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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]

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]

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]

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 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]