Steven E. Franklin

Remote sensing for sustainable forest management.

INTRODUCTION Forest Management Questions Remote Sensing Data and Methods Categories of Applications of Remote Sensing Organization of the Book SUSTAINABLE FOREST MANAGEMENT Definition of Sustainable Forest Management Ecosystem Management Criteria and Indicators of Sustainable Forest Management Information Needs of Forest Managers Role of Remote Sensing ACQUISITION OF IMAGERY Field, Aerial, and Satellite Imagery Data Characteristics Resolution and Scale Aerial Platforms and Sensors Satellite Platforms and Sensors General Limits of Airborne and Satellite Remote Sensing Data IMAGE CALIBRATION AND PROCESSING Georadiometric Effects and Spectral Response Image Processing Systems and Functionality Image Analysis Support Functions Image Information Extraction Image Understanding FOREST MODELING AND GIS Geographical Information Science Ecosystem Process Models Spatial Pattern Modeling FOREST CLASSIFICATION Information on Forest Classes Classification Systems for Use with Remote Sensing Data Level I Classes Level II Classes Level III Classes FOREST STRUCTURE ESTIMATION Information on Forest Structure Forest Inventory Variables Biomass Volume and Growth Assessment FOREST CHANGE DETECTION Information on Forest Change Harvesting and Silviculture Activity Natural Disturbances Change in Spatial Structure CONCLUSION The Technological Approach - Revisited References

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.

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.

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis

In digital image processing of remotely sensed data, texture analysis, filtering, and edge detection techniques, among others, may be improved through the use of variable window sizes which extend the analysis beyond the immediate pixel to a larger geographic area. In this paper, semivariograms are used to generate geographic windows, which are customized to the scale of observation. Three examples are used to illustrate the improvements over the use of arbitrarily selected fixed geometric windows in remote estimation of forest inventory, forest structure characteristics, and in land-cover classification. A program to handle the semivariance calculations is described. The code was written in the C programming language under AIX-Unix on an IBM RISC 6000 24-bit color workstation to support a common pixel-interleaved digital image format, and has been tested on optical and radar remote sensing imagery in three mapping studies.

Imaging radar for ecosystem studies

Recently a number of satellites have been launched with radar sensors, thus expanding opportunities for global assessment. In this article we focus on the applications of imaging radar, which is a type of sensor that actively generates pulses of microwaves and, in the interval between sending pulses, records the returning signals reflected back to an antenna.

Classification of SPOT HRV imagery and texture features

Abstract Spatial co-occurrence matrices were computed for a SPOT HRV multispectral image for a moderate-relief environment in eastern Canada. The texture features entropy and inverse difference moment were used with the spectral data in landcover classification, and substantive increases in accuracy were noted. These range from 10 per cent for exposed bedrock to over 40 per cent in forest and wetland classes. The average classification accuracies were increased from 511 per cent (spectral data alone) to 86.7 per cent (spectral data plus entropy measured in band 2 and inverse difference moment in band 3). Classes that are homogeneous on the ground were characterized adequately by spectral tone alone, but classes containing mixed vegetation patterns or strongly related to structure were characterized more accurately by using a mixture of spectral tone and texture.

An automated approach to the classification of the slope units using digital data

Abstract Digital elevation and remote sensing data sets contain different, yet complementary, information related to geomorphological features. Digital elevation models (DEMs) represent the topography, or land form, whereas remote sensing data record the reflectance/emittance, or spectral, characteristics of surfaces. Computer analysis of integrated digital data sets can be exploited for geomorphological classification using automated methods developed in the remote sensing community. In the present study, geomorphological classification in a moderate- to high-relief area dominated by slope processes in southwest Yukon Territory, Canada, is performed with a combined set of geomorphometric and spectral variables in a linear discriminant analysis. An automated method was developed to find the boundaries of geomorphological objects and to extract the objects as groups of aggregated pixels. The geomorphological objects selected are slope units, with the boundaries being breaks of slope on two-dimensional downslope profiles. Each slope unit is described by variables summarizing the shape, topographic, and spectral characteristics of the aggregated group of pixels. Overall discrimination accuracy of 90% is achieved for the aggregated slope units in ten classes.

High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes

An automated approach to identifying landslides using a combination of high-resolution satellite imagery and digital elevation derivatives is offered as an alternative to aerial photographic interpretation. Previous research has demonstrated that per pixel spectral response patterns are ineffective in discriminating mass movements. This technique utilizes image segmentation and digital elevation data in order to identify mass movements based not only on their reflectance but also on their shape properties and their geomorphic context. Dividing the classification by process into debris slides, debris flows, and rock slides makes the method far more useful than methods that group all mass movements together. A hierarchical classification scheme is utilized to eliminate areas that are not of interest and to identify areas where mass movements are probable. A supervised classification is then conducted using spectral, shape, and textural properties to identify failures that were greater than 1 ha in area. The resulting accuracy was 90 percent for debris slides, 60 percent for debris flows, and 80 percent for rock slides.