Background:National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests.These systems are especially important in a country like Ba...Background:National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests.These systems are especially important in a country like Bangladesh,which is characterised by a large population density,climate change vulnerability and dependence on natural resources.With the aim of supporting the Government’s actions towards sustainable forest management through reliable information,the Bangladesh Forest Inventory(BFI)was designed and implemented through three components:biophysical inventory,socio-economic survey and remote sensing-based land cover mapping.This article documents the approach undertaken by the Forest Department under the Ministry of Environment,Forests and Climate Change to establish the BFI as a multipurpose,efficient,accurate and replicable national forest assessment.The design,operationalization and some key results of the process are presented.Methods:The BFI takes advantage of the latest and most well-accepted technological and methodological approaches.Importantly,it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities.Overall,1781 field plots were visited,6400 households were surveyed,and a national land cover map for the year 2015 was produced.Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map,an object-based national land characterisation system,consistent estimates between sample-based and mapped land cover areas,use of mobile apps for tree species identification and data collection,and use of differential global positioning system for referencing plot centres.Results:Seven criteria,and multiple associated indicators,were developed for monitoring progress towards sustainable forest management goals,informing management decisions,and national and international reporting needs.A wide range of biophysical and socioeconomic data were collected,and in some cases integrated,for estimating the indicators.Conclusions:The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future.Reliable information on the status of tree and forest resources,as well as land use,empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources.The integrated socioeconomic data collected provides information about the interactions between people and their tree and forest resources,and the valuation of ecosystem services.The BFI is designed to be a permanent assessment of these resources,and future data collection will enable monitoring of trends against the current baseline.However,additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.展开更多
District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency i...District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.展开更多
基金financial support from projects GCP/BGD/058/USA and UNJP/BGD/057/UNJ。
文摘Background:National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests.These systems are especially important in a country like Bangladesh,which is characterised by a large population density,climate change vulnerability and dependence on natural resources.With the aim of supporting the Government’s actions towards sustainable forest management through reliable information,the Bangladesh Forest Inventory(BFI)was designed and implemented through three components:biophysical inventory,socio-economic survey and remote sensing-based land cover mapping.This article documents the approach undertaken by the Forest Department under the Ministry of Environment,Forests and Climate Change to establish the BFI as a multipurpose,efficient,accurate and replicable national forest assessment.The design,operationalization and some key results of the process are presented.Methods:The BFI takes advantage of the latest and most well-accepted technological and methodological approaches.Importantly,it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities.Overall,1781 field plots were visited,6400 households were surveyed,and a national land cover map for the year 2015 was produced.Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map,an object-based national land characterisation system,consistent estimates between sample-based and mapped land cover areas,use of mobile apps for tree species identification and data collection,and use of differential global positioning system for referencing plot centres.Results:Seven criteria,and multiple associated indicators,were developed for monitoring progress towards sustainable forest management goals,informing management decisions,and national and international reporting needs.A wide range of biophysical and socioeconomic data were collected,and in some cases integrated,for estimating the indicators.Conclusions:The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future.Reliable information on the status of tree and forest resources,as well as land use,empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources.The integrated socioeconomic data collected provides information about the interactions between people and their tree and forest resources,and the valuation of ecosystem services.The BFI is designed to be a permanent assessment of these resources,and future data collection will enable monitoring of trends against the current baseline.However,additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.
文摘District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.
