Ratri 2016 IOP Conf Ser Earth Environ Sci 47 012035

Spatial Information about rice planting season (RPS) in a wide areas, particularly during periods of El Nino, is important to support an information about the availability of rice continously. Application of remote sensing and geographic information system (GIS) technology can support it's information continuously and accurate. In this study, we attempted to identify the rice planting season during El Nino years of 1997, 2006 and 2015 in the Pringsewu district, Lampung and we compared with meteorological drought index. Spatial information of the RPS obtained through Interpretation of multitemporal Landsat data aquired in 1997, 2006 and 2015 using normalized difference vegetation index (NDVI) and the humidity index. While standardized precipitation index (SPI) is used as a indicator of meteorological drought. This study has shown that the application of remote sensing and GIS could accurately monitor the rice planting season during the periods of El Nino in 1997, 2006 and 2015. The fallow land dominated during the El Nino years and there were no significant difference between years. While drought information based on SPI values showed different results between years of El Nino events. In this paper we also discussed the relationship between distribution of fallow land and meteorological drought in a spatial perspective.

IOP Conference Series: Earth and Environmental Science, 2014

Paddy field is important agriculture crop in Indonesia. Rice is a food staple for 237,6 million Indonesian people. Paddy field growth is strongly influenced by water, but the amount of precipitation is unpredictable. Annual and interannual climate variability in Indonesia is unusual. In recent years remote sensing data has been used for measurement and monitoring of precipitation, drought and vegetation index such as Global Satellite Mapping of Precipitation (GSMaP), Multipurpose Transmission SATellite (MTSAT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The objective of this research is to investigate seasonal variability of precipitation, drought and vegetation index in Indonesian paddy field based on remote sensing data. The methodology consists of collecting of enhanced vegetation index (EVI) from MODIS data, mosaicking of image, collecting of region of interest of paddy field, collecting of precipitation and drought index based on Keetch Bryam Drought Index (KBDI) from GSMaP and MTSAT, and seasonal analysis. The result of this research has showed seasonal variability of precipitation, KBDI and EVI on Indonesia paddy field from 2007 until 2012. Precipitation begins from January until May and October until December, and KBDI begins to increase from June and peak in September only in South Sumatera precipitation almost in all month. Seasonal analysis has showed precipitation and KBDI affect on EVI that can indicate variety phenology of Indonesian paddy field. Peak of EVI occurs before peak of KBDI occurs and increasing of KBDI followed by decreasing of EVI. In 2010 all province got higher precipitation and smaller KBDI so EVI has three peaks such as in West Java that can indicated increasing of rice production.

THE PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON MARITIME EDUCATION AND TRAINING (The 5th ICMET) 2021

Drought is a common, slow-moving disaster that is difficult to pinpoint its beginning and end. As a country located in the tropics, Indonesia is sensitive to the El Nino Southern Oscillation (ENSO) climate anomaly, where this event causes drought. Therefore, this study analyzes the drought in Indonesian rice fields by comparing the 2015 El Nino status with the normal 2013 status. CHIRPS data are used in this study to calculate the 9-month-based Standardized Precipitation Index (SPI) for 2013 and 2015. In 2013 the average rice field was normal to the extreme wet range with the largest area under normal conditions (43.46%) and the lowest in severely dry conditions (0.06%). However, in 2015 rice fields were normal to the extreme dry range with the largest area under normal conditions (35.28%) and the lowest in severe wet conditions (0.04%) of the total area of rice fields in Indonesia. The total increase in moderate drought in rice fields throughout Indonesia was 17333.83 km2, severe drought was 20324.69 km2, and extreme drought was 5134.44 km2. The highest increase in extreme drought after the El Nino event was in the Sumatra region with 3075.14 km2, while the highest increase in severe drought and drought occurred in the Java region with a total area of 13263.46 km2 and 12293.53 km2.

This study examined the use of remote sensing in detecting and assessing drought in Iloilo Province, Philippines. A remote sensing-based soil moisture index (SMI), rainfall anomaly data from the Tropical Rainfall Measuring Mission (TRMM), and rice production departure (P d ) data were used for drought detection and validation. The study was conducted using two drought years (2001, 2005) and one non-drought year (2002). According to SMI data, the drought distribution was classified into four major groups. SMI values > 0.3 were considered not to be drought and SMI values < 0.3 were classified as slight, moderate, and severe drought. Results based on SMI revealed that the study area experienced drought in 2001 and 2005, while 2002 exhibited no drought. On the other hand, TRMM-based rainfall anomaly data revealed negative values in 2001 and 2005 and positive values in 2002. Below-normal P d values were observed in 2005 and above-normal values in 2002, whereas nearly normal values prevailed in 2001.

Purpose-This research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield. Design/methodology/approach-A three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data. Findings-Rice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCI min and VCI min are found to be the most proper indices to predict rice yield. Practical implications-The results serve as a guideline for policy-makers and practitioners in the agrofood industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances. Originality/value-This study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010-2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.

International Journal of Remote Sensing and Earth Sciences, 2017

Long droughts experienced in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. MODIS, MTSAT, AMSR-E, TRMM and GSMaP have been used in this activity. Meteorological drought index (SPI) of the daily and monthly rainfall data from TRMM and GSMaP have analyzed for last 10-year period. While, agronomic drought index has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen- day and monthly MODIS, MTSAT, and AMSR-E data at a period of 4 years. Network for satellite data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, University of Tokyo (technical supporter), and NASA. Two information system have been developed: 1) agricultural drought ...

Procedia Environmental Sciences, 2015

Existing drought-impacted data are generally rooted from individual reports, which under-represent spatial information. To improve the report, some meteorological satellites have been employed. Nonetheless, due to lacking of spatial resolution, the scale of the data is often excessively coarse. With the availability of long-term Landsat data, estimated extent of drought has been studied. One of the latest methods for this purpose is Vegetation Supply Water Index (VSWI). VSWI is defined as a ratio between vegetation index (in this case NDVI) and land surface temperature (LST, presented in Kelvin). Both data can be derived from remote sensing data containing multispectral reflectance and thermal data, which are available in Landsat data after calibration procedure. In this research, Landsat 7 sensor was applied considering its temporal span. Landsat data were atmospherically corrected to avoid misinterpretation of the results. We found that VSWI can accommodate various state of drought in agricultural fields. Severely affected fields are represented in dark tone, illustrating the absence of vegetation cover when surface temperature rises. Nonetheless, shortcomings of the technique are visually observable. Based on two kinds of rice field (irrigated and rainfed) coupled with two states of field condition (wet and dry), we conclude that dried and waterlogged irrigated rice fields are inseparable due similar value of NDVI. In contrast, vegetated rice field has fairly high VSWI value. The results indicate that further analysis incorporating water index can improve discrimination process.

Procedia Environmental Sciences, 2016

Agricultural drought monitoring is paramount in order to maintain food security in Indonesia, particularly in Subang and Karawang as the national rice production centers. In this paper, satellite-borne remote sensing data are tested for monitoring drought extent in about 184.486 ha of both regencies. Vegetation Health Index (VHI), a vegetative drought indices based on remote sensing data, is studied in this case using long term sequence of 2000, 2005, 2010, and 2015 dry season Landsat data. VHI collates overall vegetation health, which in turn suitable to indicate agricultural drought extent. It measures either moisture vegetation/vegetation condition (VCI) or thermal condition of vegetation (TCI). Both indices were derived from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data respectively. The results revealed that VHI decreased more than 50 percent, from 30.86 in 2000 to 14.66 in 2015. This figure indicated drought extent intensified in research area, from mild drought to severe drought. The severity was mainly triggered by the rising LST from 27°C in 2000 to 40°C in 2015. In addition, there was a decreasing tendency of NDVI values in recent years, leading agricultural fields more susceptible to drought.

Drought is one of the most frequent hazards that has serious impacts on both agriculture and people's livelihood in the Central Highlands. In this study, Normalized Difference Drought Index (NDDI) retrieved from multi Landsat imageries in March from 1989 to 2017 has been used to recorded drought dynamics in the Central Highlands. The results show that areas of drought-severe area account for 8% to 22% in 1991 and 2005, respectively where mainly distribute in Kon Tum, Dak To (Kon Tum), Ea Sup, Buon Don (Dak Lak), Pleiku, Chu Se, Chu Prong (Gia Lai), Cu Jut (Dak Nong), Lam Ha (Lam Dong) in the study area. Besides historical drought zones between the years 1989 and 2009, the regions including Tu Mo Rong (Kon Tum), La Grai (Gia Lai), Dak Song, Tuy Duc, Dak Glong, Krong No (Dak Nong), Dam Rong (Lam Dong) are recognized to be extendedly drought-impacted area to the present day. Additionally, it is estimated that the drought-impacted area increases moderately from 27% to 42% of total study regions via the period 1989-1999 to 2010-2017, of which the period 2001-2009 witnessed 31% of total study areas was influenced by drought. Therefore, drought mitigation and sustainable management should be paid attention, especially in annual drought-prone areas.

Asian Journal of Geographical Research, 2019

The study is conducted to determine the correlation between climatic parameters and rice yield. The present study is also undertaken to analyze the land cover change in Sylhet district between 2013 and 2018 using LANDSAT-8 images. Local climate and rice yield data are collected from BMD (Bangladesh Meteorological Department) and BRRI (Bangladesh Rice Research Institute) and BBS (Bangladesh Bureau of Statistics). ArcGIS 10.5 and SPSS software are used to show the vegetation condition and correlation coefficient between rice yield and climatic variables respectively. It is revealed from the result that rainfall is negatively correlated with Aman and Boro (local and HYV) rice whereas temperature and relative humidity showed a positive correlation with local Aman and Boro rice. On the other hand, relative humidity showed a strong linear relationship with HYV Boro rice. Finally, both temperature and relative humidity have substantial effects on yields in the Boro rice. Furthermore, veget...