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Data Fusion for Improved Forest Inventories and Planning

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 18675

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Special Issue Editors

Dr. Svetlana Saarela

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Guest Editor

Department of Forest Resource Management, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Interests: statistical inferences applied to natural resource surveys; forest biometrics

Prof. Tuula Packalen

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Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland
Interests: forest planning; land-use planning; wood supply modelling; optimization; GIS

Prof. Piermaria Corona

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Research Centre for Forestry and Wood, Viale S. Margherita 80, 52100 Arezzo, Italy
Interests: forest management; forest monitoring; forest inventory; geomatics for natural resources assessment; landscape planning

Prof. Dr. Lorenzo Fattorini

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Department of Economics and Statistics, University of Siena, 53100 Siena, Italy
Interests: sampling theory with focus on sampling strategies for surveying animal and plant communities to estimate and to map abundance, coverage and ecological diversity

Dr. Paul L. Patterson

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Guest Editor

United States Forest Service, Rocky Mountain Research Station, 240 West Prospect Road, Fort Collins, CO, USA
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

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Keywords

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

Published Papers (4 papers)

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Research

18 pages, 2956 KiB

Open AccessArticle

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

by Wanwan Liang, Mongi Abidi, Luis Carrasco, Jack McNelis, Liem Tran, Yingkui Li and Jerome Grant

Remote Sens. 2020, 12(4), 609; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040609 - 12 Feb 2020

Cited by 13 | Viewed by 4869

Abstract

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km²) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km² in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species. Full article

(This article belongs to the Special Issue Data Fusion for Improved Forest Inventories and Planning)

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19 pages, 831 KiB

Open AccessArticle

Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators

by Dinesh Babu Irulappa-Pillai-Vijayakumar, Jean-Pierre Renaud, François Morneau, Ronald E. McRoberts and Cédric Vega

Remote Sens. 2019, 11(8), 991; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11080991 - 25 Apr 2019

Cited by 27 | Viewed by 4353

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, 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)

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24 pages, 26054 KiB

Open AccessArticle

SAR and Lidar Temporal Data Fusion Approaches to Boreal Wetland Ecosystem Monitoring

by Joshua Montgomery, Brian Brisco, Laura Chasmer, Kevin Devito, Danielle Cobbaert and Chris Hopkinson

Remote Sens. 2019, 11(2), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020161 - 16 Jan 2019

Cited by 42 | Viewed by 5968

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 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)

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19 pages, 7531 KiB

Open AccessArticle

Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals

by Aaron Thompson and Richard Kelly

Remote Sens. 2019, 11(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11010006 - 20 Dec 2018

Cited by 7 | Viewed by 2827

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 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)

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