Michael A. Wulder

Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery.

In this study we investigate the use of local maximum (LM) filtering to locate trees on high spatial resolution (1-m) imagery. Results are considered in terms of commission error (falsely indicated trees) and omission error (missed trees). Tree isolation accuracy is also considered as a function of tree crown size. The success of LM filtering in locating trees depends on the size and distribution of trees in relation to the image spatial resolution. A static-sized 3×3 pixel LM filter provides an indication of the maximum number of trees that may be found in the imagery, yet high errors of commission reduce the integrity of the results. Variable window-size techniques may be applied to reduce both the errors of commission and omission, especially for larger trees. The distribution of the error by tree size is important since the large trees account for a greater proportion of the stand basal area than the smaller trees. An investigation of the success of tree identification by tree crown radius demonstrates the relationship between image spatial resolution and LM filtering success. At an image spatial resolution of 1 m, a tree crown radius of 1.5 m appears to be the minimum size for reliable identification of tree locations using LM filtering.

High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization

Abstract Characterization of ecosystem structure, diversity, and function is increasingly desired at finer spatial and temporal scales than have been derived in the past. Many ecological applications require detailed data representing large spatial extents, but these data are often unavailable or are impractical to gather using field-based techniques. Remote sensing offers an option for collecting data that can represent broad spatial extents with detailed attribute characterizations. Remotely sensed data are also appropriate for use in studies across spatial scales, in conjunction with field-collected data. This article presents the pertinent technical aspects of remote sensing for images at high spatial resolution (i.e., with a pixel size of 16 square meters or less), existing and future options for the processing and analysis of remotely sensed data, and attributes that can be estimated with these data for forest ecosystems.

Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters:

Forests are the most widely distributed ecosystem on the earth, affecting the lives of most humans daily, either as an economic good or an environmental regulator. As forests are a complex and widely distributed ecosystem, remote sensing provides a valuable means of monitoring them. Remote-sensing instruments allow for the collection of digital data through a range of scales in a synoptic and timely manner. Accordingly, a variety of image-processing techniques have been developed for the estimation of forest inventory and biophysical parameters from remotely sensed images. The use of remotely sensed images allows for the mapping of large areas efficiently and in a digital manner that allows for accuracy assessment and integration with geographic information systems. This article provides a summary of the image-processing methods which may be applied to remotely sensed data for the estimation of forest structural parameters while also acknowledging the various limitations that are presented. Current advancements in remote-sensor technology are increasing the information content of remotely sensed data and resulting in a need for new analysis techniques. These advances in sensor technology are occurring concurrently with changes in forest management practices, requiring detailed measurements intended to enable ecosystem-level management in a sustainable manner. This review of remote-sensing image analysis techniques, with reference to forest structural parameters, illustrates the dependence between spatial resolution to the level of detail of the parameters which may be extracted from remotely sensed imagery. As a result, the scope of a particular investigation will influence the type of imagery required and the limits to the detail of the parameters that may be estimated. The complexity of parameters that may be extracted can be increased through combinations of image-processing techniques. For example, multitemporal analysis of image radiance values or multispectral image classification maps may be analysed to undertake the assessment of such forest characteristics as area of forest disturbances, forest succession and development, or sustainability of forest management practices. Further, the combination of spectral and spatial information extraction techniques shows promise for increasing the accuracy of estimates of forest inventory and biophysical parameters.

Object-based change detection

Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change detection and provide a conceptual overview of solutions, which are followed by a detailed review of current OBCD algorithms. In particular, OBCD offers unique approaches and methods for exploiting high-spatial-resolution imagery, to capture meaningful detailed change information in a systematic and repeatable manner, corresponding to a wide range of information needs.

Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI)

Abstract Leaf area index (LAI) currently may be derived from remotely sensed data with limited accuracy. This research addresses the need for increased accuracy in the estimation of LAI through integration of texture to the relationship between LAI and vegetation indices. The inclusion of texture, which acts as a surrogate for forest structure, to the relationship between LAI and the normalized difference vegetation index (NDVI) increased the accuracy of modeled LAI estimates. First-order, second-order, and a newly developed semivariance moment texture are assessed in the relationship with LAI. The ability to increase the accuracy of LAI estimates was demonstrated over a range of forest species, densities, closures, tolerances, and successional regimes. Initial assessment of LAI from spectral response over the full range of stand types demonstrated the need for stratification by stand type prior to analysis. Stratification of the stands based upon species types yields an improvement in the regression relationships. For example, deciduous hardwood stands, spanning an LAI range from ≈1.5 to 7, have a moderate initial bivariate relationship between LAI and NDVI at an r 2 of 0.42. Inclusion of additional texture statistics to the multivariate relationship between LAI and NDVI further increases the amount of variation accounted for, to an R 2 of 0.61, which represents an increase in ability to estimate hardwood forest LAI from remotely sensed imagery by approximately 20% with the inclusion of texture. Mixed forest stands, which are spectrally diverse, had an insignificant initial r 2 of 0.01 between LAI and NDVI, which improved to a significant R 2 of 0.44 with the inclusion of semivariance moment texture.

Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage

Red-attack damage caused by mountain pine beetle (Dentroctonus ponderosa Hopkins) infestation in stands of lodgepole pine (Pinus contorta) in the Prince George Forest Region of British Columbia was examined using multitemporal Landsat-7 ETM+ imagery acquired in 1999, 2000, and 2001. The image data were geometrically and atmospherically corrected, and processed using the Tasseled Cap Transformation (TCT) to obtain wetness indices. The final steps included pixel subtraction, enhancement, and thresholding of the wetness index differences. The resulting enhanced wetness difference index (EWDI) was used to interpret spectral patterns in stands with confirmed (through aerial survey) red-attack damage in 2001, and these EWDI patterns were compared to the patterns of reflectance in normal-colour composites. We stratified the aerial survey dataset into two levels and used the EWDI to discriminate classes of 10–29 red-attack trees and 30–50 red-attack trees, and a sample of healthy forest collected from inventory data. Classification accuracy of red-attack damage based on the EWDI ranged from 67% to 78% correct.

Development of a large area biodiversity monitoring system driven by remote sensing

Biodiversity is a multifaceted concept that often eludes simple operational definitions. As a result, a variety of definitions have been proposed each with varying levels of complexity and scope. While different definitions of biodiversity exist, the basic unit of measurement for the vast majority of studies is conducted at the species level. Traditional approaches to measuring species richness provide useful, yet spatially constrained information. Remote sensing offers the opportunity for large area characterizations of biodiversity in a systematic, repeatable, and spatially exhaustive manner. Based on this review we examine the potential for a national biodiversity monitoring system for Canada driven by remote sensing, a country approaching 1 billion ha in area, with the aim of producing recommendations that are transferable for regional or continental applications. A combination of direct and indirect approaches is proposed, with four selected key indicators of diversity that can be derived from Earth observation data: productivity, disturbance, topography, and land cover. Monitoring these indicators through time at an ecosystem level has the potential to provide a national early warning system, indicating where areas of potential biodiversity change may be occurring. We believe the large area biodiversity monitoring system as outlined would provide an initial stratification of key areas where regional and local scale analysis can be focused, while also providing context-specific information for species collection data.

Recent rates of forest harvest and conversion in North America

Incorporating ecological disturbance into biogeochemical models is critical for estimating current and future carbon stocks and fluxes. In particular, anthropogenic disturbances, such as forest conversion and wood harvest, strongly affect forest carbon dynamics within North America. This paper summarizes recent (2000-2008) rates of extraction, including both conversion and harvest, derived from national forest inventories for North America (the United States, Canada, and Mexico). During the 2000s, 6.1 million ha/yr were affected by harvest, another 1.0 million ha/yr were converted to other land uses through gross deforestation, and 0.4 million ha/yr were degraded. Thus about 1.0% of North Americas forests experienced some form of anthropogenic disturbance each year. However, due to harvest recovery, afforestation, and reforestation, the total forest area on the continent has been roughly stable during the decade. On average, about 110 m3 of roundwood volume was extracted per hectare harvested across the continent. Patterns of extraction vary among the three countries, with U.S. and Canadian activity dominated by partial and clear-cut harvest, respectively, and activity in Mexico dominated by conversion (deforestation) for agriculture. Temporal trends in harvest and clearing may be affected by economic variables, technology, and forest policy decisions. While overall rates of extraction appear fairly stable in all three countries since the 1980s, harvest within the United States has shifted toward the southern United States and away from the Pacific Northwest.

Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia

This Letter presents the results of textural separability tests obtained by first- and second-order texture methods on different aged Douglas-fir stands in IKONOS panchromatic imagery acquired 3 June 2000 over the Sooke River watershed in British Columbia. The effects of different measures and window sizes on the textural separability are discussed. Small windows sizes were not as effective in separating stands as larger windows sizes. Second-order (spatial co-occurrence homogeneity) texture values were the most effective in distinguishing between age classes. A first-order (variance) texture measure, though still useful, provided less separability.

Forest Disturbance and North American Carbon Flux

North Americas forests are thought to be a significant sink for atmospheric carbon. Currently, the rate of sequestration by forests on the continent has been estimated at 0.23 petagrams of carbon per year, though the uncertainty about this estimate is nearly 50%. This offsets about 13% of the fossil fuel emissions from the continent [Pacala et al., 2007]. However, the high level of uncertainty in this estimate and the scientific communitys limited ability to predict the future direction of the forest carbon flux reflect a lack of detailed knowledge about the effects of forest disturbance and recovery across the continent. The North American Carbon Program (NACP), an interagency initiative to better understand the distribution, origin, and fate of North American sources and sinks of carbon, has highlighted forest disturbance as a critical factor constraining carbon dynamics [Wofsy and Harris, 2002]. National forest inventory programs in Canada, the United States, and Mexico provide important information, but they lack the needed spatial and temporal detail to support annual estimation of carbon fluxes across the continent. To help with this, the NACP recommends that scientists use detailed remote sensing of the land surface to characterize disturbance.