Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets

Cavallo, Carmela; Papa, Maria Nicolina; Gargiulo, Massimiliano; Palau-Salvador, Guillermo; Vezza, Paolo; Ruello, Giuseppe

doi:10.3390/rs13173525

Cited by 39 publications

(22 citation statements)

References 48 publications

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“…Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Building Index (NDBI), etc. are all manifestations of the interaction between bands in a ratio [32]. Among them, NDVI is expressed as (NIR − R)/(NIR + R), which is related to the reflection value of the NIR band and the R band.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

et al. 2021

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In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

et al. 2021

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“…The algorithm adjusted from the extracted pixel values of the NDWI index successfully estimated the volume of water in the rice fields for each generated scene. The results have been compared with the winter flood duration maps of this wetland obtained by Cavallo et al [60]. They combined three multispectral indices (NDWI, MNDWI and NDVI) and developed an automatic classification method of four land cover classes.…”

Section: Discussionmentioning

confidence: 99%

“…Although the volume of water in the rice fields was not analysed in detail, their results are consistent with our observations regarding the extension and duration of submerged zones in different wetland areas. In particular, the areas delineated as Tancats, closer to the Albufera Lake, showed the maximum duration of inundation and were classified as Open Water (W) for an average of 60-80 days [60]. The indices mentioned above have been used to assess long-term land cover variations in other wetlands regions with satisfactory results.…”

Section: Discussionmentioning

confidence: 99%

“…Alonso et al [59] studied the winter flooding of this Mediterranean wetland between 2000 and 2003 using medium resolution spatial sensors. The most recent study, carried out by Cavallo et al [60], involved the development and validation of a method to assess winter flooding of the wetland using Landsat-8 and Sentinel-2 imagery to avoid cloud cover limitations. Their new approach allowed the automatic classification of the wetland cover, creating unique winter flooding duration maps for the last eight years.…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Hydrological Regime Monitoring of a Mediterranean Agro-Ecological Wetland Using Landsat Imagery: Correlation with the Water Renewal Rate of a Shallow Lake

et al. 2021

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The Natural Park of Albufera (Valencia, Spain) is one of the Spanish Mediterranean wetlands where rice is cultivated intensively. The hydrology of the Albufera Lake, located in the center, combines natural contributions with complex human management. The aim of our study was to develop a new methodology to accurately detect the volume of flood water in complex natural environments which experience significant seasonal changes due to climate and agriculture. The study included 132 Landsat images, covering a 15-year period. The algorithm was adjusted using the NDWI index and simultaneous measurements of water levels in the rice fields. The NDVI index was applied to monitor the cultivated area during the summer. Lake inflows and residence times were also evaluated to quantify how the hydrodynamic of the lake is conditioned by the agricultural management. The algorithm developed is confirmed as a useful ecological tool to monitor the flood cycle of the wetland, being able to detect even the lowest water levels. The flood dynamics are consistent over the fifteen years, being in line with the rice cultivation cycle. Water renewal in Albufera lake is altered with respect to that expected according to the rainfall recorded in the study area, so an improvement in the water management of the hydrological basin is required to optimize the runoff during the rainiest months.

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“…Based on the spectral information and morphological and other features in the images, we used the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI), the maximum likelihood method and random forest to extract the edge information of the Yellow River water bodies. MNDWI and NDWI are ratio indexes that rely on the characteristics of river water in multispectral bands (such as the highest and lowest reflectance bands); the differences among land cover types are emphasised by the ratio, with the brightness of water bodies enhanced to their maximum value, while background features are suppressed in processed images [67,68]. The maximum likelihood method is important in supervised classification.…”

Section: Edge Information Extraction For the Yellow River At The Nort...mentioning

confidence: 99%

How Can We Understand the Past from Now On? Three-Dimensional Modelling and Landscape Reconstruction of the Shuanghuaishu Site in the Central Plains of China

et al. 2022

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The Shuanghuaishu (SHS) site in China is one of the 100 most important archaeological discoveries over the past 100 years; its historical heritage can be traced directly back 5300 years. Understanding the early landscape of the site would provide important information about the origin of Chinese civilization. The SHS site is buried and surface traces are difficult to see; therefore, we attempted to reconstruct the early landscape of the site based on a current surface landscape model and environmental archaeological analysis. We created a modern three-dimensional (3D) landscape model of the study area from high spatial resolution unmanned aerial vehicle (UAV) aerial photographs and analysed the distance change between the Yellow River and SHS site in the past 60 years from CORONA and Landsat images. By combining environmental archaeological survey results, archaeological excavation data, relevant papers, and field measurements, we reconstructed the paleotopography of the SHS site during the Yangshao period (7000–5000 aBP). On this basis, 3D natural and human landscapes during the Yangshao period were rebuilt. The results show that (1) Satellite images acquired at different resolutions can provide multiscale spatial information about the site, and high-precision models of current conditions can be quickly generated from UAV aerial photography. (2) From 1960 to 2020, the shortest distance between the SHS site and the Yellow River was approximately 512 m. The location of bedrock on Mang Mountain can be used to infer the early extent of the northern terrace at the site. (3) Environmental archaeology provided information about the palaeoenvironment of the site area. By incorporating spatial information technology and 3D visualization, we can better restore the early landscape of the SHS site. Our work integrates environmental archaeology, field archaeology, and spatial technology, enabling data and modelling support for the visual interpretation of the SHS site.

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Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets

Cited by 39 publications

References 48 publications

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

Long-Term Hydrological Regime Monitoring of a Mediterranean Agro-Ecological Wetland Using Landsat Imagery: Correlation with the Water Renewal Rate of a Shallow Lake

How Can We Understand the Past from Now On? Three-Dimensional Modelling and Landscape Reconstruction of the Shuanghuaishu Site in the Central Plains of China

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