Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory
Abstract
:1. Introduction
1.1. Value of National Forest Inventory (NFI) Data: Management, Research, Policy Decisions
1.2. Value and Uses of FIA Data
1.3. Background on Efficiency
1.4. Improvement of Statistical Efficiency—Statistical Inference
1.5. Design-Based Inference
1.5.1. Simple Expansion Estimators
1.5.2. Post-Stratified Estimators
1.5.3. Model-Assisted Estimators
1.6. Model-Based Inference
1.7. Hybrid Inference
1.8. Improvement of Economic Efficiency
2. Progression of FIA’s Use of RS Data: Inception to Modern Times
2.1. Early Use of RS Data in FIA
2.2. Photointerpretation (PI)
2.3. AVHRR
2.4. Landsat
2.5. MODIS
2.6. Growth of Machine Learning
2.7. Advanced Uses of Landsat
2.7.1. Opening of the Landsat Archive
2.7.2. Vegetation Change Tracker and the North American Forest Dynamics Project
2.7.3. TimeSync and LandTrendr
2.7.4. Landscape Change Monitoring System
2.7.5. Use of LTS-Derived Covariates for Mapping of FIA Attributes
2.8. Cloud Computing
2.8.1. Cloud-Based Data Processing
2.8.2. Cloud-Based Data Hosting and Serving
2.9. Increased Use of NAIP
2.9.1. Image-Based Change Estimation (ICE) and Logistical Planning Prior to Fieldwork (Pre-Field)
2.9.2. Pixel-Based Mapping Using NAIP
2.9.3. Object-Based Image Analysis Using NAIP
2.9.4. 3-D Processing of NAIP for Structure
2.10. Airborne Light Detection and Ranging (Lidar)
2.10.1. Airborne Lidar for Wall-to-Wall Mapping
2.10.2. Airborne Lidar for Sample-Based Estimation
2.11. Spaceborne Lidar for Sample-Based Estimation
2.11.1. GLAS
2.11.2. GEDI
2.12. Unmanned Aerial Systems and Terrestrial Lidar
3. General Observations on RS Data Integration in FIA and Other NFIs
General Characteristics of FIA’s Use of RS
- ◦
- NFI data are invaluable to creating RS products. They provide a standardized source of training data for models, and their use raises the likelihood that RS-based estimates will align with NFI-based estimates. They also provide valuable validation data for users interested in conducting map accuracy assessments at both the plot-pixel scale, as well as over larger geographic areas like U.S. counties, for which NFI-based estimates and confidence intervals can be generated.
- ◦
- A successful RS program has access to RS data inputs, software, and hardware, including affordable high performance computing systems. There was a strong correlation between advances in FIA’s use of RS and improvements in Internet and personal computer technology, and, more recently, a similar increase in RS technology usage with the opening of the Landsat archive, the advent of other free RS data input sources, and the advent of cloud computing systems. It cannot be understated how the democratization of RS data acquisition and processing technologies have led to improvements in our ability to monitor forest resources, and how FIA scientists are contributing more and more to both basic and applied research aimed at advancing forest science in these areas.
- ◦
- Advances in RS usage require nimbleness and outlets for creative investigation. Support for intellectual fora such as program meetings and scientific conference attendance advances what McRoberts [254], citing Reichenbach [255], calls the “discovery” component of science, i.e., the exploratory and creative part of the scientific method that focuses on identifying research questions, forming hypotheses, and developing models. Mechanisms for scientists and technical staff to conduct research and share preliminary results in a less-formal way furthers advancements.
- ◦
- Advances in RS are incremental, beginning with discovery and leading to operationalization. Figure 4 is a conceptual model showing the process that FIA RS research has typically gone through over the last several decades, beginning with knowledge discovery and ending in operationalization. It is noteworthy that some of the studies described in this review have not yet, or never will, become operational; Figure 4 identifies several points in the research and development process where operationalization can be impeded:
- a)
- After research into methods for application is conducted, it becomes clear that it is not feasible, or results are not as expected due to poorly-conceived research ideas that attempt to integrate components of many studies and stakeholder needs.
- b)
- After prototype development, large costs of operationalization or a lack of research maturity may limit adoption likelihood.
- c)
- After operationalization of the technology, it becomes clear that the user community does not yet have the capacity to use the results of the new technology. Strategies to address this include continuous capacity building among the user community, continuous improvement of the technology, and technology transfer.
4. Future Directions of RS Technology in FIA
4.1. RS Imagery Time Series
4.2. Cloud Computing and Storage
4.3. Exploitation of the Z-Dimension
4.3.1. Airborne Lidar
4.3.2. Spaceborne Lidar
4.3.3. Radar
4.4. Improved Estimation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Lister, A.J.; Andersen, H.; Frescino, T.; Gatziolis, D.; Healey, S.; Heath, L.S.; Liknes, G.C.; McRoberts, R.; Moisen, G.G.; Nelson, M.; et al. Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory. Forests 2020, 11, 1364. https://doi.org/10.3390/f11121364
Lister AJ, Andersen H, Frescino T, Gatziolis D, Healey S, Heath LS, Liknes GC, McRoberts R, Moisen GG, Nelson M, et al. Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory. Forests. 2020; 11(12):1364. https://doi.org/10.3390/f11121364
Chicago/Turabian StyleLister, Andrew J., Hans Andersen, Tracey Frescino, Demetrios Gatziolis, Sean Healey, Linda S. Heath, Greg C. Liknes, Ronald McRoberts, Gretchen G. Moisen, Mark Nelson, and et al. 2020. "Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory" Forests 11, no. 12: 1364. https://doi.org/10.3390/f11121364