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Woodcock, C. E.

Publications and source records attributed to Woodcock, C. E..

13 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

On the nature of models in remote sensing

An explicit framework can provide a better understanding of remote sensing models and their interrelationships. This framework distinguishes between the scene, which is real and exists on the ground, and the image, which is a collection of spatially arranged masurements drawn from the scene. The scene model generalizes and parameterizes the essential qualities of the scene. Scene models may be discrete, in which the scene model consists of discrete elements with boundaries, or continuous, in which matter and energy flows are taken to be continuous and there are no clear or sharp boundaries in the scene. In the discrete case, there are two possibilities for models: H- and L-resolution. In the H-resolution case, the resolution cells of the image are smaller than the elements, and thus the elements may be individually resolved. In the L-resolution case, the resolution cells are larger than the elements and cannot be resolved. Most canopy models are L-resolution, deterministic, and noninvertible in nature; image processing models, however, tend to be H-resolution, empirical, and invertible. This taxonomy helps add insight to the development of remote sensing theory and point the way to new, productive areas of research.

Strahler, A. H.↗
NASA NTRS1986

Coniferous forest classification and inventory using Landsat and digital terrain data

Machine-processing techniques were used in a Forest Classification and Inventory System (FOCIS) procedure to extract and process tonal, textural, and terrain information from registered Landsat multispectral and digital terrain data. Using FOCIS as a basis for stratified sampling, the softwood timber volumes of the Klamath National Forest and Eldorado National Forest were estimated within standard errors of 4.8 and 4.0 percent, respectively. The accuracy of these large-area inventories is comparable to the accuracy yielded by use of conventional timber inventory methods, but, because of automation, the FOCIS inventories are more rapid (9-12 months compared to 2-3 years for conventional manual photointerpretation, map compilation and drafting, field sampling, and data processing) and are less costly.

Franklin, J.↗
NASA NTRS1985

Preliminary evaluation of the airborne imaging spectrometer for vegetation analysis in the Klamath National Forest of northeastern California

The experiences and results associated with a project entitled Preliminary Evaluation of the Airborne Imaging Spectrometer for Vegetation Analysis is documented. The primary goal of the project was to provide ground truth, manual interpretation, and computer processing of data from an experimental flight of the Airborne Infrared Spectrometer (AIS) to determine the extent to which high spectral resolution remote sensing could differentiate among plant species, and especially species of conifers, for a naturally vegetated test site. Through the course of the research, JPL acquired AIS imagery of the test areas in the Klamath National Forest, northeastern California, on two overflights of both the Dock Well and Grass Lake transects. Over the next year or so, three generations of data was also received: first overflight, second overflight, and reprocessed second overflight. Two field visits were made: one trip immediately following the first overflight to note snow conditions and temporally-related vegetation states at the time of the sensor overpass; and a second trip about six weeks later, following acquisition of prints of the images from the first AIS overpass.

Strahler, A. H.↗
NASA NTRS1985

Discrete-object modeling of remotely sensed scenes

Remotely sensed scenes can be modeled as collections of discrete, three-dimensional objects that cast shadows on a background. Approaching scenes from this perspective has led to two related lines of research. First is the geometric/optical modeling of a forest canopy, in which conifers are modeled as cones whose size and spacing vary according to functions established by field measurements. This canopy model is 'L-resolution' in nature - the objects are smaller than the resolution cells of the image and cannot be resolved individually. Second is scene modeling in which image variance is taken as a function of the relationship between the size, shape and spacing of objects and the resolution cell size of the digital image derived from the scene. This modeling is 'H-resolution' in nature - the objects in the scene are assumed to be larger than the resolution cells of the image, and thus can be individually distinguished. Both approaches illustrate the utility of the discrete-object scene model in extracting information from remotely sensed scenes.

Strahler, A. H.↗
NASA NTRS1984

Preliminary evaluation of the airborne imaging spectrometer for vegetation analysis

The primary goal of the project was to provide ground truth and manual interpretation of data from an experimental flight of the Airborne Infrared Spectrometer (AIS) for a naturally vegetated test site. Two field visits were made; one trip to note snow conditions and temporally related vegetation states at the time of the sensor overpass, and a second trip following acquisition of prints of the AIS images for field interpretation. Unfortunately, the ability to interpret the imagery was limited by the quality of the imagery due to the experimental nature of the sensor.

Strahler, A. H.↗
NASA NTRS1984

Improvements in forest classification and inventory using remotely sensed data

A Forest Classification and Inventory System (Focis) has been developed for large area forest inventories on the basis of Landsat and digital terrain data. It is a potential advantage of Focis that it can provide timely inventories at a reduced cost which are easily updated. The Klamath National Forest in Northern California was employed as test area for the initial development of Focis. Focis is constantly being changed and improved. Two recent additions to the inventory system include a spatial filtering algorithm which improves the spatial coherence in the final classified image, and a modification to the classification procedure designed to reduce the adverse effects of local topography on classification accuracy. Attention is given to a Focis overview, spatial filtering, the interface with the forest service geographic information system, and efforts to reduce the influence of topography.

Woodcock, C. E.↗
NASA NTRS1984

Image variance and spatial structure in remotely sensed scenes

Digital images derived by scanning air photos and through acquiring aircraft and spcecraft scanner data were studied. Results show that spatial structure in scenes can be measured and logically related to texture and image variance. Imagery data were used of a South Dakota forest; a housing development in Canoga Park, California; an agricltural area in Mississppi, Louisiana, Kentucky, and Tennessee; the city of Washington, D.C.; and the Klamath National Forest. Local variance, measured as the average standard deviation of brightness values within a three-by-three moving window, reaches a peak at a resolution cell size about two-thirds to three-fourths the size of the objects within the scene. If objects are smaller than the resolution cell size of the image, this peak does not occur and local variance simply decreases with increasing resolution as spatial averaging occurs. Variograms can also reveal the size, shape, and density of objects in the scene.

Woodcock, C. E.↗
NASA NTRS1983

Relating Spatial Patterns in Image Data to Scene Characteristics

In remote sensing, the primary goal is accurate scene inference, in which characteristics of the scene are inferred from the image data. More effective inference of scene characteristics can be accomplished through the use of techniques that use explicit models of spatial pattern. Spatial patterns in image data are functionally related to the size and spacing of elements in the scene and to the spatial resolution of the image data. At resolutions where variance is high, scene inference techniques should rely heavily on data from the spatial domain. As variance decreases, effective scene inference will increasingly rely on spectral data.

Strahler, A. H.↗
NASA NTRS1983

Spatial inventory integrating raster databases and point sample data

A timber inventory of the Eldorado National Forest, located in east-central California, provides an example of the use of a Geographic Information System (GIS) to stratify large areas of land for sampling and the collection of statistical data. The raster-based GIS format of the VICAR/IBIS software system allows simple and rapid tabulation of areas, and facilitates the selection of random locations for ground sampling. Algorithms that simplify the complex spatial pattern of raster-based information, and convert raster format data to strings of coordinate vectors, provide a link to conventional vector-based geographic information systems.

Strahler, A. H.↗
NASA NTRS1983

User alternatives in post-processing for raster-to-vector conversion

A number of Landsat-based coniferous forest stratum maps have been created of the Eldorado National Forest in California. These maps were produced in raster image format which is not directly usable by the U.S. Forest Service's vector-based Wildland Resource Information System (WRIS). As a solution, raster-to-vector conversion software has been developed for processing classified images into polygonal data structures. Before conversion, however, the digital classification images must be simplified to remove high spatial variance ('noise', 'speckle') and meet a USFS ten acre minimum requirement. A post-processing (simplification) strategy different from those commonly used in raster image processing may be desired for preparing maps for conversion to vector format, because simplification routines typically permit diagonal connections in the process of reclassifying pixels and forming new polygons. Diagonal connections are often undesirable when converting to vector format because they permit polygons to effectively cross over each other and occupy a common location. Three alternative methodologies are discussed for simplifying raster data for conversion to vector format.

Logan, T. L.↗
NASA NTRS1980

Stratification of forest vegetation for timber inventory using Landsat and collateral data

An automated forest stratification procedure based on Landsat and digital terrain data is reviewed. The system uses Landsat multispectral brightness values, a spatial texture channel, and digital terrain data information to divide Klamath National Forest vegetation into timber volume-homogeneous strata. The stratification process involves analyst-supervised class pooling editing, and the modeling of the regional type in a spatial manner using the digital terrain data. It is noted that for a given size and density class, the timber volume will change when the regional type changes, reflecting differences in growth potential.

Woodcock, C. E.↗
NASA NTRS1979

Forest Classification and Inventory System using Landsat, digital terrain, and ground sample data

Accurate timber inventory data for cost-effective forest management is the primary goal of a Forest Classification and Inventory System (FOCIS) designed to provide estimates of timber volume by species aggregated by compartments, townships, or other spatial management units. FOCIS uses Landsat spectral data, including a synthesized texture channel, and Forest Service ground sample data to produce timber volume-homogeneous classes through a two-step unsupervised clustering process. Registered digital terrain data, including derived slope angle and slope aspect channels, are used to predict species proportions through a trend surface model, again using ground sample data for model calibration. In the final stage of FOCIS, volume estimates and predicted species proportions are merged and aggregated to yield timber volumes by species within management areas.

Strahler, A. H.↗