E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Data Fusion for Improved Forest Inventories and Planning"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Guest Editor
Dr. Svetlana Saarela

Department of Forest Resource Management, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Website | E-Mail
Interests: statistical inferences applied to natural resource surveys; forest biometrics
Guest Editor
Prof. Tuula Packalen

Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland
Website | E-Mail
Interests: forest planning; land-use planning; wood supply modelling; optimization; GIS
Guest Editor
Prof. Piermaria Corona

Research Centre for Forestry and Wood, Viale S. Margherita 80, 52100 Arezzo, Italy
Website | E-Mail
Interests: forest management; forest monitoring; forest inventory; geomatics for natural resources assessment; landscape planning
Guest Editor
Prof. Lorenzo Fattorini

Department of Economics and Statistics, University of Siena, 53100 Siena, Italy
Website | E-Mail
Interests: sampling theory with focus on sampling strategies for surveying animal and plant communities to estimate and to map abundance, coverage and ecological diversity
Guest Editor
Dr. Paul L. Patterson

United States Forest Service, Rocky Mountain Research Station, 240 West Prospect Road, Fort Collins, CO, USA
Website | E-Mail
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

Special Issue Information

Dear Colleagues,

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
Guest Editors

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 1800 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.

Keywords

  • Data Assimilation
  • Model-Assisted Estimation
  • Model-Based Inference
  • Hierarchical Model-Based Estimation
  • Composite Estimation

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle
Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators
Remote Sens. 2019, 11(8), 991; https://doi.org/10.3390/rs11080991
Received: 14 March 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 25 April 2019
PDF Full-text (831 KB) | HTML Full-text | XML Full-text
Abstract
Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, [...] Read more.
Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. Full article
(This article belongs to the Special Issue Data Fusion for Improved Forest Inventories and Planning)
Figures

Figure 1

Open AccessArticle
SAR and Lidar Temporal Data Fusion Approaches to Boreal Wetland Ecosystem Monitoring
Remote Sens. 2019, 11(2), 161; https://doi.org/10.3390/rs11020161
Received: 25 October 2018 / Revised: 21 December 2018 / Accepted: 4 January 2019 / Published: 16 January 2019
Cited by 3 | PDF Full-text (26054 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study was to develop a decision-based methodology, focused on data fusion for wetland classification based on surface water hydroperiod and associated riparian (transitional area between aquatic and upland zones) vegetation community attributes. Multi-temporal, multi-mode data were examined from airborne [...] Read more.
The objective of this study was to develop a decision-based methodology, focused on data fusion for wetland classification based on surface water hydroperiod and associated riparian (transitional area between aquatic and upland zones) vegetation community attributes. Multi-temporal, multi-mode data were examined from airborne Lidar (Teledyne Optech, Inc., Toronto, ON, Canada, Titan), synthetic aperture radar (Radarsat-2, single and quad polarization), and optical (SPOT) sensors with near-coincident acquisition dates. Results were compared with 31 field measurement points for six wetlands at riparian transition zones and surface water extents in the Utikuma Regional Study Area (URSA). The methodology was repeated in the Peace-Athabasca Delta (PAD) to determine the transferability of the methods to other boreal environments. Water mask frequency analysis showed accuracies of 93% to 97%, and kappa values of 0.8–0.9 when compared to optical data. Concordance results comparing the semi-permanent/permanent hydroperiod between 2015 and 2016 were found to be 98% similar, suggesting little change in wetland surface water extent between these two years. The results illustrate that the decision-based methodology and data fusion could be applied to a wide range of boreal wetland types and, so far, is not geographically limited. This provides a platform for land use permitting, reclamation monitoring, and wetland regulation in a region of rapid development and uncertainty due to climate change. The methodology offers an innovative time series-based boreal wetland classification approach using data fusion of multiple remote sensing data sources. Full article
(This article belongs to the Special Issue Data Fusion for Improved Forest Inventories and Planning)
Figures

Graphical abstract

Open AccessArticle
Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals
Remote Sens. 2019, 11(1), 6; https://doi.org/10.3390/rs11010006
Received: 13 November 2018 / Revised: 14 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
PDF Full-text (7531 KB) | HTML Full-text | XML Full-text
Abstract
UWScat, a ground-based Ku- and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow [...] Read more.
UWScat, a ground-based Ku- and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow and Ice Experiment (CASIX) campaign in Churchill, Manitoba, were also included. Limited sensitivity to snow was observed at 9.6 GHz, while the forest canopy attenuated the signal from sub-canopy snow at 17.2 GHz. Forested landscapes were distinguishable using the volume scattering component of the Freeman–Durden three-component decomposition model by applying a threshold in which values ≥50% indicated forested landscape. It is suggested that the volume scattering component of the decomposition can be used in current snow water equivalent (SWE) retrieval algorithms in place of the forest cover fraction (FF), which is an optical surrogate for microwave scattering and relies on ancillary data. The performance of the volume scattering component of the decomposition was similar to that of FF when used in a retrieval scheme. The primary benefit of this method is that it provides a current, real-time estimate of the forest state, it automatically accounts for the incidence angle and canopy structure, and it provides coincident information on the forest canopy without the use of ancillary data or modeling, which is especially important in remote regions. Additionally, it enables the estimation of forest canopy transmissivity without ancillary data. This study also demonstrates the use of these frequencies in a forest canopy application, and the use of the Freeman–Durden three-component decomposition on scatterometer observations in a terrestrial snow accumulation environment. Full article
(This article belongs to the Special Issue Data Fusion for Improved Forest Inventories and Planning)
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top