1 00:00:06,920 --> 00:00:09,310 - Our next presenter is Jen Pontius 2 00:00:09,310 --> 00:00:13,480 with the university of Vermont and the USDA forest service. 3 00:00:13,480 --> 00:00:16,110 Her presentation will be on the variability 4 00:00:16,110 --> 00:00:18,670 in forest conversion rates and drivers 5 00:00:18,670 --> 00:00:22,330 across a diverse socio-ecological landscape. 6 00:00:22,330 --> 00:00:25,003 A Vermont regional planning commission case study. 7 00:00:28,420 --> 00:00:30,550 - Hi everybody, I am Jen Pontius, 8 00:00:30,550 --> 00:00:31,710 and I just wanna thank you all 9 00:00:31,710 --> 00:00:34,320 for coming to my first and hopefully 10 00:00:34,320 --> 00:00:38,020 last remote conference presentation. 11 00:00:38,020 --> 00:00:40,420 I realized of course, after I submitted the abstract 12 00:00:40,420 --> 00:00:43,900 that this may be the longest title 13 00:00:43,900 --> 00:00:46,320 for a 20 minute presentation. 14 00:00:46,320 --> 00:00:47,810 So if I were to sum it up, 15 00:00:47,810 --> 00:00:50,320 I'm really here today just to tell you a little bit about, 16 00:00:50,320 --> 00:00:53,520 some of the forest modeling work that we've been doing 17 00:00:53,520 --> 00:00:56,280 specifically in terms of forest fragmentation 18 00:00:56,280 --> 00:00:57,770 and how we might be able to use 19 00:00:57,770 --> 00:00:59,320 some of the products we have developed 20 00:00:59,320 --> 00:01:01,823 to help inform planning. 21 00:01:02,819 --> 00:01:05,780 At the FEMC, we do talk a lot about 22 00:01:05,780 --> 00:01:09,210 the various threats to forested ecosystems in the Northeast. 23 00:01:09,210 --> 00:01:12,230 And there are some common themes that always come up. 24 00:01:12,230 --> 00:01:15,340 Climate change is one, pests and pathogens, 25 00:01:15,340 --> 00:01:17,290 but also fragmentation. 26 00:01:17,290 --> 00:01:19,950 And when we're talking about fragmentation here, 27 00:01:19,950 --> 00:01:21,040 as a part of this project, 28 00:01:21,040 --> 00:01:23,620 we're not talking about civil cultural treatments 29 00:01:23,620 --> 00:01:26,690 or logging activities or even parcelization, 30 00:01:26,690 --> 00:01:29,410 the dividing up of larger parcels into smaller ones. 31 00:01:29,410 --> 00:01:31,900 But really getting at the loss of forest 32 00:01:31,900 --> 00:01:34,700 to developed land use types. 33 00:01:34,700 --> 00:01:38,270 As I mentioned, this project developed from several years 34 00:01:38,270 --> 00:01:40,260 of modeling work that we'd been doing 35 00:01:40,260 --> 00:01:42,240 and this presented several opportunities 36 00:01:42,240 --> 00:01:43,500 that really brought us to 37 00:01:43,500 --> 00:01:45,840 want to work on this project specifically. 38 00:01:45,840 --> 00:01:48,200 And one was just the availability 39 00:01:48,200 --> 00:01:50,910 of these improved forest cover maps 40 00:01:50,910 --> 00:01:54,003 that we were able to create with David (murmurs). 41 00:01:54,003 --> 00:01:56,160 And also the work of Alison Adams, 42 00:01:56,160 --> 00:01:58,440 who had used those forest cover maps 43 00:01:58,440 --> 00:02:01,410 to look at forest conversion on a very broad scale 44 00:02:01,410 --> 00:02:03,100 across the region. 45 00:02:03,100 --> 00:02:04,070 As a part of that study, 46 00:02:04,070 --> 00:02:06,840 she identified some additional questions. 47 00:02:06,840 --> 00:02:08,010 One of which was, 48 00:02:08,010 --> 00:02:11,820 how does forest conversion the drivers of forest conversion 49 00:02:11,820 --> 00:02:13,880 differ across the landscapes? 50 00:02:13,880 --> 00:02:15,540 Does it make more sense if we look at this 51 00:02:15,540 --> 00:02:17,600 at a smaller, more localized scale. 52 00:02:17,600 --> 00:02:21,540 But then what really catalyzed this project 53 00:02:21,540 --> 00:02:25,720 more our collaborators at the Vermont DEC and FPR 54 00:02:25,720 --> 00:02:28,750 as well as at regional planning commissions across Vermont, 55 00:02:28,750 --> 00:02:32,560 who worked with us on a US forest service LSR grant 56 00:02:32,560 --> 00:02:34,280 to both examine those patterns 57 00:02:34,280 --> 00:02:36,670 of forest conversion at the finer scale. 58 00:02:36,670 --> 00:02:39,790 But also, identify ways that we could actually 59 00:02:39,790 --> 00:02:42,620 integrate those products into 60 00:02:42,620 --> 00:02:44,260 land use planning. 61 00:02:44,260 --> 00:02:45,820 So we really had five objectives. 62 00:02:45,820 --> 00:02:50,530 One was just to extend those improved forest mapping 63 00:02:50,530 --> 00:02:52,330 methods across the state, 64 00:02:52,330 --> 00:02:55,230 and then to examine those to see how 65 00:02:55,230 --> 00:02:57,250 forest cover has changed over time 66 00:02:57,250 --> 00:03:00,140 between 1985 and the present, 67 00:03:00,140 --> 00:03:02,620 but then use that to actually identify 68 00:03:02,620 --> 00:03:05,220 what the drivers of those changes were, 69 00:03:05,220 --> 00:03:07,370 and use those relationships 70 00:03:07,370 --> 00:03:09,650 to model forest loss into the future. 71 00:03:09,650 --> 00:03:11,260 But then the application really came 72 00:03:11,260 --> 00:03:12,950 in that fifth objective, 73 00:03:12,950 --> 00:03:13,783 which was to say, 74 00:03:13,783 --> 00:03:18,480 can we overlay this now map of high forest conversion risk 75 00:03:18,480 --> 00:03:22,140 with other maps of high value forest 76 00:03:22,140 --> 00:03:24,940 to help prioritize conservation or planning effort? 77 00:03:24,940 --> 00:03:27,540 And to do all of this on a scale that really was appropriate 78 00:03:27,540 --> 00:03:30,550 for these regional planning commissions to be able to use. 79 00:03:30,550 --> 00:03:31,600 Now in the interest of time 80 00:03:31,600 --> 00:03:34,030 I didn't wanna get too deep into the methods. 81 00:03:34,030 --> 00:03:36,180 So please feel free to ask any questions 82 00:03:36,180 --> 00:03:37,590 and I can share some of the papers 83 00:03:37,590 --> 00:03:40,670 on the background of the modeling that we did, 84 00:03:40,670 --> 00:03:43,250 but essentially we took these historical changes 85 00:03:43,250 --> 00:03:46,460 that we had identified over time from the satellite imagery. 86 00:03:46,460 --> 00:03:48,860 We overlaid those with various spacial maps 87 00:03:48,860 --> 00:03:50,210 of potential forest drivers, 88 00:03:50,210 --> 00:03:52,900 for example distance to roads, 89 00:03:52,900 --> 00:03:56,180 topography, current parcelization or conservation status 90 00:03:56,180 --> 00:03:57,240 and so on and so forth. 91 00:03:57,240 --> 00:03:59,610 And then used Dyamica, 92 00:03:59,610 --> 00:04:02,310 this landscape scale modeling software 93 00:04:02,310 --> 00:04:04,910 to identify locations across the landscape 94 00:04:04,910 --> 00:04:06,800 at a 30 meter scale 95 00:04:06,800 --> 00:04:09,090 where you would have a higher risk of conversion 96 00:04:09,090 --> 00:04:12,218 based on these drivers of historical patterns. 97 00:04:12,218 --> 00:04:14,380 So just jumping into the results, 98 00:04:14,380 --> 00:04:18,010 we were able to find enough cloud free images historically, 99 00:04:18,010 --> 00:04:20,630 to go back through the satellite archive 100 00:04:20,630 --> 00:04:21,920 and be able to 101 00:04:21,920 --> 00:04:23,940 finish the forest cover maps 102 00:04:23,940 --> 00:04:25,710 for all of Vermont. 103 00:04:25,710 --> 00:04:28,570 And again, I can provide information and links to 104 00:04:28,570 --> 00:04:31,030 the original papers that highlight the 105 00:04:31,030 --> 00:04:34,780 improved accuracy and details associated with this method. 106 00:04:34,780 --> 00:04:37,840 And so then once we had those forest cover maps 107 00:04:37,840 --> 00:04:42,840 for 1985, 2000 and 2015 as historical timestamps, 108 00:04:43,570 --> 00:04:47,680 we were able to break down the rates of forest conversion 109 00:04:47,680 --> 00:04:49,830 by each regional RPC. 110 00:04:49,830 --> 00:04:52,150 And there are a couple of interesting patterns 111 00:04:52,150 --> 00:04:52,983 that came out. 112 00:04:52,983 --> 00:04:54,150 So just for perspective, 113 00:04:54,150 --> 00:04:57,150 these are color coded from green being the slowest 114 00:04:57,150 --> 00:04:58,340 rate of forest loss 115 00:04:58,340 --> 00:05:01,690 to red being the fastest rate of forest loss. 116 00:05:01,690 --> 00:05:04,640 And what you can see is a really clear temporal pattern, 117 00:05:04,640 --> 00:05:08,750 where we had much faster forest conversion to development 118 00:05:08,750 --> 00:05:11,450 in the 1985 to 2000 period. 119 00:05:11,450 --> 00:05:12,780 And that really slowed down 120 00:05:12,780 --> 00:05:16,400 although still was a net loss of forest cover, 121 00:05:16,400 --> 00:05:20,020 but did slow down in the 2000 to 2015 era. 122 00:05:20,020 --> 00:05:22,700 But there also is this special pattern. 123 00:05:22,700 --> 00:05:24,300 So that rate of forest loss 124 00:05:24,300 --> 00:05:27,130 was not consistent across all of the RPCs. 125 00:05:27,130 --> 00:05:29,510 Some faced much more 126 00:05:29,510 --> 00:05:32,880 extreme development pressure and forest loss. 127 00:05:32,880 --> 00:05:34,800 These temporal trends are very consistent 128 00:05:34,800 --> 00:05:36,800 with what we saw across the larger region 129 00:05:36,800 --> 00:05:38,400 in the original analysis. 130 00:05:38,400 --> 00:05:40,750 And we really linked to this back to 131 00:05:40,750 --> 00:05:43,700 development pressure which you can capture quite nicely 132 00:05:43,700 --> 00:05:45,870 in this housing price index. 133 00:05:45,870 --> 00:05:49,180 So you can see that we really were in sort of a housing boom 134 00:05:49,180 --> 00:05:50,890 with a lot of incentive for development 135 00:05:50,890 --> 00:05:54,669 as we moved from the early 1990s into around 2000, 136 00:05:54,669 --> 00:05:57,110 2003 when we hit that peak, 137 00:05:57,110 --> 00:05:59,920 and then we had this really precipitous crash. 138 00:05:59,920 --> 00:06:01,570 And this just highlights how it really 139 00:06:01,570 --> 00:06:03,360 is sort of economic conditions 140 00:06:03,360 --> 00:06:05,360 that are driving the rate of conversion. 141 00:06:05,360 --> 00:06:06,410 We still have to investigate 142 00:06:06,410 --> 00:06:08,400 the patterns of those conversions because, 143 00:06:08,400 --> 00:06:10,220 we saw that even when this 144 00:06:10,220 --> 00:06:12,580 housing price index really dropped, 145 00:06:12,580 --> 00:06:15,010 we still had a net forest loss. 146 00:06:15,010 --> 00:06:16,400 It was just much slower. 147 00:06:16,400 --> 00:06:18,080 So, this is when the pattern, 148 00:06:18,080 --> 00:06:19,970 this facial pattern becomes very important. 149 00:06:19,970 --> 00:06:22,450 So our next objective was to take 150 00:06:22,450 --> 00:06:25,920 those spatial patterns that we saw in the historical record 151 00:06:25,920 --> 00:06:27,450 and overlay that with the drivers 152 00:06:27,450 --> 00:06:30,600 to identify some potential correlates we could use, 153 00:06:30,600 --> 00:06:33,330 to quantify forest conversion risk. 154 00:06:33,330 --> 00:06:34,900 And for perspective I wanted to start 155 00:06:34,900 --> 00:06:36,120 with the regional assessment, 156 00:06:36,120 --> 00:06:37,200 that original study, 157 00:06:37,200 --> 00:06:38,970 where you can see the results were highly driven 158 00:06:38,970 --> 00:06:42,090 by population dense areas. 159 00:06:42,090 --> 00:06:43,460 So that's where the majority 160 00:06:43,460 --> 00:06:45,190 of the forest conversion was taking place. 161 00:06:45,190 --> 00:06:47,740 And so the correlations with drivers there was strongest. 162 00:06:47,740 --> 00:06:49,779 So we saw higher risk of forest conversion 163 00:06:49,779 --> 00:06:52,440 near to those high population centers, 164 00:06:52,440 --> 00:06:54,300 near other non-forested areas, 165 00:06:54,300 --> 00:06:56,090 that lower elevations that were flat 166 00:06:56,090 --> 00:06:58,120 and also those parcels that were not 167 00:06:58,120 --> 00:06:59,683 in conservation easement. 168 00:07:00,800 --> 00:07:02,500 When we repeated this now 169 00:07:02,500 --> 00:07:04,830 just for Vermont at the RPC level, 170 00:07:04,830 --> 00:07:07,580 we found that first of all, looking at the state as a whole, 171 00:07:07,580 --> 00:07:09,870 the drivers really were different from 172 00:07:09,870 --> 00:07:12,130 what we had found at the larger region. 173 00:07:12,130 --> 00:07:14,420 So instead of proximity to population density 174 00:07:14,420 --> 00:07:16,140 being one of the primary drivers, 175 00:07:16,140 --> 00:07:18,130 it really was proximity to roads 176 00:07:18,130 --> 00:07:20,000 that were featured much more prominently. 177 00:07:20,000 --> 00:07:22,730 And also we found that the topographic conditions 178 00:07:22,730 --> 00:07:24,320 were not as important. 179 00:07:24,320 --> 00:07:26,360 We also found that this wasn't even consistent 180 00:07:26,360 --> 00:07:27,410 just for the whole state. 181 00:07:27,410 --> 00:07:29,260 That across the RPCs, 182 00:07:29,260 --> 00:07:31,620 those drivers that were weighted most heavily, 183 00:07:31,620 --> 00:07:33,060 really did differ. 184 00:07:33,060 --> 00:07:35,110 While roads were consistently weighted, 185 00:07:35,110 --> 00:07:37,740 the type of road whether it was a distance to an interstate, 186 00:07:37,740 --> 00:07:39,110 the distance to a major road, 187 00:07:39,110 --> 00:07:40,210 or a distance to any road, 188 00:07:40,210 --> 00:07:42,860 did vary depending on the RPC. 189 00:07:42,860 --> 00:07:45,170 And so by doing this at the RPC level, 190 00:07:45,170 --> 00:07:48,470 we were able to get a much more nuanced and localized 191 00:07:48,470 --> 00:07:51,450 set of drivers that really reflected the pressures 192 00:07:51,450 --> 00:07:53,880 in that specific region. 193 00:07:53,880 --> 00:07:55,640 I don't wanna make your eyeballs pop out 194 00:07:55,640 --> 00:07:57,010 but if you are interested, 195 00:07:57,010 --> 00:07:59,690 we do have the report on all of the drivers 196 00:07:59,690 --> 00:08:02,280 and their rank order for their weightings, 197 00:08:02,280 --> 00:08:04,740 in the final risk modeling, 198 00:08:04,740 --> 00:08:08,010 Just to highlight it really was this proximity to roads, 199 00:08:08,010 --> 00:08:10,610 topographic limitations, some, 200 00:08:10,610 --> 00:08:12,490 distance to urban definitely did come up 201 00:08:12,490 --> 00:08:14,120 in some of the RPCs. 202 00:08:14,120 --> 00:08:16,160 But then also land cover characteristics 203 00:08:16,160 --> 00:08:18,190 and economics did show up in a few. 204 00:08:18,190 --> 00:08:22,150 So, there really were differential drivers across the state. 205 00:08:22,150 --> 00:08:25,480 Ultimately this resulted in a much more nuanced 206 00:08:25,480 --> 00:08:28,720 and fine tuned map of forest conversion risk 207 00:08:28,720 --> 00:08:30,770 across the RPCs. 208 00:08:30,770 --> 00:08:33,230 So our next step was to take the current (murmurs) 209 00:08:33,230 --> 00:08:35,940 maps of forest cover, that we had developed 210 00:08:35,940 --> 00:08:38,000 and then link that, 211 00:08:38,000 --> 00:08:40,420 to the rate of conversion that we had 212 00:08:40,420 --> 00:08:41,870 for each of these RPCs. 213 00:08:41,870 --> 00:08:43,670 And we use the average 214 00:08:43,670 --> 00:08:45,339 just to make sure we were hedging our bets 215 00:08:45,339 --> 00:08:48,050 and not falling on either extreme. 216 00:08:48,050 --> 00:08:51,430 And then overlaid that with the probability of conversion 217 00:08:51,430 --> 00:08:53,000 and use this stochastic model, 218 00:08:53,000 --> 00:08:55,780 to be able to project into the future, 219 00:08:55,780 --> 00:08:57,770 which locations were likely to be 220 00:08:57,770 --> 00:09:00,580 converted from forest to developed. 221 00:09:00,580 --> 00:09:02,540 That allows us to model, 222 00:09:02,540 --> 00:09:07,010 the projected forest cover or land cover across the state 223 00:09:07,010 --> 00:09:09,590 at several different timestamps. 224 00:09:09,590 --> 00:09:11,690 And I do wanna note that I've been talking about 225 00:09:11,690 --> 00:09:13,840 our modeling of forest conversion, right? 226 00:09:13,840 --> 00:09:17,200 Flipping from forest to developed land cover types. 227 00:09:17,200 --> 00:09:20,490 But, we also did look at fragmentation metrics 228 00:09:20,490 --> 00:09:21,850 across the state. 229 00:09:21,850 --> 00:09:25,490 And what we see generally over time is, 230 00:09:25,490 --> 00:09:28,100 that not only are we losing forest area, 231 00:09:28,100 --> 00:09:30,660 but the pattern of that forest loss 232 00:09:30,660 --> 00:09:33,240 really is magnifying how fragmented 233 00:09:33,240 --> 00:09:34,930 the forested landscape is. 234 00:09:34,930 --> 00:09:38,680 Both measured in terms of the amount of core forest area, 235 00:09:38,680 --> 00:09:40,870 but also forest edge density. 236 00:09:40,870 --> 00:09:43,670 So we're really seeing our fragmentation metrics 237 00:09:43,670 --> 00:09:46,930 continue to increase even when the rates of change 238 00:09:46,930 --> 00:09:48,160 are relatively low. 239 00:09:48,160 --> 00:09:49,430 And this gets at the importance of the 240 00:09:49,430 --> 00:09:51,210 spatial pattern of forest loss. 241 00:09:51,210 --> 00:09:53,960 Our next step and honestly I think the most interesting part 242 00:09:53,960 --> 00:09:55,030 of this project, 243 00:09:55,030 --> 00:09:57,625 was to figure out how to make it useful. 244 00:09:57,625 --> 00:10:00,810 In terms of informing planning or conservation efforts. 245 00:10:00,810 --> 00:10:03,440 And so we also did some modeling 246 00:10:03,440 --> 00:10:05,483 where we took the risk maps that we had developed 247 00:10:05,483 --> 00:10:08,160 and we overlaid that with other, 248 00:10:08,160 --> 00:10:09,800 forest mapping products, 249 00:10:09,800 --> 00:10:11,450 particularly the really great products 250 00:10:11,450 --> 00:10:15,400 that come out of bio finder and Vermont conservation design. 251 00:10:15,400 --> 00:10:19,260 And so when you overlay these high risk areas 252 00:10:19,260 --> 00:10:21,362 with these high value areas, 253 00:10:21,362 --> 00:10:24,770 you can start to create these more specialized maps 254 00:10:24,770 --> 00:10:27,659 that could identify locations that should be 255 00:10:27,659 --> 00:10:30,560 considered for conservation priority. 256 00:10:30,560 --> 00:10:33,330 Because not everybody wants to download 257 00:10:33,330 --> 00:10:35,860 a bunch of GIS data layers or read this long report. 258 00:10:35,860 --> 00:10:37,910 We've also been working to develop 259 00:10:37,910 --> 00:10:40,374 these fact sheets for each of the RPCs 260 00:10:40,374 --> 00:10:43,420 that really breaks down the key information 261 00:10:43,420 --> 00:10:46,320 such as what the differential rates of change 262 00:10:46,320 --> 00:10:47,570 have been historically 263 00:10:47,570 --> 00:10:51,410 and what that means long-term over the full time period. 264 00:10:51,410 --> 00:10:53,830 What the primary drivers are, 265 00:10:53,830 --> 00:10:57,446 of the forest conversion changes that we see, 266 00:10:57,446 --> 00:10:59,750 and then also, just that map 267 00:10:59,750 --> 00:11:01,430 of what is the current forest cover, 268 00:11:01,430 --> 00:11:03,610 where are the areas of risk? 269 00:11:03,610 --> 00:11:05,620 In this case in the central Vermont 270 00:11:05,620 --> 00:11:07,100 regional planning commission you can see 271 00:11:07,100 --> 00:11:09,520 it really is this sort of proximity to 272 00:11:09,520 --> 00:11:11,740 the I-89 interstate, 273 00:11:11,740 --> 00:11:16,740 and the Montpelier and Waterbury STO town centers. 274 00:11:16,870 --> 00:11:17,830 But when you overlay that 275 00:11:17,830 --> 00:11:19,750 with the high quality habitat blocks, 276 00:11:19,750 --> 00:11:23,090 what you're really seeing are these stands 277 00:11:23,090 --> 00:11:27,210 up in the STO area that really should be considered 278 00:11:27,210 --> 00:11:28,960 as perhaps a high priority 279 00:11:28,960 --> 00:11:30,580 or at least a special consideration 280 00:11:30,580 --> 00:11:32,510 during planning activities. 281 00:11:32,510 --> 00:11:34,110 And again, for perspective 282 00:11:34,110 --> 00:11:38,400 on how that differs across the various RPCs. 283 00:11:38,400 --> 00:11:41,070 Now, here we are in the Northeast kingdom, 284 00:11:41,070 --> 00:11:42,050 and you can see 285 00:11:42,050 --> 00:11:44,900 that these smaller roads become much more important 286 00:11:44,900 --> 00:11:46,350 in terms of the risk. 287 00:11:46,350 --> 00:11:49,510 Again, overlaying that with the high quality blocks 288 00:11:49,510 --> 00:11:53,240 we begin to see some parcels that really are singled out, 289 00:11:53,240 --> 00:11:54,870 as perhaps being 290 00:11:54,870 --> 00:11:57,370 areas that should be looked at more carefully. 291 00:11:57,370 --> 00:12:00,460 And this case of particular concern are, 292 00:12:00,460 --> 00:12:02,870 the locations around (murmurs) 293 00:12:02,870 --> 00:12:05,730 highlighting the importance of tourism in this region 294 00:12:05,730 --> 00:12:09,780 but also the impacts that can have on the forest resource. 295 00:12:09,780 --> 00:12:11,120 One last example I'll give you, 296 00:12:11,120 --> 00:12:12,630 just something that looks quite different 297 00:12:12,630 --> 00:12:14,580 would be Addison County. 298 00:12:14,580 --> 00:12:16,800 So now we're over by Lake Champlain 299 00:12:16,800 --> 00:12:19,440 in a much more highly agricultural area. 300 00:12:19,440 --> 00:12:22,560 Notice that we don't even have that much forest risk at all 301 00:12:22,560 --> 00:12:24,990 because most of this is agricultural. 302 00:12:24,990 --> 00:12:29,440 So while conversion risk is pretty high around Middlebury, 303 00:12:29,440 --> 00:12:31,840 there aren't really a concentration 304 00:12:31,840 --> 00:12:33,483 of high quality forest blocks 305 00:12:33,483 --> 00:12:36,043 that are adjacent to these high-risk areas, 306 00:12:36,043 --> 00:12:39,410 where you have higher risk and high quality is 307 00:12:39,410 --> 00:12:43,670 further up in this sort of Monkton and Bristol area. 308 00:12:43,670 --> 00:12:45,310 And so there always is some danger 309 00:12:45,310 --> 00:12:47,220 when you put up pretty maps. 310 00:12:47,220 --> 00:12:48,570 And so we think it is important 311 00:12:48,570 --> 00:12:51,680 that people are aware of what the limitations are. 312 00:12:51,680 --> 00:12:53,470 First of all, you have to remember that 313 00:12:53,470 --> 00:12:55,750 the methodology took those risk maps 314 00:12:55,750 --> 00:12:58,340 which really just was a probability of conversion 315 00:12:58,340 --> 00:13:01,430 and then applied the stochastic model where, 316 00:13:01,430 --> 00:13:04,560 conversions were just simulated over and over again. 317 00:13:04,560 --> 00:13:07,890 So it's not like you can take an exact pixel and say 318 00:13:07,890 --> 00:13:11,410 I know that by 2040, this is no longer gonna be forest. 319 00:13:11,410 --> 00:13:13,710 It's more looking at the patterns across 320 00:13:13,710 --> 00:13:15,420 the broader landscape. 321 00:13:15,420 --> 00:13:18,390 But not for that pixel level interpretation. 322 00:13:18,390 --> 00:13:20,860 And then also we wanna point out that the rates 323 00:13:20,860 --> 00:13:22,480 of change are really important 324 00:13:22,480 --> 00:13:24,700 and that's primarily driven by economic 325 00:13:24,700 --> 00:13:25,940 and development pressures. 326 00:13:25,940 --> 00:13:28,650 So again, those projections of what's going 327 00:13:28,650 --> 00:13:30,090 to be forest cover and what's not, 328 00:13:30,090 --> 00:13:32,060 or how fast we're gonna lose forest cover 329 00:13:32,060 --> 00:13:35,330 really are dependent upon the economic pressures 330 00:13:35,330 --> 00:13:38,810 that are really driving that conversion across the region. 331 00:13:38,810 --> 00:13:40,560 What can they do? 332 00:13:40,560 --> 00:13:42,890 We do think that these are really useful 333 00:13:42,890 --> 00:13:46,430 in seeing where forests are more likely to disappear 334 00:13:46,430 --> 00:13:47,263 in the future. 335 00:13:47,263 --> 00:13:48,660 So on a relative scale, 336 00:13:48,660 --> 00:13:50,284 which locations are most at risk. 337 00:13:50,284 --> 00:13:53,320 It does help inform us about what the drivers 338 00:13:53,320 --> 00:13:55,080 of historical change have been. 339 00:13:55,080 --> 00:13:58,330 And the assumption is that those drivers will continue 340 00:13:58,330 --> 00:14:00,930 to influence forest conversion patterns. 341 00:14:00,930 --> 00:14:05,570 And then it also shows us that we really are seeing 342 00:14:05,570 --> 00:14:09,150 impacts from forest conversion, 343 00:14:09,150 --> 00:14:12,530 particularly in terms of fragmentation and connectivity. 344 00:14:12,530 --> 00:14:14,870 And, we know that these are incredibly important 345 00:14:14,870 --> 00:14:16,990 from an ecological perspective. 346 00:14:16,990 --> 00:14:20,210 So what this really can do is show us where 347 00:14:20,210 --> 00:14:22,880 we have high risk and again, 348 00:14:22,880 --> 00:14:24,840 integrated with these other products, 349 00:14:24,840 --> 00:14:26,750 high priority locations 350 00:14:26,750 --> 00:14:29,780 where we should focus any of our future efforts. 351 00:14:29,780 --> 00:14:31,490 Most of the work that you see presented today 352 00:14:31,490 --> 00:14:33,060 this is ongoing work. 353 00:14:33,060 --> 00:14:36,170 We are still working to get all of these products up 354 00:14:36,170 --> 00:14:37,810 and discoverable 355 00:14:37,810 --> 00:14:40,770 so that they are easily accessed in various formats 356 00:14:40,770 --> 00:14:43,280 by anybody who might want to use them. 357 00:14:43,280 --> 00:14:48,120 We are currently working with our LSR grant collaborators 358 00:14:48,120 --> 00:14:50,770 on how we might integrate this with other tools, 359 00:14:50,770 --> 00:14:52,320 perhaps like bio finder. 360 00:14:52,320 --> 00:14:54,370 And then also collaborating directly 361 00:14:54,370 --> 00:14:57,160 with our regional planners to help us interpret 362 00:14:57,160 --> 00:14:58,630 the products that we have created. 363 00:14:58,630 --> 00:14:59,700 Does this make sense? 364 00:14:59,700 --> 00:15:02,610 Do the drivers make sense to the patterns that we see 365 00:15:02,610 --> 00:15:04,701 in your RPC makes sense. 366 00:15:04,701 --> 00:15:07,560 And perhaps put together some 367 00:15:07,560 --> 00:15:09,860 used case tests to see 368 00:15:09,860 --> 00:15:13,660 how valid it is and how useful this information is 369 00:15:13,660 --> 00:15:16,720 to inform planning at that regional level. 370 00:15:16,720 --> 00:15:19,890 So if you, or anyone that you work with 371 00:15:19,890 --> 00:15:22,500 might have interest in collaborating 372 00:15:22,500 --> 00:15:24,090 with us to determine if 373 00:15:24,090 --> 00:15:26,992 or how these products are useful, 374 00:15:26,992 --> 00:15:29,800 (background creates inaudible situation) 375 00:15:29,800 --> 00:15:31,247 to your (murmurs), 376 00:15:32,990 --> 00:15:34,080 get in touch with us. 377 00:15:34,080 --> 00:15:37,250 So with that, I will pause there 378 00:15:37,250 --> 00:15:39,960 and try to address any questions you might have. 379 00:15:39,960 --> 00:15:42,410 Thank you everybody for sitting through this 380 00:15:42,410 --> 00:15:45,210 and being a great audience. 381 00:15:45,210 --> 00:15:49,360 Is that I did put a couple of links into the chat. 382 00:15:49,360 --> 00:15:51,700 One of those is a link to the forest mapping products 383 00:15:51,700 --> 00:15:54,480 which are archived in a project at the FEMC. 384 00:15:54,480 --> 00:15:57,160 So if you wanted more details on that methodology, 385 00:15:57,160 --> 00:16:00,970 and then the other is a link to the FEMC fragment tool 386 00:16:00,970 --> 00:16:03,250 which has sort of an aggregation of all sorts 387 00:16:03,250 --> 00:16:05,630 of information on fragmentation across the region. 388 00:16:05,630 --> 00:16:09,040 So, to potentially good sources 389 00:16:09,040 --> 00:16:10,963 if you want more information. 390 00:16:13,100 --> 00:16:15,060 Have we looked at proximity to mills? 391 00:16:15,060 --> 00:16:15,960 We have not, 392 00:16:15,960 --> 00:16:19,330 but that actually is something that could be 393 00:16:19,330 --> 00:16:22,080 interesting if we're talking about forest loss 394 00:16:22,080 --> 00:16:24,670 as a more general, you know, in terms of, 395 00:16:24,670 --> 00:16:25,950 actual logging activities, 396 00:16:25,950 --> 00:16:28,730 but this really was focused on development. 397 00:16:28,730 --> 00:16:31,320 So kind of honing in on where are we seeing 398 00:16:31,320 --> 00:16:33,340 forest conversion that essentially is 399 00:16:33,340 --> 00:16:36,660 not likely to grow back. 400 00:16:36,660 --> 00:16:38,290 But that is something we could do with 401 00:16:38,290 --> 00:16:40,133 the products that we have. 402 00:16:44,020 --> 00:16:46,325 Yeah, so conservation status 403 00:16:46,325 --> 00:16:49,570 was definitely significant in that first model. 404 00:16:49,570 --> 00:16:50,650 In the second model 405 00:16:50,650 --> 00:16:52,730 it did not come up as being as important. 406 00:16:52,730 --> 00:16:54,510 And I think part of it may be 407 00:16:54,510 --> 00:16:57,010 just thought of the range of conservation status data 408 00:16:57,010 --> 00:16:58,324 that we had, 409 00:16:58,324 --> 00:17:01,080 in the database on a spatial scale. 410 00:17:01,080 --> 00:17:04,080 So across the region, and we were able to incorporate places 411 00:17:04,080 --> 00:17:05,860 like national parks 412 00:17:05,860 --> 00:17:08,270 that are clearly conserved or wilderness areas 413 00:17:08,270 --> 00:17:10,870 that are clearly conserved in perpetuity. 414 00:17:10,870 --> 00:17:13,010 So that had a higher conservation status. 415 00:17:13,010 --> 00:17:15,760 Within the state of Vermont we didn't have as much nuance 416 00:17:15,760 --> 00:17:18,140 across the types of different conservation easements. 417 00:17:18,140 --> 00:17:20,210 And I think that's really why it came 418 00:17:20,210 --> 00:17:22,110 out as not being as significant. 419 00:17:22,110 --> 00:17:24,470 So I think that this is where collaborating 420 00:17:24,470 --> 00:17:27,460 with our RPC partners is gonna become 421 00:17:27,460 --> 00:17:28,860 much more interesting because, 422 00:17:28,860 --> 00:17:31,410 they can take the risk maps and they can overlay that 423 00:17:31,410 --> 00:17:34,050 with their own parcel information, 424 00:17:34,050 --> 00:17:36,450 and see what might be explaining some 425 00:17:36,450 --> 00:17:37,860 of those patterns that we came up with 426 00:17:37,860 --> 00:17:38,950 in the historical imagery. 427 00:17:38,950 --> 00:17:40,730 But it's a really great question 428 00:17:40,730 --> 00:17:43,500 and honestly, an important one in terms of policy 429 00:17:43,500 --> 00:17:46,335 and informing different incentives that we use 430 00:17:46,335 --> 00:17:49,503 to minimize fragmentation. 431 00:17:50,710 --> 00:17:54,270 And I have to say Alice Schadler is also on this meeting. 432 00:17:54,270 --> 00:17:57,020 She is our fearless leader who is really working to 433 00:17:57,020 --> 00:17:58,500 integrate all of this work between, 434 00:17:58,500 --> 00:18:01,600 sort of the modeling and the actual planners on the ground. 435 00:18:01,600 --> 00:18:04,340 So Alice, I'm gonna throw you under the bus and say 436 00:18:04,340 --> 00:18:07,220 feel free to chat with Alice as well 437 00:18:07,220 --> 00:18:09,130 if you have other questions 438 00:18:09,130 --> 00:18:11,423 that would make more sense on the planning side.