scholarly journals Multitemporal observation of Karla reservoir in Thessaly Greece utilizing SAR and optical remotely sensing imagery

2020 ◽  
Vol 11 (4) ◽  
pp. 144-156
Author(s):  
Triantafyllos FALARAS ◽  
◽  
Maria KOILAKOU ◽  
Leonidas TSOUKALAS ◽  
◽  
...  
Keyword(s):  
Natural Environments ◽  
Segmentation Method ◽  
Mean Values ◽  
Wetland Ecosystems ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Seasonal Basis ◽  
Optical Imagery ◽  
Sar Imagery ◽  
Sentinel 2

Wetlands constitute areas with significant value and service offerings both in anthropogenic and natural environments. Taking into consideration the importance of studying wetland ecosystems and their changes, the aim of this paper is the observation of Lake Karla’s reservoir fluctuation, since its reconstitution in 2010. For this reason, annual and seasonal fluctuations of the reservoir were estimated, utilizing remote sensing Synthetic Aperture Radar (SAR) (Sentinel 1) and optical (Sentinel 2) imagery, as well as Landsat 5 imagery. For SAR imagery an image segmentation method with a dynamic threshold operator based on mean values is utilized, while for optical imagery the Normalized Difference Water Index (NDWI) is applied. The findings reveal changes in Karla’s reservoir, with its acreage being continually increased on average on an annual basis. Meanwhile, on a seasonal basis, the results indicate some variations in the reservoir, due to precipitation and irrigation purposes.

Fusion of Sentinel-1 radar and Sentinel-2 MSI imagery for water extraction in Tibetan plateau

2020 ◽  
Author(s):  
Dan Li ◽  
Baosheng Wu ◽  
Bowei Chen ◽  
Yanjun Wang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radar Data ◽  
Google Earth ◽  
Water Extraction ◽  
Tibet Plateau ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine ◽  
Optical Imagery ◽  
Sentinel 2

<p><strong>Abstract:</strong> Water plays a vital role in plants, animals and human survival, as well as water resources planning and protection. The spatial and temporal changes of rivers have a profound impact on climate change and the scientific protection of the regional ecological environment in Qingzang-Tibet plateau. Due to the influence of snow and cloud cover, optical remote sensing images in this region have less effective coverage. Many researches in the past mainly faced the challenge of misclassification caused by shadows from cloud and mountain. In this study, we proposed a method to improve the extraction of rivers by reducing the effect of shadows by fusing Sentinel-1 radar data and Sentinel-2 optical imagery. For the optical imagery, water indices including MNDWI (Modified Normalized Difference Water Index) and RNDWI (Revised Normalized Difference Water Index) and morphological operations were used to extract the river coverage. In addition, radar data is used to extract water in areas where there is no optical image coverage or where optical images are misclassified by using a combination of both the histogram and Otsu threshold methods. The GEE (Google Earth Engine) platform is used to implement the analysis using two classification datasets at a regional level. Relevant results from Sentinel-1 and Sentinel-2 data showed that the RNDWI has a more accurate water extraction results in this region. We further compared the final river width results with the manually measured samples from Google Earth and situ data of hydrological stations for accuracy assessment. The R<sup>2 </sup>value is 0.90, and the standard deviation is 18.663m. The river width can be estimated well by this method, which can provide basic data for the study of water in depopulated zone.</p><p><strong>Keywords: </strong>Remote sensing, shadow removal, water extraction, water index, Otsu threshold, Google Earth Engine</p>

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Inland Saline Wetlandscapes

2020 ◽  
pp. 242-268
Author(s):  
Laxmikant Sharma ◽  
Rajashree Naik ◽  
Alok Raj
Keyword(s):  
Salt Lake ◽  
Research Work ◽  
Vital Role ◽  
Ramsar Convention ◽  
Wetland Ecosystems ◽  
Water Index ◽  
Strategic Plans ◽  
The World ◽  
Normalized Difference Water Index

Wetland ecosystems are one of the highly productive ecosystems in the world. These ecosystems have been deteriorating at a faster rate. Ramsar Convention is putting enormous effort to protect, maintain, and restore these ecosystems. Currently, the fourth phase of Strategic Plans of Ramsar Convention is going on, in which saline wetlandscapes can play vital role to attain 19 targets of this plan. In India there are 27 Ramsar sites in all the biogeographic zones; however, research work has been carried out in the past five years in only eight Ramsar sites. Currently, four years are available for the strategic plans to encourage more wetland researches. The chapter presents a case study of Sambhar Salt Lake, a Ramsar site of India that is on the verge of extinction. Normalized Difference Water Index has been calculated for three decades in 1992, 2009, and 2019, revealing the declining phases of the lake.

scholarly journals DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2

2020 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Suwarsono Suwarsono ◽  
Fajar Yulianto ◽  
Hana Listi Fitriana ◽  
Udhi Catur Nugroho ◽  
Kusumaning Ayu Dyah Sukowati ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Area ◽  
Water Index ◽  
Index Model ◽  
Normalized Difference Water Index ◽  
Sentinel 2 ◽  
Water Vegetation

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir.

scholarly journals Detecção e delimitação de pequenos reservatórios na Bacia Hidrográfica do Rio Cachoeira, Bahia, utilizando imagens multiespectrais

2020 ◽  
Vol 20 (4) ◽  
pp. 458-475
Author(s):  
Sabrina Brandão Cardoso ◽  
Caroline Favoreto da Cunha ◽  
Bruno Zanon Engelbrecht ◽  
Hung Kiang Chang
Keyword(s):  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

No presente trabalho foram utilizadas imagens multiespectrais do satélite Sentinel-2 da Bacia Hidrográfica do Rio Cachoeira (BHRC), localizada no sul do estado da Bahia. O objetivo deste trabalho foi detectar, delimitar e quantificar a área ocupada por reservatórios de água na BHRC. Para tanto foram calculados os índices MNDWI (Modified Normalized Difference Water Index) e NDWI (Normalized Difference Water Index). A capacidade de detecção de pequenos corpos d’água pelos métodos empregados mostrou-se satisfatória, apresentando uma correspondência de até 78% entre os métodos, com superiores resultados para índice MNDWI frente ao NDWI. A partir desses índices foram observadas variações sazonais e espaciais quanto à distribuição de reservatórios na BHRC. A porção sudoeste da bacia apresentou maior concentração de pequenos reservatórios no período chuvoso. No contexto geral da bacia hidrográfica, os reservatórios de água ocupam até 0,13% da área da bacia, enquanto que em determinadas áreas do sudoeste da BHRC esse valor atinge até 0,86%.

scholarly journals Synoptic Characteristics of Torrential Rains in Southwest and Southeast Iraq: A Case Study

2021 ◽  
Vol 32 (3) ◽  
pp. 1
Author(s):  
Aqeel Ghazi Mutar ◽  
Asraa Khtan ◽  
Loay E. George
Keyword(s):  
Satellite Images ◽  
Research Work ◽  
Heavy Rain ◽  
Moisture Flux ◽  
Relative Vorticity ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Modern Era ◽  
Sentinel 2

Torrential rains cause many losses in city infrastructure, crops, and deaths in several regions of the world including Iraq as in the case that we will discuss in this work, on January 28 and 29, 2019. Torrential rain caused the flow of torrents in several areas of Iraq and the neighboring areas. This research work aims to identify the synoptic characteristics of torrential rains and the causes of this case. This will be done by analyzing and interpreting the weather maps at different pressure levels with focusing on the troughs and fronts locations, relative vorticity, polar jet stream effect as well as the moisture flux. The Geographic Information System (GIS) was used to analyze the satellite images in order to calculate the Normalized Difference Water Index (NDWI) to confirm the heavy rain case. The weather maps were obtained from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2).  As for the satellite images we used the satellite imagery from Sentinel-2 and EMUTSAT.

scholarly journals Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 945
Author(s):  
Hao Jiang ◽  
Mo Wang ◽  
Hongda Hu ◽  
Jianhui Xu
Keyword(s):  
Noise Reduction ◽  
Spatial Resolution ◽  
Recall Rate ◽  
Optical Observations ◽  
Water Index ◽  
Mountainous Regions ◽  
Data Assessment ◽  
Normalized Difference Water Index ◽  
F Measure ◽  
Sentinel 2

Accurate waterbody mapping can support water-related environment monitoring and resource management. The Sentinel series satellites provide high-quality Synthetic Aperture Radar (SAR) and optical observations that are commonly used in waterbody mapping. However, owing to the 10-m spatial resolution of Sentinel data, previous studies mostly focused on the mapping of large waterbodies. In this work, we evaluated the performance of small waterbody mapping over urban and mountainous regions with two datasets, the average annual VH backscatter coefficients (VHavg), derived from the Sentinel-1A series, and the Modified Normalized Difference Water Index (MNDWI), derived from cloud-free Sentinel-2. A proven framework of waterbody mapping based on watershed segmentation and noise reduction was employed to assess the performance of the two datasets in waterbody identification. The validation was performed by comparing their results with 1-m spatial resolution reference waterbody data. Assessment metrics, including Precision, Recall, and F-measure, were employed. Results showed that: (1) the MNDWI outperformed the VHavg by 9 percentage points of the F-measure; (2) there was more room for results of VHavg to improve the accuracy through a combination with noise reduction; and (3) the potential smallest identifiable waterbody area (recall rate larger than 0.8) was larger than 104 m2.

scholarly journals Detecção e delimitação de pequenos reservatórios na Bacia Hidrográfica do Rio Cachoeira, Bahia, utilizando imagens multiespectrais

2020 ◽  
Vol 20 (4) ◽  
pp. 458-475
Author(s):  
Sabrina Brandão Cardoso ◽  
Caroline Favoreto da Cunha ◽  
Bruno Zanon Engelbrecht ◽  
Hung Kiang Chang
Keyword(s):  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

No presente trabalho foram utilizadas imagens multiespectrais do satélite Sentinel-2 da Bacia Hidrográfica do Rio Cachoeira (BHRC), localizada no sul do estado da Bahia. O objetivo deste trabalho foi detectar, delimitar e quantificar a área ocupada por reservatórios de água na BHRC. Para tanto foram calculados os índices MNDWI (Modified Normalized Difference Water Index) e NDWI (Normalized Difference Water Index). A capacidade de detecção de pequenos corpos d’água pelos métodos empregados mostrou-se satisfatória, apresentando uma correspondência de até 78% entre os métodos, com superiores resultados para índice MNDWI frente ao NDWI. A partir desses índices foram observadas variações sazonais e espaciais quanto à distribuição de reservatórios na BHRC. A porção sudoeste da bacia apresentou maior concentração de pequenos reservatórios no período chuvoso. No contexto geral da bacia hidrográfica, os reservatórios de água ocupam até 0,13% da área da bacia, enquanto que em determinadas áreas do sudoeste da BHRC esse valor atinge até 0,86%.

scholarly journals Hasil Studi Pola Kebakaran Lahan Gambut melalui Citra Satelit Sentinel-2 dengan Pengimplementasian Machine Learning Metode Random Forest : Kajian Literatur

2021 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Annisa Rizky Kusuma ◽  
Fauzan Maulana Shodiq ◽  
Muhammad Faris Hazim ◽  
Dany Puguh Laksono
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Water ◽  
Water Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Water Level ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

Kebakaran lahan gambut merupakan peristiwa yang sulit diprediksi perilakunya. Karakteristik tanah gambut yang kompleks dan faktor-faktor alam lain seperti arah angin, status vegetasi, dan kandungan air membuat kasus ini menjadi salah satu kasus menarik yang masih menjadi objek penelitian yang belum tuntas hingga saat ini. Ketika memasuki musim kemarau kondisi kadar air di dalam tanah gambut akan semakin berkurang, maka potensi terjadinya kebakaran akan semakin tinggi. Pada studi ini dilakukan analisis faktor penyebab kebakaran dengan area cakupan yang luas melalui satelit Sentinel-2. Citra satelit yang diperoleh nantinya akan diolah oleh machine learning untuk memprediksi penyebaran api. Hasil literatur yang telah dilakukan diperoleh bahwa Ground Water Level (GWL), kematangan gambut, suhu, curah hujan dan kelembaban, serta kerapatan vegetasi dapat diidentifikasi melalui perhitungan indeks. Indeks yang digunakan diantaranya indeks Differenced Normalized Difference Vegetation Index (dNDVI) dan Normalized Difference Water Index (NDWI) yang diolah dengan algoritma machine learning metode Random Forest memilki akurasi mencapai 96%.

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