Special Issue "Data Fusion for Improved Forest Inventories and Planning"
Deadline for manuscript submissions: closed (31 March 2020).
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Data Assimilation
- Model-Assisted Estimation
- Model-Based Inference
- Hierarchical Model-Based Estimation
- Composite Estimation