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In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15 years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15 years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

Drought is the most complex climate-related disaster issue in South Asia, because of the various land-cover changes, vegetation dynamics, and climates. The aims of the current research work were to analyze the performance of AVHRR Normalized Difference Vegetation Index (NDVI) and spatiotemporal differences in vegetation dynamics on a seasonal basis by correlating the results with NASA's MERRA precipitation and air temperature for monitoring vegetation dynamics and drought over South Asia. Our approach is based on the use of AVHRR NDVI data and NASA's MERRA rainfall and air temperature data (1990-2011). Due to the low vegetation and dryness, the NDVI is more helpful in describing the drought condition in South Asia. There were rapid increases in NDVI, VHI, and VCI from April to October. Monthly NDVI, VHI, and VCI stabilize in September and improved once more in October and then show a declining trend in December. The monthly PCI, TCI, VHI, and VCI values showed that the South Asia goes through an extreme drought in 2000, which continues up to 2002, which lead the highest water stress. Spatial correlation maps among NDVI, precipitation, air temperature, VHI, and VCI on a seasonal basis. The correlation between NDVI and precipitation showed a significantly higher correlation value in JJA and SON seasons; the spatial correlation between NDVI and air temperature showed significant high values in DJF, JJA, and SON periods, while the correlation between VHI and TCI showed a significantly higher values in MAM and SON seasons, which indicated a good sign for dryness monitoring, mainly for farming regions during these seasons in South Asia. It was confirmed that these indexes are a comprehensive drought monitoring indicator and a step to monitoring the climate change in South Asia, which will play a relevant role ongoing studies on vegetation types, monitoring climate change, and drought over South Asia.

Background: This is a consensus developed by a group of expert endoscopists aiming to standardise the preparation, process and endoscopic procedural steps for diagnosis of early upper gastrointestinal (GI) cancers., Method: The Delphi method was used to develop consensus statements through identification of clinical questions on diagnostic endoscopy. Three consensus meetings were conducted to consolidate the statements and voting. We conducted a systematic literature search on evidence for each statement. The statements were presented in the second consensus meeting and revised according to comments. The final voting was conducted at the third consensus meeting on the level of evidence and agreement., Results: Risk stratification should be conducted before endoscopy and high risk endoscopic findings should raise an index of suspicion. The presence of premalignant mucosal changes should be documented and use of sedation is recommended to enhance detection of superficial upper GI neoplasms. The use of antispasmodics and mucolytics enhanced visualisation of the upper GI tract, and systematic endoscopic mapping should be conducted to improve detection. Sufficient examination time and structured training on diagnosis improves detection. Image enhanced endoscopy in addition to white light imaging improves detection of superficial upper GI cancer. Magnifying endoscopy with narrow-band imaging is recommended for characterisation of upper GI superficial neoplasms. Endoscopic characterisation can avoid unnecessary biopsy., Conclusion: This consensus provides guidance for the performance of endoscopic diagnosis and characterisation for early gastric and oesophageal neoplasia based on the evidence. This will enhance the quality of endoscopic diagnosis and improve detection of early upper GI cancers., Competing Interests: Competing interests: The consensus meeting is supported by a non-government organisation named Asian Novel Bio-Imaging and Intervention Group (ANBIG) which focused on training and education of endoscopic diagnosis and management of early gastrointestinal cancers in Asia. All the authors are members of ANBIG. The manuscript underwent editorial review by a professional academic English editor., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)