Joanne C. White

Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update

Airborne laser scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elevations to normalize DSI-derived heights. Similar metrics were calculated from the vertical information provided by both DSI and ALS. Comparisons revealed that ALS metrics provided a more detailed characterization of the canopy surface including canopy openings. Both DSI and ALS metrics had similar levels of correlation with forest structural attributes (e.g., height, volume, and biomass). DSI-based models predicted height, diameter, basal area, stem volume, and biomass with root mean square (RMS) accuracies of 11.2%, 21.7%, 23.6%, 24.5%, and 23.7%, respectively. The respective accuracies for the ALS-based predictions were 7.8%, 19.1%, 17.8%, 17.9%, and 17.5%. Change detection between ALS-derived CHM (time 1) and DSI-derived CHM (time 2) provided change estimates that demonstrated good agreement (r = 0.71) with two-date, ALS only, change outputs. For the single-layered, even-aged stands under investigation in this study, the DSI-derived vertical information is an appropriate and cost-effective data source for estimating and updating forest information. The accuracy of DSI information is based on a capability to measure the height of the upper canopy envelope with performance analogous to ALS. Forest attributes that are well captured and subsequently modeled from height metrics are best suited to estimation from DSI metrics, whereas ALS is more suitable for capturing stand density. Further investigation is required to better understand the performance of DSI-derived height products in more complex forest environments. Furthermore, the difference in variance captured between ALS and DSI-derived CHM also needs to be better understood in the context of change detection and inventory update considerations.

An accuracy assessment framework for large-area land cover classification products derived from medium-resolution satellite data

Land cover classification over large geographic areas using remotely sensed data is increasingly common as a result of the requirements of national inventory and monitoring programmes, scientific modelling and international environmental treaties. Although large‐area land cover products are more prevalent, standard operational protocols for their validation do not exist. This paper provides a framework for the accuracy assessment of large‐area land cover products and synthesizes some of the key decision points in the design and implementation of an accuracy assessment from the literature. The fundamental components of a validation plan are addressed and the framework is then applied to the land cover map of the forested area of Canada that is currently being produced by the Earth Observation for Sustainable Development programme. This example demonstrates the compromise between the theoretical aspects of accuracy assessment and the practical realities of implementation, over a specific jurisdiction. The framework presented in this paper provides an example for others embarking on the assessment of large‐area land cover products and can serve as the foundation for planning a statistically robust validation.

Forest Monitoring Using Landsat Time Series Data: A Review

Abstract Unique among Earth observation programs, the Landsat program has provided continuous earth observation data for the past 41 years. Landsat data are systematically collected and archived following a global acquisition strategy. The provision of free, robust data products since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat time series (LTS) for multitemporal characterizations. The science and applications capacity has developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of particular relevance to management and scientific activities is ensured in the short term. In particular, forest monitoring benefits from LTS, whereby a baseline of conditions can be interrogated for both abrupt and gradual changes and attributed to different drivers. Such benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, as well as sophisticated image processing approaches. In this review, we present the status of remote sensing of forests and forest dynamics using LTS, including issues related to the sensors, data availability, data preprocessing, variables used in LTS, analysis approaches, and validation issues.

Comparison of airborne and satellite high spatial resolution data for the identification of individual trees with local maxima filtering

High spatial resolution airborne remotely sensed data have been considered a test bed for the utility of future satellite sensors. Techniques developed on airborne data are now being applied to high spatial resolution imagery collected from remote sensing satellites. In this Letter we compare the results of local maxima (LM) filtering for the identification of individual trees on a 1 m spatial resolution airborne Multi-detector Electro-optical Imaging Sensor II (MEIS II) image and a 1 m IKONOS image. With a relatively large spatial extent, comparative ease of acquisition, and radiometric consistency across the imagery, IKONOS 1 m spatial resolution data have potential utility for forestry applications. However, the results of the LM filtering indicate that although the IKONOS data accurately identify 85% of individual trees in the study area, the commission error is large (51%) and this error may be problematic for certain applications. This is compared to an overall accuracy of 67% for the MEIS II with a commission error of 22%. Further work in developing LM techniques for IKONOS data is required. These methods may be useful to forest stewards, who increasingly seek spatially explicit information on individual trees to serve as the foundation for more accurate modelling of forest structure and dynamics.

Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery

Information needs associated with forest management and reporting requires data with a steadily increasing level of detail and temporal frequency. Remote sensing satellites commonly used for forest monitoring (eg, Landsat, SPOT) typically collect imagery with sufficient temporal frequency, but lack the requisite spatial and categorical detail for some forest inventory information needs. Aerial photography remains a principal data source for forest inventory; however, information extraction is primarily accomplished through manual processes. The spatial, categorical, and temporal information requirements of large-area forest inventories can be met through sample-based data collection. Opportunities exist for very high spatial resolution (VHSR; ie, <1 m) remotely sensed imagery to augment traditional data sources for large-area, sample-based forest inventories, especially for inventory update. In this paper, we synthesize the state-of-the-art in the use of VHSR remotely sensed imagery for forest inventory and monitoring. Based upon this review, we develop a framework for updating a sample-based, large-area forest inventory that incorporates VHSR imagery. Using the information needs of the Canadian National Forest Inventory (NFI) for context, we demonstrate the potential capabilities of VHSR imagery in four phases of the forest inventory update process: stand delineation, automated attribution, manual interpretation, and indirect attribute modelling. Although designed to support the information needs of the Canadian NFI, the framework presented herein could be adapted to support other sample-based, large-area forest monitoring initiatives.

Lidar plots * a new large-area data collection option: context, concepts, and case study

Forests are an important global resource, playing key roles in both the environment and the economy. The implementation of quality national monitoring programs is required for the generation of robust national statistics, which in turn support global reporting. Conventional monitoring initiatives based on samples of field plots have proven robust but are difficult and costly to implement and maintain, especially for large jurisdictions or where access is difficult. To address this problem, air photo- and satellite-based large area mapping and monitoring programs have been developed; however, these programs also require ground measurements for calibration and validation. To mitigate this need for ground plot data we propose the collection and integration of light detection and ranging (lidar) based plot data. Lidar enables accurate measures of vertical forest structure, including canopy height, volume, and biomass. Rather than acquiring wall-to-wall lidar coverage, we propose the acquisition of a sample of scanned lidar transects to estimate conditions over large areas. Given an appropriate sampling framework, statistics can be generated from the lidar plots extracted from the transects. In other instances, the lidar plots may be treated similar to ground plots, providing locally relevant information that can be used independently or integrated with other data sources, including optical remotely sensed data. In this study we introduce the concept of “lidar plots” to support forest inventory and scientific applications, particularly for large areas. Many elements must be considered when planning a transect-based lidar survey, including survey design, flight and sensor parameters, acquisition considerations, mass data processing, and database development. We present a case study describing the acquisition of over 25 000 km of lidar data in Canadas boreal forests in the summer of 2010. The survey, which included areas of managed and unmanaged forests, resulted in the production of more than 17 million 25 × 25 m lidar plots with first returns greater than 2 m in height. We conclude with insights gained from the case study and recommendations for future surveys.

Monitoring Canada’s forests. Part 2: National forest fragmentation and pattern

Canada is one of the world’s largest nations, with a land area of nearly one billion hectares. This vast area is home to a number of unique ecosystems, comprised of different climate, land cover, topography, and disturbance characteristics. Depiction of forest composition, based on satellite-derived land cover, is a common means to characterize and identify trends in forest conditions and land use. Forest pattern analyses that consider the size, distribution, and connectivity of forest patches can provide insights to land use, habitat, and biodiversity. In this communication, we present the pattern characteristics of Canada’s forests as determined by the Earth Observation for Sustainable Development of Forests (EOSD) product, a new land cover classification of the forested area of Canada. The EOSD product (EOSD LC 2000) represents conditions circa the year 2000, mapping each 25 m × 25 m pixel into one of 23 categories. We used the EOSD data to assess forest patterns nationally at four spatial extents: level 1, 13 000 km2 (corresponding to the area of a single 1:250 000 scale National Topographic System (NTS) map sheet); level 2, 800 km2 (corresponding to the area of a single 1:50 000 scale NTS map sheet); level 3, 1 km2; and level 4, 1 ha. For levels 1–3, a total of 95 landscape pattern metrics were calculated; for the 1 ha units, a subset of eight metrics were calculated. The results of this analysis indicate that Canada’s forest pattern varies by ecozone, with some ecozones characterized by large areas of contiguous forest (i.e., Boreal Shield, Atlantic Maritime, and Montane Cordillera), while other ecozones have less forest and are characterized by large numbers of small forest patches, reflecting the complex mosaic of land cover types present (Taiga Shield, Taiga Cordillera). Trends for the subset of metrics used to characterize national conditions are relatively consistent across levels 1-3. Level 4 metrics, where the analysis extent is 1 ha, are well-suited to regional or local analyses. As the first regional assessments of the patterns contained in the EOSD LC 2000, these measures of Canada’s forest landscape patterns add value to the national land cover baseline.

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

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.

Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices

The mountain pine beetle (Dendroctonus ponderosae) is the most destructive insect of mature pine forests in western North America. Time series of wetness transformations generated from Landsat imagery have been used to detect mountain pine beetle red attack damage over large areas. With the recent availability of high spatial (QuickBird) and high spectral (Hyperion) resolution satellite sensor imagery, the relationship between spectral moisture indices and levels of red attack damage may be investigated. Six moisture indices were generated from Hyperion data and were compared to the proportion of the Hyperion pixel having red attack damage. Results indicate the Hyperion moisture indices incorporating both the shortwave infrared (SWIR) and near infrared (NIR) regions of the electromagnetic spectrum concurrently, such as the Moisture Stress Index, were significantly correlated to levels of damage (r 2 = 0.51; p = 0.0001). The results corroborate the hypothesis that changes in foliage moisture resulting from mountain pine beetle attack are driving the broad‐scale temporal variation in Landsat derived wetness indices. Furthermore, the results suggest that Hyperion data may be used to map low levels of mountain pine beetle red attack damage over large areas that are not consistently captured with Landsat data.

A history of habitat dynamics: Characterizing 35 years of stand replacing disturbance

Landscape change, specifically habitat loss and modification, is thought to have an impact on the health, productivity, distribution, and survival of grizzly bears (Ursus arctos L.). Although grizzly bears may preferentially seek out areas of anthropogenic disturbances for foraging opportunities, research has found that grizzly bears experience greater mortality in these areas as a result of increased human access. Additional insights on the location and rates of anthropogenic-driven landscape change are required to better understand related impacts upon grizzly bears. In this study, a time series of 14 Landsat MSS, TM, and ETM+ images were used to retrospectively document and quantify the rate of landscape change over a 35-year period from 1973 to 2008 in a 13507km2 analysis area in western Alberta, Canada. The study area is located within a larger region that contains the highest density of grizzly bears in Alberta and has experienced increasingly intensive forest harvesting and oil and gas exploration activities during this period. To accommodate the differing spectral channels from MSS to TM/ETM+ sensors, the arctangent of the angle of the Tasseled Cap greenness-to-brightness components was computed for each image year, with sequential image pairs differenced and a threshold applied to identify stand-replacing disturbance events. Results indicated that 11% of the analysis area experienced some form of stand-replacing disturbance (e.g., cutblocks, roads, oil and gas well sites, seismic lines, power lines, pipelines, blowdown) between 1973 and 2008. The greatest proportion of this change (by area) occurred between 2004 and 2006 (24%), while the lowest proportion occurred between 2000 and 2001 (2%). Although the number of change events has fluctuated over time, with a minimum of 2888 change events between 1976 and 1978 (2%) and a maximum of 36623 change events between 2004 and 2006 (29%), the mean size of change events has decreased over time: prior to 1995, mean event size was greater than 1.5ha; after 1995, it was less than 1.5ha. The annual rate of change was greatest between 2004 and 2006 (−1.25%), and lowest between 1981 and 1990 (−0.04%). Consideration of changes within the context of units relevant to grizzly bear management (i.e., grizzly bear watershed units and core or secondary habitat areas) indicate that the amount and rate of change was not spatially or temporally uniform across the study area. While the average change event size has decreased over time, the increasing number of change events has resulted in a larger aggregate area of change in more recent years. Landsat imagery provided a large-area, synoptic, and consistent characterization of 35years of stand-replacing disturbance in our study area, providing information that enables an improved understanding of the complex interactions between grizzly bear distribution, abundance, health, survival, and habitat.