I

I ANALYSIS OF THE CANADIAN BOREAL FOREST USING ENHANCED RESOLUTION I ERS-1 SCATTEROMETER IMAGERY I Clarence J. Wilson ill and

I David G. Long

Brigham Young University I 459 CB, Provo, UT 84602 Voice: 801-378-4884, FAX: 801-378-6586 I e-mail: [email protected] Abstract -- Scatterometer backscatter measurements >50 km/pixel to ...... 25 km/pixel. SIR imagery, along with 0 (u ) are primarily and traditionally used to estimate wind the frequent coverage of spacebome scatterometers like I speed and direction over the ocean. This paper presents ERS-1, can aid in the study of large land regions with an investigation of the backscatter coefficient of bo­ ecological significance. Such research has already shown real forest and neighboring vegetation regions. ERS-1 SIR's usefulness in the study of the Amazon rain forests backscatter A-values of the Canadian boreal forest are [5] and Antarctic sea ice [6]. I imaged using a resolution enhancement algorithm for this analysis. Regions of boreal forest, tundra, and grassland Boreal Forests: Description and Scientific Interest · are individually analyzed over the extent of the ERS-t Boreal forests are vast coniferous forests characterized I scatterometer's data set (1992-1995). The annual varia­ by needleleaf deciduous and needleleaf evergreen trees tion of the mean u 0 value for each region is presented. (common varieties are spruce, pine, fir, aspen, birch, Distinct seasonal variations exist for these vegetation hemlock, and larch), and lichen woodlands. Typically, types. Boreal forests exhibit a stronger response (...... 2.5 a few species will dominate the forest stand making I dB) during warm summer months than during the snow the boreal forest a rather homogeneous ecosystem [7]. and ice covered winter months. The results of this study Boreal forests are found in cool, temperate zones of the indicate good potential for further analysis of boreal northern hemisphere. They are typically found north of I forest regions using ERS-1 scatterometer data. the 50th North parallel and south of the Arctic Circle. Thus, Canada, Alaska, Siberia, and Scandinavia are INTRODUCTION the primary political entities containing boreal forests. I The European Remote Sensing satellite, ERS-1, carries a Ecologically, they fit between arctic tundra and broadleaf scatterometer radar which measures the radar backscat­ deciduous hardwoods. 0 tering coefficient ( u ) over the entire earth. The primary Because they cover such massive regions (approxi­ purpose of this measurement is to determine wind speeds mately 15 million km2 worldwide), boreal forests are an I and directions over the ocean. Recently, consideration of important factor in understanding the global ecosystem. scatterometer measurements over land surfaces has been For most of this century, boreal forests have periodi­ investigated[ I, 2, 3]. The ERS-1 scatterometer measure­ cally been studied to access their condition and monitor I ment data provides 50 km resolution of these surfaces changes that occur in their ecosystems. Research has and repeated coverage every 4-10 days. While this revisit sought to determine how timber resources are being time is very good, the spatial resolution is low and thus managed (especially with regard to wood production), limits applications of such land studies. the effects of increasing pollution from modem society 'I Researchers in the Microwave Earth Remote Sensing (i.e., acid rain and global warming theories). (MERS) group at BYU have been actively developing a Natural cycles of the forest and how the forest adapts technique for enhancing the resolution of scatterometer to subsequent change is the subject of other research. I data [4]. This resolution enhancement technique, called Remote sensing techniques including aerial photography Scatterometer Image Reconstruction (SIR), appears to and SAR imagery have found use in studies requiring I have the ability to increase resolution from the original infrequent image collection (on the order of years) and 1 I 2 studies of small ( < 10 km ) sections of forest. However, the quantity of data produced by scatterometer mea­ I as Shubert et al. have suggested, ''The processes that surements is much more manageable than that of SAR control [boreal forest] patterns can change in importance measurements. These characteristics make spaceborne as the space- and time-scales of the pattern of interest scatterometer u 0 measurements an attractive resource for are changed.'' Thus, the understanding of boreal forest study of surface vegetation on a regional or global scale. I cycles over small sections or short time periods does In an effort to improve the resolution of scatterometer not necessarily apply to the cycles of the forest on measurements, an image reconstruction algorithm has macroscopic levels or over long time periods. This been developed and refined by members of the BYU paper investigates the use of ERS-1 u 0 measurement Microwave Earth Remote Sensing gro~p [4]. The Scat­ imagery enhanced by the SIR algorithm as a technique terometer Image Reconstruction (SIR) algorithm takes for studying boreal forests over long time periods and advantage of spatial overlap in scatterometer measure­ I large forest regions. ments made at different times in order to enhance image resolution. It produces estimates of the A and B co­ 0 Scatterometer u Measurements and SIR Imagery efficients discussed above at a pixel spacing of 8.9 km The ERS-1 scatterometer' s primary purpose is to measure for ERS-1 data. Thus, spacebome scatterometer mea­ I vector wind velocities over the ocean. Of course, these surements in conjunction with SIR image resolution measurements are not made directly. Rather, it measures enhancement provide medium-scale resolution of veg­ the radar backscatter coefficient u 0 of the ocean's surface. etation regions around the world with a short interim I A model function is then used to estimate wind direction fly-over time. This characteristic of SIR scatterometer and speed. The normalized radar backscatter coefficient, imagery is exploited as a tool for studying boreal forests or normalized radar cross-section, is defined by the radar over vast land regions and long time periods. I equation to be ANALYSIS OF BOREAL FOREST u 0 PATTERNS o _ (4r)3 R4 L Ei (J - G2 A2 A Pt The potential of SIR imagery for studying boreal forest regions is investigated by first making SIR imagery I where R is the range to the surface, L accounts for system of the Canadian boreal forest region from originat tr0 losses, G is antenna gain, A is the reflecting surface area, measurements. Next, small subregions of the different A is the wavelength of the electromagetic energy, Ps is vegetation types in the study area are examined over a I the received backscatter power, and Pt is the transmitted four-year period to investigate interannual backscatter backscatter. Thus, a simple interpretation of u 0 is the variations. ratio of received power to transmitted power nonnalized for geometric and system considerations. SIR Imagery I Kennett and Li demonstrated that u 0 values over land SIR imagery is created from the raw ERS-1 u 0 data set are well modeled by the first-order linear function of the region enclosed by latitudes 50° N and 70° N and longitudes 140° W and 55° W over the period Jan. 1, I 1992 through Oct. 31, 1995 (the extent of the data set at where 8 is the incidence angle of the observation. Thus, press). In order to assure quality SIR images, the data was A may be interpreted as a constant backscatter coeffi­ grouped into 15 day periods and then processed using the 1: cient for the land type of the reflecting surface. B yields SIR algorithm, producing an 8.9 Ian pixel-spaced image the interpretation as a measure of the backscatter coeffi­ of each time period. cient's dependence on incidence angle for the land type Fig. 1 shows two SIR images thus produced. Fig. II of the reflecting surface. Prior research has demonstrated l(a) is the image for February 15-28, 1993. Snow and a strong correlation between A and B and vegetation type ice present during this winter period result in a nearly and surface topology [3, 5, 6]. homogeneous backscatter A-value over the entire image.

The major drawback of using spaceborne scatterometer However, prominent geographic features (such as the III u 0 measurements to study vegetation and land surfaces is Pacific Ocean, Rocky Mountains, and Hudson Bay) are the rather coarse resolution of the scatterometer measure­ still apparent. There is not much distinction, however, ments (for the ERS-1 scatterometer ..... so km/pixel). On over the majority of the land mass due to the snow I the other hand, spaceborne scatterometers offer a distinct and ice cover mentioned above. Differentiating boreal advantage over other common remote sensing meth­ forests from other vegetation types does not appear to be ods (namely, aerial and SAR): frequent and consistent straightforward during the winter months. fly-overs. The ERS-1 scatterometer provides repeated Fig. l(b) shows the region for August 15-31, 1993. I coverage of the entire world every 4-10 days. Further, Here, a distinctive band running diagonally from the northwest to southeast corners through the center of li 2 I

I Seasonal Variations The backscatter characteristics of smaller, localized, ho­ mogeneous vegetation subregions are next analyzed over I several annual periods. Six such subregions are selected ~om the Canadian boreal forest and neighboring vegeta­ tion types. A map of Canada displaying the location of the six subregions is found in Figure 2. Two regions of I needleleaf evergreen forest, one in the north and the other in the south (Sub 1 & 2) are chosen. Two regions oflichen woodland are selected, again one in the north and the I v.. r: e:s. Dayo: 045-059, Raglan Name: - other in the south (Sub 4 & 3). Finally, a region of tundra oin-•bo,...IIL045-(1511_113.oir, ERSI, COGEN-SIR Capvltghl\1196, BYU r.lcruwa""' Earth Remol. Senolng Lab (Sub 5) and a region of grassland at the northern extreme -20 -16 -12 -e -4 of the Great Plains (Sub 6) are chosen to demonstrate I (a) Feb. 15-28, 1993 further backscatter characteristic differences.

ION eiiN

I 55N

I 45N

I 120W HOW 100W lOW lOW 70W eow Figure 2: A map of Canada indicating the six subregi~ns Y.ar: e3, Dip: 225-239, Raglan~ E1orN12 used for temporal analysis of Boreal forest backscatter. The oiot-+-bonN112_225-238.83.olr, ERSI, COiiEN-eiR Copy!iglol111811, BYU Mionloovw E•rlh R.,GIII 8loNing lAb vegetation types of the subregions are as follows: Sub 1 & 2, I -20 -16 -12 -8 -4 Boreal Forest; Sub 3 & 4, lichen Woodland; Sub 5, Tundra; and, Sub 6, Grassland. (b) Aug. 15-31, 1993 Fig. 3 is a graph of the mean A-values estimated I Figure 1: Enhanced Resolution Images of Canada. The overall darkness in the upper image is the result of snow and ice by the SIR algorithm for the needleleaf evergreen forest coverage. The bright band running across central Canada in the subregions (#1 and #2). In these plots, the dashed line lower image is the Boreal forest region. represents the weighted average of the A-values from I all four years. Portions of the subregions missing data (as discussed above) are not included in calculating such Canada is readily apparent. This feature is the boreal statistics. The backscatter response is 2-3 dB stronger forest. The northern extent of the grasslands/farmlands during the summer than the winter. Both plots display I of the Great Plains appears in this image south and west a .distinctly increasing slope during early spring that of the boreal forest. The tundra region appears north climaxes around JD 120 (May 1). This is assumed to and east of the boreal forest. The many small black represent spring vegetation growth following the melt­ spots throughout the image are lakes which were covered I off of winter snow and ice. A symmetric feature occurs with ice in the February image and now appear as other in the late summer season as the autumn vegetation bodies of water. This image appears to be more useful cycle and winter freezing begins. Note that this occurs for vegetation studies than the February image due to the earlier for the northern subregion (about JD 260, or mid­ I overall higher contrast levels of the backscatter A-values Sept~mber) than the southern subregion (about JD 280, among different vegetation regions. or rrud-October). One noticeable problem with 1(b) is the missing image Fig. 4 is a similar plot for lichen woodland subre­ I data at the bottom center and other places in the image. gions (#3 and #4). Here, again, the spring and autumn This is a result of missing scatterometer data over this vegetation cycles are apparent yielding a 2.5-3 dB differ­ region during this time period. The scatterometer was ence between winter and summer. The lichen woodland probably turned off and the satellite's SAR turned on I spring and autumn transitions are more rapid than those over these regions. Missing data is a problem for many of the boreal forest. Note that for both vegetation types I of the images produced in this series. 3 I I

15-day Mean A-values, Subregion #1: Boreal Forest (northern) I

-9 ...... ) ...... ···!············ : . ~··············~··············f········· . . . . +1994~= .. -1o ···········-+············-i····¢-~t-~·e··~---) ...... j...... ~.~~---· I : : ' : '~-o o: 0 0 9: : -11 dB -1 ~: ~1-i~;~~i·~f:-••·:•~•·:·:·•·•:·t''~:·:r:·~·:;l_;.:;;Q I ! ! I --:weighted average I j ! 15 350 " o 50 100 150 200 250 300 350 I Julian Day 15-day Mean A-values, Subregion #4: Uchen Woodland (southern) ~~----~~----~--~----~----.---~----~ I

-11 dB li -1 I

: :x : : : : -14 ...... ; ...... ; ...... •...... ; ...... ; ...... •...... li ! ! I -- :W.shted average I ( ! 15 50 100 150 200 250 300 350 " o 50 100 150 200 250 300 350 Julian Day Julian Day Figure 3: Mean A-values for subregions 1 and 2 (Boreal forest) Figure 4: Mean A-values for subregions 3 and 4 (Uchen for 1992-1995 Woodland) for 1992-1995 li the northern subregions (#1 and #3) have more abrupt backscatter coefficient is less than the boreal forest and transitions than their southern counterparts. lichen woodlands during summer by about 1.5 dB, but Figure 5 is the mean A-value plot for the tundra during the winter season is approximately the same or subregion (subregion #5) and a section of the grasslands slightly greater. It appears the boreal forest and lichen at the northern extent of the Great Plains (subregion #6). woodland regions all reach a peak in spring around JD 1: The tundra region exhibits several singular behaviors. 120. However, date of the autumn fall-off varies from Around JD 135 (mid-May), a significant decrease (1.5 region to region. Thus, the seasonal patterns of boreal dB) in the mean A-value is observed which returns to its forests and the surrounding vegetation regions are very li prior value within 30 days. It is thought that this behavior pronounced. corresponds to the spring melt. Later, around JD 255, a sharp decrease in mean A-value again occurs and then CONCLUSION returns to its nominal value about JD 0 (three months Study of the boreal forest regions using enhanced resolu­ I later). No explanation appears readily appropriate for this tion imagery produced by the SIR algorithm demonstrates phenomena. In the lower plot, an interesting behavior that a dramatic seasonal transition exists for the boreal for the grassland subregion appears. Two interesting 0 forest's backscatter coefficient (u ). This interannual II features appear around JD 105 and JD 270. These days variation is characterized by a much stronger response correspond to mid-April and late September. The plot (2-3 dB) during the warm growing season throughout the around these days has sharp dips in the mean A-values. summer than that of the snow and ice covered winter. An I It seems reasonable to suggest these correspond to the even more rapid transition is observed for neighboring planting and harvesting seasons for the farmers in this lichen woodlands. Tundra has a similar response dur­ region. This demonstrates the sensitivity of backscatter ing winter but differs during summer by about 1.5 dB. coefficient measurements to changes in soil moisture Grasslands exhibit a much lower response year-round. I content when the land is plowed and turned over. Thus, the markedly different responses of these neigh­ In Fig. 6 the weighted mean A-values for all six boring vegetation types should allow discrimination of subregions are plotted for comparison. From this figure Boreal forest and lichen woodland regions from tundra I it is clear that the grassland subregion (#5) backscatter and grasslands during the summer. is significantly less than the boreal forests, lichen wood­ lands, and tundra year-round. The tundra region (#6) I 4 I I

I measurements for the study of boreal forest vegetation regions.

: x1992 -9 . .. ; ...... ;.o-199S .. - REFERENCES I +1994 -to [ 1] E. Mougin, A. Lopes, P. Frison, and C. Proisy, ''Preliminary Analysis ofERS-1 Wind Scatterometer Data over Land Surfaces,'' International Journal of I Remote Sensing, vol. 16, no. 2, pp. 391--398, 1995. [2] R. Kennett and F. Li, ''Seasat over Land Scatterom­ I eter Data, Part 1: Global Overview of the Ku-band - 15o'----so...___ 1..~..oo_...;+:;...1..J.so--2.00~-~2s~o -~300:-:----::!350 Backscatterer Coefficients,'' IEEE Transactions on Julian Day Geoscience and Remote Sensing, vol. 27, pp. 592-- 605,1989. I 15-day Mean A-va!UM, Subregion 16: Grassland [3] R. Kennett and F. Li, "Seasat over Land Scat­ -11 ...... : ...... •..... : ...... :l: ... ~ ...... :...... x...... j...... ~············· o' • : x1992 terometer Data, Part IT: Selection of Extended Area -12 ...... ···)(;...... ; ...... , ....: ...... :· .. o-199S·· Land-target Sites for the Calibration of Spacebome I x o: to . o :+1994 -13 ...... x .. 15 ..... :...... :.. ~\··.--/Ti-:-·'\··· ...... :...... 995·· Scatterometers,'' IEEE Transactions on Geoscience 0 0 : ': ~·. • 9,¥,x/il( x •' to + ~ and Remote Sensing, vol. 27, pp. 779--788, 1989. @-14 ··)(···· )~'+ "'":·· ~\······,·;~"'"·i/······~•····:·· .. ··+···:·~~ .. ·•··~·~· :······· .. ····0 a 0; • + )e ~ Iii • : : \ I ,; I [4] D. Long, P. Hardin, and P. Whiting, "Resolution ~:: z· ~·······~ ·~···~ :•••···~••• :... ••• [~ ··r~-f~ Enhancement of Spaceborne Scatterometer Data,'' -.i.~at·.. gl IEEE Transactions on Geoscience and Remote Sens­ -17 ...... - . . ~...... ' . ' .... ~...... " . ... ~. . -.. --. ' . )(. ing, vol. 31, pp. 700--715, May 1993 . I 0 50 100 150 2.00 250 350 Julian Day Figure 5: Mean .A-values for subregions 5 and 6 (Tundra and [5] D. Long and P. J. Hardin, "Vegetation Studies Grassland)for 1992-1995 of the Amazon Basin Using Enhanced Resolution Seasat Scatterometer Data,'' IEEE Transactions on I Geoscience and Remote Sensing, vol. 32, pp. 449-- The use of spaceborne scatterometer measurements in 460, March 1994. combination with SIR image resolution enhancement is I a valuable resource for the study of land regions such [6] D. Long, D. Early, and M. Drinkwater, "Enhanced as boreal forests. With this technique, we have ex­ Resolution ERS-1 Scatterometer Imaging of South­ amined the boreal forest on a macroscopic level over em Hemisphere Polar Ice,'' IGARRS '94, vol. 1, several annual cycles. These successful results are sub­ pp. 156--158, 1994. I stantial motivation for expanded research exploring the use of spacebome scatterometer backscatter coefficient [7] H. Shugart, R. Leemans, and G. Bonan, eds., A Systems Analysis ofthe Global Boreal Forest. Cam­ I bridge, Great Britain: Cambridge University Press, 1992.

-10 I I ,,I I -11 ,, ·~}.... ~-~·- ~.,;:.., ,. . sub2 />·: \ ..-" \ .. j~ub4 ./,' \// !!l-13 ~ --, I 1 .b3-',

-14

sub& I -15 - aub 1 & 2: boreal forest -- -sub 3 & 4: lichen woodland -16. · - ·- sub 5: tundra .. · · .. · sub 6: grassland -17 I 0 50 100 150 200 250 300 350 Julian Day Figure 6: Weighted Mean .A-values over all six subregions I 5 I