Land utilization and ecological aspects in the Sylhet-Mymensingh Haor Region of Bangladesh: An analysis of LANDSAT data

The use of remote sensing data from LANDSAT (ERTS) imageries in identifying, evaluating and mapping land use patterns of the Haor area in Bangladesh was investigated. Selected cloud free imageries of the area for the period 1972-75 were studied. Imageries in bands 4, 5 and 7 were mostly used. The method of analysis involved utilization of both human and computer services of information from ground, aerial photographs taken during this period and space imageries

Sustainable Intensification of Agriculture: Opportunities and Challenges for Food Security and Agrarian Adaptation to Environmental Change in Bangladesh

This dissertation investigates three unique aspects of sustainable agricultural intensification (SAI) in the context of Bangladeshi rice production. The first article presents a qualitative analysis of SAI and farmer surveys in the embanked polder region of coastal Bangladesh. The second article investigates the global food security and environmental impacts of already adopted High Yielding Variety (HYV) rice and double-cropped rice systems in Bangladesh using a spatial partial equilibrium trade model and a Life Cycle Assessment (LCA). The final article demonstrates a remote sensing methodology for monitoring dry season rice production at 30 m resolution in Bangladesh using a harmonic time series model, the Landsat archive, and Google Earth Engine. Major findings from this dissertation include: (1) agrarian communities in the polder region face food insecurity during the peak of monsoonal paddy rice production and could improve production by adopting HYV or second season crops, (2) agrarian communities in the polders identify water management issues as the primary agricultural concern, followed by pest infestation and soil salinity, (3) HYV rice provides enough additional production in Bangladesh to feed nearly 26 million Bangladeshis per annum and is more environmentally efficient than traditional rice in terms of global warming potential, land use, water use, and fertilizer use, and (4) the combination of a harmonic time series model, spectral indices, and rice phenology can produce relatively accurate predictions of dry season rice in Bangladesh compared to district-level reference information. Overall, the findings from this investigation of SAI support continued efforts to improve food security, increase agricultural output, and decrease environmental impacts in Bangladesh

Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery

Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation seasonal mapping both on forest and agricultural site. In order to provide a long-terms of vegetation characteristic maps, a wide time-series images analysis is needed which require high-performance computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of paddy field vegetation information. This research is aimed to develop a method to produce the optimized solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both CPU and GPU to find the efficient and the robust result. The developed method has been tested and implemented using the sample case of paddy fields in Java Island. The best system which can accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%

Report on Ganges BDC Reflection Workshop

Phase II of the CGIAR Challenge Program for Water and Food (CPWF) is a multi-institutional and inter-disciplinary research for development initiative focused on increasing the resilience of social and ecological systems through better water management for food production.Phase II in the Ganges Basin began in 2011 and is scheduled to end in 2014. With the aim of improving the livelihoods of Ganges coastal zone farmers in Bangladesh and West Bengal India, the five projects comprising the Challenge are focusing on areas where there is already some level of water control, especially within the polders of Bangladesh but also extending to areas outside polders in India.The goal of the Challenge is to reduce poverty and improve livelihood resilience. The first most important function of the Reflection Workshop is that it allows Ganges BDC project teams to share their individual project activities, findings, issues, opportunities and visions.This enables the BDC team as a whole to discuss, collectively, any adjustments that need to be made to better address the goal of the BDC, and to identify early results that should be built on, particularly for out and upscaling

Application of machine learning to prediction of vegetation health

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern

Hydrological impacts of climate change on rice cultivated riparian wetlands in the Upper Meghna River Basin (Bangladesh and India)

Riparian depressional wetlands (haors) in the Upper Meghna River Basin of Bangladesh are invaluable agricultural resources. They are completely flooded between June and November and planted with Boro rice when floodwater recedes in December. However, early harvest period (April/May) floods frequently damage ripening rice. A calibrated/validated Soil and Water Assessment Tool for riparian wetland (SWATrw) model is perturbed with bias free (using an improved quantile mapping approach) climate projections from 17 general circulation models (GCMs) for the period 2031–2050. Projected mean annual rainfall increases (200–500 mm or 7–10%). However, during the harvest period lower rainfall (21–75%) and higher evapotranspiration (1–8%) reduces river discharge (5–18%) and wetland inundation (inundation fraction declines of 0.005–0.14). Flooding risk for Boro rice consequently declines (rationalized flood risk reductions of 0.02–0.12). However, the loss of cultivable land (15.3%) to increases in permanent haor inundation represents a major threat to regional food security

Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010

Rice is the most consumed staple food in the world and a key crop for food security. Much of the world’s rice is produced and consumed in Asia where cropping intensity is often greater than 100% (more than one crop per year), yet this intensity is not sufficiently represented in many land use products. Agricultural practices and investments vary by season due to the different challenges faced, such as drought, salinity, or flooding, and the different requirements such as varietal choice, water source, inputs, and crop establishment methods. Thus, spatial and temporal information on the seasonal extent of rice is an important input to decision making related to increased agricultural productivity and the sustainable use of limited natural resources. The goal of this study was to demonstrate that hyper temporal moderate-resolution imaging spectroradiometer (MODIS) data can be used to map the spatial distribution of the seasonal rice crop extent and area. The study was conducted in Bangladesh where rice can be cropped once, twice, or three times a year

The impact of irrigation return flow on seasonal groundwater recharge in northwestern Bangladesh

Peer reviewedPublisher PD