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<br />00839 <br /> <br />natural springs, previous TI R data collected just after dawn and after sunset were capable <br />of mapping natural springs and should also detect archaeological structures. However. <br />such da(a would require a separate data collection for just these two resources and not all <br />parts of the corridor would be illuminated during early morning collections. <br /> <br />Remote-sensing technologies were evaluated for two important terrestrial aspects <br />of the physical resource program: (I) mapping the distribution of sediment deposits and <br />(2) mapping the topography and volumes of fine- and coarse-grained sediment deposits. <br />We found remote-sensing technologies to be vcry useful for both of these aspects. Our <br />evaluations showed that (I) digital color infrared (CIR) image data are more accurate <br />than digital or film natural-color and panchromatic imagery for mapping terrestrial <br />sediment deposits; (2) digital CIR image data provide a relatively rapid and accurate <br />mcans for this mapping; and (3) these deposits can be accurately mapped with 22-44-cm <br />resolution data. Our evaluations of different airborne approaches for monitoring <br />terrestrial sediment volumes showed that (I) low-resolution Light Detection and Ranging <br />(L1DAR) (one point every four to six meters) produced 26-1 03-cm vertical accuracies on <br />bare ground, whereas moderate resolution L1DAR (one point every one to two meters) <br />produced 9-26-cm vertical accuracies; (2) although the moderate-resolution L1DAR did <br />provide acceptable elevation accuracies on barc ground, these data wcre only acceptable <br />after correction for a vertical offset that varied with river reach; (3) the accuracies at both <br />L1DAR resolutions decreased (average 1.5-m error) in vegetated terrain; (4) the accuracy <br />and precision of high-resolution L1DAR data (10 points every meter) is 8 cm and 3 em <br />respectively, with essentially no vertical offsets. Although the cost for the high-rcsolution <br />L1DAR data is high ($6,200 per river km), it has wide applicability across many GCMRC <br />programs for detailed, site-specific monitoring requirements; and (5) photogrammetric <br />methods using I :4,000-scale aerial photography provide acceptable elevation data on <br />bare and vegetated ground (28-cm accuracy), but the cost analysis is about $3,000 per <br />river km, the analysis requires control panels within the study sites, and its accuracy is <br />lower in winter months due to shadows. <br /> <br />Remote-sensing technologies were evaluated for two major components of the <br />terrestrial biologic resources: (I) estimating canopy volumes and (2) inventorying the <br />vegetation. Remote sensing technologies were found to bc useful for both program <br />components with some qualifications. Manual photogrammetric methods using 1:4000- <br />scale aerial photography provided 85% accuracy in canopy height compared to ground <br />surveys. However, manual photogrammetry requires the placement of ground panels and <br />is both invasive and expensive. Automated photogrammetric methods and very high <br />resolution L1DA R data sets (15-30 points per square meter), which do not require ground <br />control panels, are currently being evaluated and may provide similar accuracies to that <br />provided by manual photogrammetry in a less invasive manner and at a lower cost. In <br />terms of vegetation inventories, our evaluations showed that calibrated, digital four-band <br />data with 30-cm resolution provide acceptable spectral and textural discrimination of <br />CRE vegetation commul/ities for developing inventory maps. However, certain <br />vegetation species were not effectively discriminated, due to either (I) miscalibration of <br />onc of the sensor's detectors, such that the two or three of the highest retlectance species <br />appear similar in the data. or (2) the inherent inability of the four wavelength bands to <br /> <br />3 <br />