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Driscoll, R. S.

Publications and source records attributed to Driscoll, R. S..

At least 19 records

Evaluation of Skylab (EREP) data for forest and rangeland surveys

The author has identified the following significant results. Four widely separated sites (near Augusta, Georgia; Lead, South Dakota; Manitou, Colorado; and Redding, California) were selected as typical sites for forest inventory, forest stress, rangeland inventory, and atmospheric and solar measurements, respectively. Results indicated that Skylab S190B color photography is good for classification of Level 1 forest and nonforest land (90 to 95 percent correct) and could be used as a data base for sampling by small and medium scale photography using regression techniques. The accuracy of Level 2 forest and nonforest classes, however, varied from fair to poor. Results of plant community classification tests indicate that both visual and microdensitometric techniques can separate deciduous, conifirous, and grassland classes to the region level in the Ecoclass hierarchical classification system. There was no consistency in classifying tree categories at the series level by visual photointerpretation. The relationship between ground measurements and large scale photo measurements of foliar cover had a correlation coefficient of greater than 0.75. Some of the relationships, however, were site dependent.

Aldrich, R. C.↗

Inventory of forest and rangeland resources, including forest stress

The author has identified the following significant results. Road systems being developed within the Manitou, Colorado area for human habitation are readily discernible on the S192 normal-color photographs. These are dirt roads, some of which are about 20 feet wide. These data should provide the District Ranger of the Pike National Forest required information on the size and extent of these developing areas, information which he does not now have but is required for total management of the District.

Aldrich, R. C.↗

Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available.

Heller, R. C.↗

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Controlled visual interpretation of one ERTS-1 scene taken at the peak of the growing season has indicated that classification to the ECOCLASS Series level is not entirely satisfactory. For five forest classes, aspen, Douglas-fir, lodgepole pine, ponderosa pine, and Spruce/fir, correct identification ranged from 60 to 70 percent. With the exception of shortgrass and wet shrubby meadow classes in the nonforest categories (81 and 100 percent correct, respectively), correct identification of the nonforest classes is so far unacceptable. The low accuracies are believed due to: (1) edge effects due to ecotones between plant community classes with apparent similar image characteristics; (2) confounding effects of amount of plant crown cover and ground surface material in the scene; and (3) variable land slope degree and aspect as it affects the image signature.

Heller, R. C.↗

Inventory of forest and rangeland resources, including forest stress

The author has identified the following significant results. Some current beetle-killed ponderosa pine can be detected on S190-B photography imaged over the Bear Lodge mountains in the Black Hills National Forest. Detections were made on SL-3 imagery (September 13, 1973) using a zoom lens microscope to view the photography. At this time correlations have not been made to all of the known infestation spots in the Bear Lodge mountains; rather, known infestations have been located on the SL-3 imagery. It was determined that the beetle-killed trees were current kills by stereo viewing of SL-3 imagery on one side and SL-2 on the other. A successful technique was developed for mapping current beetle-killed pine using MSS imagery from mission 247 flown by the C-130 over the Black Hills test site in September 1973. Color enhancement processing on the NASA/JSC, DAS system using three MSS channels produced an excellent quality detection map for current kill pine. More importantly it provides a way to inventory the dead trees by relating PCM counts to actual numbers of dead trees.

Heller, R. C.↗

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Disturbances in a forest environment that cause reductions in forest area, timber volume, and timber growth can be detected on ERTS-1 combined color composites. However, detection depends on comparing a conventional aerial photograph taken at some base year with an ERTS-1 image taken in some subsequent year. In a test made on the Atlanta site, 1:63,360 scale aerial photo index sheets made in 1966 were compared with ERTS-1 image 1264-15445 (April 1973). Five factors were found important to detection reliability: (1) the quality of the imagery; (2) the season of the imagery; (3) the size of the disturbed area; (4) the number of years since the disturbances; and (5) the type of cutting treatment. Of 209 disturbances verified on aerial photography, 165 (or approximately 80%) were detected on the ERTS-1 image by one independent interpreter. Improved training and additional experience in using this low resolution imagery should improve detection. Of the two seasons of data studies (fall and early spring), early spring is the best for detecting land use changes. Generally speaking, winter, early spring, and early summer are the best times of year for detecting forest disturbances.

Heller, R. C.↗

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Eucalyptus tree stands killed by low temperatures in December 1972 were outlined by image enhancement of two separate dates of ERTS-1 images (January 22, 1973-I.D. 1183-18175 and April 22, 1973-I.D. 1273-18183). Three stands larger than 500 meters in size were detected very accurately. In Colorado, range and grassland communities were analyzed by visual interpretation of color composite scene I.D. 1028-17135. It was found that mixtures of plant litter, amount and kind of bare soil, and plant foliage cover made classification of grasslands very difficult. Changes in forest land use were detected on areas as small as 5 acres when ERTS-1 color composite scene 1264-15445 (April 13, 1973) was compared with 1966 ASCS index mosaics (scale 1:60,000). Verification of the changes were made from RB-57 underflight CIR transparencies (scale 1:120,000).

Heller, R. C.↗