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Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations
摘要： The number of reservoirs is rapidly increasing owing to the growth of the world’s economy and related energy and water needs. Yet, for the vast majority of reservoirs around the world, their locations and related information, especially for newly dammed reservoirs, are not readily available due to financial, political, or legal considerations. This study proposes an automated method of identifying newly dammed reservoirs from time series of MODIS-derived NDWI (normalized difference water index) images. Its main idea lies in the detection of abrupt changes in the NDWI time series that are associated with land-to-water conversion due to the reservoir impoundment. The proposed method is tested in the upper reach of the Yellow River that is severely regulated by constructed reservoirs. Our results show that five newly dammed reservoirs were identified in the test area during 2000–2018. Validated against high-resolution Google Earth imagery, our method is effective to determine both locations of the emerging medium-size reservoirs and the timing of their initial water impoundments. Such information then allows for a refined calculation of the reservoir inundation extents and storage capacities through the combination of higher-resolution Landsat imagery and SRTM DEM. The comparison of our estimated reservoir areas and capacities against documented information further indicates that the integration of multi-mission remote sensing data may provide useful information for understanding reservoir operations and impacts on river discharges. Our method also demonstrates a potential for regional or global inventory of emerging reservoirs, which is crucial to assessing human impacts on river systems and the global water cycle.
关键词： reservoir,time series,NDWI,remote sensing,BFAST,Yellow River
更新于2025-09-23 15:23:52
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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 - Normalized Compression Distance for SAR Image Change Detection
摘要： With a continuous increase in multi-temporal synthetic aperture radar (SAR) images, leading to enable mapping applications for Earth environmental observation, the number of algorithms for detection of different types of terrain changes has greatly expanded. In this paper, a SAR image change detection method based on normalized compression distance (NCD) is proposed. The procedure mainly consists in dividing two time series images in patches, computing a collection of similarities corresponding to each pair of patches and generating the change map with a histogram-based threshold. The experimental results were computed using 2 Sentinel 1A images over the city of Bucharest, Romania and 2 TerraSAR-X images over the Elbe River and its surrounding area, Germany.
关键词： Change detection,SAR,NCD,satellite image time series (SITS)
更新于2025-09-23 15:23:52
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Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS)
摘要： Landsat time series commonly contain missing observations, i.e., gaps, due to the orbit and sensing geometry, data acquisition strategy, and cloud contamination. A spectral-angle-mapper (SAM) based spatio-temporal similarity (SAMSTS) gap-filling algorithm is presented that is designed to fill small and large area gaps in Landsat data, using one year or less of data and without using other satellite data. Each gap pixel is filled by an alternative similar pixel that is located in a non-missing region of the image. The alternative similar pixel locations are identified by comparison of reflectance time series using a SAM metric revised to be adaptive to missing observations. A time series segmentation-and-clustering approach is used to increase the search efficiency. The SAMSTS algorithm is demonstrated using six months of Landsat 8 Operational Land Imager (OLI) reflectance time series over three 150 × 150 km (5000 × 5000 30 m pixels) areas in California, Minnesota and Kansas. The three areas contain different land cover types, especially crops that have different phenology and abrupt changes due to agricultural harvesting, which make gap filling challenging. Fillings on simulated gaps, which are equivalent to 36% of 5000 × 5000 images in each test area, are presented. The gap filling accuracy is assessed quantitatively, and the SAMSTS algorithm is shown to perform better than the simple closest temporal pixel substitution gap filling approach and the sinusoidal harmonic model-based gap filling approach. The SAMSTS algorithm provides gap-filled data with five-band reflective-wavelength root-mean-square differences less the 0.02, which is comparable to the OLI reflectance calibration accuracy.
关键词： Landsat,reflectance,time series,spectral angle mapper,gap filling
更新于2025-09-23 15:23:52
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On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping
摘要： (1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery.
关键词： Echinococcus multilocularis,random forests,spatial epidemiology,SAR,land cover,Ellobius tancrei,Microtus gregalis,time-series,Sentinel
更新于2025-09-23 15:23:52
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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 - A New Semi-Automatic Seamless Cloud-Free Landsat Mosaicing Approach Tracks Forest Change Over Large Extents
摘要： The extensive and freely available archive of Landsat satellite data is used throughout the world to assess forest changes over large areas and long time periods (30-40 years). But analyzing Landsat data in time-series is not free of challenges (e.g. data processing and storage capabilities, dealing with cloud cover and other data gaps, and accounting for changes in illumination conditions due to atmospheric effects, sun angle and vegetation phenology). In this research, we present a method used to create annual seamless cloud-free mosaics for the entire state of Victoria, Australia (19 Landsat tiles), for a 30 year period. These mosaics were created by first constructing yearly Best Available Pixel (BAP) composites from over 3000 individual scenes. Then, forested areas were analyzed in time-series to determine breakpoints (e.g. a disturbance event such as fire). Following this, the breakpoints were used to fit a piece-wise linear regression model through each pixel’s temporal trajectory. In this way, data gaps and other radiometric anomalies were removed. These gap-free composites can be used by a variety of stakeholders for land management, statutory reporting and decision making activities. This ensures state-wide consistency, and offers significant savings in processing and storage requirements.
关键词： Compositing,Landsat,Time Series
更新于2025-09-23 15:22:29
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Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms
摘要： The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.
关键词： spectral tracking,time series,plant phenotyping,hyperspectral imaging,disease detection
更新于2025-09-23 15:22:29
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Remote sensing of cropping practice in Northern Italy using time-series from Sentinel-2
摘要： Maps of cropping practice, including the level of weed infestation, are useful planning tools e.g. for the assessment of the environmental impact of the crops, and Northern Italy is an important example due to the large and diverse agricultural production and the high weed infestation. Sentinel-2A is a new satellite with a high spatial and temporal resolution which potentially allows the creation of detailed maps of cropping practice including weed infestation. To explore the applicability of Sentinel-2A for mapping cropping practice, we analysed the Normalised Differential Vegetation Index (NDVI) time series from five weed-infested crop fields as well as the areas designated as non-irrigated agricultural land in Corine Land Cover, which also contributed to an increased understanding of the cropping practice in the region. The analysis of the case studies showed that the temporal resolution of Sentinel-2A was high enough to distinguish the gross features of the cropping practice, and that high weed infestations can be detected at this spatial resolution. The analysis of the entire region showed the potential for mapping cropping practice using Sentinel-2. In conclusion, Sentinel-2A is to some extent applicable for mapping cropping practice with reasonable thematic accuracy.
关键词： Clustering,Phenology,Weed infestation,NDVI,Time-series analysis
更新于2025-09-23 15:22:29
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Mapping Two-Dimensional Deformation Field Time-Series of Large Slope by Coupling DInSAR-SBAS with MAI-SBAS
摘要： Mapping deformation field time-series, including vertical and horizontal motions, is vital for landslide monitoring and slope safety assessment. However, the conventional differential synthetic aperture radar interferometry (DInSAR) technique can only detect the displacement component in the satellite-to-ground direction, i.e., line-of-sight (LOS) direction displacement. To overcome this constraint, a new method was developed to obtain the displacement field time series of a slope by coupling DInSAR based small baseline subset approach (DInSAR-SBAS) with multiple-aperture InSAR (MAI) based small baseline subset approach (MAI-SBAS). This novel method has been applied to a set of 11 observations from the phased array type L-band synthetic aperture radar (PALSAR) sensor onboard the advanced land observing satellite (ALOS), spanning from 2007 to 2011, of two large-scale north–south slopes of the largest Asian open-pit mine in the Northeast of China. The retrieved displacement time series showed that the proposed method can detect and measure the large displacements that occurred along the north–south direction, and the gradually changing two-dimensional displacement fields. Moreover, we verified this new method by comparing the displacement results to global positioning system (GPS) measurements.
关键词： synthetic aperture radar,deformation field time-series,open-pit slope,multiple-aperture InSAR,DInSAR
更新于2025-09-23 15:22:29
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Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery
摘要： Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.
关键词： afforestation,forest inventory,Three-North Shelter Forest Program,time-series,forest monitoring
更新于2025-09-23 15:22:29
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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 - Deformation of Bhuj Earthquake Area Obtained with Persistent Scatterer Interferometric Analysis of Alos L-Band Sar Data
摘要： Long-term ground surface displacements of seismically active Bhuj area in India between two time spans are derived from L-band ALOS-1 and ALOS-2 images using Persistent Scatterer Interferometry approach. The work also focuses on studying the influence of high resolution TanDEM-X Global DEM on topographic phase removal and it's effect on deformation estimates. The results over the 2001 Bhuj earthquake epicentral region indicate minor deformation rates in the range of ±20 mm year?1. Interpretation of results revealed new subsidence areas between 2014 and 2017.
关键词： time series,surface deformation,bhuj earthquake,TanDEM-X Global DEM,persistent scatterer interferometry
更新于2025-09-23 15:21:21