scholarly journals Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

2020 ◽  
Vol 12 (14) ◽  
pp. 2188 ◽  
Author(s):  
Simona Niculescu ◽  
Jean-Baptiste Boissonnat ◽  
Cédric Lardeux ◽  
Dar Roberts ◽  
Jenica Hanganu ◽  
...  
Keyword(s):  
Time Series ◽  
Aquatic Macrophytes ◽  
Aquatic Vegetation ◽  
Myriophyllum Spicatum ◽  
Danube Delta ◽  
Backscatter Coefficient ◽  
Macrophyte Communities ◽  
Optical Images ◽  
Detailed Classification ◽  
Floating Vegetation

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.

scholarly journals The effect of ice phenology exerted on submerged macrophytes through physicochemical parameters and the phytoplankton abundance

2015 ◽  
Author(s):  
Wojciech Ejankowski ◽  
Tomasz Lenard
Keyword(s):  
Physicochemical Parameters ◽  
Aquatic Vegetation ◽  
Myriophyllum Spicatum ◽  
Ice Cover ◽  
Water Transparency ◽  
Ceratophyllum Demersum ◽  
Total Biomass ◽  
Eutrophic Lakes ◽  
Macrophyte Species ◽  
Tn:Tp Ratio

<p>The physicochemical parameters of water, the concentration of chlorophyll-<em>a</em> and the submerged aquatic vegetation (SAV) were studied to evaluate the effects of different winter seasons on the biomass of macrophytes in shallow eutrophic lakes. We hypothesised that a lack of ice cover or early ice-out can influence the physicochemical parameters of water and thus change the conditions for the development of phytoplankton and SAV. The studies were conducted in four lakes of the Western Polesie region in mid-eastern Poland after mild winters with early ice-out (MW, 2011 and 2014) and after cold winters with late ice-out (CW, 2010, 2012 and 2013). The concentrations of soluble and total nitrogen, chlorophyll-<em>a</em> and the TN:TP ratio in the lakes were considerably higher, whereas the concentration of soluble and total phosphorus and water transparency were significantly lower after the MW compared with after the CW. No differences were found in water temperature, reaction and electrolytic conductivity. Low water turbidity linked with low concentration of chlorophyll-<em>a</em> after the CW resulted in increased water transparency and the total biomass of the SAV. The negative effect of the MW on the macrophyte species was stronger on more sensitive species (<em>Myriophyllum spicatum</em>,<em> Stratiotes aloides</em>) compared with shade tolerant <em>Ceratophyllum demersum</em>. Our findings show that the ice cover phenology affected by climate warming can change the balance between phytoplankton and benthic vegetation in shallow eutrophic lakes, acting as a shift between clear and turbid water states. We speculate that various responses of macrophyte species to changes in the water quality after two winter seasons (CW and MW) could cause alterations in the vegetation biomass, particularly the expansion of shade tolerance and the decline of light-demanding species after a series of mild winters.</p>

CHEMICAL COMPOSITION OF AQUATIC MACROPHYTES I. INVESTIGATION OF ORGANIC CONSTITUENTS AND NUTRITIONAL POTENTIAL

1978 ◽  
Vol 58 (3) ◽  
pp. 829-841 ◽  
Author(s):  
A. JABBAR MUZTAR ◽  
S. J. SLINGER ◽  
J. H. BURTON
Keyword(s):  
Dry Matter ◽  
Aquatic Macrophytes ◽  
Nutritive Value ◽  
Myriophyllum Spicatum ◽  
Ash Content ◽  
Neutral Detergent Fiber ◽  
Mineral Matter ◽  
Cladophora Glomerata ◽  
Additional Processing ◽  
Gross Energy

Chemical analyses were conducted on unwashed samples of four aquatic plant species harvested at three progressive dates during 1974. All species showed an extremely high ash content. The ash content increased in Potamogeton spp. with progress in the harvesting time and varied only slightly in Cladophora glomerata. Myriophyllum spicatum and Vallisneria americana, harvested in September, were second growth, which was reflected in the much lower ash and considerably higher organic nutrient levels. The neutral-detergent fiber (NDF) level tended to be higher in all species for samples harvested in September. Acid-detergent fiber (ADF) was also higher during the same month except in Potamogeton spp. With the exception of Potamogeton spp., the level of acid-detergent lignin (ADL) was similar in all species at the different dates. Both NDF and ADF values were inflated because of unavoidable contamination with mineral matter. A further experiment with washed and unwashed plant samples harvested the following year showed that washing decreased the ash content markedly, in most cases with a concomitant increase in proximate constituents and gross energy values. All species, either washed or unwashed, were very low in dry matter (5–20%); the NDF and ADF levels for most plants were similar to those of alfalfa, while ADL content was relatively low. Results indicated that aquatic macrophytes would have nutritive value similar to alfalfa; however, their high ash and low dry matter contents would necessitate quality control and additional processing for possible use as feedstuffs.

Classification of Danube Delta lakes based on aquatic vegetation and turbidity

1999 ◽  
pp. 187-191
Author(s):  
Hugo Coops ◽  
Jenica Hanganu ◽  
Marian Tudor ◽  
Willem Oosterberg
Keyword(s):  
Aquatic Vegetation ◽  
Danube Delta

scholarly journals DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1619 ◽  
Author(s):  
Zhou Ya’nan ◽  
Luo Jiancheng ◽  
Feng Li ◽  
Zhou Xiaocheng
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Hunan Province ◽  
Spatial Features ◽  
Optical Images ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

scholarly journals In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

2019 ◽  
Vol 11 (2) ◽  
pp. 118 ◽  
Author(s):  
Valérie Demarez ◽  
Florian Helen ◽  
Claire Marais-Sicre ◽  
Frédéric Baup
Keyword(s):  
Time Series ◽  
Shuttle Radar Topography Mission ◽  
Irrigation Scheduling ◽  
Landsat 8 ◽  
Radar Images ◽  
Optical Images ◽  
Multi Temporal ◽  
Irrigated Crops ◽  
Elevation Model ◽  
The Impact

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome.

scholarly journals Crop Mapping from Sentinel-1 Polarimetric Time-Series with a Deep Neural Network

2020 ◽  
Vol 12 (15) ◽  
pp. 2493
Author(s):  
Yang Qu ◽  
Wenzhi Zhao ◽  
Zhanliang Yuan ◽  
Jiage Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Learning Strategy ◽  
Series Data ◽  
Security Assessment ◽  
Multiple Perspectives ◽  
Backscatter Coefficient ◽  
Crop Mapping ◽  
Agricultural Areas

Timely and accurate agricultural information is essential for food security assessment and agricultural management. Synthetic aperture radar (SAR) systems are increasingly available in crop mapping, as they provide all-weather imagery. In particular, the Sentinel-1 sensor provides dense time-series data, thus offering a unique opportunity for crop mapping. However, in most studies, the Sentinel-1 complex backscatter coefficient was used directly which limits the potential of the Sentinel-1 in crop mapping. Meanwhile, most of the existing methods may not be tailored for the task of crop classification in time-series polarimetric SAR data. To solve the above problem, we present a novel deep learning strategy in this research. To be specific, we collected Sentinel-1 time-series data in two study areas. The Sentinel-1 image covariance matrix is used as an input to maintain the integrity of polarimetric information. Then, a depthwise separable convolution recurrent neural network (DSCRNN) architecture is proposed to characterize crop types from multiple perspectives and achieve better classification results. The experimental results indicate that the proposed method achieves better accuracy in complex agricultural areas than other classical methods. Additionally, the variable importance provided by the random forest (RF) illustrated that the covariance vector has a far greater influence than the backscatter coefficient. Consequently, the strategy proposed in this research is effective and promising for crop mapping.

Superpixel-based time-series reconstruction for optical images incorporating SAR data using autoencoder networks

2020 ◽  
Vol 57 (8) ◽  
pp. 1005-1025
Author(s):  
Ya’nan Zhou ◽  
Xianzeng Yang ◽  
Li Feng ◽  
Wei Wu ◽  
Tianjun Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Optical Images ◽  
Sar Data

scholarly journals Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 820 ◽  
Author(s):  
Haifeng Tian ◽  
Ni Huang ◽  
Zheng Niu ◽  
Yuchu Qin ◽  
Jie Pei ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Windows ◽  
Vegetation Indices ◽  
Optical Images ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Winter Crop ◽  
Winter Crops ◽  
Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Effects of aquatic macrophytes on physico-chemical conditions of three contrasting lowland streams: a consequence of diffuse pollution from agriculture?

2001 ◽  
Vol 43 (5) ◽  
pp. 163-168 ◽  
Author(s):  
R. J. Wilcock ◽  
J. W. Nagels
Keyword(s):  
Aquatic Macrophytes ◽  
Aquatic Vegetation ◽  
Submerged Macrophytes ◽  
Third Stream ◽  
Lowland Streams ◽  
Riparian Plants ◽  
Agricultural Catchments ◽  
Physico Chemical ◽  
Photosynthetic Production ◽  
Chemical Conditions

Three lowland streams in developed pasture catchments with different farming intensities exhibited contrasting summer diurnal variations in pH, DO and temperature. These are ascribed to differences in dominant aquatic vegetation and their respective effects on shade, and on photosynthetic production and respiration within each stream. The stream dominated by submerged macrophytes had the greatest amplitude swings in DO and pH, and DO levels of 86–128% saturation. Floating marginal macrophytes reduced photosynthetic inputs while providing additional organic loading for respiration, with consequent flat DO and pH curves and conditions not conducive to healthy stream ecosystems. The third stream was shaded by riparian plants, which inhibited photosynthetic effects on DO and pH so that diurnal variation was intermediate between the other two streams. The interaction between nutrients and increased insolation in agricultural catchments, in stimulating aquatic plants, needs to be better understood for managing the sustainability of stream habitats and ecosystems.

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