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Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification. [ABSTRACT FROM AUTHOR]

Studying ecosystems using remote sensing technologies is very relevant since it checks the accuracy of the results of modern research. This study aims to monitor environmental changes in ecosystems of the Shahdagh National Park and its surrounding areas in Azerbaijan using Sentinel 2A MSI data. The study aimed to examine and monitor changes in vegetation, water resources, and drought conditions of the study area in recent years. For analyzing and observing these ecosystems Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were calculated using multi-band methods. Obtained indices were compared and changes were investigated analyzing satellite-derived methods. For proper monitoring and assessment of relevant ecosystems, there had been determined 3,825 fishnet points for the study area. This made it possible to compare and coordinate the results of the indices more accurately. After linking fishnet points to raster indices, classification had been made for measuring ecosystems indicators. Vegetation assessments revealed a partial expansion of sparse vegetation or bare rocks, river valleys, as well as nival, subnival, and partial subalpine meadows from 15.1% to 18.1%. Another growth indicator is a significant increase of dense forest ecosystems from 2.3% to 9.2%. According to the results decreases are observed in sparse forests, arable lands, pastures, and shrubs, which are more sensitive to anthropogenic factors. Monitoring of the indices shows that low-humidity areas increase as droughts intensify, especially in plain areas. Finally, the study revealed that the introduction of a specially protected regime within the national park makes ecosystems more sustainable.

Drought has the potential to lead to forest fires. Forest fires generally occur during the dry season when the mountain slope forest experiences a water deficit. Drought identification based on remote sensing is useful for mapping potential fires in Arjuno-Welirang Forest and TNBTS Forest (in Bromo Tengger Semeru National Park). This research used Landsat-8 images in 118/065 and 118/066 in August and November 2015-2018. Validation data were obtained using high resolution planet scope images and rainfall data. Three drought indices were tested to identify fires, namely TVDI, VHI and NDDI. The indices were tested visually using high resolution images and tested meteorologically using SPI. From the results of the accuracy test and correlation, TVDI had the highest accuracy in the Arjuno-Welirang forest (96% accurate), while the best index for TNBTS was the VHI index (96% accurate). [ABSTRACT FROM AUTHOR]

A mapping model of drought-prone zones for agricultural land based on Geographic Information Systems is needed to determine the distribution of drought vulnerability levels that occured in Bantul Regency, DI Yogyakarta. This study aims to determine the estimated area of agricultural land drought based on the interpretation of aerial images. This study compares the performance of the drought potential index of agricultural land using the Normalized Difference Drought Index (NDDI) algorithm based on remote sensing technology/Landsat 8 satellite imagery to identify the estimated zones indicated for agricultural land drought that occurred in Bantul Regency, based on trends in spatio-temporal data with recording intervals from the 2015 until 2020 data representative during the dry season. Comparisons were made by looking at the performance between indices extracted from Landsat 8 imagery data based on the value of the green vegetation parameter/Normalized Difference Vegetation Index (NDVI) and the soil and vegetation moisture parameters/Normalized Difference Water Index (NDWI). The method used in this research is descriptive correlative method: quantitative and qualitative deductive using geostatistical indicators based on big data analysis to measure and compare various data variables spatio-temporal. The distribution of agricultural land drought through the NDDI index transformation method on a normal, mild, moderate, to severe scale occurs in almost all areas of Bantul Regency. This happened, due to the influence of natural activities of the global climate phenomenon ENSO, the impact of the transition of the El Nino phenomenon to La Nina (wet drought) which was more dominant in 2016. The average area affected by drought in Bantul Regency on a normal scale affected was ± 6.500,49 ha, affected by mild drought was ± 17.192,16 ha, affected by moderate-scale drought was ± 8.636,155 ha, and affected by drought of heavy scale agricultural land was ± 2.407,485 ha. [ABSTRACT FROM AUTHOR]

ریز گردها بر وجوه مختلف زندگی و سلامت انسان‌ها اثر می‌گذارند؛ ازاین­رو ترکیب این ذرات و اثرات زیست‌محیطی آن به یک نگرانی بزرگ تبدیل‌شده است. شهرستان شهریار دارای قریب به ۱۲ واحد معدن ازجمله سیلیس، نمک، شن و ماسه و خاک است که این معادن، شهرستان را به یک منبع تولید گردوغبار در استان تهران تبدیل کرده است. گردوغباری که به استان نفوذ می‌کند، از جهت‌های شمال غربی در جهت باد شهریار است. پژوهش حاضر میزان تأثیر معادن شهریار را بر تشدید گردوغبار استان تهران، موردبررسی قرار داده است. جهت ترسیم جهت و سرعت باد منطقه، داده‌های روزانه باد این ایستگاه در بازه‌ زمانی 2007 تا 2015 تهیه گردید. سرعت و جهت باد منطقۀ موردمطالعه با استفاده از نرم‌افزار WRPLOT ترسیم شد. برای استخراج میزان گردوغبار، تصاویر سطح یک سنجنده‌ مودیس ماهوارۀ ترا و آکوا از سایت ناسا تهیه گردید، سپس میزان گردوغبار با استفاده از شاخص‌های NDDI،DBAAEL،AOD همچنین تراکم پوشش گیاهی و رطوبت به ترتیب از شاخص­های NDVI و NDMI استخراج شد. نتایج نشان داد که در فصل تابستان، بهار و پاییز تأثیر معادن شن و ماسه در تشدید ریز گردها فراوان است؛ به‌طوری‌که علاوه برشدت گردوغبار، عمق ذرات گردوغبار در فصول بهار، تابستان و پاییز در استان تهران افزایش می‌یابد که می‌تواند ناشی از حاکمیت شرایط خشک و وجود معادن شن و ماسه در شهرستان باشد؛ بنابراین باد شهریار در انتقال گردوغبار در غرب و جنوب استان سهم قابل‌ملاحظه­ای دارد؛ زیرا معادن شن و ماسه در مسیر باد شهر استقراریافته‌اند؛ بنابراین می‌توان گفت این معادن یکی از عوامل تشدید گردوغبار در استان تهران به‌ویژه جنوب و غرب آن است.

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

In this paper, several spectral indices based on spectral reflectance data from the Terra satellite’s Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor were evaluated in terms of their practical applicability for quantifying drought intensity and its geographical effects to support the drought monitoring applications. A total number of 358 MODIS 8-day composite images for the period between 2000 and 2014 were acquired and processed for the analysis; the frequency of drought phenomenon was increased during this time period. Vegetation indices, water indices, and combinations of both, which were called drought indices, were used. To validate the results, regression analyses were performed and Pearson’s r values were calculated. The Pálfai Drought Index (PAI) and the average annual yields of different crops were used as reference data. The Normalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), the Normalized Difference Water Index (NDWI), the Difference Water Index (DWI), the Difference Drought Index (DDI), and the Normalized Difference Drought Index (NDDI) were found to be significant in quantifying drought intensity. Anomalies relative to the average of the period between 2000 and 2014 were calculated and standardized using the standard deviation, enabling the identification of above-average, drought-stricken areas. The results of this study can be used to create a cost-effective, near real-time and currently missing national drought monitoring system with high temporal resolution to detect regional climate changes, and to assess forest damage probability through changes in the chlorophyll and water content of forest vegetation.

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The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001–2020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001–2020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania.

This research aims to study the spatial characteristics of drought throughout the year in Kuan Kreng Peat Swamp (KKPS) by using various drought indices. Meteorological drought indices were analysed by using data of precipitation during the period of the study 1984 - 2013. The standardized precipitation index (SPI) was calculated on the basis of precipitation deficit. Vegetation-based drought indices were also derived from the analysis of Landsat satellite images based on the normalized difference drought index (NDDI). In addition, hydrological drought indices were studied based on the water table level (WTL) and drought assessments were also based on the standardized water level index (SWI) calculated from data on surface water and the groundwater level in the peat swamp forest. The results are presented in the form of maps of geographic information system (GIS) based on the SPI, NDDI, WTL and SWI. The study focused on the droughts in 2 years: 2010 and 2012. The year 2010 was subject to the El Niño phenomenon while 2012 was not. However, peat fires occurred in both years. The assessment of drought using the SPI, WTL and SWI reveals that drought occurred from April to October due to there being less rainfall during that period. The NDDI reveals that vegetation was affected by the drought between February and September due to this being the summer season with high temperatures and less moisture in the air. The 3 types of drought indices used, meteorological, vegetation and hydrological for the period of April to September indicate the likelihood of peat fires in the KKPS area during that period. The results of this study contribute to the understanding of how spatial and temporal data can be used to predict and measure the severity of drought, to which the study area is vulnerable and to the concomitant risk of peat fires. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this review was to investigate the prevalence of depression in people with epilepsy (PWE) in different countries in Asia.Methods: We searched the electronic database PubMed on June 13, 2017 for articles in English that included the following search terms: "epilepsy" AND "depression" AND "country name" for all Asian countries since 1947. Relevant original studies from Asia were included if they reported the prevalence of depression in PWE. Papers studying special populations (e.g., elderly, veterans, etc.) were not included. In addition, experts in epilepsy field were invited from some Asian countries for an in-depth assessment.Results: Six hundred eighty-seven papers were reviewed and 26 related studies were included in this study. Depression is highly prevalent in PWE in different countries in Asia and the prevalence rates are consistent with rates reported in the literature from other countries: overall, about 25% of PWE suffer from depression.Conclusion: In Asian countries, as elsewhere, depression is common in PWE. High quality data is scarce in many countries and validated screening tools [e.g., Neurological Disorders Depression Inventory for Epilepsy (NDDI-E)] to appropriately investigate the prevalence of depression in PWE are still lacking in many languages. Considering the high prevalence of depression among PWE, routine and periodic screening of all PWE for early detection and appropriate management of depression would be a reasonable approach. [ABSTRACT FROM AUTHOR]

In this paper, several spectral indices based on spectral reflectance data from the Terra satellite's Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor were evaluated in terms of their practical applicability for quantifying drought intensity and its geographical effects to support the drought monitoring applications. A total number of 358 MODIS 8-day composite images for the period between 2000 and 2014 were acquired and processed for the analysis; the frequency of drought phenomenon was increased during this time period. Vegetation indices, water indices, and combinations of both, which were called drought indices, were used. To validate the results, regression analyses were performed and Pearson's r values were calculated. The Pálfai Drought Index (PAI) and the average annual yields of different crops were used as reference data. The Normalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), the Normalized Difference Water Index (NDWI), the Difference Water Index (DWI), the Difference Drought Index (DDI), and the Normalized Difference Drought Index (NDDI) were found to be significant in quantifying drought intensity. Anomalies relative to the average of the period between 2000 and 2014 were calculated and standardized using the standard deviation, enabling the identification of above-average, drought-stricken areas. The results of this study can be used to create a cost-effective, near real-time and currently missing national drought monitoring system with high temporal resolution to detect regional climate changes, and to assess forest damage probability through changes in the chlorophyll and water content of forest vegetation. [ABSTRACT FROM AUTHOR]

Sand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31⁻32); (c) BTD (20⁻31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31⁻32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20⁻31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types.

The purpose of this work is the benefit exploitation of spectral indices such as NDVI, NDWI and NDDI for obtaining drought maps. To this objective, the geographical region of Poopo Lake (Bolivia) is selected as a study area. A set of multitemporal multiband scenes of this geographical area, sensed by Landsat7-ETM+ and Landsat8-OLI/TIRS sensors have been chosen. Experiments on this images datasets provide encouraging results which may be further used for the analysis of change detection, the decision-making and implementing future action plans. [ABSTRACT FROM PUBLISHER]

Currently dust storms are among the most prevalent global environmental issues. They are the early warning indicators for desertification and climate change. The study intends to analyse dust event in Pakistan using normalized dust detection index (NDDI), brightness temperature (BT) and MODIS data of aerosol optical depth (AOD), deep blue angstrom exponent (AE), aerosol index (AI) and HYSPLIT mass trajectory methods. It also highlighted the relationship between NDDI, BT and AOD. The threshold value of NDDI was 0.19 - 0.36 and BT ≤ 310.5 K. Data from CALIPSO, NCEP/NCAR reanalysis datasets were used to validate research's findings. The results found NDDI and BT value for Pakistan and track the movement of dust storm from west to east especially at the height of 5-10km. Dust storms are inversely proportional to relative humidity and pressure. A case study of Dust Storm Events in Pakistan using Brightness Temperature and NDDI [ABSTRACT FROM AUTHOR]

Abstract: Background:: Progress has been made in research on the effects of drug therapy on pediatric patients, but neonates are still an understudied population. Those most likely to receive drug therapy (eg, preterm infants) are least likely to be studied. Objectives:: The purposes of this article are to summarize an initiative developed jointly by the National Institute of Child Health and Human Development (NICHD) and the US Food and Drug Administration (FDA) and to introduce a series of articles developed as a result of this initiative. Methods:: Information for this article was gathered from the proceedings of a workshop cosponsored by the NICHD and the FDA that took place March 29 and 30, 2004, in Rockville, Maryland. Results:: Dosing based on use in adults and older children has resulted in adverse events among newborn infants, and may have long-term effects. Moreover, formulations appropriate for use in neonates are often unavailable, and compensatory efforts such as mixing crushed tablets into formula may interfere with accurate dose delivery. Under the Best Pharmaceuticals for Children Act of 2002, government agencies work with experts in pediatrics and pediatric research to develop and prioritize a list of off-patent drugs for which pediatric studies are urgently needed. Four such listings were published in the Federal Register from January 2003 through January 2005. The NICHD and FDA have also initiated the Newborn Drug Development Initiative (NDDI), a multiphase program to determine gaps in knowledge concerning neonatal pharmacology and clinical trial design and to explore novel study designs for use in newborns, with the ultimate goal of increasing our knowledge about the safety and efficacy of drugs used to treat newborns. Conclusions:: Most drugs used to treat newborns still lack appropriate dosing, efficacy, and safety studies in this vulnerable population. The NICHD and FDA developed the NDDI as an ongoing process to identify and suggest strategies for addressing obstacles to conducting drug trials in the newborn. [Copyright &y& Elsevier]

The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001–2020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001–2020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania. [ABSTRACT FROM AUTHOR]

Sand and dust storms (SDSs), which present environmental risks and affect the regional climate, have been worsened in the East Asian regions over the last decade. Monitoring SDS from space using satellite remote sensing (RS) has become one of the most important issues in this field. At present, satellite RS of SDS is limited to using true-color images or aerosol optical thickness (AOT), or a new algorithm called "Deep Blue". Using current existing approaches makes it difficult to identify SDS from clouds. The authors have detected SDS by combining Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) solar reflectance band (SRB) measurements. Based on the dust spectral characteristic, this letter proposes a normalized difference dust index (NDDI) using MODIS reflectance measurements and applies it to the Asian SDS cases. The simple NDDI index is found to be able to identify SDS and clouds easily. The results suggest that NDDI could be used to detect SDS over bright surfaces where the MODIS AOT product is not available [ABSTRACT FROM PUBLISHER]

Sand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31–32); (c) BTD (20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for 'dust-present' versus 'dust-free' conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31–32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20–31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types. [ABSTRACT FROM AUTHOR]