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Barbara Koch - One of the best experts on this subject based on the ideXlab platform.
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mapping Forest Biomass from space fusion of hyperspectral eo1 hyperion data and tandem x and worldview 2 canopy height models
International Journal of Applied Earth Observation and Geoinformation, 2015Co-Authors: Teja Kattenborn, Joachim Maack, Fabian Fasnacht, Fabian Ensle, Jorg Ermert, Barbara KochAbstract:Abstract Spaceborne sensors allow for wide-scale assessments of Forest ecosystems. Combining the products of multiple sensors is hypothesized to improve the estimation of Forest Biomass. We applied interferometric (Tandem-X) and photogrammetric (WorldView-2) based predictors, e.g. canopy height models, in combination with hyperspectral predictors (EO1-Hyperion) by using 4 different machine learning algorithms for Biomass estimation in temperate Forest stands near Karlsruhe, Germany. An iterative model selection procedure was used to identify the optimal combination of predictors. The most accurate model (Random Forest) reached a r 2 of 0.73 with a RMSE of 14.9% (29.4 t/ha). Further results revealed that the predictive accuracy depended highly on the statistical model and the area size of the field samples. We conclude that a fusion of canopy height and spectral information allows for accurate estimations of Forest Biomass from space.
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status and future of laser scanning synthetic aperture radar and hyperspectral remote sensing data for Forest Biomass assessment
Isprs Journal of Photogrammetry and Remote Sensing, 2010Co-Authors: Barbara KochAbstract:This is a review of the latest developments in different fields of remote sensing for Forest Biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for Forest mapping issues. Forest mapping is a wide field and a variety of Forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a Forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess Forest Biomass. This information is especially required by the REDD (reducing of emission from avoided deForestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for Forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on Biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.
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status and future of laser scanning synthetic aperture radar and hyperspectral remote sensing data for Forest Biomass assessment
Isprs Journal of Photogrammetry and Remote Sensing, 2010Co-Authors: Barbara KochAbstract:This is a review of the latest developments in different fields of remote sensing for Forest Biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for Forest mapping issues. Forest mapping is a wide field and a variety of Forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a Forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess Forest Biomass. This information is especially required by the REDD (reducing of emission from avoided deForestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for Forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on Biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.
Taraneh Sowlati - One of the best experts on this subject based on the ideXlab platform.
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assessment and optimization of Forest Biomass supply chains from economic social and environmental perspectives a review of literature
Renewable & Sustainable Energy Reviews, 2014Co-Authors: Claudia Cambero, Taraneh SowlatiAbstract:Forest Biomass is a renewable source that has the potential to substitute fossil fuels in many applications, from the generation of bioenergy (heat, electricity or transportation fuels) to the production of bioproducts (chemicals and other materials). The increased use of Forest Biomass could support the reduction of anthropogenic carbon emissions to the environment and could help Forest-dependent communities achieve energy independence while generating jobs. The viability and feasibility of generating valuable products from Forest Biomass depend on ensuring the long-term availability of Biomass supply with the required quality at a competitive cost. This calls for a cost-efficient design of the Forest Biomass supply chain. Social and environmental aspects have to be considered in the design as well to guarantee sustainable use of this renewable resource. In this paper, we present a review of studies that assessed or optimized economic, social and environmental aspects of Forest Biomass supply chains for the production of bioenergy and bioproducts. The majority of studies so far considered either economic (techno-economic and optimization studies) or environmental (life cycle assessment studies) aspects of bioenergy projects. Nevertheless, there is a recent trend to integrate economic, environmental and social aspects in the assessment and optimization of Forest Biomass supply chains. Combined approaches integrating multi-objective optimization and life cycle assessment have started to flourish. In these studies, GHG emissions are the most frequently used environmental indicator, production and capital costs are the preferred economic measures and the number of created jobs is the most considered social criterion. Further research has to be done to study and assess the potential social impacts of using Forest Biomass. There is a need for further development of decision support tools that consider economic, environmental and social criteria to aid the design and planning of Forest Biomass supply chains.
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economic feasibility of utilizing Forest Biomass in district energy systems a review
Renewable & Sustainable Energy Reviews, 2014Co-Authors: Shaghaygh Akhtari, Taraneh Sowlati, Ken DayAbstract:Abstract Recent global protocols and agreements have motivated countries to use Biomass for energy generation. However, the barriers in Biomass utilization including variations in Biomass availability, high moisture content, low bulk density and dispersed distribution of Biomass have made investors reluctant to invest in bioenergy projects in some parts of the world. In this paper, in addition to a brief summary of the conversion technologies used for energy generation, a review of the world literature on techno-economic assessment of district energy systems using Forest Biomass as the primary fuel with references extending over two decades is provided. Although energy generation from Forest Biomass is found to be expensive in many countries, the review of literature revealed important factors that increased the share of Biomass in energy production in other countries. These important factors include using more efficient technologies, providing governmental grants and subsidies, setting new policies in favor of Biomass utilization, increasing emission reduction targets, and introducing tradable carbon credits. The feasibility of utilizing Forest Biomass in district heating systems has been examined in the literature mainly based on the costs, while considering social and environmental profiles of these systems could improve their acceptance. Future research studies on assessing the performance of Biomass district energy systems should consider environmental and social impacts of these systems in addition to their costs.
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Tactical supply chain planning for a Forest Biomass power plant under supply uncertainty
Energy, 2014Co-Authors: Nazanin Shabani, Mustapha Ouhimmou, Taraneh Sowlati, Mikael RönnqvistAbstract:Uncertainty in Biomass supply is a critical issue that needs to be considered in the production planning of bioenergy plants. Incorporating uncertainty in supply chain planning models provides improved and stable solutions. In this paper, we first reformulate a previously developed non-linear programming model for optimization of a Forest Biomass power plant supply chain into a linear programming model. The developed model is a multi-period tactical-level production planning problem and considers the supply and storage of Forest Biomass as well as the production of electricity. It has a one-year planning horizon with monthly time steps. Next, in order to incorporate uncertainty in monthly available Biomass into the planning, we develop a two-stage stochastic programming model. Finally, to balance the risk and profit, we propose a bi-objective model. The results show that uncertainty in availability of Biomass has an additional cost of $0.4 million for the power plant. Using the proposed stochastic optimization model could reduce this cost by half.
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value chain optimization of Forest Biomass for bioenergy production a review
Renewable & Sustainable Energy Reviews, 2013Co-Authors: Nazanin Shabani, Shaghaygh Akhtari, Taraneh SowlatiAbstract:Abstract Forest Biomass is one of the renewable and sustainable sources of energy that can be used for producing electricity, heat, and biofuels. The complex supply chain of Forest Biomass for energy generation, which consists of different players and products and is affected by Biomass characteristics, such as low density and unpredictable quality, makes the energy generation cost from Biomass higher than that of the conventional sources of energy, such as fossil fuels. Moreover, variability and uncertainty in this supply chain, mainly due to the nature of material, economic condition and market fluctuation, affect the amount of produced energy and its cost. Mathematical modeling, in particular optimization techniques, can be employed to manage the supply chain and achieve the optimum design. This paper reviews studies which used deterministic and stochastic mathematical models to optimize Forest Biomass supply chains for electricity, heat and biofuels production. Optimization models were used to provide the optimum solution for decisions related to the network design, technology choice, plant size and location, storage location, mix of products and raw materials, logistics options, supply areas, and material flows. Mainly, economic objectives were considered in these models. Further studies should consider environmental and social objectives, in addition to the economic ones, in the models. In non-deterministic models uncertainty mainly in the demand, supply, prices, and conversion yields were incorporated. Although material quality is an important uncertain parameter in the Forest Biomass supply chain that affects the amount and cost of produced energy, its variation was not considered in previous studies.
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Forest Biomass supply logistics for a power plant using the discrete event simulation approach
Applied Energy, 2011Co-Authors: Mahdi Mobini, Taraneh Sowlati, Shahab SokhansanjAbstract:This study investigates the logistics of supplying Forest Biomass to a potential power plant. Due to the complexities in such a supply logistics system, a simulation model based on the framework of Integrated Biomass Supply Analysis and Logistics (IBSAL) is developed in this study to evaluate the cost of delivered Forest Biomass, the equilibrium moisture content, and carbon emissions from the logistics operations. The model is applied to a proposed case of 300Â MW power plant in Quesnel, BC, Canada. The results show that the Biomass demand of the power plant would not be met every year. The weighted average cost of delivered Biomass to the gate of the power plant is about C$ 90 per dry tonne. Estimates of equilibrium moisture content of delivered Biomass and CO2 emissions resulted from the processes are also provided.
Brian J Enquist - One of the best experts on this subject based on the ideXlab platform.
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variation in above ground Forest Biomass across broad climatic gradients
Global Ecology and Biogeography, 2011Co-Authors: James C Stegen, Nathan G Swenson, Brian J Enquist, Ethan P White, Oliver L Phillips, Peter M Jorgensen, Michael D WeiserAbstract:AimAn understanding of the relationship between Forest Biomass and climate is needed to predict the impacts of climate change on carbon stores.Biomass patterns have been characterized at geographically or climatically restricted scales,making it unclear if Biomass is limited by climate in any general way at continental to global scales.Using a dataset spanning multiple climatic regions we evaluate the generality of published Biomass‐climate correlations.We also combine metabolic theory and hydraulic limits to plant growth to first derive and then test predictions for how Forest Biomass should vary with maximum individual tree Biomass and the ecosystem water deficit.
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above ground Forest Biomass is not consistently related to wood density in tropical Forests
Global Ecology and Biogeography, 2009Co-Authors: James C Stegen, Nathan G Swenson, Brian J Enquist, Renato Valencia, Jill ThompsonAbstract:Aim It is increasingly accepted that the mean wood density of trees within a Forest is tightly coupled to above-ground Forest Biomass. It is unknown, however, if a positive relationship between Forest Biomass and mean community wood density is a general phenomenon across Forests. Understanding spatial variation in Biomass as a function of wood density both within and among Forests is important for predicting changes in stored carbon in response to global change, and here we evaluated the generality of a positive Biomass‐wood density relationship within and among six tropical Forests. Location Costa Rica, Panama, Puerto Rico and Ecuador.
Jingyun Fang - One of the best experts on this subject based on the ideXlab platform.
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Biomass carbon stocks in china s Forests between 2000 and 2050 a prediction based on Forest Biomass age relationships
Science China-life Sciences, 2010Co-Authors: Zhaodi Guo, Shilong Piao, Jingyun FangAbstract:China’s Forests are characterized by young Forest age, low carbon density and a large area of planted Forests, and thus have high potential to act as carbon sinks in the future. Using China’s national Forest inventory data during 1994–1998 and 1999–2003, and direct field measurements, we investigated the relationships between Forest Biomass density and Forest age for 36 major Forest types. Statistical approaches and the predicted future Forest area from the national Forestry development plan were applied to estimate the potential of Forest Biomass carbon storage in China during 2000–2050. Under an assumption of continuous natural Forest growth, China’s existing Forest Biomass carbon (C) stock would increase from 5.86 Pg C (1 Pg=1015 g) in 1999–2003 to 10.23 Pg C in 2050, resulting in a total increase of 4.37 Pg C. Newly planted Forests through afForestation and reForestation will sequestrate an additional 2.86 Pg C in Biomass. Overall, China’s Forests will potentially act as a carbon sink for 7.23 Pg C during the period 2000–2050, with an average carbon sink of 0.14 Pg C yr−1. This suggests that China’s Forests will be a significant carbon sink in the next 50 years.
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inventory based estimates of Forest Biomass carbon stocks in china a comparison of three methods
Forest Ecology and Management, 2010Co-Authors: Zhaodi Guo, Jingyun Fang, Yude Pan, Richard A BirdseyAbstract:Abstract Several studies have reported different estimates for Forest Biomass carbon (C) stocks in China. The discrepancy among these estimates may be largely attributed to the methods used. In this study, we used three methods [mean Biomass density method (MBM), mean ratio method (MRM), and continuous Biomass expansion factor (BEF) method (abbreviated as CBM)] applied to Forest inventory data to estimate China's Forest Biomass C stocks and their changes from 1984 to 2003. The three methods generated various estimates of the Biomass C stocks: the lowest (4.0–5.9 Pg C) from CBM and the highest (5.7–7.7 Pg C) from MBM, with an intermediate estimate (4.2–6.2 Pg C) from MRM. Forest age class is a major factor responsible for these method-induced differences. MBM overestimates Biomass for young-aged Forests, but underestimates Biomass for old-aged Forests; while the reverse is true for MRM. Further, the three methods resulted in different estimates of Biomass C stocks for different Forest types. For temperate/subtropical mixed Forests, MBM generated a 92% higher estimate than CBM and MRM generated a 14% lower than CBM. The degree of the overestimates is closely related with the proportion of young-aged Forest within total area of each Forest type.
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Forest Biomass and root shoot allocation in northeast china
Forest Ecology and Management, 2008Co-Authors: Xiangping Wang, Jingyun FangAbstract:Abstract Temperate and boreal Forests act as major sinks for atmospheric CO2. To assess the magnitude and distribution of the sinks more precisely, an accurate estimation of Forest Biomass is required. However, the determinants of large-scale Biomass pattern (especially root Biomass) are still poorly understood for these Forests in China. In this study, we used 515 field measurements of Biomass across the northeast part of China, to examine factors affecting large-scale Biomass pattern and root–shoot Biomass allocation. Our results showed that, Picea & Abies Forest and coniferous & broadleaf mixed Forest had the highest mean Biomass (178–202 Mg/ha), while Pinus sylvestris Forest the lowest (78 Mg/ha). The root:shoot (R/S) Biomass ratio ranged between 0.09 and 0.67 in northeast China, with an average of 0.27. Forest origin (primary/secondary/planted Forest) explained 31–37% of variation in Biomass (total, shoot and root), while climate explained only 8–15%, reflecting the strong effect of disturbance on Forest Biomass. Compared with shoot Biomass, root Biomass was less limited by precipitation as a result of Biomass allocation change. R/S ratio was negatively related to water availability, shoot Biomass, stand age, height and volume, suggesting significant effects of climate and ontogeny on Biomass allocation. Root–shoot Biomass relationships also differed significantly between natural and planted Forests, and between broadleaf and coniferous Forests. Shoot Biomass, climate and Forest origin were the most important predictors for root Biomass, and together explained 83% of the variation. This model provided a better way for estimating root Biomass than the R/S ratio method, which predicted root Biomass with a R2 of 0.71.
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Forest Biomass carbon stocks in china over the past 2 decades estimation based on integrated inventory and satellite data
Journal of Geophysical Research, 2005Co-Authors: Shilong Piao, Jingyun Fang, Biao Zhu, Kun TanAbstract:[1] Forests are major contributor of terrestrial ecosystem carbon (C) pools, and are thus crucial components for assessing the global C budget. On the basis of Forest inventory data for three inventory periods of 1984–1988, 1989–1993, and 1994–1998, and synchronous NDVI (Normalized Difference Vegetation Index) data, we developed a satellite-based approach for estimating China's Forest total Biomass C stocks. Using this approach, we analyzed the changes in Forest C stocks over the last 2 decades to identify the size and distribution of C sinks/sources in the Forests. The total Forest Biomass of China averaged 5.79 Pg C (1 Pg = 1015 g) during the study period, with an average Biomass density of 45.31 Mg C/ha (1 Mg = 106 g). The Forest Biomass C density showed a large spatial heterogeneity: high in southwestern and northeastern areas, and low in the eastern coastal regions. Over the past 2 decades, the total Forest Biomass C stock increased from 5.62 Pg C in the early 1980s (average for 1981–1983) to 5.99 Pg C by the end of the 1990s (average for 1997–1999), giving a total increase of 0.37 Pg C and an annual sequestration rate of 0.019 Pg C/yr. The C sink appeared mainly in regions with lower C density. Both environmental changes and human activities are likely major drivers of such spatiotemporal patterns.
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changes in Forest Biomass carbon storage in china between 1949 and 1998
Science, 2001Co-Authors: Jingyun Fang, Anping Chen, Changhui Peng, Shuqing Zhao, Longjun CiAbstract:The location and mechanisms responsible for the carbon sink in northern mid-latitude lands are uncertain. Here, we used an improved estimation method of Forest Biomass and a 50-year national Forest resource inventory in China to estimate changes in the storage of living Biomass between 1949 and 1998. Our results suggest that Chinese Forests released about 0.68 petagram of carbon between 1949 and 1980, for an annual emission rate of 0.022 petagram of carbon. Carbon storage increased significantly after the late 1970s from 4.38 to 4.75 petagram of carbon by 1998, for a mean accumulation rate of 0.021 petagram of carbon per year, mainly due to Forest expansion and regrowth. Since the mid-1970s, planted Forests (afForestation and reForestation) have sequestered 0.45 petagram of carbon, and their average carbon density increased from 15.3 to 31.1 megagrams per hectare, while natural Forests have lost an additional 0.14 petagram of carbon, suggesting that carbon sequestration through Forest management practices addressed in the Kyoto Protocol could help offset industrial carbon dioxide emissions.
Shezhou Luo - One of the best experts on this subject based on the ideXlab platform.
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fusion of airborne lidar data and hyperspectral imagery for aboveground and belowground Forest Biomass estimation
Ecological Indicators, 2017Co-Authors: Shezhou Luo, Cheng Wang, Feifei Pan, Dailiang Peng, Jie Zou, Sheng Nie, Haiming QinAbstract:Abstract Vegetation Biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of Biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate Forest Biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for Biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal Biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground Biomass (BGB), aboveground Biomass (AGB) and total Forest Biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of Forest Biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate Biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for Forest Biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R 2 = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p 2 by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that Biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving Biomass estimation accuracy.