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forest mapping and Classification with Compact PolInSAR Data

Journal of Beijing Institute of Technology 2018年第3期27卷 391-398页

作者： Ningxiao Sun Yuejin Zhao Lin Sun Qiongzhi Wu School of Optoelectronics Beijing Institute of Technology School of Information and Electronics Beijing Institute of Technology

An unsupervised classification method was applied to compact polarimetric-interferometric SAR（C-PolInSAR）data to investigate its potential for forest mapping and *** classification requires an initial class as a training *** this paper,the compact polarimetric entropy H and the optimal coherence spectrumA were computed,and their capabilities for initial classification were *** on the Hand A,apartition method was proposed to subdivide the H-A plane,and initial classes were hence ***,unsupervised C-PolInSAR segmentation procedures based on H-A and the complex coherence matrix J;were *** effectiveness of the unsupervised classification of C-PolInSAR data was demonstrated by using an E-SAR L-band PolInSAR dataset of the Traunstein test site.

关键词： forest mapping unsupervised classification Wishart classifier optimal coherence set com-pact polarimetric-interferometric SAR(C-PolInSAR)

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forest mapping with ERS SAR Tandem Data

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Chinese forestry Science and Technology 2002年第2期1卷 5-9页

作者： Mike WOODING Franck RANERA Betlem ROSICH Remote Sensing Applications Consultants Ltd. 2 Prospect Place Mill Lane Alton GU34 2SX UKEarth Observation Application Department ESA/ESRIN Via Galileo Galilei 00044 Frascati ItalyEarth Observation Application Department ESA/ESRIN Via Galileo Galilei 00044 Frascati Italy

The feasibility of ERS SAR Tandem data for mapping forest and non-forest cover in China was evaluated over Zengcheng County in the South China. An accuracy of 75% has been achieved. Then, the MACFERST (mapping China forest with ERS SAR Tandem data) project started by the Ministry of Science and Technology (MOST) of China and the European Space Agency (ESA) in 1999. The generation of a large-scale forest map requires solving problems such as the georeferencing and mosaicking of very long image strips cov...

关键词： forest mapping ERS SAR Tandem ILU image

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mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

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forest Ecosystems 2020年第3期7卷 562-578页

作者： Svetlana Saarela AndréWästlund Emma Holmström Alex Appiah Mensah Sören Holm Mats Nilsson Jonas Fridman Göran Ståhl Faculty of forest Sciences Swedish University of Agricultural SciencesSLU Skogsmarksgränd 17UmeåSE-90183Sweden

Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging(LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based ***: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m×18 m map units was found to range between 9 and 447 Mg·ha^-1. The corresponding root mean square errors ranged between 10 and 162 Mg·ha^-1. For the entire study region, the mean aboveground biomass was 55 Mg·ha^-1 and the corresponding relative root mean square error 8%. At this level 75%of the mean square error was due to the uncertainty associated with tree-level ***: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level bio

关键词： Aboveground biomass assessment forest mapping Gauss-Newton Regression Hierarchical Model-Based inference LiDAR maps National forest Inventory Uncertainty estimation Uncertainty map

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