1 00:00:08,530 --> 00:00:11,120 - [Woman] So our next presenter is Chris Williams 2 00:00:11,120 --> 00:00:15,060 with the Graduate School of Geography at Clark University. 3 00:00:15,060 --> 00:00:16,517 And he will be presenting on 4 00:00:16,517 --> 00:00:20,267 "Plots to Pixels to Policy The Intrepid Journey 5 00:00:20,267 --> 00:00:23,070 "of a Wall-to-Wall Forest Carbon Monitoring 6 00:00:23,070 --> 00:00:23,903 "and Assessment System." 7 00:00:23,903 --> 00:00:24,736 - Fantastic. All right. 8 00:00:24,736 --> 00:00:26,090 I changed it from Intrepid to Foolhardy 9 00:00:26,090 --> 00:00:28,840 because I knew that Chris Woodall would all would be here 10 00:00:28,840 --> 00:00:31,950 and that I expect great hard questions 11 00:00:31,950 --> 00:00:35,653 because what we're trying to do is a really hard thing. 12 00:00:36,760 --> 00:00:38,430 I want to, before I get too far into this, 13 00:00:38,430 --> 00:00:41,960 thank some of the funding sources. 14 00:00:41,960 --> 00:00:44,480 For example, NASA has supported a lot of this work, 15 00:00:44,480 --> 00:00:46,960 so has the Nature Conservancy, a bit of support from NSF. 16 00:00:46,960 --> 00:00:48,630 I'm a professor at Clark University 17 00:00:48,630 --> 00:00:50,800 in geography and environmental science. 18 00:00:50,800 --> 00:00:54,300 And I have a group, a team of individuals working with me 19 00:00:54,300 --> 00:00:57,590 to try and cobble together 20 00:00:57,590 --> 00:00:59,810 a comprehensive wall-to-wall picture 21 00:00:59,810 --> 00:01:03,695 of forest carbon dynamics across the conterminous U.S., 22 00:01:03,695 --> 00:01:05,020 and that's what I'm going to talk to you about today. 23 00:01:05,020 --> 00:01:08,769 So thanks to Natalia Hasler, Huan Gu, Yu Zhou, 24 00:01:08,769 --> 00:01:11,233 and other members of my group for their contributions. 25 00:01:15,828 --> 00:01:17,730 We all know that carbon is under the microscope 26 00:01:17,730 --> 00:01:20,860 for good reason in forests because we recognize 27 00:01:20,860 --> 00:01:24,460 that forest carbon plays a really significant role 28 00:01:24,460 --> 00:01:28,160 in carbon emissions and carbon uptake 29 00:01:28,160 --> 00:01:31,890 across certainly the U.S. and much of the world, 30 00:01:31,890 --> 00:01:34,990 forests being chief among the natural working lands 31 00:01:34,990 --> 00:01:38,173 that contribute this kind of ecosystem service. 32 00:01:39,100 --> 00:01:42,880 But in order for us to size up policies 33 00:01:42,880 --> 00:01:45,030 and assess the current state, 34 00:01:45,030 --> 00:01:48,470 we need great baseline measurement capabilities, 35 00:01:48,470 --> 00:01:51,430 reporting capabilities, opportunities to verify, 36 00:01:51,430 --> 00:01:54,160 and also just assessment and understanding systems 37 00:01:54,160 --> 00:01:56,030 so that we can detect, monitor, 38 00:01:56,030 --> 00:01:58,750 and assess changes over time, 39 00:01:58,750 --> 00:02:02,310 and project where we think things are going in the future. 40 00:02:02,310 --> 00:02:03,300 So what do we really need? 41 00:02:03,300 --> 00:02:05,690 We need to stand-level estimates of carbon emissions 42 00:02:05,690 --> 00:02:08,250 and removals from the atmosphere; 43 00:02:08,250 --> 00:02:10,500 annually resolved, repeatable, everywhere, 44 00:02:10,500 --> 00:02:14,460 wall-to-wall, and ideally attributed to drivers. 45 00:02:14,460 --> 00:02:16,960 Many of the methods on the market today to do this 46 00:02:16,960 --> 00:02:20,120 have emphasized appropriately live aboveground biomass, 47 00:02:20,120 --> 00:02:21,700 because it's something we can see remotely 48 00:02:21,700 --> 00:02:26,050 and detect spatially, but really much of what we're after 49 00:02:26,050 --> 00:02:28,020 is carbon emissions and removals 50 00:02:28,020 --> 00:02:29,510 or exchange with the atmosphere, 51 00:02:29,510 --> 00:02:32,450 at least with respect to the policy world 52 00:02:32,450 --> 00:02:34,570 concerned with climate change. 53 00:02:34,570 --> 00:02:36,050 And so existing methods 54 00:02:36,050 --> 00:02:38,640 are often ambiguous about mechanisms. 55 00:02:38,640 --> 00:02:41,440 They often lack wall-to-wall coverage 56 00:02:41,440 --> 00:02:44,283 and may not have full forecasting capability. 57 00:02:45,250 --> 00:02:47,760 In my group some time ago, we recognized 58 00:02:47,760 --> 00:02:51,310 a really significant opportunity to take remote sensing 59 00:02:51,310 --> 00:02:53,410 of disturbances, forest disturbances, 60 00:02:53,410 --> 00:02:56,520 remote sensing of biomass at an epoch, 61 00:02:56,520 --> 00:02:57,790 or a moment in time, 62 00:02:57,790 --> 00:03:02,440 combine that with FIA inventory data to assess 63 00:03:02,440 --> 00:03:04,790 that it can help us understand stock accumulation 64 00:03:04,790 --> 00:03:08,260 and change over time in forests of different types 65 00:03:08,260 --> 00:03:11,070 with also a carbon cycle model that helps us extend 66 00:03:11,070 --> 00:03:15,210 beyond the the measured quantities in the field 67 00:03:15,210 --> 00:03:20,210 in plots and also understand full forest carbon dynamics. 68 00:03:20,750 --> 00:03:23,290 And so recognizing this opportunity, 69 00:03:23,290 --> 00:03:25,270 I developed what I'm calling now 70 00:03:25,270 --> 00:03:27,720 the National Forest Carbon Monitoring System 71 00:03:27,720 --> 00:03:31,090 or Carbon Monitoring Reporting and Assessment System, 72 00:03:31,090 --> 00:03:33,750 and it has a flow diagram that looks like this. 73 00:03:33,750 --> 00:03:35,750 We're gonna take it one piece at a time. 74 00:03:37,350 --> 00:03:40,047 First we'll look at how our team has been using 75 00:03:40,047 --> 00:03:43,120 FIA yield curves and plot-based data 76 00:03:43,120 --> 00:03:46,260 served through a validator to generate above-ground biomass 77 00:03:46,260 --> 00:03:50,110 with age curves, and then how we derive carbon stock 78 00:03:50,110 --> 00:03:53,993 and flux assessments at a stand scale with those data. 79 00:03:55,290 --> 00:03:58,070 This is just one example from, happens to be, 80 00:03:58,070 --> 00:03:59,460 from the Pacific Northwest, 81 00:03:59,460 --> 00:04:02,283 a Douglas fir high-productivity stand forest. 82 00:04:03,130 --> 00:04:05,140 And overall, this isn't a singular forest. 83 00:04:05,140 --> 00:04:08,300 This is actually a collection of forest stands 84 00:04:08,300 --> 00:04:12,200 where in-situ plot measurements were made by the FIA, 85 00:04:12,200 --> 00:04:13,890 and they can be arrayed in this fashion 86 00:04:13,890 --> 00:04:16,670 on the lower left-hand curve where we show 87 00:04:16,670 --> 00:04:21,380 above-ground live wood with stand age in age classes. 88 00:04:21,380 --> 00:04:23,740 And we are forcing our cost of carbon cycle 89 00:04:23,740 --> 00:04:27,230 ecosystem process model to match the yield curve 90 00:04:28,260 --> 00:04:31,870 carbon accumulation in a forest of this type in this region, 91 00:04:31,870 --> 00:04:34,220 and of this site productivity class. 92 00:04:34,220 --> 00:04:37,510 When we do that, we then derive curves, for example, 93 00:04:37,510 --> 00:04:41,050 of net ecosystem productivity as a function of stand age. 94 00:04:41,050 --> 00:04:44,190 And this can be produced for a whole array 95 00:04:44,190 --> 00:04:46,240 of forest type groups 96 00:04:46,240 --> 00:04:49,440 and of high and low-productivity classes, 97 00:04:49,440 --> 00:04:51,840 which is a really important additional variable. 98 00:04:53,510 --> 00:04:56,130 We've done that for all of the dominant forest types 99 00:04:56,130 --> 00:04:58,950 and regions across the United States. 100 00:04:58,950 --> 00:05:02,770 We now couple that with observations from remote sensing 101 00:05:02,770 --> 00:05:06,530 of biomass and of disturbance, as well as ancillary data 102 00:05:06,530 --> 00:05:10,740 on forest type distribution and site productivity class. 103 00:05:10,740 --> 00:05:13,170 And we do that so that we can derive information 104 00:05:13,170 --> 00:05:15,890 about the stand age, disturbance legacy, 105 00:05:15,890 --> 00:05:20,760 and site conditions of individual 30-meter pixels 106 00:05:21,620 --> 00:05:23,423 all across the conterminous U.S. 107 00:05:24,360 --> 00:05:27,060 So just to give you a quick glimpse of how we do this, 108 00:05:27,060 --> 00:05:31,100 this is, we're currently using the Kellndorfer NACP 109 00:05:31,100 --> 00:05:34,233 Aboveground Biomass Carbon Baseline Dataset. 110 00:05:35,824 --> 00:05:38,920 And so, for example, this is again our Pacific Northwest 111 00:05:38,920 --> 00:05:41,610 Douglas fir stand. 112 00:05:41,610 --> 00:05:44,050 If it was high-productivity, and we had a measurement 113 00:05:44,050 --> 00:05:47,080 in an individual pixel of about 10 kilograms of carbon 114 00:05:47,080 --> 00:05:51,000 per meter squared, we can use our FIA derived curves 115 00:05:51,000 --> 00:05:55,210 to characterize what the stand age distribution would be 116 00:05:55,210 --> 00:05:57,330 that we would assume is represented 117 00:05:58,220 --> 00:06:02,070 by this 10 kilogram of carbon per meter squared condition. 118 00:06:02,070 --> 00:06:04,640 If it's a low-productivity stand, 119 00:06:04,640 --> 00:06:08,540 we can intersect that observation with some uncertainty 120 00:06:08,540 --> 00:06:12,853 and derive a stand age band shown with the green lines. 121 00:06:14,330 --> 00:06:16,320 So what we're doing here is we're taking 122 00:06:16,320 --> 00:06:18,090 aboveground biomass as a, 123 00:06:18,090 --> 00:06:20,330 to using it as a baseline initialization 124 00:06:20,330 --> 00:06:22,700 in the year of the observations, 125 00:06:22,700 --> 00:06:24,700 this was around the year 2000, 126 00:06:24,700 --> 00:06:28,130 and we're deriving a biomass equivalent stand age 127 00:06:28,130 --> 00:06:31,510 that can then be a marker of the current stocks, 128 00:06:31,510 --> 00:06:32,830 where they were previously 129 00:06:32,830 --> 00:06:35,800 if the stand stayed free of disturbance, 130 00:06:35,800 --> 00:06:38,334 and where it would be going in the next, say, 131 00:06:38,334 --> 00:06:39,333 10, 20, 50 years. 132 00:06:40,700 --> 00:06:43,890 Also, we're relying on a Landsat-derived 133 00:06:43,890 --> 00:06:45,640 disturbance data products. 134 00:06:45,640 --> 00:06:48,630 These originated as the North American Forest Dynamics 135 00:06:48,630 --> 00:06:52,790 Data Products 30-meter mapping of disturbances. 136 00:06:52,790 --> 00:06:55,410 They initially were not attributed to the disturbance type. 137 00:06:55,410 --> 00:06:58,380 But in my group, we intersected those 30-meter 138 00:06:58,380 --> 00:07:01,210 Landsat-derived forest disturbance events 139 00:07:01,210 --> 00:07:05,290 with MTBS fire scars or burn scar information, 140 00:07:05,290 --> 00:07:08,740 as well as aerial detection survey data on bark beetles 141 00:07:08,740 --> 00:07:11,520 and other biotic agents to try to attribute, 142 00:07:11,520 --> 00:07:15,640 in a cursory way, each of those individual 30- meter scale 143 00:07:15,640 --> 00:07:19,340 year-specific disturbance events to a driver 144 00:07:19,340 --> 00:07:20,963 and to map these out in space. 145 00:07:23,220 --> 00:07:28,150 Those two pieces, biomass and disturbance, can be combined 146 00:07:28,150 --> 00:07:32,060 in a way to derive a continuous map of stand age. 147 00:07:32,060 --> 00:07:34,530 And this is continuously mapped for us 148 00:07:34,530 --> 00:07:38,573 across the conterminous U.S. from 1986 to 2010 at present. 149 00:07:40,037 --> 00:07:41,370 And once we have these two pieces, 150 00:07:41,370 --> 00:07:44,660 our yield curve derived carbon sequestration, for example, 151 00:07:44,660 --> 00:07:46,480 or carbon stock dynamics, 152 00:07:46,480 --> 00:07:49,980 as well as information about every pixel's forest type, 153 00:07:49,980 --> 00:07:53,400 site productivity class, biomass, or disturbance 154 00:07:53,400 --> 00:07:54,870 from which we derive stand age, 155 00:07:54,870 --> 00:07:59,070 we can estimate net ecosystem productivity, for example. 156 00:07:59,070 --> 00:08:01,240 And we're gonna zoom in on New England plus New York 157 00:08:01,240 --> 00:08:02,830 in just a second. 158 00:08:02,830 --> 00:08:04,600 So that's the sort of data inputs 159 00:08:04,600 --> 00:08:06,380 and the intermediate variables, 160 00:08:06,380 --> 00:08:08,360 and now I'm going to try to transition 161 00:08:08,360 --> 00:08:10,130 to some applications that give you a sense 162 00:08:10,130 --> 00:08:11,470 of what we can do with this framework. 163 00:08:11,470 --> 00:08:14,300 We will also take a look at some evaluation. 164 00:08:14,300 --> 00:08:16,380 Now we can, we have our annual maps 165 00:08:16,380 --> 00:08:17,850 of carbon stocks and fluxes 166 00:08:17,850 --> 00:08:20,310 at a 30-meter resolution nationally. 167 00:08:20,310 --> 00:08:23,380 And we're also looking at applying them to understand 168 00:08:23,380 --> 00:08:25,580 regional carbon balance and change, 169 00:08:25,580 --> 00:08:28,270 to try to attribute those changes to drivers, 170 00:08:28,270 --> 00:08:31,160 to estimate carbon stock potential of landscapes, 171 00:08:31,160 --> 00:08:33,523 and also to assess carbon emissions risks. 172 00:08:35,590 --> 00:08:37,580 So zooming a little closer to home, 173 00:08:37,580 --> 00:08:40,820 this is a map of forest carbon stocks in the year 2010 174 00:08:40,820 --> 00:08:42,530 according to our data product 175 00:08:42,530 --> 00:08:45,130 across New York plus New England States. 176 00:08:45,130 --> 00:08:48,730 And I just zoomed in on one particular track to show 177 00:08:48,730 --> 00:08:50,210 not just aboveground biomass 178 00:08:50,210 --> 00:08:54,300 but the total live biomass in this one box. 179 00:08:54,300 --> 00:08:57,720 We can report it at a pixel level or over the region. 180 00:08:57,720 --> 00:09:01,410 This happens to hold 68 million metric tons of carbon. 181 00:09:01,410 --> 00:09:04,760 It's mapped here in kilograms of carbon per meter squared. 182 00:09:04,760 --> 00:09:06,150 And our technique also extends 183 00:09:06,150 --> 00:09:08,240 beyond these biomass estimates 184 00:09:08,240 --> 00:09:12,273 to total ecosystem carbon stocks as shown in this lower box. 185 00:09:13,270 --> 00:09:15,710 Now, another thing we can do is, as I said, 186 00:09:15,710 --> 00:09:18,650 zoom in on a singular pixel and ask not just 187 00:09:18,650 --> 00:09:21,620 what it held in terms of its ecosystem carbon stocks 188 00:09:21,620 --> 00:09:23,460 in the year 2010, 189 00:09:23,460 --> 00:09:25,100 but based on its forest type, 190 00:09:25,100 --> 00:09:28,240 for example a maple-beech birch forest type group, 191 00:09:28,240 --> 00:09:30,600 if the stand had about six kilograms of carbon 192 00:09:30,600 --> 00:09:33,270 per meter squared in a year, I don't know, 2000, 193 00:09:33,270 --> 00:09:35,313 how much might it hold in the future? 194 00:09:37,060 --> 00:09:41,340 So we now can take our yield curve information, 195 00:09:41,340 --> 00:09:42,810 for example on the left-hand side 196 00:09:42,810 --> 00:09:46,920 for a Northeast maple-beech birch of high productivity, 197 00:09:46,920 --> 00:09:50,650 the FIA data are sampled with the red pluses. 198 00:09:50,650 --> 00:09:53,030 These curves are our fits through those data 199 00:09:53,030 --> 00:09:57,573 with the curved turf fitting routine that we have. 200 00:09:58,410 --> 00:10:01,020 And let's focus in on just this window 201 00:10:01,020 --> 00:10:03,773 of about 40- to 90-year-old stands. 202 00:10:04,767 --> 00:10:07,760 If it started at about six kilograms of carbon 203 00:10:07,760 --> 00:10:11,550 per meter squared and it was going to acquire 204 00:10:11,550 --> 00:10:14,260 about 10 after another 50 years, 205 00:10:14,260 --> 00:10:16,650 we can see the aboveground biomass expected 206 00:10:16,650 --> 00:10:20,930 in this maple-beech birch forest in the Northeast, 207 00:10:20,930 --> 00:10:22,820 but we also with our framework are able to see 208 00:10:22,820 --> 00:10:25,590 what we expect in terms of soil carbon dynamics, 209 00:10:25,590 --> 00:10:28,740 coarse woody debris dynamics, below ground biomass dynamics, 210 00:10:28,740 --> 00:10:30,310 and so forth. 211 00:10:30,310 --> 00:10:33,060 Furthermore, over that 50-year-period 212 00:10:33,060 --> 00:10:34,990 from a stand age of 40 to 90, 213 00:10:34,990 --> 00:10:37,960 we expect about six kilograms of carbon 214 00:10:37,960 --> 00:10:41,220 to be stored in this ecosystem if it remains 215 00:10:41,220 --> 00:10:44,770 free of disturbance from some initial year 216 00:10:44,770 --> 00:10:45,710 over that 50 years. 217 00:10:45,710 --> 00:10:47,520 And we see that most of it is in biomass 218 00:10:47,520 --> 00:10:49,630 but there's also significant accumulation 219 00:10:49,630 --> 00:10:51,710 in coarse woody debris. 220 00:10:51,710 --> 00:10:53,530 The framework also allows us to estimate 221 00:10:53,530 --> 00:10:56,690 year-specific fluxes of carbon net exchange 222 00:10:56,690 --> 00:11:00,490 with the atmosphere in terms of net ecosystem productivity. 223 00:11:00,490 --> 00:11:03,240 And that's what's shown here with a slight decline 224 00:11:03,240 --> 00:11:07,460 over this 50-year period as the yield curves would suggest 225 00:11:07,460 --> 00:11:10,023 due to a tendency towards saturation. 226 00:11:12,440 --> 00:11:15,500 We've done this for every pixel in our landscape 227 00:11:15,500 --> 00:11:19,370 to just look at the 20-year carbon sequestration opportunity 228 00:11:19,370 --> 00:11:23,100 that we expect at this 30-meter scale across New York 229 00:11:23,100 --> 00:11:24,663 and New England States, 230 00:11:25,920 --> 00:11:28,373 and that's shown in this map illustration. 231 00:11:30,460 --> 00:11:32,490 There are some areas where we see recent 232 00:11:32,490 --> 00:11:34,760 clearcutting having a legacy of carbon emissions 233 00:11:34,760 --> 00:11:36,030 in those sites. 234 00:11:36,030 --> 00:11:38,450 That's why we see some negative values. 235 00:11:38,450 --> 00:11:40,010 And even over this 20-year period, 236 00:11:40,010 --> 00:11:42,330 that can result in a net emission 237 00:11:42,330 --> 00:11:46,210 for that 20-year period as it tends towards regrowth 238 00:11:46,210 --> 00:11:49,520 and carbon sinks following a clearing event. 239 00:11:49,520 --> 00:11:52,280 But much of the landscape is shown to have moderate 240 00:11:52,280 --> 00:11:55,400 to high carbon uptake sequestration potential, 241 00:11:55,400 --> 00:11:57,370 and this can be a really useful tool 242 00:11:57,370 --> 00:12:00,200 for policy makers and state-level accounting 243 00:12:00,200 --> 00:12:03,203 and for assessing opportunities going forward. 244 00:12:06,499 --> 00:12:07,950 So that's a nice prediction, 245 00:12:07,950 --> 00:12:09,990 but of course people would be wondering 246 00:12:09,990 --> 00:12:14,990 how effective we expect, how how successful this is 247 00:12:15,000 --> 00:12:17,190 when we compare it to other data products. 248 00:12:17,190 --> 00:12:20,520 So this now is a time series of aboveground biomass 249 00:12:20,520 --> 00:12:23,100 from 1986 to 2010. 250 00:12:23,100 --> 00:12:24,910 And we're gonna compare this aboveground biomass 251 00:12:24,910 --> 00:12:28,630 to two data products, actually three, excuse me. 252 00:12:28,630 --> 00:12:30,510 Our data product is in the red line, 253 00:12:30,510 --> 00:12:32,630 and this is for the Pacific Northwest, 254 00:12:32,630 --> 00:12:35,380 a place where we've started to look in depth 255 00:12:35,380 --> 00:12:37,640 at what we've seen over the region. 256 00:12:37,640 --> 00:12:39,470 Starting on the left, if we focus in 257 00:12:39,470 --> 00:12:41,710 on recently undisturbed forests, 258 00:12:41,710 --> 00:12:45,430 we see that our estimate is agreeing quite well 259 00:12:45,430 --> 00:12:48,270 with the data set that we train it to agree with, 260 00:12:48,270 --> 00:12:50,820 the NBCD calendar for data set. 261 00:12:50,820 --> 00:12:53,380 So that's no surprise but it is reassuring. 262 00:12:53,380 --> 00:12:56,550 And it also agrees well with Steven Hagen's dataset 263 00:12:56,550 --> 00:12:57,870 with Sussan Saatchi and others, 264 00:12:57,870 --> 00:13:00,320 I think Grant Domke was involved in this as well, 265 00:13:01,660 --> 00:13:03,180 over the Pacific Northwest region 266 00:13:03,180 --> 00:13:06,370 where we saw a tendency towards increased carbon stocks 267 00:13:06,370 --> 00:13:08,860 particularly in the undisturbed forests. 268 00:13:08,860 --> 00:13:12,010 When we include the areas where North American 269 00:13:12,010 --> 00:13:14,750 forest dynamics told us disturbances had occurred, 270 00:13:14,750 --> 00:13:17,760 we find a little bit of a different story where instead 271 00:13:17,760 --> 00:13:20,540 of a tendency for regional accumulation of carbon, 272 00:13:20,540 --> 00:13:22,863 really there's a relatively flat, 273 00:13:23,710 --> 00:13:26,120 relatively constant aboveground biomass. 274 00:13:26,120 --> 00:13:28,940 And that also agrees well with what we were seeing 275 00:13:28,940 --> 00:13:31,750 with our other data products. 276 00:13:31,750 --> 00:13:34,110 Furthermore, eMapR is a data product, 277 00:13:34,110 --> 00:13:37,370 also 30-meter biomass, annually-resolved, 278 00:13:37,370 --> 00:13:41,390 generated by Robert Kennedy at Oregon State University 279 00:13:41,390 --> 00:13:45,030 with his LandTrendr and some Google Earth engine 280 00:13:46,050 --> 00:13:48,110 neural network approaches. 281 00:13:48,110 --> 00:13:49,990 And he has shown, well, 282 00:13:49,990 --> 00:13:52,210 our products show very good agreement. 283 00:13:52,210 --> 00:13:55,990 They're both related to Landsat-derived information 284 00:13:55,990 --> 00:13:58,900 but they're using that in very different ways. 285 00:13:58,900 --> 00:14:02,400 Also on the right, I would show good agreement overall 286 00:14:02,400 --> 00:14:04,470 between bio at a forest-type level 287 00:14:04,470 --> 00:14:05,960 across the Pacific Northwest 288 00:14:05,960 --> 00:14:08,410 between our data product in the red bands 289 00:14:08,410 --> 00:14:11,530 showing that year-to-year variability range 290 00:14:11,530 --> 00:14:13,830 for this 25-year period and what we see 291 00:14:13,830 --> 00:14:17,440 in the NBCD and Hagen et al data sets. 292 00:14:17,440 --> 00:14:19,863 We see that across the dominant forest types. 293 00:14:22,460 --> 00:14:23,720 Another thing I'd like to show you 294 00:14:23,720 --> 00:14:26,380 that we can do is to zoom in on a local assessment. 295 00:14:26,380 --> 00:14:27,213 And this is showing, 296 00:14:27,213 --> 00:14:28,520 sorry for the busy graph, 297 00:14:28,520 --> 00:14:30,840 one particular postage stamp, 298 00:14:30,840 --> 00:14:34,857 and it's showing 1986, 94, 2002, and 2010, 299 00:14:34,857 --> 00:14:37,293 and this period of increased harvest. 300 00:14:38,330 --> 00:14:40,410 Another thing we can do is track 301 00:14:40,410 --> 00:14:42,920 regional scale monitoring and assessment. 302 00:14:42,920 --> 00:14:44,860 So we're looking at carbon stock dynamics, 303 00:14:44,860 --> 00:14:47,940 harvest removals, emissions, fire emissions, 304 00:14:47,940 --> 00:14:50,910 and then net biome productivity and ecosystem productivity 305 00:14:50,910 --> 00:14:54,833 on annual resolution across the entire Pacific Northwest. 306 00:14:55,950 --> 00:14:58,060 I see that we have one minute. 307 00:14:58,060 --> 00:14:59,220 Another thing I'll just tell you 308 00:14:59,220 --> 00:15:01,810 is that we're doing state-level reporting and monitoring 309 00:15:01,810 --> 00:15:03,380 and we're starting to zoom in 310 00:15:03,380 --> 00:15:06,740 on the carbon sequestration opportunities, carbon stocks, 311 00:15:06,740 --> 00:15:09,080 in the '90s, 2000s, 2010s, 312 00:15:09,080 --> 00:15:12,460 and out to 2030 and '50. 313 00:15:12,460 --> 00:15:15,610 For example, here for New Hampshire. 314 00:15:15,610 --> 00:15:17,200 We're excited to be working with states 315 00:15:17,200 --> 00:15:19,280 who are participating in the U.S. Climate Alliance 316 00:15:19,280 --> 00:15:20,263 to do this work. 317 00:15:21,980 --> 00:15:23,890 In future work we're continuing to look 318 00:15:23,890 --> 00:15:27,200 at forest conversions and loss of forests. 319 00:15:27,200 --> 00:15:31,570 And we're also looking at this at a national scale 320 00:15:31,570 --> 00:15:34,030 to come up with some very important conclusions. 321 00:15:34,030 --> 00:15:35,890 For example, that forests are currently holding 322 00:15:35,890 --> 00:15:39,040 only about 50% of their carbon stock potential; 323 00:15:39,040 --> 00:15:41,530 disturbances are being shown to push some regions 324 00:15:41,530 --> 00:15:43,610 toward net carbon emission; 325 00:15:43,610 --> 00:15:46,180 we're seeing that avoided forest carbon loss 326 00:15:46,180 --> 00:15:49,300 could nationwide prevent an emission 327 00:15:49,300 --> 00:15:52,270 of about 20 teragrams of carbon per year; 328 00:15:52,270 --> 00:15:54,710 and that harvest emissions are, as known, 329 00:15:54,710 --> 00:15:58,293 a major offset to carbon uptake in forest lands. 330 00:16:00,500 --> 00:16:02,090 We're doing a lot of innovations. 331 00:16:02,090 --> 00:16:05,590 We intend to work with repeat measures 332 00:16:05,590 --> 00:16:08,790 of FIA plots to test our framework 333 00:16:08,790 --> 00:16:11,293 as well as to perform some scenario testing. 334 00:16:14,010 --> 00:16:15,580 How was the final estimate comparing 335 00:16:15,580 --> 00:16:17,160 with field measure data? 336 00:16:17,160 --> 00:16:19,420 And if this assessment can be used to put arrow bars 337 00:16:19,420 --> 00:16:21,220 in some of the predictions? 338 00:16:21,220 --> 00:16:23,660 Yes. We haven't done that work yet and we should. 339 00:16:23,660 --> 00:16:24,880 We're looking forward to doing it. 340 00:16:24,880 --> 00:16:27,950 So we've been so fixated on generating estimates 341 00:16:27,950 --> 00:16:30,870 at pixel scales where pixel for us is 30 meters 342 00:16:30,870 --> 00:16:32,390 we haven't yet intersected that 343 00:16:32,390 --> 00:16:34,600 with individual plot measurements 344 00:16:34,600 --> 00:16:38,070 either for a given epoch or repeat measurements. 345 00:16:38,070 --> 00:16:40,330 So as I briefly mentioned, 346 00:16:40,330 --> 00:16:43,660 that's one of the, say, innovations or pieces of future work 347 00:16:45,420 --> 00:16:46,820 that we'd really like to do. 348 00:16:48,730 --> 00:16:51,650 Some of our maps are publicly available, James. 349 00:16:51,650 --> 00:16:54,960 So for example, we are, we have posted 350 00:16:54,960 --> 00:16:58,203 with TNC on the Resilient Lands Mapping Tool. 351 00:17:00,310 --> 00:17:01,883 Many of our data sets, and we're, 352 00:17:03,250 --> 00:17:05,050 and we're also generating this for 353 00:17:07,630 --> 00:17:10,500 the Oak Ridge National Lab DAAC, 354 00:17:10,500 --> 00:17:12,320 Data Archive Distribution Center, 355 00:17:12,320 --> 00:17:14,900 and that should be live within weeks. 356 00:17:14,900 --> 00:17:15,733 Who is we? 357 00:17:15,733 --> 00:17:17,539 This is Clark University, 358 00:17:17,539 --> 00:17:20,530 a team of mine in geography and environmental science. 359 00:17:20,530 --> 00:17:24,750 And we are generating state-level results for, well, 360 00:17:24,750 --> 00:17:26,340 like I show here, New Hampshire, 361 00:17:26,340 --> 00:17:28,410 but also all of the New England and York States. 362 00:17:28,410 --> 00:17:31,063 And ultimately we plan to do this nationwide.