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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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Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2
摘要： Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.
关键词： MNDWI,OSH,SVM,AWEI,water mapping,water classification,shadow,NDWI,Sentinel-2,MuWI,Landsat,water index,multi-spectral water index,sunglint,machine learning
更新于2025-09-23 15:21:01
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Airborne Hyperspectral Evaluation of Maximum Gross Photosynthesis, Gravimetric Water Content, and CO2 Uptake Efficiency of the Mer Bleue Ombrotrophic Peatland
摘要： Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m2) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake ef?ciency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our ?ndings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the ?rst high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 μmol m?2 s?1 and 12 μmol m?2 s?1 were predominant (86.3% of the total area). For hollows, our results revealed, for the ?rst time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake ef?ciency for the whole bog.
关键词： Shortwave Airborne Spectrographic Imager (SASI),Compact Airborne Spectrographic Imager (CASI),carbon uptake,gravimetric water content,normalized difference water index (NDWI),photosynthesis,airborne hyperspectral,bog,Mer Bleue,peatlands
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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A FFT-Based Approach to Explore Periodicity of Vines/Soil Properties in Vineyard from Time Series of Satellite-Derived Spectral Indices
摘要： From literature, NDVI proved to be correlated to vigour and midday stem water potential of vines; NDWI to soil water content. It is thus expected that significant periodicities can be found looking at spectral indices time series. This can be useful to better interpret vines behaviour and, possibly, relate intra-vineyard macroscopic differences to this type of information. To preliminarily test this hypothesis 25 Landsat 8 OLI images, Level-2 Data Products, were processed to compute NDVI and NDWI time series. An interpolation step aimed at generating daily estimates of indices in the explored period (May 2013-February 2016) was performed. Successively vegetation effects were removed from NDWI to emphasize soil signal. Finally a FFT analysis of both NDVI and NDWI interpolated time series was achieved to explore their periodicity, giving a first agronomic interpretation of results.
关键词： NDVI,volumetric water content,water potential,Landsat 8 OLI,NDWI,FFT
更新于2025-09-04 15:30:14