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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Aerosol Optical Depth Characterization in Middle and Polar Latitudes
摘要： The Aerosol Optical Depth (AOD) is a very important atmospheric parameter which knowledge will help to understand the atmospheric processes and to propose different scenarios related with climate change. AOD plays a main role in the energy radiation balance in the atmosphere and in the aerosol radiative forcing. In this paper an analysis of time series of AOD, from MYD04_3K (MODIS Aqua Aerosols product gridded in a resolution of 3 kilometres), is analysed by means of Fourier Harmonics tool. Results shown in this paper are preliminary results because a more comprehensive study is envisaged where data coming from sun and lunar photometers installed in Artic stations will be analysed jointly.
关键词： sun photometers,AOD,Aeronet,MODIS,time series
更新于2025-09-09 09:28:46
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Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor
摘要： The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16?day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.
关键词： pastures,time series,growth index,Zona da Mata,remote sensing
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Development of an Approach for Monitoring Sugarcane Harvested and Non-Harvested Conditions Using Time Series Sentinel-1 Data
摘要： With the recent launch of Sentinel-1 constellations and frequent availability of C-band synthetic aperture radar (SAR) data at no cost, there is an opportunity to monitor crops on regular basis, which is still not explored. Therefore, in this paper an approach has been proposed to monitor sugarcane harvest status using time series Sentinel-1 data. The proposed approach uses knowledge based classification and temporal profile for obtaining harvest status of crops. The approach is able to identify harvested and non-harvested sugarcane areas from other crops with an overall accuracy of 82.17%.
关键词： sugarcane,time series analysis,Sentinel-1,Synthetic Aperture Radar (SAR)
更新于2025-09-09 09:28:46
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Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi
摘要： Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.
关键词： Sentinel,land cover,data combination,SAR,dry forest,time series data,REDD+,optical
更新于2025-09-04 15:30:14
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Roma, Italy (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Robust Detection and Estimation of Change-Points in a Time Series of Multivariate Images
摘要： In this paper, we study the problem of detecting and estimating change-points in a time series of multivariate images. We extend existent works to take into account the heterogeneity of the dataset on a spatial neighborhood. The classic complex Gaussian assumption of the data is replaced by a complex elliptically symmetric assumption. Then robust statistics are derived using Generalized Likelihood Ratio Test (GLRT). These statistics are coupled to an estimation strategy for one or several changes. Performance of these robust statistics have been analyzed in simulation and compared to the one associated with standard multivariate normal assumption. When the data is heterogeneous, the detection and estimation strategy yields better results with the new statistics.
关键词： Multivariate Images,Complex Elliptically Symmetric,Robust Change Detection,Image Time Series
更新于2025-09-04 15:30:14
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Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band
摘要： For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.
关键词： Satellite images,Functional time series analysis,Forest monitoring,Online random regression forests,Autoregressive integrated moving average
更新于2025-09-04 15:30:14
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A center-median filtering method for detection of temporal variation in coronal images
摘要： Events in the solar corona are often widely separated in their timescales, which can allow them to be identified when they would otherwise be confused with emission from other sources in the corona. Methods for cleanly separating such events based on their timescales are thus desirable for research in the field. This paper develops a technique for identifying time-varying signals in solar coronal image sequences which is based on a per-pixel running median filter and an understanding of photon-counting statistics. Example applications to "EIT waves" (named after EIT, the EUV Imaging Telescope on the Solar and Heliospheric Observatory) and small-scale dynamics are shown, both using 193 ? data from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory. The technique is found to discriminate EIT waves more cleanly than the running and base difference techniques most commonly used. It is also demonstrated that there is more signal in the data than is commonly appreciated, finding that the waves can be traced to the edge of the AIA field of view when the data are rebinned to increase the signal-to-noise ratio.
关键词： Flares,Solar image processing,Coronal-Moreton waves,Time series analysis,Corona
更新于2025-09-04 15:30:14
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Comprehensive Remote Sensing || Multitemporal Analysis of Remotely Sensed Image Data
摘要： In the last years, a large interest has been devoted to the development of novel methodologies for multitemporal information extraction and analysis. This is demonstrated by the sharp increase in the number of papers published in the major remote sensing journals, the increased number of sessions in international conferences, and the increased number of projects related to multitemporal images and data.
关键词： change detection,unsupervised bitemporal image analysis,image time series,supervised/semi-supervised bitemporal image analysis,multitemporal analysis,remote sensing
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automatic Mapping of Irrigated Areas in Mediteranean Context Using Landsat 8 Time Series Images and Random Forest Algorithm
摘要： Groundwater withdrawals by farmers, in Morocco, are very numerous and informal. Therefore, the need for information on the location of irrigated areas is becoming increasingly important. Our main objective, in this study, is to evaluate the use of high-resolution Landsat 8 (L8) time series images and Random forest (RF) method to produce a land cover map with a sufficient precision to monitor the extension of irrigated areas. In the first part of this study, four parameters were evaluated: Number of trees, min split samples, max features and max depth. The results proves that the last parameter is the most important and has more impact on the oob score, which can reach 91%. The second part of this study was devoted to reduce furthermore the number of features taken as input in the classification process. This was done through feature reduction then selection. The computational time was highly reduced and the best level of classification accuracy was reached by using only Landsat 8 (L8) time series images, statistics on the temporal spectral indices (NDVI, MNDWI) and Range texture.
关键词： Random forest,NDVI,Landsat 8,Irrigated areas,Feature selection,time series,MNDWI,tuning,Range texture
更新于2025-09-04 15:30:14
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Integrating Grey Data Preprocessor and Deep Belief Network for Day-ahead PV Power Output Forecast
摘要： Generation output forecasting is a crucial task for planning and sizing of a photovoltaic (PV) power plant. The purpose of this paper is to present an effective model for day-ahead forecasting PV power output of a plant based on deep belief network (DBN) combined with grey theory-based data preprocessor (GT-DBN), where the DBN attempts to learn high-level abstractions in historical PV output data by utilizing hierarchical architectures. Test results obtained by the proposed model are compared with those obtained by other five forecasting methods including autoregressive integrated moving average model (ARIMA), back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector regression (SVR), and DBN alone. It shows that the proposed model is superior to other models in forecasting accuracy and is suitable for day-ahead PV power output prediction.
关键词： supervised learning,time series analysis,power generation planning,neural networks,Renewable energy
更新于2025-09-04 15:30:14