BOREAS RSS-15 SIR-C and Landsat TM Biomass and Landcover Maps of the NSA and SSA Summary As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C , X- SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on September 2, 1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994. Note that some of the data files on the BOREAS CD-ROMs have been compressed using the Gzip program. See section 8.2 for details. Table of Contents * 1 Data Set Overview * 2 Investigator(s) * 3 Theory of Measurements * 4 Equipment * 5 Data Acquisition Methods * 6 Observations * 7 Data Description * 8 Data Organization * 9 Data Manipulations * 10 Errors * 11 Notes * 12 Application of the Data Set * 13 Future Modifications and Plans * 14 Software * 15 Data Access * 16 Output Products and Availability * 17 References * 18 Glossary of Terms * 19 List of Acronyms * 20 Document Information 1. Data Set Overview 1.1 Data Set Identification BOREAS RSS-15 SIR-C and TM Biomass and Landcover Maps of the NSA and SSA 1.2 Data Set Introduction Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with Synthetic Aperture Radar (SAR) and Landsat Thematic Mapper (TM) data and development of simple mapping techniques using combinations of SAR channels. 1.3 Objective/Purpose The purpose of this study is to provide maps of dry biomass over the modeling grids in the BOReal Ecosystem-Atmosphere Study (BOREAS) Southern Study Area (SSA) and Northern Study Area (NSA). Such information is useful for determining a portion of the carbon stored as woody biomass and for estimating the potential maintenance respiration of trees on a per area basis. 1.4 Summary of Parameters The data products include landcover and biomass maps covering most of the NSA and SSA modeling grids. Specific map products are: SSA Landcover map SSA Total above ground dry woody biomass SSA Stem dry biomass SSA Branch dry biomass SSA Foliage dry biomass NSA Total above ground dry woody biomass NSA Landcover map 1.5 Discussion The total biomass product was generated using multiple linear regression of SAR channels and field-measured above-ground woody biomass. Forest stand measurements of stem diameter and species were used with allometric equations (weight tables) to estimate biomass within several fixed radius circular plots. Forest data from the BOREAS Terrestrial Ecosystem (TE)-06 and TE-20 (auxiliary sites) were used for the analysis. Over 60 plots were measured in 1993 and 1994 by Remote Sensing Science (RSS)-15, RSS-16, and TE-20. These measurements were used to develop and test the biomass vs. SAR relationships. Another part of the study was to determine component-level biomass estimates for foliage, branches, and boles. This required classification of the area into three major forest types: pine, spruce, and aspen. It also required separate biomass equations for each of the forest types. Component biomass was estimated from total biomass using relationships developed from data acquired by TE-06. A seven-class map was developed for both NSA and SSA that included spruce-, pine-, and aspen-dominated forest categories. Classification accuracy’s of training areas were greater than 90% for both forest and non-forest classes. Accuracy’s determined from the classifications of small forest stands, including auxiliary sites, were better than 90% for pine and aspen stands, but only about 70% for spruce stands. Most of the errors were the result of spruce being misclassified as pine. The results indicate that above-ground biomass can be estimated to within about 1.6 kg/m2 for the range of measured stands (0-30 kg/m2). Because of increased variance and lack of data points at higher average stand biomass levels, there is greater uncertainty for total biomass levels above 15 kg/m2. For the SSA only, biomass mapping was extended to bole, branch, and foliage components from relationships with total above-ground biomass developed from detailed tree measurements. Average biomass within the imaged area was estimated to be about 7.3 kg/m2 with biomass components of bole, branch, and foliage comprising 83%, 12%, and 5% of the total. Average biomass within the NSA imaged area was found to be about 4.6 kg/m2. 1.6 Related Data Sets BOREAS TE-06 1994 Tower and Carbon Evaluation Site Allometry and Biomass Data BOREAS TE-13 1994 Tower and Auxiliary Site Biometry BOREAS TE-20 1994 Tower and Auxiliary Site Biometrics BOREAS RSS-16 AIRSAR Landcover and Biomass Maps 2. Investigator(s) 2.1 Investigator(s) Name and Title Dr. K. Jon Ranson, Co-PI NASA GSFC Dr. Roger Lang, Co-PI George Washington University Dr. Guoqing Sun, Co-I SSAI at NASA GSFC 2.2 Title of Investigation Distribution and Structure of Above Ground Woody Biomass 2.3 Contact Information Contact 1 --------- K. Jon Ranson NASA GSFC Greenbelt, MD (301) 286-4041 (301) 286-0239 (fax) Jon.Ranson@gsfc.nasa.gov Contact 2 --------- Guoqing Sun Dept.of Geography University of Maryland, College Park College Park, MD (301) 286-4041 (301) 286-0239 (fax) Guoqing.Sun@gsfc.nasa.gov Contact 3 ------------- Jaime Nickeson Raytheon ITSS NASA’s GSFC Greenbelt, MD (301) 286-3373 (301) 286-0239 (fax) Jaime.Nickeson@gsfc.nasa.gov 3. Theory of Measurements It has been demonstrated that SAR's capability to penetrate forest canopies can provide improved estimates of above-ground woody biomass. Because of the correlation between the different biomass components of vegetation canopies (i.e., foliage, branch, bole), SAR images with different wavelengths that each contain information related to total biomass, can be used together to estimate total biomass. Previous studies using airborne SAR (AIRSAR) and Spaceborne Imaging Radar-C/X- band Synthetic Aperture Radar SIR-C/X-SAR by Ranson and Sun [1994], and Ranson et al., [1995a] has shown that total above-ground dry biomass (transformed by logarithm or cube root) have a strong linear relationship with radar backscattering coefficients (in dB). The combination of cross-polarization backscattering (HV, horizontal transmit-vertical receive, or VH, vertical transmit-horizontal receive) of a longer wavelength and a shorter wavelength (e.g., PHV-CHV, P and C bands, or LHV-CHV, L and C bands) was successfully used in biomass estimation for a northern forest in Maine and BOREAS sites in Saskatchewan, Canada. In these studies, the two-band combination was found to be useful in increasing the sensitivity of radar signature to total biomass and in reducing the effects of radar incidence angle, forest species, and spatial structure. In view of the complexity of forest structure, especially in the Maine study site, these previous studies have emphasized reducing these effects so that a more general, simple model may be used for biomass retrieval for forests with various spatial structures and species compositions. In general, researchers have noticed that the sensitivity of biomass to backscatter is diminished at levels between 10 kg/m2 and 25 kg/m2 depending on the radar wavelength(s) used [e.g., LeToan et al., 1992; Ranson et al., 1995b; Dobson et al., 1995]. It is well documented that the shorter the radar wavelength, the lower the sensitivity to forest biomass. Studies using the Earth Resources Satellite-1 (ERS-1) C-band VV (CVV) have shown limited utility for mapping forest cover type or biomass [Leckie and Yatabe, 1994; Rignot et al. 1994a]. Dobson et al. [1995b] recently demonstrated improved forest classification using a combination of ERS-1 and the Japanese Earth Resources Satellite-1 (JERS-1) data. Harrell et al. [1995] reported poor sensitivity of ERS-1, but slightly better results using JERS-1 data to estimate to boreal forest biomass in Alaska. The ability to estimate biomass up to 15-25 kg/m2 such as reported by Ranson et al., [1995a] and Dobson et al. [1995a] using longer wavelength radar (i.e., L-band) makes SIR-C data suitable for boreal zone forest studies. A relationship between radar backscatter and field-measured biomass transformed by the cube root was developed using a stepwise regression routine to determine a best set of SIR-C/X-SAR channels from LHH, LHV, LVV, CHH, CHV, CVV, and XVV backscatter (s°). A two-step approach to retrieve forest biomass was used: 1) classify forests using SIRC/X-SAR data; and 2) develop models for each category and retrieve total biomass. The three major types of forests (pine, spruce, and aspen) discussed earlier were considered. Overall, the two methods produced similar results; however, the two-step method is required to estimate component biomass. 4. Equipment 4.1 Sensor/Instrument Description SIR-C/X-SAR has three radars, C-band and L-band with HH, VV, HV, and VH polarizations and X-band with VV polarization. The table below summarizes the characteristics of the radars. The mission was a cooperative experiment between National Aeronautics and Space Administration (NASA) and the Jet Propulsion Laboratory (JPL), which provided the C- and L-band multipolarization radars, and the German and Italian Space Agencies, which jointly provided the X-SAR. SIR- C/X-SAR data were used in this study because of the relatively large area covered (80 by 20 km) and the small change in illumination angle (< 5°) across an image. Characteristics of SIR-C/X-SAR. Radar Band Frequency (GHz) Wavelength (cm) Polarization SIR-C L-band 1.25 24.0 HH, HV, VV, VH C-band 5.3 5.7 HH, HV, VV, VH X-SAR X-band 9.6 3.1 VV 4.1.1 Collection Environment Selected meteorological parameters from the Saskatchewan Research Council (SRC) tower located at the SSA-Old Jack Pine (OJP) site for SIR-C/X-SAR data takes in April and October 1994. Data are 15-minute averages. Date GMT_Time Sol_Down Air Temp Soil_10c m 1994 HHMM W/m2 Deg C Deg C 10-April 1528 245.03 -0.34 -0.16 11-April 1510 249.69 9.87 -0.09 12-April 1452 234.18 7.29 -0.02 13-April 1432 238.18 4.37 0.02 14-April 1413 37.69 -0.30 0.02 15-April 1352 252.62 -1.76 0.03 16-April 1331 169.14 2.34 0.02 17-April 1310 61.92 6.83 0.02 18-April 1249 49.18 1.617 0.05 1-Oct. 1540 23.53 2.93 7.25 2-Oct. 1521 16.99 3.65 7.03 3-Oct. 1502 41.35 -0.11 4.50 4-Oct. 1443 110.22 0.12 4.50 5-Oct. 1424 56.67 3.62 5.51 6-Oct. 1404 51.20 5.08 6.27 6-Oct. 1536 243.68 7.60 6.17 7-Oct. 1344 35.75 -1.30 4.17 4.1.2 Source/Platform SIR-C/X-SAR is part of a series of spaceborne imaging radar missions that began with the June 1978 launch of Seasat SAR and continued with the November 1981 SIR-A and October 1984 SIR-B missions. The SIR-C/X-SAR missions were successfully conducted during 09-19-Apr-1994 and 30-Sep-1994 through 10-Oct-1994 and demonstrated the design and capabilities of a spaceborne multifrequency polarimetric SAR. SIR-C/X-SAR was launched on space shuttle Endeavour and acquired multiple data takes covering over 6% of Earth's surface, including a variety of land, ocean, and polar ice targets. The BOREAS study areas were added to the mission plan in 1991 as Backup Supersites, ensuring several data takes during the missions. 4.1.3 Source/Platform Mission Objectives SIR-C/X-SAR mission objectives were to demonstrate the capabilities of multifrequency and multipolarization data for Earth science research. 4.1.4 Key Variables Backscatter coefficient (dB) 4.1.5 Principles of Operation Active microwave, SAR 4.1.6 Sensor/Instrument Measurement Geometry The advantages of the SIR-C/X-SAR system are its three-dimensional illumination parameters (wavelength, polarization, and angle of incidence). The SIR-C instrument, developed for NASA at JPL, uses active phased array antennas for L- band and C-band that not only provide images of magnitudes of HH, VV, and HV polarization, but also provide images of the phase difference between the polarized returns. In addition, the electronic beam steering capability in the range direction (23°) from a fixed antenna position of 38° look angle makes it possible to acquire multiple incidence angle data (15° - 55°) without tilting the antenna. The X-SAR radar, built jointly by the Deutsche Forschungsantalt Für Luft-und Raumfarht (DLR) in Germany and the Agenzia Spaziale Italiana (ASI) in Italy, operates at 9.6 GHz and has only VV polarization. The SIR-C/X-SAR design includes bandwidths of 10, 20, and 40-MHz with the 40 MHz bandwidth providing better resolution. Data acquisitions for BOREAS sites used the 20-MHz bandwidth and 4-look averaging, resulting in a ground resolution of approximately 25 m. 4.1.7 Manufacturer of Sensor/Instrument NASA/JPL, 4800 Oak Grove Drive, Pasadena, CA 4.2 Calibration 4.2.1 Specifications During the two SIR-C/X-SAR missions, April 1994 (Space Radar Laboratory, SRL-1) and October 1994 (SRL-2), the BOREAS study area was imaged on several orbits [Ranson et al., 1995a]. The absolute calibration of SIR-C data was found to be +2.3 dB and +2.2 dB for L-band and C-band, respectively, for SRL-1. SRL-2 calibration was reported to be +2.0 dB and +3.2 dB for C-band and L-band, respectively [Freeman et al., 1995]. X-SAR calibration was very good and reported to be +1 dB for both missions [Zink and Bamler, 1995]. The mission plan called for similar orbits and radar parameters (e.g., illumination, data take mode, resolution) during the two missions, which facilitated the use of the temporal data. In addition, a Landsat TM image was also used for the forest type classification. The image was acquired on 02 Sep-1995 (Path 37, Row 22- 23). 4.2.1.1 Tolerance See Section 4.2.1. 4.2.2 Frequency of Calibration See Freeman et al. (1995). 4.2.3 Other Calibration Information None. 5. Data Acquisition Methods The data were acquired from NASA and JPL, which provided the C- and L-band multipolarization radars, and the German and Italian Space Agencies, which jointly provided the X-SAR. The BOREAS Landsat TM imagery was acquired through the Canadian Centre for Remote Sensing (CCRS). 6. Observations 6.1 Data Notes None given. 6.2 Field Notes An additional nine stands were measured in August 1996 (i.e., sites with numbers greater than or equal to 70). These were used for testing purposes only. A summary of the data is given in the following tables. Summary of SSA biomass sampling points. --------------------------------------- Site: from RSS15-TE20-1 to -36 were 'randomly' sampled along major roads during Intensive Field Campaign (IFC)-2 and described by TE-20. Other names: AL - an old jack pine stand shaped like the head of an alligator, located to the south of SSA-Young Jack Pine (YJP) tower site NofAL - young jack pine, north of 'Alligator' SofYJP - young jack pine south of SSA-YJP tower site YJP - SSA young jack pine tower site EofOJP - east of SSA-OJP EofYJP - east of SSA-YJP OJP - SSA old jack pine tower site OJPstem - stem map near SSA-OJP WS+ASL - white spruce and aspen mixture near Swan lake BSmed - medium density black spruce along the boardwalk to SSA-Old Black Spruce (OBS) tower BSwet - small density black spruce along the boardwalk to SSA-OBS tower BS - black spruce near SSA-OBS tower JPsl - Jack Pine near Swan Lake OAop - old aspen near SSA BOREAS Operations Center MoA - medium aspen WsAcl - white spruce and aspen mixture near Candle Lake Class: 1 - aspen, 2 - dry conifer, 3 - wet conifer Image Windows for each site: st-ln: starting line st-px: starting pixel nl: number of lines np: number of pixels Biomass: mean, stdv: mean and standard deviation of field measured biomass (kg/m2). radar: estimated biomass extracted from radar-derived biomass image (kg/m2). site name Class st-ln st-px nl np mean stdv radar 1 rss15-te20-1 1 1582 837 3 3 11.050 6.913 9.4771 2 rss15-te20-2 3 1533 902 3 3 4.182 1.047 2.2484 3 rss15-te20-3 3 1487 950 3 3 6.528 3.048 8.1438 4 rss15-te20-4 0 1452 1007 3 3 0.100 0.100 2.3791 5 rss15-te20-5 2 1398 1047 3 3 0.100 0.100 0.8758 6 rss15-te20-6 2 1332 1037 3 3 6.902 4.996 9.7647 7 rss15-te20-7 3 1266 1017 3 3 7.846 2.638 8.4706 8 rss15-te20-8 3 1201 1046 3 3 10.114 4.254 12.1180 9 rss15-te20-9 3 1165 1106 3 3 8.678 5.273 10.6930 10 rss15-te20-10 1 1113 1115 3 3 8.174 3.988 10.0000 11 rss15-te20-11 3 1053 1140 3 3 5.874 3.564 7.3464 12 rss15-te20-12 3 1000 1180 3 3 4.062 4.849 3.8301 13 rss15-te20-13 3 968 1228 3 3 11.758 3.001 10.1570 14 rss15-te20-14 1 961 1294 2 2 5.452 4.912 7.1765 15 rss15-te20-15 2 962 1360 2 2 1.317 0.898 2.5294 17 rss15-te20-17 1 943 1492 2 2 2.040 1.680 4.4412 18 rss15-te20-18 3 919 1556 3 3 2.844 2.683 6.5621 19 rss15-te20-19 3 888 1620 3 3 13.984 8.394 13.1900 20 rss15-te20-20 3 848 1669 3 3 4.230 3.888 4.5098 21 rss15-te20-21 3 818 1729 3 3 11.124 4.290 14.9670 22 rss15-te20-22 2 796 1795 3 3 0.304 0.615 0.0000 23 rss15-te20-23 2 753 1841 3 3 7.070 2.367 9.4771 24 rss15-te20-24 2 689 1862 3 3 12.960 2.316 12.6280 25 rss15-te20-25 2 627 1890 2 2 14.876 5.633 19.2050 26 rss15-te20-26 2 580 1942 3 3 1.714 2.126 3.6471 27 rss15-te20-27 2 522 1985 3 3 0.032 0.072 1.7908 28 rss15-te20-28 2 474 2030 3 3 0.880 1.311 5.1634 30 rss15-te20-30 2 508 2051 3 3 1.400 1.416 5.0196 31 rss15-te20-31 2 569 2070 2 2 0.324 0.511 0.1471 32 rss15-te20-32 1 631 2099 2 2 6.142 8.998 8.0588 33 rss15-te20-33 1 700 2101 2 2 28.752 14.172 23.4710 34 rss15-te20-34 2 762 2126 3 3 7.426 5.072 8.5752 35 rss15-te20-35 2 827 2139 3 3 5.798 1.473 5.2288 36 rss15-te20-36 3 887 2158 3 3 8.992 9.708 14.7060 37 rss15-te20-37 2 916 1937 3 3 7.485 5.916 7.0980 38 rss15-te20-38 1 895 2218 3 3 15.544 6.162 13.7910 39 rss15-te20-39 2 1047 2037 2 2 15.210 5.200 8.9412 40 rss15-te20-40 2 1035 2023 3 3 8.210 8.270 8.4314 41 F7J0P 3 968 1221 3 3 7.893 1.792 12.0920 42 G4K8P 2 838 1855 3 3 7.960 7.357 8.7712 43 G9I4S 3 541 1082 3 3 10.030 2.190 10.2220 44 G1K9P 2 873 1885 3 3 8.057 2.409 8.8105 45 NofAL 2 986 2086 3 3 0.640 0.300 0.3007 46 SofYJP 2 999 2125 3 3 1.000 0.500 4.8497 47 YJP 2 986 2119 3 3 2.400 1.050 5.7778 48 EofOJP 2 855 2117 3 3 8.430 4.380 11.3730 49 AL 2 1002 2069 3 3 11.540 3.000 16.5360 50 EofYJP 2 986 2128 3 3 11.340 3.290 10.1960 51 OJP 2 842 2000 10 10 6.400 2.000 7.5424 52 OJPstem 2 851 2027 3 3 7.810 2.500 6.7190 53 D9G4A 1 1495 302 3 3 9.001 2.868 16.9410 54 D9I1M 3 1546 879 3 3 8.654 2.676 6.7451 55 F1N0M 1 1252 2358 3 3 17.163 4.176 14.3400 56 F5I6P 2 1032 1088 3 3 7.037 2.123 7.9346 57 F7J1P 2 973 1262 3 3 13.318 2.800 9.8824 58 G2I4S 3 795 1025 2 2 13.065 10.585 13.1470 59 G2L7S 3 891 2126 3 3 3.367 1.115 3.7386 60 G4I3M 1 767 1022 3 3 16.670 2.495 17.8430 61 G6K8S 3 740 1861 3 3 13.620 1.346 14.1180 62 G7K8P 2 680 1833 3 3 9.007 0.525 9.6601 63 G8L6P 3 673 2132 3 3 1.258 0.634 1.7647 64 G9L0P 2 624 1907 3 3 14.347 1.929 16.1170 70 BSmed 3 567 1089 3 3 8.710 1.910 8.3791 71 BSwet 3 549 1084 3 3 3.400 0.790 4.0261 72 BS 3 556 1113 3 3 13.250 2.440 9.9608 73 JPsl 2 532 1113 3 3 14.690 4.870 16.2880 74 WsAsl 3 478 1146 3 3 8.470 5.250 8.4837 77 OAop 1 1479 787 3 3 16.040 2.800 21.5950 78 MOA 1 1368 1022 3 3 9.300 2.270 10.4710 80 WsAcl 3 1418 823 3 3 16.850 11.160 16.8890 NSA biomass data were derived from data provided to the BOREAS Information System (BORIS) by TE-06 or TE-13. The values used are listed below. Also included are the SIR-C image locations for measured above-ground total dry biomass for NSA stands. BOREAS Mean Biomass Sdev Biomass Op-Grid st-ln st-px nl ns kg/m2 kg/m2 T2Q6A 1186 484 4 4 9.96 0.7 T3R8T 1203 888 4 4 9.47 0.63 T3U9S 1384 1931 4 4 3.77 1.16 T4U5A 1326 1794 3 3 4.93 4.7 T4U9S 1385 1920 3 3 8.3 1.88 T5Q7S 1083 559 3 3 13.42 0.49 T6R5S 1107 812 3 3 11.04 1.18 T6T6S 1210 1510 3 3 3.76 1.67 T7Q8P 1041 590 3 3 3.81 0.46 T7R9S 1084 957 3 3 2.39 2.08 T7S9P 1151 1263 3 3 4.4 1.76 T8Q9P 1000 644 3 3 14.53 2.25 T8S4S 1072 1116 3 3 1.85 0.45 T8S9T 1152 1289 3 3 0.96 0.37 T8S9P 1122 1299 3 3 0.86 0.57 T8T1P 1114 1343 3 3 1.4 0.24 T9Q8P 1017 622 3 3 1.85 0.06 7. Data Description 7.1 Spatial Characteristics 7.1.1 Spatial Coverage The SSA images represent a 20 x 80 km swath covering 75% of the SSA modeling grid. The center point coordinates are approximately 104° 45' W, 53° 52' N, see map below. The corner points of the SSA images are: UTM UTM Longitude Latitude Easting (m) Northing (m) X (deg) Y (deg) Upper Left 460059.160 5999525.927 105.61141° W 54.14229° N Upper Right 550596.384 5999525. 104.22549° W 54.14135° N Lower Left 460059.160 5942856.342 105.60403° W 53.63297° N Lower Right 550596.384 5942856.342 104.23484° W 53.63205° N The NSA image covers most of the modeling grid, except for the small extension on the western edge. The center point coordinates are approximately 98° 18' 07.7" W, 55° 54' 17.3" N, see map below.. The corner points of the NSA images are: UTM UTM Longitude Latitude Easting (m) Northing (m) X (deg) Y (deg) Upper Left 505789.00 6229238.100 98.90668° W 56.20804° N Upper Right 577489.00 6229238.100 97.75100° W 56.20177° N Lower Left 505789.00 6153818.100 98.90829° W 55.53030° N Lower Right 577489.00 6153818.100 97.77255° W 55.52427° N 7.1.2 Spatial coverage Map 7.1.2.1 Spatial Coverage Map for SSA SIR-C/X-SAR Images 7.1.2.2 Spatial Coverage Map for NSA SIR-C/X-SAR Images 7.1.3 Spatial Resolution The data were acquired at about 25 m spatial resolution and were resampled to 30 m resolution in Albers Equal Area Conic (AEAC) projection selected by BOREAS. 7.1.4 Projection The projection is AEAC. 7.1.5 Grid Description The origin of the grid is at 111° W, 51° N and the standard parallels are set to 52.5° N and 58.5° N as prescribed in 'Map Projections - A Working Manual,' USGS Professional Paper 1395, John P. Snyder, 1987. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage Characteristics of data takes over BOREAS sites for SIR-C/X-SAR flights SRL-1 (09-19-Apr-1994), SRL-2 (30Sep-1994 through 10-Oct-1994). PA is Prince Albert (SSA) , NH is Nelson House (NSA). Site Name Orbit # Date (1994) Look Angle(deg) Orbit Direction Ascending / Descendin g Mode* SRL-1 SRL-2 SRL-1 SRL-2 SRL-1 SRL-2 PA 20.1 20.2 10- April 1-Oct. 28.86 29.42 A 16X PA 36.3 36.3 11 April 2-Oct. 33.14 33.41 A 16X PA 52.3 52.3 12- April 3-Oct. 39.97 36.86 A 16X PA 68.2 68.2 13 April 4-Oct. 39.50 39.52 A 16X PA 84.2 84.12 14- April 5-Oct. 42.00 41.77 A 16X PA 100.2 100.12 15 April 6-Oct. 43.50 43.63 A 16X PA - 101.1 - 6-Oct. 55.83 55.55 D 11 PA 116.3 116.22 16 April 7-Oct. 44.86 45.17 A 16X PA 132.4 - 17 April - 45.86 - A 16X PA 148.2 - 18 April - 46.69 - A 16X PA 164.2 - 19 April - 47.25 - A 16X NH 21.1 21.1 10- April 1-Oct. 24.96 25.35 D 16X NH 37.1 37.1 11 April 2-Oct. 23.18 23.68 D 16X NH 53.1 53.1 12- April 3-Oct. 21.50 21.90 D 16X NH 69.1 69.1 13 April 4-Oct. 19.62 19.69 D 16X NH 70.0 70.1 13- April 4-Oct. 58.06 58.04 D 11X NH 85.1 85.1 14 April 5-Oct. 17.98 17.84 D 16X *16X = C-,L-band quad-polarization, X-band VV 11X = C- ,L-band HH, HV polarization, X-band VV 11= C-, L-band HH, HV polarization only 7.2.2 Temporal Coverage Map SSA biomass map developed from 06-Oct-1994 SIR-C data. NSA biomass map developed from 13-Apr-1994 SIR-C data. Landsat TM data used for the SSA landcover map was acquired on 02-Sep-1995 7.2.3 Temporal Resolution See Section 7.2.1. 7.3 Data Characteristics 7.3.1 Parameter/Variable Land cover classification Total above ground dry woody biomass Dry woody stem biomass Dry woody branch biomass Dry foliage biomass 7.3.2 Variable Description/Definition The data products include landcover and biomass maps covering most of the NSA and SSA modeling grids. Individual parameter maps include: Land cover map with categories of Pine, Spruce, broadleaf Aspen, shrubland, fen, clearing, and water. (See table below): Total above ground dry woody biomass in each image resolution cell (nominally 30 meters squared): Dry woody stem biomass, Dry woody branch biomass and dry foliage biomass - SSA only . Land cover classification set. Class Description ---------- --------------------------------------------------------- Pine Consists of mature jack pine with lichen, mature jack pine with alder, young jack pine and regenerating jack pine. Spruce Includes black spruce and white spruce. Also includes areas of low biomass treed muskeg. Aspen Consists of mature, intermediate and young aged aspen stands. Shrubland Treeless areas of willow and other deciduous shrubs. Includes very young aspen regeneration. Fen Treeless wetlands mostly covered with bog birch or other low shrubs. Clearing Areas of recent logging activity where tree cover is removed. Water Lakes, ponds and larger rivers. SSA Land Cover Map class 0 - background 1 - pine 2 - spruce 3 - aspen 4 - shrub land 5 - clearing 6 - fen 7 - water NSA Land Cover Map class 0 - background 1 - pine 2 - spruce 3 - aspen 4 - shrub land 5 - clearing 6 - fen 7 - water Total above ground dry woody biomass - kg/m2 of total standing woody biomass. Dry woody stem biomass - kg/m2 of woody stem or bole biomass Dry woody branch biomass - kg/m2 of woody branch biomass Dry foliage biomass - kg/m2 of woody foliage (leaves or needles) biomass (SSA only) 7.3.3 Unit of Measurement Classification maps - coded but unitless values. In the following biomass images, the pixel values 0 - 255 correspond linearly to the specified ranges of biomass Dry above ground woody total biomass, 0 - 30 kg/m2 for total biomass image. To calculate biomass as kg/m2, divide image values by 8.50 Dry woody stem biomass in gridded (image) format. 0 - 27.13 kg/m2 for stem biomass image. To calculate biomass as kg/m2, divide image values by 9.40 Dry woody branch biomass in gridded (image) format. 0 - 4.07 kg/m2 for branch biomass image. To calculate biomass as kg/m2, divide image values by 62.65 Dry foliage biomass in gridded (image) format. 0 - 3.95 kg/m2 for foliage biomass image. To calculate biomass as kg/m2, divide image values by 64.56 7.3.4 Data Source The data described here are derivative products of SIR-C/X-SAR and Landsat TM data. 7.3.5 Data Range Classification maps, 0-7. Biomass maps, 0-255. 7.4 Sample Data Record Not applicable to image data. 8. Data Organization 8.1 Data Granularity The smallest unit of data tracked by BORIS is the entire set of images from each of study area. 8.2 Data Format 8.2.1 Uncompressed Files This data set contains the following 9 files: file description samples lines ------ ----------------------------- ------- ----- 1 *NSA list NA NA 2 NSA SIR-C Cover Map 2390 2500 3 NSA SIR-C Total Biomass Map 2390 2500 4 *SSA list NA NA 5 SSA SIR-C Branch Biomass Map 3023 1887 6 SSA SIR-C Cover Map 3023 3023 1887 7 SSA SIR-C Foliage Biomass Map 3023 1887 8 SSA SIR-C Stem Biomass Map 3023 1887 9 SSA SIR-C Total Biomass Map 3023 1887 ----------------------------------------------------------------------------- *contain the list of sites, image coordinates and biomass measurements used to construct and/or test the SSA and NSA biomass algorithms. All images files 1-7 are single byte binary image files with no header. Pixel size is 30 m x 30 m. Files 8 and 9 are ASCII files with 80 characters per line. 8.2.2 Compressed CD-ROM Files On the BOREAS CD-ROMs, the image files been compressed with the Gzip (GNU zip) compression program (file_name.gz). These data have been compressed using gzip version 1.2.4 and the high compression (-9) option (Copyright (C) 1992-1993 Jean-loup Gailly). Gzip uses the Lempel-Ziv algorithm (Welch, 1994) also used in the zip and PKZIP programs. The compressed files may be uncompressed using gzip (with the -d option) or gunzip. Gzip is available from many websites (for example, the ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both executable and source code form. Versions of the decompression software for various systems are included on the CD-ROMs. 9. Data Manipulations 9.1 Formulae Forest Type Classification The purpose of the landcover classification was to identify areas of forest and non-forest and stratify the forest classes into three major forest categories: pine, spruce, and aspen. The separation of the forest types allows the use of type-specific biomass vs. backscatter relationships as discussed below. L-band (HH, HV, VV), C-band (HH, HV, VV), and X-band (VV) channels from 15-Apr and 06- Oct SIR-C/X-SAR images were combined into a common radar data set. In addition, the six reflective channels from a 02-Sep-1994 Landsat TM image, Channel 1 : 0.45-0.52 mm, Channel 2: 0.52-0.60 mm, Channel 3: 0.63-0.69 mm, Channel 4: 0.76- 9.90 mm, Channel 5: 1.55-0.1.75 mm, and Channel 7: 2.08-2.35 mm were included. A principal component analysis was performed to transform the data set and reduce the number of channels used. The first eight components accounted for over 90% of the variance. After each of the principal component images was examined, components 1, 2, 3, 4, 7 and 8 were selected for use in the classifier. Principal components 5 and 6 were not selected because the apparent information content of the images was low or redundant with other components. See Ranson et al., 1997, for a complete description. SIR-C channels for October data and TM channels contributed the most to the classifier information content. For radar channels, L-band contributed more than C-band channels and X-band contributed the least. Near-IR and shortwave-IR bands contributed the most from the TM data, while visible band 2 contributed the least of any channel. Commercially available imaging processing software (PCI) was used for image classification. Training set locations were identified on the images for the three forest classes and four non-forest classes listed in Section 7.3.2. Global Positioning system (GPS)-derived site coordinates and aerial photography were used to aid in site location. Spectral signatures were extracted from the transformed images and used as inputs for the Maximum Likelihood Classifier (MLC) to produce a forest type map. Classification performance was determined by analyzing classified training sets and also checking classification results for the measured stands discussed in Section 10.2.3. Biomass Estimation A set of forest stand measurements was acquired by BOREAS investigators during the summers of 1993 and 1994 that consisted of identifying tree species, measuring diameter and heights, and determining age. Eight stands were sampled in August 1993, and 40 stands were sampled in 1994. The sites covered a wide variety of forest types and in many cases, were very heterogeneous within and between plot samples (these data are described elsewhere by Knox et al., TE-20). An additional nine plots were sampled in August 1996. Above-ground dry woody biomass (kg/m2) was determined by using the measured dbh (diameter at breast height or 1.3 m from the ground) for every tree in a plot and by applying biomass or weight tables developed for boreal forest species occurring in the Prairie Provinces of Canada. The weight tables were derived from equations developed on-site by University of Wisconsin personnel (Gower et al., 1997) and from other published studies (Young et al., 1980; Singh, 1982). In addition, the study used data made available by Forestry Canada (Haliwell and Apps, 1997) [TE-13] and University of Wisconsin personnel [TE-06] for BOREAS auxiliary and tower flux sites. The University of Wisconsin allometry results were expressed as kg carbon/ha. Since dry biomass contains approximately 50% carbon [Waring and Schlesinger, 1985], the expression: Biomass density = kg Carbon/ha . CF / 10000 was used with a conversion factor (CF) = 2.0 to convert mass of carbon to woody biomass density. A CF equal to 2.222 was used for the foliage component. Plot data were then averaged. It was desirable to use larger, more homogeneous stands (determined from between sample plot density variance) to extract at least a 3x3 array of SIR-C/X-SAR pixels to obtain a representative sample size. A total of 62 of the stands were suitable for this purpose and were assumed to represent most of the forest conditions. Data from these stands were used for algorithm development and testing as discussed below. 9.1.1 Derivation Techniques and Algorithms The method for mapping biomass from SAR uses a relationship between radar backscatter and field-measured biomass transformed by the cube root. A stepwise regression routine was used to determine a best set of SIR-C/X-SAR channels from LHH, LHV, LVV, CHH, CHV, CVV, and XVV backscatter (s°). A two-step approach to retrieve forest biomass was used: 1) classify forests using SIR-C/X-SAR data; and 2) develop models for each category and retrieve total biomass. The three major types of forests (pine, spruce and aspen) discussed earlier were considered. To examine the importance of forest type, the results from the two- step approach will be compared with results obtained from a general relationship that combines data without regard for forest type. To explore the usefulness of the SIR-C/X-SAR backscatter channels for total biomass estimation, a routine for stepwise selection of the best independent variables was used to determine the multiple regression models [MathSoft, 1993]. Average backscatter in each of the seven SIR-C/X-SAR channels was used as the independent variable with dependent variable, biomass, for multiple linear regression analysis. The routine starts from an intercept-only model, i.e., no independent variable (SAR channel backscatter), and calculates an ANOVA table showing the residual sum of squares and Cp statistics. (Cp is the estimator for the standardized total squared error.) An independent variable is added to the model and the resulting Cp value compared with the original. The routine automatically adds or drops variables based on a criterion of minimum Cp value. The stepwise selections were conducted for data sets for the three forest categories separately and for data for all forest types combined. As discussed above, data from 62 stands were used to develop the regression model from the SIR-C/X-SAR data. Of these 62 stands, there were 30 with pine, 21 with spruce, and 11 with aspen, including two with very low biomass. The independent variables selected by the stepwise process are shown in below. In addition, the next variable to be added if the process were to be continued one more step is also shown. Each of the equations listed for forest type is different since either the bands selected or the magnitude of the coefficients is different. This indicates that biomass estimation is dependent to a certain degree on forest type as discussed by Dobson et al. [1995]. Note that each of the forest type biomass equations contains L- and C-band cross-polarized channels with positive and negative coefficients, respectively. If the absolute value of the coefficients were equal for LHV and CHV, the form of the equation would be similar to that reported by Ranson et al. [1994, 1995a]. Results for mapping with individual forest type biomass equations were compared with those from a single combined biomass equation and very little actual difference was found. For total-above ground biomass, either method could be used. For component biomass, individual forest type equations must be used. The biomass equations listed were applied on a pixel-by-pixel basis to the SAR image data to create biomass images or maps. The SAR-predicted biomass of all sample stands was extracted by averaging over a 3x3 window from biomass maps. Since the variation in field biomass measurements introduced an uncertainty in the comparisons of field-estimated and SAR-mapped biomass, a weighted least squares analysis was used. The weights used were the inverse of the standard deviations for field-sampled biomass. SSA multiple regression models for biomass estimation from SIR-C backscatter data. Note that an additional variable CHV was added in the second model for Aspen. NS = Not Selected. Total Biomass = b0 + b1* LHV + b2 CHV + b3 LHH. Category Intercept (b0) LHV (b1) CHV (b2) LHH (b3) r2 n obs Pine 3.031 0.245 -0.175 NS 0.80 30 Spruce 3.475 0.229 -0.131 NS 0.86 21 Aspen 5.905 0.259 NS NS 0.81 11 Aspen 3.417 0.251 -0.161 NS 0.94 11 All 3.420 0.208 -0.163 0.092 0.85 62 Regression coefficients for estimating component biomass (kg/m2) from measured total biomass. Component Biomass = b0 + b1 * Total biomass. Forest Type Component Intercept(b 0) Slope(b1 r2 num obs Stem 0.0000 0.8199 0.997 24 Pine Branch -0.0376 0.1370 0.901 24 Foliage 0.0054 0.0356 0.992 8 Stem -0.06758 0.7500 0.982 12 Spruce Branch 0.0294 0.1329 0.971 12 Foliage 0.0425 0.1301 0.866 12 Stem 0.0216 0.9037 0.999 22 Aspen Branch -0.02017 0.0856 0.890 22 Foliage -0.0015 0.0118 0.953 22 NSA multiple regression models for biomass estimation from SIR-C backscatter data. Category Intercept (b0) LHV (b1) CHV (b2) LHH (b3) r2 N All Forest Types 2.6589 0.3822 -0.3476 -0.0540 0. 79 17 9.2 Data Processing Sequence 9.2.1 Processing Steps None given. 9.2.2 Processing Changes None. 9.3 Calculations 9.3.1 Special Corrections/Adjustments None. 9.3.2 Calculated Variables None given. 9.4 Graphs and Plots See Ranson, et al., 1997. 10. Errors 10.1 Sources of Error Sources of error include natural stand variations, measurement errors, error in radar backscatter from speckle and noise, and location errors extracting backscatter for measured forest stands. 10.2 Quality Assessment All field plot data were quality checked to reduce transcription errors. Several plots not included in the regression analysis were used to check the veracity of biomass and classification maps. Preliminary error analysis with SIR-C data over a portion of SSA shows residual standard error of 1.6 kg/m2 for a range of over 30 kg/m2. However, because of the increased error in relationship and paucity of data points at higher biomass levels, the relationship gives best results for biomass 15 kg/m2 or less. 10.2.1 Data Validation by Source Below is a comparison of biomass (kg/m2) estimates for four jack pine stands from bole and branch volume measurements and allometry using dbh data. Also included are estimates from the SAR biomass equation. Estimated Biomass (kg/m2) Stand Geometry Allometry SIR-C SAR RJP 0.88 1.00 0.96 YJP 1.99 2.40 2.83 OJP 9.19 7.80 8.73 MJP 9.02 11.54 11.22 10.2.2 Confidence Level/Accuracy Judgment None given. 10.2.3 Measurement Error for Parameters The results of the classification are presented as contingency tables of the SAR classification vs. training set class and SAR classification vs. field plot sampling data. The table below gives the classification results for training set data using the combined SIR-C/X-SAR and Landsat images. The classification accuracy for all classes is greater than or equal to 90% with no major confusion with other classes. Classification contingency table for SAR classification of training sets. Classes are described in Table 2. Average accuracy = 94.6% Classified As (%) Class Pine Spruce Aspen Shrubland Fen Clearing Water Training Pixels Pine 94.1 2.6 1.0 0.1 0.2 2.1 0.0 3581 Spruce 1.5 98.0 0.0 0.0 0.6 0.0 0.0 545 Aspen 4.1 1.5 93.4 0.0 0.0 1.0 0.0 1593 Shrublan d 1.4 1.7 1.8 90.0 2.5 2.5 0.0 711 Fen 2.7 1.6 0.6 0.1 95.1 0.0 0.0 1032 Clearing 1.3 0.0 0.0 0.0 0.0 98.7 0.0 697 Water 0.0 0.0 0.0 0.0 0.0 0.0 100.0 13225 As a final check on the classification performance, the most abundant forest type recorded in the field plots was compared to the classes mapped in a 3x3 array of pixels for the plot locations. The test site (3x3 array) was labeled as the forest type occurring in 5 or more pixels. If no single forest type was dominant in the test site, the site was not included in the analysis. The results are listed in the following table and show high classification accuracy for pine (96.4%) and aspen (94.1%). However, only 71.4% of the spruce sites were correctly identified partly because most of the spruce sites were small and very heterogeneous. However, there were a few cases of spruce being misclassified as pine with alder understory, a mesic site condition. At this time, it is not clear why this is the case, but it may have implications when it is necessary to separate conifer forests on wet and dry conditions for modeling purposes and will also have an impact on the calculation of biomass using forest-type-specific biomass equations. However, given the high accuracy for the training sets for pine and spruce classes, the classification should be adequate for the purposes of this paper. Class contingency table for SAR classification and field measurement sampling plots. Average accuracy = 87.3% Classified As: Class Pine Spruce Aspen Pine 96.4 4.6 0.0 Spruce 21.4 71.4 7.2 Aspen 5.9 0.0 94.1 The biomass equations listed above were applied on a pixel-by-pixel basis to the SAR image data to create biomass images or maps. The SAR-predicted biomass of all sample stands was extracted by averaging over a 3x3 window from biomass maps. Since the variation in field biomass measurements introduced an uncertainty in the comparisons of field-estimated and SAR-mapped biomass, a weighted least squares analysis was used. The weights used were the inverse of the standard deviations for field-sampled biomass. This reduces the effects of sample points with higher field biomass variances. The results are listed in the following table as the regression coefficients (intercept and slope), coefficient of determination (r2), residual standard error (RSE), and a 95% confidence interval (CI) for biomass estimation. Model Intercept Slope r2 RSE (kg/m2) 95% CI One-step 1.275 0.925 0.88 1.551 0.896 Two-step 1.507 0.943 0.89 1.527 0.895 The 95% CI listed above is the average over all sample points. The CI changes with biomass and is lower at small biomass and larger at higher biomass levels. The equivalent results for the two methods indicate that the species effects on total above-biomass estimation were not important for this data set in the study area. However, the capability of stratifying biomass by forest type lends itself to estimates of component biomass. The component biomass results discussed above were used to produce the maps for bole, branch, and foliage biomass. From these data it can be seen that the highest bole biomass estimates occur in areas of pine and aspen. Most of the SSA clearings identified in the classification maps are located primarily in the high biomass pine areas. Areas with greatest foliage biomass are located within predominantly spruce forests. Spruce trees have a much greater proportion of foliage biomass than the other forest types as seen by comparing slope coefficients in Section 9.1. Dobson et al. [1995a] showed higher levels of crown layer biomass for "lowland conifer," which includes black spruce and tamarack in northern Michigan. On average, the total biomass was about 6.8 kg/m2 across the entire image or 7.3 kg/m2 for only forested areas. Boles, branches, and foliage comprise about 83%, 12%, and 5%, of the total biomass, respectively. Coupling these data with ecosystem models should improve estimates of maintenance respiration and decomposition rates across the landscape. 10.2.4 Additional Quality Assessments None. 10.2.5 Data Verification by Data Center BORIS has viewed the biomass images to verify image size, type, and value range. 11. Notes 11.1 Limitations of the Data The results indicate that above-ground biomass can be estimated to within about 1.6 kg/m2 and up to about 15 kg/m2 across the SIR-C image evaluated. A general method also produced results equivalent to those obtained by treating forest types separately. 11.2 Known Problems with the Data None given. 11.3 Usage Guidance Because of the increased error in the relationship and paucity of data points at higher biomass levels, the relationship gives best results for biomass 15 kg/m2 or less. 11.4 Other Relevant Information None given. 12. Application of the Data Set These data may be used as estimates of forest type and above-ground woody biomass for ecosystem modeling purposes. 13. Future Modifications and Plans Similar analysis for NSA is ongoing. 14. Software 14.1 Software Description Software used in the analyses included commercial packages: PCI, ARC/INFO, and IDL. A public-domain image analysis software package called Image Processing Workbench, developed at the University of California-Santa Barbara, was also used. 14.2 Software Access 15. Data Access 15.1 Contact for Data Center/Data Access Information These BOREAS data are available from the Earth Observing System Data and Information System (EOS-DIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory Oak Ridge, TN (865) 241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 15.2 Procedures for Obtaining Data BOREAS data may be obtained through the ORNL DAAC World Wide Web site at http://www-eosdis.ornl.gov/ or users may place requests for data by telephone, electronic mail, or fax. 15.3 Output Products and Availability Requested data can be provided electronically on the ORNL DAAC's anonymous FTP site or on various media including, CD-ROMs, 8-MM tapes, or diskettes. The complete set of BOREAS data CD-ROMs, entitled "Collected Data of the Boreal Ecosystem-Atmosphere Study", edited by Newcomer, J., et al., NASA, 1999, are also available. 16. Output Products and Availability 16.1 Tape Products The image data are available as band-sequential files on 8 mm tape media. 16.2 Film Products None. 16.3 Other Products None. 17. References 17.1 Platform/Sensor/Instrument/Data Processing Documentation Freeman, A., M. Alves, B. Chapman, J. Cruz, Y. Kim, S. Shaffer, J. Sun, E. Turner, and K. Sarabandi. 1995. SIR-C data quality and calibration results. IEEE Trans. Geosci. Remote Sens. 33:848-857. Welch, T.A. 1984, A Technique for High Performance Data Compression, IEEE Computer, Vol. 17, No. 6, pp. 8 - 19. Zink, M. and R. Bamler. 1995. X-SAR radiometric calibration and data quality. IEEE Trans. Geosci. Remote Sens. 33:840-847. 17.2 Journal Articles and Study Reports Apps, M.J., P. Albu, D. Halliwell, J. Niederleitner, D.T. Price, D. Seburn, M. Siltanen, and Varem-Sanders. 1994. BOREAS biometry and auxiliary sites, locations and descriptions. Northern Forestry Centre, Edmonton, Alberta, Version 2.0, August. Dobson, M.C., F.T. Ulaby, L.E. Pierce, T.L. Sharik, K.M. Bergen, J. Kellndorfer, J.R. Kendra, E. Li, Y.C. Lin, A. Nashashibi, K. Sarabandi, and P. Siqueira. 1995a. Estimation of forest biophysical characteristics in northern Michigan with SIR-C/X-SAR. IEEE Trans. Geosci. Remote Sens. 33:877-895. Dobson, M.C., F.T. Ulaby, and L.E. Pierce. 1995b. Land cover classification and estimation of terrain attributes using synthetic aperture radar. Remote Sensing of Environ. 51(1): 199-214. Dobson, M.C., F.T. Ulaby, T. LeToan, A. Beaudoin, E.S. Kasischke, and N. Christensen. 1992. Dependence of radar backscatter on conifer forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30:412-415. Gower, S.T., J.G. Vogel, J.M. Norman, R.J. Kucharik, S.J. Stole, and T.K. Stow. 1997. Carbon distribution and above-ground net primary production in aspen, jack pine, and black spruce stands in Saskatchewan and Manitoba, Canada. JGR, Vol. 102, No. D24:29,029-29,041. Haliwell, D.H., M.J. Apps et al. 1999. Boreal Ecosystem-Atmosphere Study (BOREAS) biometry and auxiliary sites: overstory and understory data. Nat Reours. Can., Can. For. Service, North. For. Cent., Edmonton, Alberta. Harrell, P.A., L.L. Bourgeau-Chavez, E.S. Kasischke, N.H.F. French, and N.L. Christensen, Jr. 1995. Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest. Remote Sensing of Environment 54:247-260. Leckie, D.G. and S.M. Yatabe. 1994. Discriminating forest cuts with ERS-1 radar imagery. SPIE, Proc. EUROPTO Series, Vol. 2314, Rome, Italy, 26-30 Sep. pp. 414-420. LeToan, T., A. Beaudoin, J. Riom, and D. Guyon. 1992. Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing, 30: 403-411. Newcomer, J., D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. Morrell, J. Nickeson, A. Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers, eds. 2000. Collected Data of The Boreal Ecosystem-Atmosphere Study. NASA. CD- ROM. Ranson, K.J. and G. Sun. 1994. Mapping biomass for a northern forest using multifrequency SAR data. IEEE Transactions on Geoscience. Remote Sensing, 32(3):388-396. Ranson, K.J., S. Saatchi, and G. Sun. 1995a. Boreal forest ecosystem characterization with SIR-C/X-SAR. IEEE Trans. Geosci. Remote Sens. 33:867- 876. Ranson, K.J., R.H. Lang, G. Sun, N.S. Chauhan, and R.J. Cacciola. 1995b. Mapping of boreal forest biomass using synthetic aperture radar measurements and modeling. Retrieval of Bio- and Geophysical Parameters from SAR for Land Applications, Toulouse France, 10-13 October. Ranson, K.J., G. Sun, B. Montgomery, and R.H. Lang. 1996. Mapping of boreal forest biomass using SAR. IGARSS'96, Lincoln, Nebraska. Ranson, K.J, G. Sun, R. Lang, N.S. Chauhan, R.J. Cacciola, and O. Kilic. 1997. Mapping of boreal forest biomass from spaceborne synthetic aperture radar, JGR 102:29599-29610. Rignot, E., J. Way, C. Williams, L. Viereck. 1994. Radar estimates of abouve- ground biomass in boreal forests of interior Alaska. IEEE Trans. Geoscience Remote Sensing. 32(5): 1,117-1,124. Sellers P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1994-3.0 NASA BOREAS Report (EXPLAN 94). Sellers P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study: Experiment Plan. Version 1996-2.0 NASA BOREAS Report (EXPLAN 96). Sellers P., F. Hall, and K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). Sellers P., F. Hall and K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere Study: 1996 Operations. NASA BOREAS Report (OPS DOC 96). Sellers P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. Wickland. 1995. The boreal ecosystem-atmosphere study (BOREAS): an overview and early results from the 1994 field year. Bulletin of the American Meteorological Society. 76(9):1549-1577. Sellers,P.J., F.G. Hall, R.D. Kelly, A. Black, D. Baldocchi, J. Berry, H. Margolis, M. Ryan, J. Ranson, P.M. Crill, D.P. Lettenmeier, J. Cihlar, J. Newcomer, D. Halliwell, D. Fitzjarrald, P.G. Jarvis, S.T. Gower, D. Williams, B. Goodison, D.E. Wickland, and F.E. Guertin. 1997. BOREAS in 1997: Scientific results, experiment overview and future directions. BOREAS Special Issue, JGR- Atmospheres, 102:28,731-28,770, No. D24.. Singh, T. 1982. Biomass equations for ten major tree species of the Prairie Provinces. Info. Report NOR-X-242, Northern Forest Research Centre, Canadian Forestry Service, Environment Canada. Waring, R.H. and W.H. Schlesinger. 1985. Forest Ecosystem: Concepts and Management. Academic Press, Inc., New York, p. 40. Young, H.E., J.H. Ribe, and K. Wainwright. 1980. Weight Tables for Tree and Shrub Species in Maine, Miscellaneous Report 230. Life Sciences and Agriculture Experiment Station, University of Maine at Orono, September. 17.3 Archive/DBMS Usage Documentation None. 18. Glossary of Terms None given. 19. List of Acronyms AEAC - Albers Equal-Area conic AIRSAR - Airborne SAR ASI - Agenzia Spaziale Italiana BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System CHH - Radar channel designation for C-band frequency and Horizontal transmit - Horizontal receive polarization CHV - Radar channel designation for C-band frequency and Horizontal transmit - Vertical receive polarization (also known as cross-pol) CCRS - Canada Centre for Remote Sensing CF - Conversion Factor CI - Confidence Interval CVV - Radar channel designation for C-band frequency and Vertical transmit - Vertical receive polarization DAAC - Distributed Active Archive Center DBH - Diameter at Breast Height DLR - Deutsche Forschung santalt Fur Loft-und Raumfahrt EOS - Earth Observing System EOSDIS - EOS Data and Information System ERS-1 - Earth Resources Satellite-1 GMT - Greenwich Mean Time GPS - Global Positioning System GSFC - Goddard Space Flight Center IFC - Intensive Field Campaign JERS - Japanese Earth Resources Satellite-1 JPL - Jet Propulsion Laboratory LHH - Radar channel designation for L-band frequency and Horizontal transmit - Horizontal receive polarization LHV - Radar channel designation for L-band frequency and Horizontal transmit - Vertical receive polarization (also known as cross-pol) LVV - Radar channel designation for L-band frequency and Vertical transmit - Vertical receive polarization MLC - Maximum Likelihood Classifier NASA - National Aeronautics and Space Administration NSA - Northern Study Area OBS - Old Black Spruce OJP - Old Jack Pine ORNL - Oak Ridge National Laboratory PANP - Prince Albert National Park PHV - Radar channel designation for PL-band frequency and Horizontal transmit - Vertical receive polarization (also known as cross-pol) RADAR - RAdio Detection and Ranging RSE - Residual Standard Error RSS - Remote Sensing Science SAR - Synthetic Aperture Radar SIR-C - Shuttle Imaging Radar, C-band SRC - Saskatchewan Research Council SSA - Southern Study Area SRL - Space Radar Laboratory TE - Terrestrial Ecology TM URL - Uniform Resource Locator X-SAR - X-band Synthetic Aperture Radar XVV - Radar channel designation for X-band frequency and Vertical transmit - Vertical receive polarization YJP - Young Jack Pine 20. Document Information 20.1 Document Revision Date Written: 05-Apr-1997 Last Updated: 11-Feb-1999 20.2 Document Review Date(s) BORIS Review: 16-Oct-1997 Science Review: 31-Jan-1999 20.3 Document ID 20.4 Citation When using these data, please include the following acknowledgement as well as citations of relevant papers in Section 17.2: K.J. Ranson, BOREAS RSS-15 SIR-C and TM Biomass and Landcover maps of the NSA and SSA, Biospheric Sciences Branch, Code 923, NASA GSFC, Greenbelt, MD 20771 If using data from the BOREAS CD-ROM series, also reference the data as: Ranson, K.J, G. Sun, R. Lang ,"Distribution and Structure of Above Ground Woody Biomass." in Collected Data of The Boreal Ecosystem-Atmosphere Study. Eds. J. Newcomer, D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A.Morrell, J. Nickeson, A. Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers. CD-ROM. NASA, 2000. Also, cite the BOREAS CD-ROM set as: Newcomer, J., D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. Morrell, J. Nickeson, A. Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers, eds. Collected Data of The Boreal Ecosystem-Atmosphere Study. CD-ROM. NASA, 2000. 20.5 Document Curator 20.6 Document URL Keywords: Radar Biomass Forest Type SIR-C X-SAR RSS15_SIRC_Biomass.doc 03/03/99