Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017

Although a detailed analysis of land use and land cover (LULC) change is essential in providing a greater understanding of increased human-environment interactions across the coastal region of Bangladesh, substantial challenges still exist for accurately classifying coastal LULC. This is due to the existence of high-level landscape heterogeneity and unavailability of good quality remotely sensed data. This study, the first of a kind, implemented a unique methodological approach to this challenge. Using freely available Landsat imagery, eXtreme Gradient Boosting (XGBoost)-based informative feature selection and Random Forest classification is used to elucidate spatio-temporal patterns of LULC across coastal areas over a 28-year period (1990-2017). We show that the XGBoost feature selection approach effectively addresses the issue of high landscape heterogeneity and spectral complexities in the image data, successfully augmenting the RF model performance (providing a mean user's accuracy > 0.82). Multi-temporal LULC maps reveal that Bangladesh's coastal areas experienced a net increase in agricultural land (5.44%), built-up (4.91%) and river (4.52%) areas over the past 28 years. While vegetation cover experienced a net decrease (8.26%), an increasing vegetation trend was observed in the years since 2000, primarily due to the Bangladesh government's afforestation initiatives across the southern coastal belts. These findings provide a comprehensive picture of coastal LULC patterns, which will be useful for policy makers and resource managers to incorporate into coastal land use and environmental management practices. This work also provides useful methodological insights for future research to effectively address the spatial and spectral complexities of remotely sensed data used in classifying the LULC of a heterogeneous landscape

Scope to predict soil properties at within-field scale from small samples using proximally sensed gamma-ray spectrometer and EM induction data

Spatial prediction of soil properties are needed for various purposes, including site-specific management, soil quality assessment, soil mapping, and solute transport modelling, to mention a few. However, the costs associated with soil sampling and laboratory analysis are substantial. One way to improve efficiencies is to combine measurement of soil properties with collection of cheaper-to-measure ancillary data. There are two possible approaches. The first is the formation of classes from ancillary data. A second is the use of a simple predictive linear model of the target soil property on the ancillary variables. Here, results are presented and compared where proximally sensed gamma-ray (gamma-ray) spectrometry and electromagnetic induction (EMI) data are used to predict the variation in topsoil properties (e.g. clay content and pH). In the first instance, the proximal data is numerically clustered using a fuzzy k-means (FKM) clustering algorithm, to identify contiguous classes. The resultant digital soil maps (i.e. k = 2 - 10 classes) are consistent with a soil series map generated using traditional soil profile description, classification and mapping methods at a highly variable site near the township of Shelford, Nottinghamshire UK. In terms of prediction, the calculated expected value of mean squared prediction error (i.e. sigma2p,C) indicated that values of k = 7 and 8 were ideal for predicting clay and pH. Secondly, a linear mixed model (LMM) is fitted in which the proximal data are fixed effects but the residuals are treated as a combination of a spatially correlated random effect and an independent and identically distributed error. In terms of prediction, the expected value of the mean squared prediction error from a regression (sigma2p,R) suggested that the regression models were able to predict clay content, better than FKM clustering. The reverse was true with respect to pH, however. It is concluded that both methods have merit. In the case of the clustering, the approach is able to account for soil properties which have non-linearity with the ancillary data (i.e. pH), whereas the LMM approach is best when there is a strong linear relationship (i.e. clay)

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Evaluating flood control and drainage management systems from a productive efficiency perspective : a case study of the southwest coastal zone of Bangladesh

PhD ThesisPerformance evaluation of flood defence systems is invariably carried out from an engineering perspective overlooking the productivity perspective, thereby leaving a gap in the literature of performance evaluation in the water resources management sector. Two competing flood control and drainage management (FCDM) systems, namely, the ‘silt-dredging and regulative-drainage management (SRM)’ and the ‘tidal river-basin management (TRM)’ systems were implemented in the Southwest coastal zone of Bangladesh as safeguards to protect agricultural production. There is a longstanding debate over the appropriateness of these systems in terms of providing conditions for sustainable agriculture. The lead executing agency, Bangladesh Water Development Board, was adamant to implement the hard engineering structural system, the SRM, while the stakeholders (i.e., the farmers and the fisher folk) insisted on the non-structural system, the TRM. However, this work evaluates these two contrasting and competing FCDM systems in terms of productive efficiency, in order to address primarily the gap in the literature of performance evaluation. The study develops separate econometric models for paddy production and fisheries production with each of the FCDM systems and estimates these models using stochastic frontier analysis to obtain technical efficiency (TE), yield-gap and potential yield increment (PYI) for paddy production, and cost efficiency (CE), cost-gap and potential cost saving (PCS) for fisheries production. The study results reveal that mean TE, CE, yield-gap and cost-gap are respectively 0.782, 0.807, 719.181 (kg) and 12542.71 (tk) with the SRM system, while these estimates are 0.769, 0.762, 807.324 (kg) and 14440.39 (tk) with the TRM in order. These findings indicate that SRM system marginally outperforms TRM system in terms of agricultural productivity. This is despite the SRM being more expensive to deliver, as well as the fact that, due to rise of relative sea-level with the SRM system, it is likely to become increasingly more expensive in the future. In contrast, the TRM system benefits from counteracting the rise of relative sea-level through land accretion by sedimentation in the floodplains in an environmentally friendly way, keeping the maintenance costs low

Climate Change and Community Resilience

This open access book documents myriads of ways community-based climate change adaptation and resilience programs are being implemented in South Asian countries. The narrative style of writing in this volume makes it accessible to a diverse audience from academics and researchers to practitioners in various governmental, non-governmental and international agencies. At a time when climate change presents humanity with a gloomy future, the stories of innovation, creativity, grassroots engagement and locally applicable solutions highlighted in this book provides insights into hopeful ways of approaching climate solutions. South Asian countries have been dealing with the impact of climate change for decades and thus offer valuable learning opportunities for developing countries within and beyond the region as well as many western countries that are confronting the wrath of climate induced natural disasters more recently. SANDEE has been a pioneer in the development of research and training in environmental economics and related issues in South Asia and Prof Maler has been throughout SANDEE's history, its mentor, and its strongest supporter. Many young economists in South Asia have significantly benefited from Prof Maler's guidance and inputs. The present volume on “Climate Change and Community Resilience: Insights from South Asia” is a fitting tribute and an excellent reflection of Prof Maler's contributions to the SANDEE programme throughout his association. - Mahesh Banskota, Ph.D. Professor, Development Studies School of Arts, Kathmandu University This comprehensive volume aptly identifies grassroots initiatives as the core of the problem of adaptation to climate change. The analysis of the different experiments is lucid, inclusive, and full of interesting detail. The methodologies used and the subjects covered span a range of frameworks and narratives. Put together, the studies are a fitting tribute to Karl-Goran Maler, who spent years putting his impeccable expertise to use for the cause of enhancing research in South Asia. - Kanchan Chopra, Ph.D. Former Director and Professor, Institute of Economic Growth, Delhi, and Fellow, SANDEE The slow international policy response to climate change elevates the importance of understanding how communities can respond to climate change’s many threats. This unusually accessible volume provides that understanding for South Asia while being relevant to the rest of the world. Its emphasis on research by scholars from the region makes it a wonderful tribute to Prof. Karl-Göran Mäler, who contributed so much to the growth of environmental economics research capacity in South Asia. - Jeffrey R. Vincent, Ph.D. Clarence F. Korstian Professor of Forest Economics & Management Nicholas School of the Environment, Duke University, US

Integrated Watershed Management for Land and Water Conservation and Sustainable Agricultural Production in Asia: Proceedings of the ADB-ICRISAT-IWMI Project Review and Planning Meeting 10–14 December 2001, Hanoi, Vietnam

Erratic rainfall, land degradation, soil erosion, poverty and burgeoning population characterize the dry regions in Asia. To develop sustainable natural resource management options for increasing the agricultural productivity and income of the rural poor in these regions, a new Integrated Farmer Participatory Watershed Management Model was developed by ICRISAT in partnership with the national agricultural research systems (NARS). This model was applied at selected benchmark locations in Asia by ICRISAT through the project RETA 5812 “Improving management of natural resources for sustainable rainfed agriculture in Asia”, funded by the Asian Development Bank (ADB). The challenge for catchment research is to generate technologies and management systems that is now being addressed by the Management of Soil Erosion Consortium (MSEC) project RETA 5803 “Catchment approach to managing soil erosion in Asia”. This project is funded by ADB and the International Water Management Institute (IWMI) serves as the facilitator. The workshop “Integrated Watershed Management for Land and Water Conservation and Sustainable Agricultural Production in Asia” was held to review these projects. Forty-five scientists from China, India, Indonesia, Laos, Nepal, Philippines, Thailand, and Vietnam participated in the workshop. The objectives of the workshop were to: (1) Review the progress made at benchmark watersheds/catchments and synthesize the findings from the work of the projects RETA 5812 and RETA 5803; (2) Discuss work plans for 2002, identify emerging issues and future strategies for sustainable use of natural resources for improving rural livelihoods through new initiatives; and (3) Discuss watershed development and management technologies. During the period, a one-day workshop on Watershed Methodologies was also organized. The research papers based on the work conducted for three years at different benchmark sites in Asia are covered in this publication. The multi-country and multi-institutional research findings about watershed/catchment management reported here will serve as a valuable resource for the researchers, policymakers and students working in the area of sustainable management of natural resource

New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Figures are re-used under an Attribution 4.0 International (CC BY 4.0) license, or are not copyrighted.Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water