Dynamics and drivers of land use land cover changes in Bangladesh

Land is scarce in Bangladesh: Bangladesh occupies ~0.03 % of world’s land area, but supports over ~2% of human population. This high population to land ratio, combined with socioeconomic development has placed tremendous pressure on Bangladesh’s land resources for food, feed, and fuel. This study assesses the dynamics of land use land cover changes and its subsequent drivers at national and sub-national scales. We show contemporary spatial estimates of land change in Bangladesh using national-level analysis of Landsat imageries for 2000 and 2010. This analysis uses our newly compiled extensive socioeconomic database which covers ~480 sub-districts along with biophysical data. We also synthesized information from over 80 survey-based case studies on land use drivers in Bangladesh to complement our macro-scale analysis. We present a detailed analysis of contemporary land change both in terms of national extent and the use of detailed spatial information on land change, socioeconomic factors, and synthesis of case studies. Our results showed eight broad land cover types, of which majority is covered by agriculture (~70%), waterbody (rivers and shrimp ponds) (~10%) and forests (~8%). We found that agriculture, forest and mangrove areas showed a decreasing trend while bare soil, shrub land, waterbody and settlement showed an increasing trend. We identified three major land conversion types: agriculture to shrimp ponds, forest to shrub land and shrimp ponds to bare soil, and their hotspot regions at a sub-district level. Based on our analysis, we find both biophysical and socioeconomic variables contributing to the land conversions. We find that conversion of agriculture to shrimp ponds is driven by increasing rate of population, urban household size and rural household number, access to highways and variation in temperature. Drivers related to forest to shrubland conversion include increasing rate of population, access to rivers, highways and cities, and increased rate of precipitation. Lastly, shrimp ponds to bare soil conversion is driven by access to highway, cities and rivers, elevation and increasing rate of precipitation

Flood Mapping of Recent Major Hurricane Events with Synthetic Aperture Radar, Commercial Imaging, and Aerial Observations

Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratorys Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018

Synthetic Aperture Radar and Optical Remote Sensing of Crop Damage Attributed to Severe Weather in the Central United States

Damaging hail and wind from severe thunderstorms threatens agricultural areas annually, especially across the central United States where agriculture is prevalent. On average, these storms produce $160 to$580 million worth of damage in the US every year and contribute significantly to food prices, crop insurance, and agricultural related stocks. However, hail damage is not regularly ground-surveyed like tornadoes. Optical (visible, NIR (Near Infra-Red), and SWIR (Short-Wave Infra-Red) remote sensing techniques have been shown to successfully identify and monitor hail damage swaths. Techniques of identification and monitoring hail damage swaths from synthetic aperture radar (SAR) are currently unexplored. We hypothesize that hail-damaged cropland will exhibit lower power return than surrounding healthy vegetation due to changes in the geometry of the targets. Further analysis is needed to determine a threshold for future automated monitoring of hail damage swaths