Members’ Publication

Accurate and timely information regarding the locations and types of crops cultivated is essential for sustainable agriculture and ensuring food security. However, accurately mapping season-specific crop types in tropical and subtropical regions is challenging due to smallholder farms, fragmented fields, predominant clouds, and limited seasonal reference data. To address these limitations, this study employed optical and radar satellite data in conjunction with machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Gradient Tree Boosting (GBoost), utilizing a large number of reference datasets across crop seasons. To validate the results, extensive field visits were undertaken throughout the year. Our focus centered on two regions in Thailand recognized for their small fields and frequent overcast conditions. Utilizing over 8000 reference points, we mapped 12 crop types in Chaiyaphum province and 13 crop types in Suphan Buri province for three cropping seasons in 2023. The RF algorithm proved to be the most effective, demonstrating superior performance across all seasons in comparison to the other models, achieving an overall accuracy exceeding 85%, with classifications for sugarcane and rice exceeding 90%. The resultant maps identified sugarcane, rice, and cassava as the principal crops in the region. This research exemplifies a methodology for producing highly accurate seasonal crop maps, providing valuable tools for making informed decisions for crop sustainable management, thereby supporting sustainable agriculture practices. Our findings underscore the potential of Earth observation satellites and machine learning algorithms in addressing significant agricultural challenges and facilitating the development of more resilient strategies for food security.

Predominant water scarcity and environmental degradation in Bangladesh's coastal communities arise due to extreme meteorological conditions, unrestrained water usage, and extensive saline intrusion into both surface and groundwater. This study leverages both quantitative data from a household survey and qualitative data from a Participatory Rural Appraisal to explore the perspectives of 150 official experts and 240 coastal public respondents regarding the implementation of a "Community-Based Rainwater Harvesting System" (CBRWHS) as a sustainable water supply technology in six major coastal districts of Bangladesh. Though CBRWHS surpasses the benefits of an individual harvesting system (IHS) due to its collective approach, high storage capacity, social cohesiveness, environmental benefits, and potential for scalability, findings suggest that the experts identify financial insolvency, lack of technical skills, and insufficient education as principal barriers against adopting CBRWHS. They propose that providing technical instructions, education, and establishing a CBRWHS pilot could serve as effective incentives. While expressing a sufficient understanding of IHS, respondents are willing to adopt CBRWHS only if financial support is provided. Given the residents' experience with IHS, they are cognizant of the financial and technical challenges intrinsic to such systems. CBRWHS, unlike IHS, operates as a shared system with collective property rights and regulations. Majority of respondents perceive CBRWHS as a potential significant mitigation tool against the water shortage situation since the challenges associated with harvesting, maintaining, and using rainwater could be addressed collectively by the user community. Similar to the official experts, public respondents identify a lack of technical expertise, financial constraints, and regulations as the most significant obstacles to IHS adoption. The findings of this study hold substantial implications not only for the local region but also for similar coastal regions worldwide, suggesting a crucial need for the prioritization of community-based solutions to water scarcity within the wider global discourse on sustainable water management.

The very fast economic and population growth of Bangladesh, along with its high urbanization rate, have been seen since its independence in 1971. Sylhet City Corporation (SCC) has also experienced high population growth as well as rapid urbanization since 2002. The solid waste generation rate is also increasing along with the high economic and population growth in SCC. SCC and its extended areas are now producing more solid waste than ever before because of accelerated unplanned urbanization, ever-growing tourist pressure, and an overgrowing urban population. Sooner than later, resource recovery and capacity development in existing solid waste management systems are required to allow the sustainable and resilient growth of SCC. This study aimed to explore the total amount of solid waste generation per day as well as the waste composition and potentiality for resource recovery from the generated solid waste in SCC and its extended areas. This study has found that SCC is now generating a total of 375.68 tons of solid waste daily from households, daily kitchen markets, medical institutes, resident hotels, commercial shops, and restaurants. This study has also found that about 90% of daily-generated solid waste is perishable and has a high moisture content. This study has found that the generated waste in SCC has the potential to produce compost fertilizer, solid recovered fuel (SRF), and electricity. On the other hand, this study can directly help the conservancy department of SCC to have a brief idea about the existing solid waste generation rate, composition, categories, and potentialities of resource recovery from the generated solid waste. It can also succor other conservancy departments of other city corporations or urban areas of Bangladesh as well as other researchers in a similar way.

Whether micromobility is hurting or boosting transit ridership remains a matter of debate. Previous studies on this topic mainly use either individual level data or system level data. This paper provides insights into this debate through analyses of the connection between bike-share use and transit use at both the individual-level and the system-level. The analysis uses data from an intercept survey of bike-share users and system-level data on bike-share trips from the Sacramento region’s dockless electric bike-share system prior to the COVID-19 pandemic. Our individual-level analysis results suggest that people in the Sacramento region are more likely to replace their transit use with bike-share than to use bike-share as a first- or last-mile transit connector. Certain socio-demographic groups, however, are more likely to use bike share to connect to transit compared to others. Analysis of the system-level data shows that the number of bike-share trips that begin or end near transit stops is positively associated with transit boarding or alightings at those stops conditional on variables known to directly influence transit ridership. In this study, individual- and system-level analyses lead to different conclusions about the relationship between bike-share and transit, suggesting that reliance on system-level data alone may not provide an accurate assessment of the relationship between bike-share and transit use. A detailed understanding of the relationship using both sources of data can assist in better policy formulation that benefits both modes.

The multi-scale assessment of surface-groundwater interactions in hard rock (granite), water limited environments was carried out first in the ~80 km2 Sardon catchment and finally in a small, 7.6 ha Trabadillo study area (Western Spain). Three different thematic studies (Chapters 2-4) had been carried out applying various techniques including: field data acquisition, automated monitoring, remote sensing and integrated hydrological modelling using GSFLOW code. Chapter 2 addresses the assessment of dynamics of surface-groundwater interactions at the Sardon catchment scale; the main findings were: (1) intense groundwater exfiltration; (2) groundwater flow characterized by short groundwater flow-path and short residence time; (3) declining trend of catchment groundwater outflow; and (4) large variability of infiltration influenced by land cover type. That impact of land cover upon dynamics of surface-groundwater interactions triggered expansion of further research in Chapters 3 and 4, but also underpinned some hardcoded deficiencies of GSFLOW, related to formulation of driving forces. That initiated the study in Chapter 3, addressing spatiotemporal tree rainfall interception loss (EI). Experimental measurements of EI on selected evergreen Quercus ilex and deciduous Quercus pyrenaica oaks were carried out during two years and spatiotemporally upscaled into two homogeneous (1 ha) plots and into the entire Sardon catchment; the main findings were: (1) yearly EI was larger in wet than in dry hydrological years but if reflected as percent of rainfall, then it was opposite; (2) EI of Quercus ilex trees were larger than of Quercus pyrenaica but for the catchment scale EI, it was opposite, because of much larger catchment population of the latter; (3) catchment EI was primarily dependent on tree density and species type. The Chapter 4 focusses on simulating surface-groundwater interactions at very fine grid (5x5 m) and at temporal daily resolution, using 20-year time-series observation to assess the net recharge dependence upon land cover type expressed through hydrological terrain units (hydrotopes). The findings of this study confirmed all findings of the Chapter 2, adding the following: (1) hydrotope-dependent variability of water fluxes; (2) non-negligible grass interception; and (3) much lower net recharge under tree than grass hydrotopes, meaning more trees, less water resources.