water index
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2022 ◽  
Vol 14 (2) ◽  
pp. 374
Author(s):  
Xueying Zhou ◽  
Zhaoqiang Huang ◽  
Youchuan Wan ◽  
Bin Ni ◽  
Yalong Zhang ◽  
...  

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.


2022 ◽  
Author(s):  
Jiawei Zhou ◽  
Xiaohong Chen ◽  
Chuang Xu ◽  
Pan Wu

Abstract Socioeconomic drought is a phenomenon of water shortage caused by an imbalance between the supply and demand of water resources in natural and human socioeconomic systems. Occurrence of these droughts is closely related to sustainable socioeconomic development. However, compared with meteorological drought, hydrological drought and agricultural drought, socioeconomic drought has received relatively little attention. Therefore, this paper proposes a universal and relatively simple socioeconomic drought assessment index, the Standardized Supply and Demand Water Index (SSDWI). Taking the Jianjiang River Basin (JJRB) in Guangdong Province, China as an example, socioeconomic drought characteristics and trends during 1985-2019 were analyzed. The return period of different levels of drought were calculated using a copula function to estimate the risk of socioeconomic drought in the basin, and the relationship between socioeconomic, meteorological, and hydrological droughts and their potential drivers were discussed. The results showed that: (1) SSDWI was a better index for characterizing socioeconomic drought in the JJRB. 29 socioeconomic droughts occurred in the basin during the past 35 years, with an average duration of 6.16 months and an average severity of 5.82 per events. Socioeconomic droughts mainly occurred in autumn and winter, which also had more severe droughts than other seasons. (2) In the JJRB, the joint return periods of ‘∪’ and ‘∩’ for moderate drought, severe drought and extreme drought were 8.81a and 10.81a, 16.49a and 26.44a, and 41.68a and 91.13a, respectively; (3) Due to the increasing outflow from Gaozhou Reservoir, the risk of socioeconomic drought and hydrological drought in the JJRB has significantly declined since 2008. The reasonable operation of the reservoir has played an important role in alleviating the hydrological and socioeconomic drought in the basin.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Max Kistler ◽  
Hannes Köhler ◽  
Jan Theopold ◽  
Ines Gockel ◽  
Andreas Roth ◽  
...  

AbstractTo investigate, whether hyperspectral imaging (HSI) is able to reliably differentiate between healthy and damaged cartilage tissue. A prospective diagnostic study was performed including 21 patients undergoing open knee surgery. HSI data were acquired during surgery, and the joint surface’s cartilage was assessed according to the ICRS cartilage injury score. The HSI system records light spectra from 500 to 1000 nm and generates several parameters including tissue water index (TWI) and the absorbance at 960 nm and 540 nm. Receiver operating characteristic curves were calculated to assess test parameters for threshold values of HSI. Areas with a cartilage defect ICRS grade ≥ 3 showed a significantly lower TWI (p = 0.026) and higher values for 540 nm (p < 0.001). No difference was seen for 960 nm (p = 0.244). For a threshold of 540 nm > 0.74, a cartilage defect ICRS grade ≥ 3 could be detected with a sensitivity of 0.81 and a specificity of 0.81. TWI was not suitable for cartilage defect detection. HSI can provide reliable parameters to differentiate healthy and damaged cartilage. Our data clearly suggest that the difference in absorbance at 540 nm would be the best parameter to achieve accurate identification of damaged cartilage.


Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 172
Author(s):  
Sara Azadi ◽  
Hojat Yazdanpanah ◽  
Mohammad Ali Nasr-Esfahani ◽  
Saeid Pourmanafi ◽  
Wouter Dorigo

The Gavkhouni wetland provides many environmental and economic benefits for the central region of Iran. In recent decades, it has completely dried up several times with substantial impacts on local ecosystems and climate. Remote sensing-based Land Surface Temperature (LST), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) in combination with in-situ data were used to investigate the trend of the Gavkhouni wetland dryness and the associated impact on the variability of local air temperature. The results indicate that the wetland has increasingly experienced drier conditions since the year 2000. The wetland was almost completely dry in 2009, 2011, 2015 and 2017. In addition, the results show that Gavkhouni wetland dryness has a significant impact on local climate, increasing the mean seasonal air temperature by ~1.6 °C and ~1 °C in spring and summer, respectively. Overall, this study shows that remote sensing imagery is a valuable source for monitoring dryness and air temperature variations in the region. Moreover, the results provide a basis for effective water allocation decisions to maintain the hydrological and ecological functionality of the Gavkhouni wetland. Considering that many factors such as latitude, cloud cover, and the direction of prevailing winds affect land surface and air temperatures, it is suggested to use a numerical climate model to improve a regional understanding of the effects of wetland dryness on the surrounding climate.


2022 ◽  
Author(s):  
tao su ◽  
Jian Wang ◽  
Xingyuan Cui ◽  
Lei Wang

Abstract Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.


2022 ◽  
pp. 1098-1117
Author(s):  
Raphael Muli Wambua

Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ³ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.


2021 ◽  
Vol 2 (2) ◽  
pp. 9-20
Author(s):  
Fajar Setiawan ◽  
Iwan Ridwansyah ◽  
Luki Subehi

Wetlands are vulnerable natural habitats that should be preserved to protect habitat for fish and wildlife, flood mitigation, improve water quality, recharge area, and maintain surface water flow during dry periods. Water bodies and swamp areas are two primary components of the wetland. Considering its essential roles for the ecosystem, Lake Sentarum was set as a national park area (Lake Sentarum National Park – TNDS), Indonesia's 15 national priority lakes, and; designated as a Ramsar site (The Convention on Wetlands) in Indonesia. Despite the significant roles for the ecosystem, providing the limnological characteristic of Lake Sentarum remains a challenge due to its remote location. This study aims to identify the rainfall and inundation characteristics in the Lake Sentarum area and develop the rainfall-inundation relationship in the TNDS area. First, we carried out rainfall analysis using the Climate Forecast System Reanalysis (CFSR) data. Second, we utilized a remote-sensing-based global surface water map from the Joint Research Centre (JRC) to describe the historical inundation pattern. Third, we applied the Normalized Different Water Index (NDWI) combined with Modification Normalized Different Water Index (MNDWI) to the selected Landsat dataset to extract the inundation area. Finally, we developed a rainfall-inundation relationship in the TNDS area. The result indicated that the yearly rainfall in the TNDS area has an increasing trend, with the highest peak in December and the second peak in April. Historical Landsat data shows that the TNDS has a complex pattern of inundation. The maximum water extent was 649 km2, with a 95 km2 as permanent (90>- 100 % water occurrence). These areas were constantly flooded, even in the dry season. The most significant non-permanent water was 161 km2 (80>- 90 % water occurrence). This permanent and larger temporary water area provides fish and other aquatic biotas habitats. It temporarily stores the water flowing slowly into the River Kapuas through the Tawang River. We captured the spatial inundation pattern and its relationship with the temporal regional rainfall. The developed relationship showed a lag of -60 days of accumulated rainfall correlated with the inundation area (R2 of 0.48, n=11). These findings will thus provide valuable data for lake managers and policy-makers to protect the biota and habitat in Lake Sentarum National Park area.


2021 ◽  
Author(s):  
Claudiu Valeriu Angearu ◽  
Irina Ontel ◽  
Anisoara Irimescu ◽  
Burcea Sorin

Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.


Author(s):  
Annisa Rizky Kusuma ◽  
Fauzan Maulana Shodiq ◽  
Muhammad Faris Hazim ◽  
Dany Puguh Laksono

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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