Abstract. Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.
New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive.. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had two additional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-3 8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program.
A best practices guide for the use of airborne laser scanning data (ALS; also referred to as Light Detection and Ranging or LiDAR) in forest inventory applications is now available for download from the Canadian Forest Service bookstore (White et al., 2013; http://cfs.nrcan.gc.ca/publications?id= 34887). The guide, produced by the Canadian Forest Service, Natural Resources Canada, brings together state-of-the-art approaches, methods, and data to enable readers interested in using ALS data to characterize large forest areas in a costeffective manner. The best practices presented in the guide are based on more than 25 years of scientific research on the application of ALS data to forest inventory. The guide describes the entire process for generating forest inventory attributes from ALS data and recommends best practices for each step of the process-from ground sampling through to metric generation and model development. The collection of ground plot data for model calibration and validation is a critical component of the recommended approach and is described in detail in the guide. Appendices to the guide provide additional details on ALS data acquisition and metric generation.The area-based approach is typically accomplished in two steps ( Fig. 1). In the first step, ALS data are acquired for the entire area of interest (wall-to-wall coverage), tree-level measures are acquired from sampled ground plots and summarized to the plot level, and predictive models are developed (e.g., using regression or non-parametric methods). For the purposes of model development, the ALS data is clipped to correspond to the area and shape of each ground plot. A set of descriptive statistics (referred to as "metrics") are calculated from the clipped ALS data and include measures such as mean height, height percentiles, and canopy cover (Woods et al. 2011). Inventory attributes of interest are either measured by ground crews (i.e., height, diameter) or modelled (i.e., volume, biomass) for each ground plot. It is critical that ground plots represent the full range of variability in the attribute(s) of interest and to accomplish this, the use of a stratified sampling approach is recommended, preferably with strata that are defined using the ALS metrics themselves. Thus, the ALS data must be acquired and processed prior to ground sampling.Finally, predictive models are constructed using the ground plot attributes as the response variable and the ALS-derived metrics as predictors.In the second step of the area-based approach, models that were developed using co-located ground plots and ALS data are then applied to the entire area of interest to generate the desired wall-to-wall estimates and maps of specific forest inventory attributes. The same metrics that are calculated for the clipped ALS data (as described above) are generated for the wall-to-wall ALS data and the predictive equations developed from the modelling in the first step are applied to the entire area of interest using the wall-to-wall metrics. The prediction unit for this...
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