The Human Impact Land Use Layer (HILL) dataset was developed with visual interpretation of high-resolution satellite imagery. 26,388 2 x 2 km photo plots from the Canadian National Forest Inventory program were examined for any visible evidence of human impact within and outside of the photo plot boundary. The interpreter provided a detailed description of all human impacts and a measure of the total area of all the impacts in the plot. Human impact classes were developed from the interpreter's plot descriptions. 45.7% of the photo plots have evidence of human impact within, or close to, the plot boundary. The HILL dataset could be used as a validation layer for human impact map products produced using Geographic Information System processes.

In our previous studies, we demonstrated the usefulness of TanDEM-X interferometric bistatic mode with single polarization to obtain forest heights for the purposes of large area mapping. A key feature of our approach has been the use of a simplified Random Volume Over Ground (RVOG) model that locally estimates forest height. The model takes TanDEM-X interferometric coherence amplitude as an input and uses an external Digital Surface Model (DSM) to account for local slope variations due to terrain topography in order to achieve accurate forest height estimation. The selection of DSM for use as a local slope reference is essential, as an inaccurate DSM will result in less accurate terrain-correction and forest height estimation. In this paper, we assessed TanDEM-X height estimates associated with scale variations in different DSMs used in the model over a remote sensing supersite in Petawawa, Canada. The DSMs used for assessments and comparisons included ASTER GDEM, ALOS GDSM, airborne DRAPE DSM, Canadian DSM and TanDEM-X DSM. Airborne Laser Scanning (ALS) data were used as reference for terrain slope and forest height comparisons. The results showed that, with the exception of the ASTER GDEM, all DSMs were sufficiently accurate for the simplified RVOG model to provide a satisfactory estimate of stand-level forest height. When compared to the ALS 95th height percentile, the modeled forest heights had R2 values greater than 80% and Root-Mean-Square Errors (RMSE) less than 2 m. For a close similarity in slope estimation with the ALS reference, coverage across Canada and open data access, the 0.75 arc-second (20 m) resolution Canadian DSM was selected as a preferred choice for the simplified RVOG model to provide TanDEM-X height estimation in Canada.

Forests are dynamic ecosystems, subject to both natural and anthropogenic agents of change. Wildfire, harvesting and other human activities alter the tree-covered area present in forests. From national and international reporting perspectives, forests include areas currently treed, as well as those disturbed forest areas that are not currently treed but will be, given time for regeneration and the advancement of natural successional processes. As a consequence, forest area can be depicted at a particular point in time, informed by a retrospective temporal context. Using time series of Landsat imagery, annual land cover maps can be generated that are informed by knowledge of past disturbance history (such as wildfire and harvesting). In this research, we use over three decades of annual land cover data generated from Landsat time series to generate a spatially explicit estimate of the forest area of Canada in 2010. We demonstrate how land cover and disturbance information can be combined to map the area of 'forest', as defined by the Food and Agricultural Organization of the United Nations (FAO), within Canada's 650 Mha of forested ecozones. Following this approach, we estimated Canada's total forest area in 2010 to be 354.5 Mha. This estimate includes 324.5 Mha of current forest cover in 2010, plus an additional 33.2 Mha (or 9.4 per cent) of temporally informed forest area where tree cover had been temporarily lost due to fire or harvest, less 3.3 Mha that were removed to meet a definitional minimum size (0.5 ha) for contiguous forest area. Using Canada's National Forest Inventory (NFI) as an independent reference source, the spatial agreement between the two estimates of forest area was similar to 84 per cent overall. Aspatially, the total area of the Landsat-derived estimate of 2010 forest area and the NFI baseline estimates differed by only 3 per cent, with notable regional differences in the wetland-dominated Hudson Plains Ecozone. Satellite-derived time series land cover and change information enable spatially explicit depictions of forest area (distinct from representations of forest cover) in a robust and transparent fashion, producing information of value to science, management and reporting information needs.

In this paper, we demonstrate the feasibility of using TanDEM-X (TX) interferometric coherence data for mapping forest height with 25-m pixels across a study area near Fort Simpson, Northwest Territories (NWT), Canada. Our simplified RVOG model locally estimates forest height by combining an optimized estimation of TX interferometric coherence amplitude with the 20-m resolution Canadian Digital Elevation Model (CDEM) accounting for local slope variations. The initial map of TX height estimates provided R2 values of 0.78 and 0.88, mean errors (ME) of 1.66 m and 1.90 m, and root-mean-square errors (RMSE) of 2.7 m and 2.9 m when compared to independent height estimates derived from field plots and airborne LiDAR, respectively. We corrected the bias of TX height estimates using two variants of a LiDAR-based linear model. An application of three cover-specific linear adjustments provided the final TX height map with absolute ME ≤0.05 m and RMSE ≤2.09 m. The approach was tailored to poorly inventoried northern boreal regions through the use of archived TX data, the CDEM, a land cover map and airborne LiDAR transects. Our encouraging results support the perspective of wall-to-wall mapping of forest height across northern boreal forests in the NWT and beyond.

In this paper, we consider the problem of radar estimation of forest canopy height in regions with dense forests and severe topography. We combine a reference digital elevation model with multiple satellite baselines from ascending and descending orbits to develop a merging algorithm relating single pass interferometric coherence to forest canopy height. We first describe the algorithm and processing steps used for height estimation and then apply the technique to a mountainous study site in British Columbia, Canada, using data from the Tandem-X satellite pair. We devise a new masking scheme to isolate potential problem areas in sloped terrain and apply the new merging algorithm by using multiple Tandem-X tracks to overcome the gaps left due to the masking procedure. The radar height products are validated by using a network of ground forest measurement sites and supporting lidar. The regression statistics show an r2 of 0.70 and rmse of 4.1 m between the radar and the field measured heights. By examining height errors, we implement a new test for the presence of canopy extinction, or subcanopy surface scattering, and demonstrate that in the dense and mountainous forests of British Columbia, there are significant canopy extinction effects in X-band imagery.

Forest height is a key measurement for determining aboveground carbon in forests and forest biomass. DLR's TANDEM-X mission provides paired scenes that can be used for forest height measurement. We have demonstrated this in [1,2,3] for scenes with some moderate topography. This paper discusses issues for estimating forest canopy heights in mountainous terrain. Multiple dates and baselines of TanDEM-X data were obtained for a study site with much more severe topography and taller trees in BC, Canada. The results of analysis of large-slope induced errors and mitigation of some of these effects are discussed below.

Methods were developed and tested for mapping the distribution of Scotch broom, an invasive shrub species expanding its range and disrupting native species and habitats in several parts of the world. During spring, the Scotch broom produces yellow flowers. Landsat imagery during the flower bloom period and during summer was acquired for several years for a study area on Vancouver Island, British Columbia, Canada. Ground-based reflectance measurements plus statistical separability tests were conducted to determine the effectiveness for identifying Scotch broom with Landsat spectral bands, band ratios, vegetation indices, and combinations of bloom and nonbloom imagery. Maximum likelihood classifications of three Scotch broom density classes (dense, ≥ 75% cover; moderate, 25 to 75%; low, 10 to 25%) and other land covers were run with various image and band sets and tested against independent reference sites. Accuracies of classifications using the better band combinations for moderate and dense Scotch broom patches combined were on the order of 80%, with unreliable results for sites of low Scotch broom density. Scotch broom patches less than 0.5 ha were often missed. Some commission error occurred (areas erroneously classified as Scotch broom). Suggested improvements are the use of time series of classifications over multiple years, incorporating knowledge of Scotch broom spread mechanisms or temperature and elevation limitations, and use of higher resolution satellites if the expense warrants it. Despite some limitations, a satellite-based remote sensing approach may be useful for aspects of Scotch broom management.

A hyper-temporal dataset of 97 Radarsat C-Band SAR imagery (acquired during 1996-2004) and supporting ground data over an established test site at Petawawa Research Forest was assembled and used to examine the characteristics of multitemporal radar data. Consistent data sets can be created but the backscatter pattern over time can be strongly influenced by different environmental and phenological conditions. In this paper, the backscatter from different forest types and open areas were examined. Factors such as freezing temperatures, rain wetting, dew, wet snow, phenological condition and flooding cause anomalies and changes in backscatter pattern that affect applications such as land cover typing, temporal filtering, detecting changes and identifying specific ecological niches.

Lidar technology has reached a point where ground and forest canopy elevation models can be produced at high spatial resolution. Individual tree crown isolation and classification methods are developing rapidly for multispectral imagery. Analysis of multispectral imagery, however, does not readily provide tree height information and lidar data alone cannot provide species and health attributes. The combination of lidar and multispectral data at the individual tree level may provide a very useful forest inventory tool. A valley following approach to individual tree isolation was applied to both high resolution digital frame camera imagery and a canopy height model (CHM) created from high-density lidar data over a test site of even aged (55 years old) Douglas-fir plots of varying densities (300, 500, and 725 stems/ha) on the west coast of Canada. Tree height was determined from the laser data within the automated crown delineations. Automated tree isolations of the multispectral imagery achieved 80%‐90% good correspondence with the ground reference tree delineations based on ground data. However, for the more open plot there were serious commission errors (false trees isolated) mostly related to sunlit ground vegetation. These were successfully reduced by applying a height filter to the isolations based on the lidar data. Isolations from the lidar data produced good isolations with few commission errors but poorer crown outline delineations especially for the densest plot. There is a complimentarity in the two data sources that will help in tree isolation. Heights of the automated isolations were consistently underestimated versus ground reference trees with an average error of 1.3 m. Further work is needed to test and develop tools and capabilities, but there is an effective synergy of the two high resolution data sources for providing needed forest inventory information.

We report herein the direct digital remote sensing of schools of Pacific herring (Clupea harengus pallasi), using a newly developed Compact Airborne Spectrographic Imager (CASI). We use the spectrometer capabilities of the instrument to obtain the spectral signature of the schools and their natural background. The imaging capabilities of the instrument are then used to collect spatial data for five different spectral bands. Simple image processing procedures are used to calculate school areas. Compared to aerial photography, digital remote sensing of fish schools offers several advantages including rapid turn around of results, digital image processing and archiving, as well as the possibility of studying the distribution of fish in relation to other parameters which can be remotely measured. Such as phytoplankton (via chlorophyll fluorescence or the blue/green colour), sediment concentration, oil slicks, and water mass boundaries.