Your search found 5 records
1 Pani, Peejush; Alahacoon, Niranga; Amarnath, Giriraj; Bharani, Gurminder; Mondal, S.; Jeganathan, C. 2016. Comparison of SPI [Standardized Precipitation Index] and IDSI [Integrated Drought Severity Index] applicability for agriculture drought monitoring in Sri Lanka. Paper presented at the 37th Asian Conference on Remote Sensing (ACRS): Promoting Spatial Data Infrastructure for Sustainable Economic Development, Colombo, Sri Lanka, 17-21 October 2016. 8p.
Precipitation ; Drought ; Monitoring ; Agriculture ; Climate change ; Meteorology ; Remote sensing ; Vegetation ; Rain ; Temperature ; Soil moisture ; Spatial distribution ; Statistical analysis / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H047942)
Increasing frequency of drought events coupled uncertainty imparted by climate change pose grave threat to agriculture and thereby overall food security. This is especially true in South Asian region where world’s largest concentration of people depends on agriculture for their livelihood. Indices derived from remote sensing datasets signifying different bio-physical aspects are increasingly used for operational drought monitoring. This study focuses on evaluating a newly created index for agricultural drought referred as Integrated Drought Severity Index (IDSI) in comparison with the traditional Standardized Precipitation Index (SPI) primarily representing precipitation condition to delineate drought using custom created ArcGIS toolbox for a period of fourteen years (2001-2014) in Sri Lanka. SPI created using remotely sensed PERSIANN precipitation dataset was compared with the IDSI created using hybrid datasets. IDSI is created based on seamless mosaic of remotely sensed multi-sensor data that takes vegetation (computed from MODIS data product MOD09A1), temperature (MOD11A2) and precipitation (TRMM & GPM) status into consideration. The comparative study was made to assess the efficiency of newly created index and ArcGIS toolbox techniques for near real-time monitoring of spatio-temporal extent of agricultural drought. The result showed significant correlation of 0.85 between the two indices signifying the potential of using IDSI that integrates the response of agriculture drought variables (vegetation, rainfall, temperature and soil moisture) in monitoring shortterm drought and application in risk reduction measures.
2 Alahacoon, Niranga; Pani, Peejush; Matheswaran, Karthikeyan; Samansiri, S.; Amarnath, Giriraj. 2016. Rapid emergency response mapping for the 2016 floods in Kelani river basin, Sri Lanka. Paper presented at the 37th Asian Conference on Remote Sensing (ACRS): Promoting Spatial Data Infrastructure for Sustainable Economic Development, Colombo, Sri Lanka, 17-21 October 2016. 9p.
Natural disasters ; Disaster preparedness ; Flooding ; Emergency relief ; River basins ; Radar satellite ; Satellite imagery ; Landslides ; Remote sensing / Sri Lanka / Kelani River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H047943)
Beginning on 14 May 2016, a low pressure area over the Bay of Bengal caused torrential rain to fall across Sri Lanka. Some locations saw over 350 mm (13.77 inches) of rain fall in 24 hours. Floods and landslides have caused havoc in as many as 19 districts of the country, including around Colombo, causing floods and landslides which affected half a million people with causality reported over 100 and estimated economic losses closer to $2billion. In recent years, due to an increasing number in the frequency and intensity of extreme meteorological events potentially related to climate change, a growing attention has been paid to the operational use of satellite remote sensing applied to emergency response and relief measures. This is mainly due to the large and timely availability of different types of remotely sensed data as well as geospatial information acquired in the field which may be potentially exploited in the different phases of the disaster management cycle. IWMI jointly with Disaster Management Centre (DMC), Sri Lanka activated disaster charter with Sentinel Asia and escalated International Disaster Charter to access satellite images during the crisis response phase to support government agencies in relief and rescue measures. A total of 13 satellite images both microwave and optical datasets (ALOS-2, Sentinel-1, RISAT-1, RADARSAT-2, TerraSAR-X, FORMOSAT, Landsat-8) were provided by various space agencies to generate flood situation maps on a daily basis. The emergency flood situation maps were regularly shared to national and international organizations within 3-4 hours after the post-event image is acquired by the space agencies to support in relief measures. The derived flood maps were overlaid with local administrative division to give specific information on the priority area to the DMC and Air Force authorities to focus relief measures. These rapid response maps can further be used for postdisaster relief policy and damage assessment.
3 Mondal, S.; Jeganathan, C.; Amarnath, Giriraj; Pani, Peejush. 2017. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring. GIScience and Remote Sensing, 54(2):202-229. [doi: https://doi.org/10.1080/15481603.2017.1286726]
Climate change ; Drought ; Clouds ; Noise ; Monitoring ; Vegetation ; Satellite observation ; Satellite imagery ; Land cover mapping ; Remote sensing ; Spatial distribution ; Models ; Statistical methods ; Performance evaluation ; Homogenization ; Agriculture / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H048010)
Moderate Resolution Imaging Spectro-radiometer (MODIS) time-series Normalized Differential Vegetation Index (NDVI) products are regularly used for vegetation monitoring missions and climate change analysis. However, satellite observation is affected by the atmospheric condition, cloud state and shadows introducing noise in the data. MODIS state flag helps in understanding pixel quality but overestimates the noise and hence its usability requires further scrutiny. This study has analyzed MODIS MOD09A1 annual data set over Sri Lanka. The study presents a simple and effective noise mapping method which integrates four state flag parameters (i.e. cloud state, cloud shadow, cirrus detected, and internal cloud algorithm flag) to estimate Cloud Possibility Index (CPI). Usability of CPI is analyzed along with NDVI for noise elimination. Then the gaps generated due to noise elimination are reconstructed and performance of the reconstruction model is assessed over simulated data with five different levels of random gaps (10–50%) and four different statistical measures (i.e. Root mean square error, mean absolute error, mean bias error, and mean absolute percentage error). The sample-based analysis over homogeneous and heterogeneous pixels have revealed that CPI-based noise elimination has increased the detection accuracy of number of growing cycle from 45–60% to 85–95% in vegetated regions. The study cautions that usage of time-series NDVI data without proper cloud correction mechanism would result in wrong estimation about spatial distribution and intensity of drought, and in our study 50% of area is wrongly reported to be under drought though there was no major drought in 2014.
4 Alahacoon, Niranga; Matheswaran, Karthikeyan; Pani, Peejush; Amarnath, Giriraj. 2018. A decadal historical satellite data and rainfall trend analysis (2001–2016) for flood hazard mapping in Sri Lanka. Remote Sensing, 10(3):1-18. [doi: https://doi.org/10.3390/rs10030448]
Satellite imagery ; Satellite observation ; Radar satellite ; Rain ; Mapping ; Flooding ; Flood control ; Natural disasters ; Economic situation ; River basins ; Monsoon climate ; Risk management ; Catchment areas / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H048581)
(10.8 MB)
Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.
5 Amarnath, Giriraj; Pani, Peejush; Alahacoon, Niranga; Chockalingam, J.; Mondal, S.; Matheswaran, K.; Sikka, Alok; Rao, K. V.; Smakhtin, Vladimir. 2019. Development of a system for drought monitoring and assessment in South Asia. In Mapedza, Everisto; Tsegai, D.; Bruntrup, M.; McLeman, R. (Eds.). Drought challenges: policy options for developing countries. Amsterdam, Netherlands: Elsevier. pp.133-163. (Current Directions in Water Scarcity Research Volume 2) [doi: https://doi.org/10.1016/B978-0-12-814820-4.00010-9]
Drought ; Monitoring ; Assessment ; Temperature ; Rain ; Precipitation ; Satellite observation ; Weather forecasting ; Land use ; Land cover ; Remote sensing ; Vegetation index ; Agriculture ; Crop yield / South Asia / India / Sri Lanka / Pakistan
(Location: IWMI HQ Call no: IWMI Record No: H049369)
(15.10 MB)
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