Special Issue "Data Fusion for Improved Forest Inventories and Planning"
Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 14185
Interests: statistical inferences applied to natural resource surveys; forest biometrics
Interests: forest planning; land-use planning; wood supply modelling; optimization; GIS
Interests: forest management; forest monitoring; forest inventory; geomatics for natural resources assessment; landscape planning
Interests: sampling theory with focus on sampling strategies for surveying animal and plant communities to estimate and to map abundance, coverage and ecological diversity
Interests: in finite and infinite sampling strategies for forest and rangeland inventories; with an emphasis on the statistical properties of the estimators. Also in statistical properties of models used in natural resource monitoring and change detection
The utilization of several sources of remotely-sensed data and field data that may differ in spatial resolution, spatial–temporal coverage, correlation with forest attributes, and sensor origins for the assessment of the state and change of forest variables is becoming increasingly popular due to the recent rapid development in remote sensing techniques. Data fusion is an umbrella term for combining several sources of data for such purposes. In forest inventory and planning, several statistical applications use data fusion, such as multi-phase, model-assisted estimation, composite estimation, hierarchical model-based estimation and data assimilation based on the Kalman filter or similar techniques. Model-assisted estimation was introduced in the 1990s as a generalized version of classical regression and ratio estimation in design-based inference. Although the technique has been known to statisticians for a long time, the main exploration of the technique for forest inventories is rather recent and due to recent developments in remote sensing. Data assimilation has been widely applied for a long time in areas such as meteorology. In forest inventories, so far there are only case studies and, thus, not yet any applications in practical forestry. Hierarchical model-based estimation is a newly-introduced method within model-based inference, which facilitates forest inventories in areas where only sparse samples of field data exist by taking advantage of multiple levels of RS data. This Special Issue is being published in connection with the Technical Session "Data Fusion for Improved Inventories and Planning" at the IUFRO 2019 Congress in Curitiba, Brazil, 29 September—5 October 2019.
Dr. Svetlana Saarela
Prof. Tuula Packalen
Prof. Piermaria Corona
Prof. Lorenzo Fattorini
Dr. Paul L. Patterson
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- Data Assimilation
- Model-Assisted Estimation
- Model-Based Inference
- Hierarchical Model-Based Estimation
- Composite Estimation