborne Data Acquisition and Registration (ADAR), a sensor with high spatial resolution; (iv) Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), a sensor with high spectral resolution; and (v) Scanning Lidar Imager Of Canopies By Echo Recovery (SLICER), a lidar sensor that directly measures the height and canopy structure of forest vegetation. To evaluate the ability of each of the sensors to predict stand structure attributes, we assembled a data set consisting of 92 field plots within the Willamette National Forest in the vicinity of the H.J. Andrews Experimental Forest. Stand structure attributes included age, basal area, aboveground biomass, mean diameter at breast height (DBH) of dominant and codominant stems, mean and standard deviation of the DBH of all stems, maximum height, and the density of stems with DBH greater than 100 cm. SLICER performed better than any other remote sensing system in its predictions of forest structural attributes. The performance of the imaging sensors (TM, multitemporal TM, ADAR, and AVIRIS) varied with respect to which forest structural variables were being examined. For one group of variables there was little difference in the ability of the these sensors to predict forest structural attributes. For the remaining variables, we found that multitemporal TM was as or more effective than either ADAR or AVIRIS. These results indicate that multitemporal TM should be investigated as an alternative to either hyperspectral or hyperspatial sensors, which are more expensive and more difficult to process than multitemporal Landsat TM.

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Gao, Jianhao, Yang Yi, Tang Wei, and Guanhao Zhang. "Sentinel-2 Cloud Removal Considering Ground Changes by Fusing Multitemporal SAR and Optical Images." Remote Sensing 13, no. 19 (October 7, 2021): 3998. http://dx.doi.org/10.3390/rs13193998.

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Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information is important. Existing reconstruction methods can hardly reflect the real-time information because they mainly make use of multitemporal optical images as reference. To capture the real-time information in the cloud removal process, Synthetic Aperture Radar (SAR) images can serve as the reference images due to the cloud penetrability of SAR imaging. Nevertheless, large datasets are necessary because existing SAR-based cloud removal methods depend on network training. In this paper, we integrate the merits of multitemporal optical images and SAR images to the cloud removal process, the results of which can reflect the ground information change, in a simple convolution neural network. Although the proposed method is based on deep neural network, it can directly operate on the target image without training datasets. We conduct several simulation and real data experiments of cloud removal in Sentinel-2 images with multitemporal Sentinel-1 SAR images and Sentinel-2 optical images. Experiment results show that the proposed method outperforms those state-of-the-art multitemporal-based methods and overcomes the constraint of datasets of those SAR-based methods.

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Feng, Quanlong, Jianyu Yang, Dehai Zhu, Jiantao Liu, Hao Guo, Batsaikhan Bayartungalag, and Baoguo Li. "Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta." Remote Sensing 11, no. 9 (April 28, 2019): 1006. http://dx.doi.org/10.3390/rs11091006.

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Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

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Wijaya, Muhammad Sufwandika, Ulfa Aulia Syamsuri, Irfan Zaki Irawan, Prima Widayani, Projo Danoedoro, and Sigit Heru Murti. "The Compatibility Study of Sentinel 1 Multitemporal Analysis For River-Flood Detection, Study Case: Bogowonto River." Journal of Applied Geospatial Information 7, no. 2 (July 9, 2023): 853–60. http://dx.doi.org/10.30871/jagi.v7i2.5365.

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Flooding is a common natural disaster in Purworejo District, which can be caused by the overflowing of the Bogowonto River. The use of multitemporal analysis with Synthetic Aperture Radar (SAR) images, such as Sentinel-1, has the potential to aid in flood inundation detection for disaster mitigation in the area. However, there has not been any research examining the compatibility of flood inundation detection using multitemporal Sentinel-1 images with the flood susceptibility characteristics of the Bogowonto River. This study aims to evaluate this using a SWOT analysis. The results show that multitemporal analysis using Sentinel-1 images is not suitable for detecting flood inundation in the Bogowonto River due to difficulties in finding the right acquisition time at the time of the flood event. The duration of floods in the Bogowonto River is approximately 1-2 days, while the earliest reacquisition time for Sentinel-1 images for this study is 12 days. Additionally, Sentinel-1 images using band C have limitations in detecting floods under vegetation.

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Perros, Nikolas, Dionissios Kalivas, and Rigas Giovos. "Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation." Agriculture 11, no. 9 (August 26, 2021): 809. http://dx.doi.org/10.3390/agriculture11090809.

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In this study, a spatial analysis of agronomic and remote sensing data is carried out to derive accurate rice crop yield estimation. The variability of a series of vegetation indices (VIs) was calculated from remote sensing data obtained via a commercial UAS platform (e-Bee) at four dates (per stage of development), and the development of estimation models was conducted. The study area is located in the region of Chalastra (municipality of Thessaloniki, North Greece) and the primary data were obtained during the 2016 growing season. These data include ultra-high resolution remote sensing multispectral images of 18 plots totaling 58 hectares of Ronaldo and Gladio rice crop varieties, 97 sample point data related to yield, and many other pieces of information recorded in the producer’s field log. Ten simple and compound VIs were calculated, and the evolution of their values during the growing season as well as their comparative correlation were studied. A study of the usability of each VI was conducted for the different phenological stages of the cultivation and the variance of VIs and yield; the more correlated VIs were identified. Furthermore, three types of multitemporal VI were calculated from combinations of VIs from different dates, and their contribution to improving yield prediction was studied. As Ronaldo is a Japonica type of rice variety and Gladio is Indica type, they behave differently in terms of maturation time (Gladio is approximately 20 days earlier) and the value of every VI is affected by changes in plant physiology and phenology. These differences between the two varieties are reflected in the multitemporal study of the single-date VIs but also in the study of the values of the multitemporal VIs. In conclusion, Ronaldo’s yield is strongly dependent on multitemporal NDVI (VI6th July + VI30 Aug, R2 = 0.76), while Gladio’s yield is strongly dependent on single-date NDVI (6 July, R2 = 0.88). The compound VIs RERDVI and MCARI1 have the highest yield prediction (R2 = 0.77) for Ronaldo (VI6th July + VI30 Aug) and Gladio (R2 = 0.95) when calculated in the booting stage, respectively. For the Ronaldo variety, the examination of the multitemporal VIs increases yield prediction accuracy, while in the case of the Gladio variety the opposite is observed. The capabilities of multitemporal VIs in yield estimation by combining UAVs with more flights during the different growth stages can improve management and the cultivation practices.

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Sun, Jing, and Suwit Ongsomwang. "Multitemporal Land Use and Land Cover Classification from Time-Series Landsat Datasets Using Harmonic Analysis with a Minimum Spectral Distance Algorithm." ISPRS International Journal of Geo-Information 9, no. 2 (January 21, 2020): 67. http://dx.doi.org/10.3390/ijgi9020067.

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An understanding of historical and present land use and land cover (LULC) information and its changes, such as urbanization and urban growth, is critical for city planners, land managers and resource managers in any rapidly changing landscape. To deal with this situation, the development of a new supervised classification method for multitemporal LULC mapping with long-term reliable information is necessary. The ultimate goal of this study was to develop a new classification method using harmonic analysis with a minimum spectral distance algorithm for multitemporal LULC mapping. Here, the Jiangning District of Nanjing City, Jiangsu Province, China was chosen as the study area. The research methodology consisted of two main components: (1) Landsat data selection and time-series spectral reflectance reconstruction and (2) multitemporal LULC classification using HA with a minimum spectral distance algorithm. The results revealed that the overall accuracy and Kappa hat coefficients of the four LULC maps in 2000, 2006, 2011, and 2017 were 97.03%, 90.25%, 91.19%, 86.32% and 95.35%, 84.48%, 86.74%, 80.24%, respectively. Further, the average producer accuracy and user accuracy of the urban and built-up land, agricultural land, forest land, and water bodies from the four LULC maps were 92.30%, 90.98%, 94.80%, 85.65% and 90.28%, 93.17%, 84.40%, 99.50%, respectively. Consequently, it can be concluded that the newly developed supervised classification method using harmonic analysis with a minimum spectral distance algorithm can efficiently classify multitemporal LULC maps.

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Kim, Taeheon, and Youkyung Han. "Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction." Remote Sensing 13, no. 19 (October 6, 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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Ullah, Sana, Zhengkang Zuo, Feizhou Zhang, Jianghua Zheng, Shifeng Huang, Yi Lin, Imran Iqbal, Yiyuan Sun, Ming Yang, and Lei Yan. "GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm." Remote Sensing 12, no. 19 (September 26, 2020): 3162. http://dx.doi.org/10.3390/rs12193162.

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To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

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Duong, N. D., N. M. Phuong, and N. B. Thi. "DEVELOPMENT OF PHENOLOGY BASED ALGORITHM FOR CROPLAND AND CROP TYPE MAPPING WITH MULTITEMPORAL LANDSAT IMAGE DATA - CASE STUDY IN THE NORTHWEST OF VIETNAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W7 (March 1, 2019): 11–17. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w7-11-2019.

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Abstract. Cropland mapping is very important for food security, policy development, land use planning, and environmental protection. Scientists have developed methods and techniques for cropland mapping with remote sensing image data. Both single date and multitemporal image data are used for generation of cropland and crop type maps. Multitemporal image data has advantages over single date image data from reliability and accuracy point of view because multitemporal image data allows to eliminate seasonality of vegetation. In this paper, the authors present new algorithm for cropland and crop type mapping with multitemporal Landsat image data. The algorithm requires for analysis of all Landsat scenes observed during one year and if needed, scenes in some years back to compensate clouds and cloud shadows. Phenology of land cover is constructed based on six bimonthly cloud free land covers that were automatically classified using the selected scenes. By grouping land covers within two months to six land covers of periods January&ndash;February, March&ndash;April, May-June, July&ndash;August, September&ndash;October, and November&ndash;December we create six bimonthly cloud free land covers that formulate a database for mapping cropland and crop types. By analysis of 50 Landsat scenes of path/row number 128/45 (northwest of Vietnam) observed mainly from 2017, 2016 and 2015 we success to map upland cropland and 14 crop types with area ranging from 145,143&thinsp;ha to 3,373&thinsp;ha per crop type. The study pointed out that phenology characterized by six bimonthly land covers is acceptable to identify cropland distribution and some specific crop types. For better results, apparently we need higher temporal resolution of image data. Due to uncertainty of the atmosphere, it is almost impossible to rely only on optical remote sensing data to achieve high temporality of data so application of multitemporal SAR data could be a way to overcome this obstacle.

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Cahyaningtyas, Andriana Hetty, and Heri Tjahjono. "TINGKAT PENGETAHUAN DAN SIKAP MASYARAKAT MENGENAI ABRASI MELALUI EDUKASI DENGAN CITRA SATELIT MULTITEMPORAL DI DESA PANDANGAN KULON KABUPATEN REMBANG." Edu Geography 11, no. 1 (May 14, 2023): 103–10. http://dx.doi.org/10.15294/edugeo.v11i1.68590.

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Penyempitan kawasan pantai dan kerusakan infrastruktur desa adalah bukti kerusakan akibat bencana abrasi. Pendidikan bencana dengan media edukasi sangat penting diberikan kepada masyarakat agar memiliki pengetahuan dan sikap peduli dengan lingkungan dan dapat meminimalisir dampak bencana. Penelitian ini bertujuan untuk (1) mengetahui tingkat pengetahuan masyarakat sebelum dan sesudah diberikan edukasi dengan citra satelit multitemporal mengenai abrasi (2) mengetahui sikap masyarakat sebelum dan sesudah diberikan edukasi dengan citra satelit multitemporal mengenai abrasi (3) mengetahui perbedaan tingkat pengetahuan dan sikap masyarakat sebelum dan sesudah diberikan edukasi dengan citra satelit multitemporal mengenai abrasi. Penelitian ini dilaksanakan pada masyarakat Desa Pandangan Kulon Kecamatan Kragan Kabupaten Rembang. Jenis penelitian ini adalah penelitian kuantitatif pre-eksperimental design dengan desain one group pretest posttest. Sampel diambil 46 responden secara purposive sampling. Variabel pada penelitian ini yaitu tingkat pengetahuan masyarakat mengenai abrasi dan sikap masyarakat dalam menghadapi abrasi. Teknik pengumpulan data yang digunakan meliputi tes, angket dan dokumentasi. Teknik analisis data menggunakan uji paired sample t-test dan uji n-gain. Hasil penelitian menunjukkan bahwa pengetahuan dan sikap masyarakat mengenai abrasi mengalami peningkatan yang signifikan. Pengetahuan masyarakat sebelum diberi edukasi tergolong kurang dengan nilai sebesar 45,54% dan setelah diberi edukasi tergolong baik dengan nilai sebesar 83,91%. Sikap masyarakat sebelum diberi edukasi tergolong cukup dalam menghadapi abrasi yang memperoleh nilai sebesar 50,28% dan setelah diberikan edukasi tergolong kategori sangat baik dengan nilai sebesar 86,35% .Hasil uji perbedaan variabel pengetahuan masyarakat mengenai abrasi diperoleh nilai sebesar 0,842 masuk dalam kategori peningkatan tinggi dan variabel sikap masyarakat dalam menghadapi abrasi diperoleh nilai sebesar 0,725 masuk dalam kategori peningkatan tinggi. Kesimpulan dalam penelitian ini adalah pengetahuan dan sikap masyarakat setelah diberikan edukasi dengan citra satelit multitemporal mengalami peningkatan yang signifikan sehingga pada tingkat pengetahuan masuk dalam kategori baik dan sikap masuk dalam kategori sangat baik. Peningkatan yang terjadi didapatkan dari informasi yang telah diperoleh masyarakat melalui edukasi dengan citra satelit multitemporal. Perlunya edukasi sekaligus simulasi dalam menghadapi bencana agar masyarakat tanggap ketika bencana datang.

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Xu, Su, Xiping He, Xiaoli Cao, and Jian Hu. "Damaged Building Detection with Improved Swin-Unet Model." Wireless Communications and Mobile Computing 2022 (July 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2124949.

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Automatic detection of damaged buildings from satellite remote sensing data has become an urgent problem to rescue planners and military personnel. Unfortunately damaged buildings are in different dimensions and shapes with different roofs depending on the type of the material to be painted. In this study, we present an improved Swin-Unet approach that comprises three main operations. First, improved Swin-Unet as a Unet-like pure Transformer is used for multitemporal image segmentation. Second, different multitemporal features are extracted using hyperspectral image classification algorithm. Finally, a binary change map is generated, and evaluation results are obtained. This article takes AIST building change detection scene as the example, and compared with the conventional approaches tested, overall accuracy, mean intersection over union, and separated Kappa in the proposed method were improved by at least 23.36, 0.1725, and 0.202, respectively. Furthermore, different scenes, such as Gaofen-2/Jilin-1 multitemporal optical images and satellite imagery dataset (xBD), have also come to the same conclusion. Thus, it provides advantageous capabilities for monitoring damaged buildings along coastal areas.

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Ma, Xiaoshuang, and Penghai Wu. "Multitemporal SAR Image Despeckling Based on a Scattering Covariance Matrix of Image Patch." Sensors 19, no. 14 (July 11, 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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Zhang, Xiang, Xinming Tang, Xiaoming Gao, and Hui Zhao. "Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data." Advances in Meteorology 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/7914581.

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The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

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Han, Youkyung, Anjin Chang, Seokkeun Choi, Honglyun Park, and Jaewan Choi. "An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles." Journal of Sensors 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/9702612.

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Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

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Lin, Yuanhang, Susan Meerdink, and Paul Gader. "Spectral Transformations for Multitemporal Hyperspectral Classification." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2021.3136569.

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Mesev, V. "MULTISCALE AND MULTITEMPORAL URBAN REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B2 (July 25, 2012): 17–21. http://dx.doi.org/10.5194/isprsarchives-xxxix-b2-17-2012.

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Biday,. "Radiometric Correction of Multitemporal Satellite Imagery." Journal of Computer Science 6, no. 9 (September 1, 2010): 1027–36. http://dx.doi.org/10.3844/jcssp.2010.1027.1036.

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Haertel, V., Y. E. Shimabukuro, and R. Almeida-Filho. "Fraction images in multitemporal change detection." International Journal of Remote Sensing 25, no. 23 (December 2004): 5473–89. http://dx.doi.org/10.1080/01431160412331269751.

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La, Hien Phu, Yang Dam Eo, Soo Bong Lee, Wan Yong Park, and Jee Hee Koo. "Image simulation from multitemporal landsat images." GIScience & Remote Sensing 52, no. 5 (July 24, 2015): 586–608. http://dx.doi.org/10.1080/15481603.2015.1062676.

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Candra, Danang Surya. "Deforestation detection using multitemporal satellite images." IOP Conference Series: Earth and Environmental Science 500 (July 4, 2020): 012037. http://dx.doi.org/10.1088/1755-1315/500/1/012037.

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IVASHCHUK, VLADIMIR D., and VITALY N. MELNIKOV. "MULTITEMPORAL GENERALIZATION OF THE SCHWARZSCHILD SOLUTION." International Journal of Modern Physics D 04, no. 02 (April 1995): 167–73. http://dx.doi.org/10.1142/s0218271895000119.

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The n-time generalization of the Schwarzschild solution is considered. The equations of geodesics for the metric are integrated. The multitemporal analogues of Newton’s laws for the extended objects described by the solution are suggested. The scalar-vacuum generalization of the solution is also presented.

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De Castro, Cristina, Fabio Grandi, and Maria Rita Scalas. "SCHEMA VERSIONING FOR MULTITEMPORAL RELATIONAL DATABASES." Information Systems 22, no. 5 (July 1997): 249–90. http://dx.doi.org/10.1016/s0306-4379(97)00017-3.

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Broncano-Mateos, Carlos Javier, Carlos Pinilla, Rub�n Gonzalez-Crespo, and Andr�s Castillo-Sanz. "Relative Radiometric Normalization of Multitemporal images." International Journal of Interactive Multimedia and Artificial Intelligence 1, no. 3 (2010): 53. http://dx.doi.org/10.9781/ijimai.2010.139.

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Byeungwoo Jeon and D. A. Landgrebe. "Decision fusion approach for multitemporal classification." IEEE Transactions on Geoscience and Remote Sensing 37, no. 3 (May 1999): 1227–33. http://dx.doi.org/10.1109/36.763278.

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Shirzaei, Manoochehr. "A seamless multitrack multitemporal InSAR algorithm." Geochemistry, Geophysics, Geosystems 16, no. 5 (May 2015): 1656–69. http://dx.doi.org/10.1002/2015gc005759.

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Helgason, Sigurdur. "Integral geometry and multitemporal wave equations." Communications on Pure and Applied Mathematics 51, no. 9-10 (September 1998): 1035–71. http://dx.doi.org/10.1002/(sici)1097-0312(199809/10)51:9/10<1035::aid-cpa5>3.0.co;2-e.

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Zheng, Yongjie, Sicong Liu, Qian Du, Hui Zhao, Xiaohua Tong, and Michele Dalpont. "Corrections to “A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification”." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 12103. http://dx.doi.org/10.1109/jstars.2021.3127364.

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Wang, Li, and Yong Zhou. "Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land." Agriculture 13, no. 1 (December 20, 2022): 8. http://dx.doi.org/10.3390/agriculture13010008.

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Soil organic matter (SOM) is vital for assessing the quality of arable land. A fast and reliable estimation of SOM is important to predict the soil carbon stock in cropland. In this study, we aimed to explore the potential of combining multitemporal Sentinel-2A imagery and random forest (RF) to improve the accuracy of SOM estimates in the plough layer for cultivated land at a regional scale. The field data of SOM content were utilized along with multitemporal Sentinel-2A images acquired over three years during the bare soil period to develop spectral indices. The best bands and spectral indices were selected as prediction variables by using the RF algorithm. Partial least squares (PLS), geographically weighted regression (GWR), and RF were employed to calibrate spectral indices for the SOM content, and the optimal calibration model was used for the mapping of the SOM content in arable land at a regional scale. The results showed the following. (1) The multitemporal image estimation model outperformed the single-temporal image estimation model. The estimation model that utilized the optimal bands and spectral indices as prediction variables usually had better accuracy than the models based on full spectral data. (2) For the SOM content estimates, the performance was better with RF than with PLS and GWR in almost all cases. (3) The most accurate SOM estimation in the case area was achieved by using multitemporal images from 2018 and the RF calibration model based on the optimal bands and spectral indices as prediction variables, with R2val (coefficient of determination of the validation data set) = 0.67, RMSEval (root mean square error of the validation dataset) = 2.05, and RPIQval(ratio of performance to interquartile range of the validation dataset) = 3.36. (4) The estimated SOM content in the plough layer for cultivated land throughout the study area ranged from 16.17 to 36.98 g kg−1 and exhibited an increasing trend from north to south. In the current study, we developed a framework that combines multitemporal remote sensing imagery and RF for the SOM estimation, which can improve the accuracy of quantitative SOM estimations, provide a dynamic, rapid, and low-cost technique for understanding soil fertility, and offer an early warning of changes in soil quality.

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Dubois, C., F. Stoffner, A. C. Kalia, M. Sandner, M. Labiadh, and 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 (September 26, 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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Sun, Jing, and Suwit Ongsomwang. "Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China." ISPRS International Journal of Geo-Information 10, no. 12 (November 30, 2021): 809. http://dx.doi.org/10.3390/ijgi10120809.

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Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

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Rahul Singh, S., and Renu Dhir. "Change Monitoring of Burphu Glacier from 1963 to 2011 Using Remote Sensing." Asian Review of Civil Engineering 3, no. 1 (May 5, 2014): 14–17. http://dx.doi.org/10.51983/tarce-2014.3.1.2203.

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Himalayas has one of the largest resources of snow and ice, which act as a freshwater reservoir for all the rivers originating from it. Monitoring of these resources is important for the assessment of availability of water in the Himalayan Rivers. The mapping of Glaciers is very difficult task because of the inaccessibility and remoteness of the terrain. Remote sensing techniques are often the only way to analyze glaciers in remote mountains and to monitor a large number of glaciers in multitemporal manner. This paper presents the results obtained from the analysis of a set of multitemporal Landsat MSS, TM and ETM+images for the monitoring and analysis of Burphu Glacier.

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Henits, L., C. Jürgens, and L. Mucsi. "Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data." International Journal of Remote Sensing 37, no. 15 (January 13, 2016): 3439–54. http://dx.doi.org/10.1080/01431161.2015.1125558.

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OLIVEIRA, A. P. G., C. L. MIOTO, F. B. DALMAS, E. A. ALBREZ, A. MELOTTO, E. M. FACINCANI, R. M. GAMARRA, and A. C. P. FILHO. "Multitemporal Analysis of the Vegetation Cover of the São Gabriel do Oeste, MS." Anuário do Instituto de Geociências - UFRJ 40, no. 3 (November 30, 2017): 254–65. http://dx.doi.org/10.11137/2017_3_254_265.

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Le Breton-Miller, Isabelle, and Danny Miller. "Commentary: Family Firms and the Advantage of Multitemporality." Entrepreneurship Theory and Practice 35, no. 6 (November 2011): 1171–77. http://dx.doi.org/10.1111/j.1540-6520.2011.00496.x.

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In this commentary, we argue the need for multitemporality (MT)—the ability to achieve enduring success by meeting both short– and long–term challenges—and we show how many family firms are in a superior position to do that. We situate Lumpkin and Brigham's (L&B's) long–term orientation (LTO) components within a multitemporal frame in part by relating them to their primary mechanisms of intertemporality. We then extend L&B's LTO conception by moving from an organizational mindset to its effective actualization via multitemporal facilitators as they manifest in family firms, arguing also the conditions under which family firms have an advantage in achieving MT. We conclude with a discussion of suggested research directions.

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Li, Jiaying, Weidong Wang, Zheng Han, Yange Li, and Guangqi Chen. "Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides." Applied Sciences 10, no. 7 (April 6, 2020): 2518. http://dx.doi.org/10.3390/app10072518.

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Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

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Ghuvita Hadi, Z. N., T. Hariyanto, and N. Hayati. "Estimation of Total Suspended Sediment Solid in Porong River Waters Using Multitemporal Satellite Imagery." IOP Conference Series: Earth and Environmental Science 936, no. 1 (December 1, 2021): 012006. http://dx.doi.org/10.1088/1755-1315/936/1/012006.

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Abstract Monitoring the concentration of Total Suspended Solid (TSS) is one method to determine water quality, because a high TSS value indicates a high level of pollution. Remote sensing data can be used effectively in generating suspended sediment concentrations. Nowdays, Google Earth Engine platform has provided a large collection of remote sensing data. Therefore, this study uses Google Earth Engine which is processed for free and aims to calculate the TSS value in the Kali Porong area. This research was conducted multitemporal in the last ten years, namely from 2013-2021 using multitemporal satellite imagery landsat-8 and sentinel-2 by applying empirical algorithms for calculating TSS. The results of this study are the value of TSS concentration at each sample point and a multitemporal TSS concentration distribution map. The year 2016, 2017, and 2021, the distribution of TSS concentration values was higher than in other years. At the sample point, the lowest TSS concentration value was 16.55 mg/L in 2013. Meanwhile, the highest TSS concentration value of 266.33 mg/L occurred in 2014 precisely in the Porong River estuary area which is the border area between land and water. the sea so that a lot of TSS material is concentrated in the area due to waves and ocean currents.

Journal articles on the topic 'Multitemporel'

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