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| 1 | 大堡礁和大峡谷世界遗产区的适应性管理显示文摘关于人类和环境相互关系的传统观念正在迅速改变。那种认为人类与自然是相互隔离的,自然资源是取之不尽用之不竭的,而且认为世界是稳定的、可以预测的并且是平衡的旧观点已经过时了。在动态的社会-生态景观中强调学习和灵活性管理为基础的适应性方法的新的理念框架已经迅速初现端倪。我们以两个典型的世界遗产地区(大堡礁和大峡谷)为案例来研究如何在自然资源管理中通过改善科学和社会两个方面的结合来指导多尺度管理系统的变革从而应对和处理人类越来越占据主导的世界中出现的不确定性、风险和变化。 | Terence P.Hughes Lance H.Gunderson Carl Folke Andrew H.Baird David Bellwood Fikret Berkes Beatrice Crona Ariella Helfgott Heather Leslie Jon Norberg Magnus Nystrm Per Olsson Henrik sterblom Marten Scheffer Heidi Schuttenberg Robert S.Steneck Maria Teng Max Troell Brian Walker James Wilson Boris Worm 丁莉 | 2007 | AMBIO-人类环境杂志2007，36，B11： | 4 |
| 2 | Automatic design of pulse coupled neurons for image segmentation显示文摘 | Henrik Berg Roland Olsson Thomas Lindblad José Chilo | 2008 | Neurocomputing2008，，10： | 2 |
| 3 | Friction generated limit cycles显示文摘 | Olsson Henrik Astrom K J | 2001 | IEEE Transactions on Control Systems Technology2001，9，4： | 1 |
| 4 | Automatic design of pulse coupled neurons for image segmentation 显示文摘 | Berg Henrik Olsson Roland Lindblad Thomas | 2008 | Neurocomputing(S0925-2312)2008，71，1012： | 1 |
| 5 | Cost-effi- cient Drilling Using Industrial Robots with High- band-width Force Feedback显示文摘 | Olsson T Mathias H Henrik K | 2010 | Robotics and Com- puter-integrated Nanufacturing2010，26，1： | 1 |
| 6 | Friction Generated Limit Cycles显示文摘 | Henrik Olsson Karl Astrom | 1996 | Proceeding of IEEE International Conference on Control Applications1996，1，： | 1 |
| 7 | Cost-efficient drilling using industrial robots with high-bandwidth force feedback 显示文摘 | Olsson Tomas Haage Mathias Kihlman Henrik | 2010 | Robotics and Computer-Integrated Manufacturingo2010，26，1： | 1 |
| 8 | Rational Krylov for Eigenvalue Computation and Model Order Reduction 显示文摘 | Henrik K Olsson A | 2006 | BIT Numerical Mathematics2006，46，： | 1 |
| 9 | Cost-efficient Drilling Using Industrial Robots with High-band- width Force Feedback显示文摘 | Olsson T Mathias H Henrik K | 2010 | Robotics and Computer- Integrated Manufacturing2010，26，1： | 1 |
| 10 | Study of the transverse liquid flow paths in pine and spruce using scanning electron microscopy显示文摘 | Tomas Olsson Modris Megnis Janis Varna Henrik Lindberg | 2001 | Journal of Wood Science2001，，4： | 1 |
| 11 | Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications显示文摘Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking.However,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of information.One such feature is that model-based predictors can be considered both unbiased and biased at the same time,which has important implications in several areas of application.In this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary data.From this point of view,model-based predictors are typically unbiased.Secondly,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely so.We suggest distinguishing between conventional model-bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted.We show that model-based estimators(or predictors)are typically design-biased,and that there is a trend in the design-bias from overestimating small true values to underestimating large true values.Further,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend.We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data. | Goran Ståhl Terje Gobakken Svetlana Saarela Henrik J.Persson Magnus Ekstrom Sean P.Healey Zhiqiang Yang Johan Holmgren Eva Lindberg Kenneth Nystrom Emanuele Papucci Patrik Ulvdal Hans OleØrka Erik Næsset Zhengyang Hou Håkan Olsson Ronald E.McRoberts | 2024 | Forest Ecosystems2024，11，1： | 0 |
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