1 00:00:10,710 --> 00:00:15,710 - This next talk is gonna be from our very own James Duncan 2 00:00:16,910 --> 00:00:19,960 from the Forest Ecosystem Monitoring Cooperative, 3 00:00:19,960 --> 00:00:23,900 presenting this FEMC Sampler, 4 00:00:23,900 --> 00:00:26,140 new releases of regional data and tools 5 00:00:26,140 --> 00:00:28,003 from the cooperative in 2020. 6 00:00:31,630 --> 00:00:34,703 - Thank you Alex and thank you all for being here. 7 00:00:35,610 --> 00:00:38,220 As Alex said, I'm the director for the FEMC, 8 00:00:38,220 --> 00:00:39,880 and I'm also a member of the research staff 9 00:00:39,880 --> 00:00:41,530 at the Rubenstein School of Environment 10 00:00:41,530 --> 00:00:43,110 and Natural Resources. 11 00:00:43,110 --> 00:00:46,230 And today I'm excited to do something in between 12 00:00:46,230 --> 00:00:49,140 a five second blurb at the plenary 13 00:00:49,140 --> 00:00:51,090 and a more detailed talk on each of these, 14 00:00:51,090 --> 00:00:54,230 but to give a little sampler platter for you, 15 00:00:54,230 --> 00:00:56,400 of some of the products that have come out 16 00:00:56,400 --> 00:00:59,010 from FEMC over the past two years. 17 00:00:59,010 --> 00:01:00,700 Each of these products speaks to themes 18 00:01:00,700 --> 00:01:02,840 that have arisen again and again, 19 00:01:02,840 --> 00:01:06,140 over our time in the cooperative at the regional scale, 20 00:01:06,140 --> 00:01:08,550 and I think we've heard connections to some of these 21 00:01:08,550 --> 00:01:10,940 throughout the last two days, as well. 22 00:01:10,940 --> 00:01:12,590 So I hope we can make use of some of these 23 00:01:12,590 --> 00:01:15,830 and give you an idea of what they might be useful for. 24 00:01:15,830 --> 00:01:17,730 The first project was looking at 25 00:01:17,730 --> 00:01:19,560 forest cover and water quality, 26 00:01:19,560 --> 00:01:22,880 and how to make these connections 27 00:01:22,880 --> 00:01:24,950 easier to investigate and understand. 28 00:01:24,950 --> 00:01:26,860 So we know forests play an important role 29 00:01:26,860 --> 00:01:29,300 in regulating water quality and water flow, 30 00:01:29,300 --> 00:01:31,640 and these relationships are complex, 31 00:01:31,640 --> 00:01:34,250 and there's a robust community of people already working on 32 00:01:34,250 --> 00:01:36,300 how to bring these together, 33 00:01:36,300 --> 00:01:38,020 as you just saw from Rebecca's talk, 34 00:01:38,020 --> 00:01:40,040 and there's some great work coming out 35 00:01:40,040 --> 00:01:42,590 all over the region about these issues. 36 00:01:42,590 --> 00:01:45,080 However, one time, oftentimes, 37 00:01:45,080 --> 00:01:47,710 a starting point for researchers is the processing 38 00:01:47,710 --> 00:01:49,100 and combining of data steps. 39 00:01:49,100 --> 00:01:51,310 So we're trying to take that step out of the equation 40 00:01:51,310 --> 00:01:55,040 in the Northeast by bringing together data more quickly. 41 00:01:55,040 --> 00:01:58,370 So we identified a key list of 30 large data sets 42 00:01:58,370 --> 00:02:01,240 that takes some time and expertise to process, 43 00:02:01,240 --> 00:02:02,930 to raster data down into something 44 00:02:02,930 --> 00:02:05,730 that can be usable more quickly, 45 00:02:05,730 --> 00:02:09,480 we processed this data and provided the underlying code, 46 00:02:09,480 --> 00:02:14,050 so that people can more quickly access that spatial data. 47 00:02:14,050 --> 00:02:17,910 So we also looked at summarizing it similarly 48 00:02:17,910 --> 00:02:20,580 to a watershed level, so you can see a broader picture 49 00:02:20,580 --> 00:02:21,690 of what's going on, but really, 50 00:02:21,690 --> 00:02:23,990 the underlying data is the product. 51 00:02:23,990 --> 00:02:26,140 So if you're interested in working with spatial data 52 00:02:26,140 --> 00:02:29,010 that looks at some of these issues 53 00:02:29,010 --> 00:02:30,860 around forest cover and water quality, 54 00:02:30,860 --> 00:02:32,580 the data sets are kind of ready to go. 55 00:02:32,580 --> 00:02:35,720 And you can also grab our scripts and adapt them 56 00:02:35,720 --> 00:02:37,540 to fit your own needs if you'd like. 57 00:02:37,540 --> 00:02:41,420 And there's a story map, available through the project page 58 00:02:41,420 --> 00:02:45,230 for this that provides some trends summaries, 59 00:02:45,230 --> 00:02:47,140 which some of them are kind of interesting. 60 00:02:47,140 --> 00:02:49,600 In particular, if you wanna check something out, 61 00:02:49,600 --> 00:02:52,610 I think looking at the snow depth and duration trend maps 62 00:02:52,610 --> 00:02:55,313 are kind of fun to ponder. 63 00:02:57,690 --> 00:03:00,300 The next sampler that I'm gonna give 64 00:03:00,300 --> 00:03:02,730 is on our forest regeneration data network, 65 00:03:02,730 --> 00:03:06,280 I mentioned this in the plenary yesterday. 66 00:03:06,280 --> 00:03:08,850 But as we know, forest regeneration is, 67 00:03:08,850 --> 00:03:10,480 it's kind of a key to understanding 68 00:03:10,480 --> 00:03:12,120 how our forests are gonna respond 69 00:03:12,120 --> 00:03:13,550 and all the different pressures 70 00:03:13,550 --> 00:03:17,510 that are being placed on regeneration in our forests. 71 00:03:17,510 --> 00:03:19,390 Seedlings and saplings of today 72 00:03:19,390 --> 00:03:20,930 are gonna become the forests of tomorrow, 73 00:03:20,930 --> 00:03:23,620 and there's a lot of work going on to collect data 74 00:03:23,620 --> 00:03:25,930 around the region on regeneration, 75 00:03:25,930 --> 00:03:28,980 and how it intersects with things like deer brows, 76 00:03:28,980 --> 00:03:30,690 invasive plants, climate change, 77 00:03:30,690 --> 00:03:32,690 and different types of management. 78 00:03:32,690 --> 00:03:36,320 The region network was put together as a way to 79 00:03:37,840 --> 00:03:40,530 pull together some of these sources of understanding. 80 00:03:40,530 --> 00:03:42,140 As a research and assessment community, 81 00:03:42,140 --> 00:03:44,140 we spend a lot of time collecting information 82 00:03:44,140 --> 00:03:46,270 on seedlings and saplings. 83 00:03:46,270 --> 00:03:50,340 And we wanted to aggregate that and compare these 84 00:03:50,340 --> 00:03:52,920 to try and help people more quickly face 85 00:03:52,920 --> 00:03:55,690 the challenges we have and provide methods 86 00:03:55,690 --> 00:03:57,870 that are gonna be easily compared. 87 00:03:57,870 --> 00:04:01,010 And that is one of the key features of this project 88 00:04:01,010 --> 00:04:04,690 is to aggregate and compare methods. 89 00:04:04,690 --> 00:04:07,500 And we can share those techniques through 90 00:04:07,500 --> 00:04:09,800 the region portal so that people can grab methods 91 00:04:09,800 --> 00:04:10,960 that might be useful for them 92 00:04:10,960 --> 00:04:14,230 and start using them today to set up programs that 93 00:04:14,230 --> 00:04:16,190 are gonna be comparable at the regional scale, 94 00:04:16,190 --> 00:04:19,230 but also make it faster if you're taking data 95 00:04:19,230 --> 00:04:23,140 that's already been collected, to compare those 96 00:04:23,140 --> 00:04:24,500 different data sources 97 00:04:24,500 --> 00:04:26,570 using the method information you have. 98 00:04:26,570 --> 00:04:28,580 So you can quickly narrow down using this tool. 99 00:04:28,580 --> 00:04:30,450 Some studies they'll be compatible for use, 100 00:04:30,450 --> 00:04:33,100 you might have such as looking at sapling mortality 101 00:04:33,100 --> 00:04:35,290 or browse impacts, see the methods, 102 00:04:35,290 --> 00:04:37,520 grab the data and begin work. 103 00:04:37,520 --> 00:04:40,340 We've got 65 programs in there so far, 104 00:04:40,340 --> 00:04:41,900 that have a method assessment, 105 00:04:41,900 --> 00:04:44,660 we compared their utility for Beverly, mortality, 106 00:04:44,660 --> 00:04:48,380 composition, and provide links to those downloads. 107 00:04:48,380 --> 00:04:53,380 And you can use this to find those programs, compare them, 108 00:04:53,420 --> 00:04:56,530 get a snapshot of where we have current information, 109 00:04:56,530 --> 00:04:59,933 and finally get access to those methods and data yourself. 110 00:05:00,830 --> 00:05:02,050 So I recommend checking that out, 111 00:05:02,050 --> 00:05:03,490 if you're interested in looking at 112 00:05:03,490 --> 00:05:05,650 how people are collecting forest regeneration data 113 00:05:05,650 --> 00:05:06,483 in the region. 114 00:05:08,220 --> 00:05:11,580 A third project that we had is what we called 115 00:05:11,580 --> 00:05:13,740 our Urban Pests Project. 116 00:05:13,740 --> 00:05:18,000 We know that urban forests are fundamentally different, 117 00:05:18,000 --> 00:05:19,310 than less developed places 118 00:05:19,310 --> 00:05:21,420 and provide real economic value to residents, 119 00:05:21,420 --> 00:05:24,890 from shade and lower cooling costs, to air pollution, 120 00:05:24,890 --> 00:05:28,540 and cleaner air, air pollution roof on cleaner air. 121 00:05:28,540 --> 00:05:31,450 They're planted at some expense to the municipalities, 122 00:05:31,450 --> 00:05:32,980 they have to withstand a lot of stress 123 00:05:32,980 --> 00:05:34,680 and abuse in urban areas, 124 00:05:34,680 --> 00:05:37,390 and then you add on top of that forest pests and diseases 125 00:05:37,390 --> 00:05:40,830 that can enter urban areas and damage or kill these trees. 126 00:05:40,830 --> 00:05:43,620 But explaining the scale of loss in its services 127 00:05:43,620 --> 00:05:47,320 is not always immediately obvious to decision makers 128 00:05:47,320 --> 00:05:48,683 and the general public. 129 00:05:50,440 --> 00:05:53,540 We know that these trees are providing services 130 00:05:53,540 --> 00:05:55,580 with a real dollar value that we can quantify 131 00:05:55,580 --> 00:05:58,330 such as avoided runoff, carbon sequestration, 132 00:05:58,330 --> 00:06:01,270 pollution removal, and in towns and cities 133 00:06:01,270 --> 00:06:03,180 where we can explain that potential damage 134 00:06:03,180 --> 00:06:04,590 in terms of dollars and cents, 135 00:06:04,590 --> 00:06:07,230 we can make it clear what risks are worth focusing on 136 00:06:07,230 --> 00:06:09,260 and why trees should really be considered 137 00:06:09,260 --> 00:06:12,630 as part of urban landscape management and the budget. 138 00:06:12,630 --> 00:06:15,460 Tree inventories are being gathered around the region 139 00:06:15,460 --> 00:06:18,200 to collect data for a variety of reasons 140 00:06:18,200 --> 00:06:22,530 from management uses to questions about what the city has, 141 00:06:22,530 --> 00:06:24,770 and these provide a window into that 142 00:06:24,770 --> 00:06:27,283 potential economic value that's at risk. 143 00:06:28,200 --> 00:06:32,300 So to try and support municipalities 144 00:06:32,300 --> 00:06:34,610 and decision makers and conservation commissions, 145 00:06:34,610 --> 00:06:38,640 we compiled 205 inventories from around the region, 146 00:06:38,640 --> 00:06:42,820 88 of, with these we can combine them together 147 00:06:42,820 --> 00:06:47,820 to create 88 economic assessments for 88 municipalities. 148 00:06:47,880 --> 00:06:50,520 And this is a standard analysis of the service values 149 00:06:50,520 --> 00:06:53,320 at risk to different key pests. 150 00:06:53,320 --> 00:06:57,140 And this economic valuation, 151 00:06:57,140 --> 00:06:59,260 or economic risk potential, 152 00:06:59,260 --> 00:07:02,383 is communicated and shared in several ways. 153 00:07:03,490 --> 00:07:05,360 People can browse a map 154 00:07:05,360 --> 00:07:07,900 to see where there's tree inventory data available. 155 00:07:07,900 --> 00:07:09,520 This shows the municipalities 156 00:07:09,520 --> 00:07:12,120 covered by tree inventories we could access 157 00:07:12,120 --> 00:07:14,100 in our first round of work on this, 158 00:07:14,100 --> 00:07:15,700 you can browse an area, 159 00:07:15,700 --> 00:07:18,350 you can click on an area of 160 00:07:19,510 --> 00:07:24,090 a polygon to find out what tree inventory data it does have, 161 00:07:24,090 --> 00:07:26,720 and you can also get access to fact sheets. 162 00:07:26,720 --> 00:07:28,950 And these fact sheets will provide 163 00:07:28,950 --> 00:07:32,710 an estimated per community of the risks they might face. 164 00:07:32,710 --> 00:07:34,630 So here is an example where 165 00:07:34,630 --> 00:07:38,560 a community has 102 host trees for emerald ash borer 166 00:07:38,560 --> 00:07:39,893 in its inventory. 167 00:07:40,780 --> 00:07:43,730 And a total annual value being provided 168 00:07:43,730 --> 00:07:47,550 from just those 102 trees is $72. 169 00:07:47,550 --> 00:07:50,060 It's not a whole lot, but it's only 102 trees. 170 00:07:50,060 --> 00:07:52,050 And together replacing those trees 171 00:07:52,050 --> 00:07:54,200 at the current size that they represent, 172 00:07:54,200 --> 00:07:56,530 there's a significant amount of money 173 00:07:56,530 --> 00:07:58,660 for a smaller municipality. 174 00:07:58,660 --> 00:08:01,620 So these breakdowns are provided for each of the, 175 00:08:01,620 --> 00:08:03,940 up to four pests per city. 176 00:08:03,940 --> 00:08:05,820 And it can make it easier to quickly communicate 177 00:08:05,820 --> 00:08:07,610 why this pest might be of interest 178 00:08:07,610 --> 00:08:09,573 in a particular municipality. 179 00:08:12,770 --> 00:08:16,640 The fourth project I wanted to talk about 180 00:08:16,640 --> 00:08:18,470 is our data rescue effort. 181 00:08:18,470 --> 00:08:21,130 We actually did talk about this last year, 182 00:08:21,130 --> 00:08:23,920 at the FEMC conference, 183 00:08:23,920 --> 00:08:26,560 but I just wanted to bring it back up because I think we, 184 00:08:26,560 --> 00:08:28,570 we keep seeing this need come up. 185 00:08:28,570 --> 00:08:31,240 Rescuing data can help fill gaps. 186 00:08:31,240 --> 00:08:33,440 Monitoring is more than just a data that results, 187 00:08:33,440 --> 00:08:35,870 it also has to do with the people and choices 188 00:08:35,870 --> 00:08:37,080 that were made, 189 00:08:37,080 --> 00:08:41,660 and it's also more than just the actual numbers themselves. 190 00:08:41,660 --> 00:08:43,240 It's what we know about them, 191 00:08:43,240 --> 00:08:45,720 and that information gets easy to leave behind. 192 00:08:45,720 --> 00:08:48,360 As our sophistication improves, 193 00:08:48,360 --> 00:08:50,700 we kind of start to move on to the next thing 194 00:08:50,700 --> 00:08:52,480 and it becomes harder and harder to go back 195 00:08:52,480 --> 00:08:54,750 and bring those old datasets forward. 196 00:08:54,750 --> 00:08:57,390 We know if we don't, we start getting holes in our puzzle, 197 00:08:57,390 --> 00:09:00,120 we lose information and institutional knowledge 198 00:09:00,120 --> 00:09:01,713 that could give data meaning. 199 00:09:03,110 --> 00:09:06,350 This data comes in all formats from paper to old discs, 200 00:09:06,350 --> 00:09:09,290 to outdated files, and there's really no, 201 00:09:09,290 --> 00:09:11,480 there's more than we can save unfortunately. 202 00:09:11,480 --> 00:09:15,330 So we're really at the mercy of some sort of prioritization. 203 00:09:15,330 --> 00:09:17,510 When FEMC compiled data rescue, 204 00:09:17,510 --> 00:09:19,460 did its sets in data rescue, 205 00:09:19,460 --> 00:09:22,850 we looked at the criteria that might make it 206 00:09:22,850 --> 00:09:24,390 meaningful to rescue it. 207 00:09:24,390 --> 00:09:26,510 Is it applicable to current issues? 208 00:09:26,510 --> 00:09:29,620 How rigorous is the study of the repeated measures, 209 00:09:29,620 --> 00:09:32,220 so we can look at some measure of change over time? 210 00:09:32,220 --> 00:09:34,820 And how significant is the risk of loss? 211 00:09:34,820 --> 00:09:37,760 Now is there an immediate or near term problem? 212 00:09:37,760 --> 00:09:40,350 Is this data sitting in a box out in a shed, 213 00:09:40,350 --> 00:09:41,693 that's exposed to rain? 214 00:09:43,590 --> 00:09:47,820 So far within the FEMC effort, 215 00:09:47,820 --> 00:09:50,230 we identified a much larger list of 216 00:09:50,230 --> 00:09:51,833 potential datasets at risk, 217 00:09:51,833 --> 00:09:54,880 compiled some information on 218 00:09:56,830 --> 00:10:00,120 the relative value or priority 219 00:10:00,120 --> 00:10:03,790 for these data sets and then rescued 178 items, 220 00:10:03,790 --> 00:10:08,490 these included upgrading file formats or scanning paper, 221 00:10:08,490 --> 00:10:10,610 in some cases, actually extracting data 222 00:10:10,610 --> 00:10:14,223 out of scan information, so that it's easier to start using. 223 00:10:15,920 --> 00:10:19,560 We also realized an opportunity to collaborate 224 00:10:19,560 --> 00:10:20,480 with this project. 225 00:10:20,480 --> 00:10:22,610 So we've worked with a couple of partners 226 00:10:22,610 --> 00:10:26,670 to get them engaged in data rescue and data preservation 227 00:10:26,670 --> 00:10:28,920 with their particular constituency. 228 00:10:28,920 --> 00:10:30,620 For example, in the Catskill region, 229 00:10:30,620 --> 00:10:32,060 the Catskill Science Collaborative 230 00:10:32,060 --> 00:10:35,410 has been digitizing an extensive set of field history notes. 231 00:10:35,410 --> 00:10:37,410 We were able to kind of jumpstart the process, 232 00:10:37,410 --> 00:10:39,610 set up a system, but then they took it over 233 00:10:39,610 --> 00:10:41,860 and they've been running with it ever since. 234 00:10:41,860 --> 00:10:45,170 So that initial effort by FEMC now is rolling along 235 00:10:45,170 --> 00:10:47,100 with an independent organization, 236 00:10:47,100 --> 00:10:48,970 continuing to build up a data record 237 00:10:48,970 --> 00:10:51,623 that spans back almost 60 years. 238 00:10:52,480 --> 00:10:54,723 And we know there's still more to go. 239 00:10:55,970 --> 00:10:57,080 And there's more that we can do 240 00:10:57,080 --> 00:10:58,330 and more than any of us can do 241 00:10:58,330 --> 00:11:00,140 probably with the resources we have 242 00:11:00,140 --> 00:11:02,930 and all the other priorities that take our time, 243 00:11:02,930 --> 00:11:04,920 but this inventory is there. 244 00:11:04,920 --> 00:11:08,150 And I just wanna encourage you just to keep it in mind, 245 00:11:08,150 --> 00:11:10,790 when you're thinking about how to collect something new, 246 00:11:10,790 --> 00:11:13,130 it might already have been done somewhere else, 247 00:11:13,130 --> 00:11:15,080 there might be a source of baseline data 248 00:11:15,080 --> 00:11:17,360 that just needs a little bit of love to get 249 00:11:17,360 --> 00:11:19,830 into a format you can use today. 250 00:11:19,830 --> 00:11:22,030 So do check out the inventory 251 00:11:22,030 --> 00:11:24,310 and take a look at what we might have out there 252 00:11:24,310 --> 00:11:26,793 that could be useful in your work. 253 00:11:29,070 --> 00:11:31,900 So why rescue data? 254 00:11:31,900 --> 00:11:34,030 Hopefully it's not, it's fairly obvious, 255 00:11:34,030 --> 00:11:36,453 we can extend our data series back in time, 256 00:11:37,310 --> 00:11:39,360 making it easier to detect subtle changes 257 00:11:39,360 --> 00:11:41,060 or identify new trends, 258 00:11:41,060 --> 00:11:42,650 because we're not starting from year zero. 259 00:11:42,650 --> 00:11:47,650 And most importantly, we expand our resources for tracking 260 00:11:47,670 --> 00:11:50,530 and tackling ecosystem management challenges ahead, 261 00:11:50,530 --> 00:11:53,130 by having a better and more complete information, 262 00:11:53,130 --> 00:11:55,900 about what has happened in the past. 263 00:11:55,900 --> 00:11:59,160 So if you're interested in learning more about data rescue 264 00:11:59,160 --> 00:12:01,310 or getting involved with some of our data rescue 265 00:12:01,310 --> 00:12:04,660 and preservation efforts, we would love to chat with you. 266 00:12:04,660 --> 00:12:06,450 And with that, I will end 267 00:12:06,450 --> 00:12:08,570 and I know blew through a lot, 268 00:12:08,570 --> 00:12:09,940 so I'd be happy to answer any questions 269 00:12:09,940 --> 00:12:13,253 if you want more details on any of those tools, thank you. 270 00:12:17,630 --> 00:12:20,563 - Well, Sarah Nelson wants the link for the story map. 271 00:12:20,563 --> 00:12:21,720 (laughs). 272 00:12:21,720 --> 00:12:22,553 - I'm on it. 273 00:12:31,420 --> 00:12:35,480 And that link has listed overall information for the project 274 00:12:35,480 --> 00:12:38,883 and you can access the story map further down. 275 00:12:53,530 --> 00:12:55,590 And while we're talking about the story map, 276 00:12:55,590 --> 00:12:57,880 I think one piece that doesn't, 277 00:12:57,880 --> 00:12:59,800 maybe didn't get emphasized very clearly 278 00:12:59,800 --> 00:13:02,980 in the kind of high level, is that we 279 00:13:02,980 --> 00:13:04,190 did kind of two different things. 280 00:13:04,190 --> 00:13:06,300 One is to actually extract data 281 00:13:06,300 --> 00:13:08,750 down to just the same coordinate system projection 282 00:13:08,750 --> 00:13:12,680 and grid cell alignment for every input that we looked at, 283 00:13:12,680 --> 00:13:14,600 but we also computed some trend information 284 00:13:14,600 --> 00:13:16,290 when there was multi-year data, 285 00:13:16,290 --> 00:13:18,800 so that's kind of a new data product 286 00:13:18,800 --> 00:13:20,440 that came out of that, 287 00:13:20,440 --> 00:13:21,290 so you can kind of get either, 288 00:13:21,290 --> 00:13:23,170 you can get the initial each year data 289 00:13:23,170 --> 00:13:24,620 or you can get the trend data 290 00:13:25,470 --> 00:13:27,723 that arises from those yearly series. 291 00:13:35,460 --> 00:13:39,387 - Jerry Carlson asked, "Do you have a graphic 292 00:13:39,387 --> 00:13:41,787 "about the increase in users of your service 293 00:13:41,787 --> 00:13:43,297 "over the past few years?" 294 00:13:46,980 --> 00:13:49,340 - The only graphic I would probably be able to pull up 295 00:13:49,340 --> 00:13:51,660 in the five minutes we have looked at are 296 00:13:53,170 --> 00:13:55,500 overall website usage, 297 00:13:55,500 --> 00:13:57,220 which is not a great proxy 298 00:13:57,220 --> 00:14:00,430 for the specific services and tools. 299 00:14:00,430 --> 00:14:02,490 I can speak to it a little bit. 300 00:14:02,490 --> 00:14:07,240 Most of our online services are relatively new. 301 00:14:07,240 --> 00:14:11,560 We do see mostly initial upticks 302 00:14:11,560 --> 00:14:14,793 around times we do presentations like these or webinars, 303 00:14:16,020 --> 00:14:18,100 more generally, we've got increasing users 304 00:14:18,100 --> 00:14:20,470 of the Data Archive, is one area we've seen steady growth, 305 00:14:20,470 --> 00:14:21,850 so people are signing up for it, 306 00:14:21,850 --> 00:14:24,350 putting data into the Data Archive. 307 00:14:24,350 --> 00:14:26,270 And we continue to have more and more 308 00:14:26,270 --> 00:14:28,420 fee for service projects, 309 00:14:28,420 --> 00:14:31,550 building a specific data visualization tool for a website 310 00:14:31,550 --> 00:14:35,663 or doing a bit of digitization work as a service. 311 00:14:42,110 --> 00:14:43,330 But Jerry, if there's any other 312 00:14:43,330 --> 00:14:45,182 kind of specific metrics you're after, 313 00:14:45,182 --> 00:14:48,903 I can follow up with you so I can add some more detail here. 314 00:14:51,640 --> 00:14:56,627 - Okay, Liam Fitsum, "Are all these data sets available 315 00:14:56,627 --> 00:14:57,737 "on your website? 316 00:14:57,737 --> 00:15:01,847 "And is there a clearinghouse or main source of datasets use 317 00:15:01,847 --> 00:15:04,297 "for an ecological forest management, 318 00:15:04,297 --> 00:15:07,197 "especially for laying people in citizen scientists." 319 00:15:11,090 --> 00:15:13,330 - Yes, so the first, 320 00:15:13,330 --> 00:15:15,080 answer the first question is absolutely, 321 00:15:15,080 --> 00:15:17,120 I'd encourage you to check out our Data Archive, 322 00:15:17,120 --> 00:15:19,970 and I'll drop a link into the chat in a second for that. 323 00:15:19,970 --> 00:15:22,090 It is just a list of all the datasets 324 00:15:22,090 --> 00:15:24,230 that we currently house or make available, 325 00:15:24,230 --> 00:15:25,900 or in some cases, links to datasets 326 00:15:25,900 --> 00:15:27,810 that others have that might be useful 327 00:15:27,810 --> 00:15:30,563 and relevant to people in our region. 328 00:15:31,400 --> 00:15:36,380 You can filter that down by types of data categories, 329 00:15:36,380 --> 00:15:39,810 whether it's air pollution, or forest pests or water, 330 00:15:39,810 --> 00:15:42,490 you can kind of cut the Data Archive up in different ways. 331 00:15:42,490 --> 00:15:44,290 But that's definitely a great place to browse. 332 00:15:44,290 --> 00:15:46,033 However, it's all about the data. 333 00:15:46,910 --> 00:15:51,310 For tools that really help kind of make that data, 334 00:15:51,310 --> 00:15:53,160 like extract information out of that data, 335 00:15:53,160 --> 00:15:54,090 I would definitely recommend 336 00:15:54,090 --> 00:15:56,560 the Vermont Indicators Dashboard, 337 00:15:56,560 --> 00:16:01,560 it's a compilation of 34 key data sets 338 00:16:02,120 --> 00:16:03,960 that have long term trend data, 339 00:16:03,960 --> 00:16:06,410 about how forests are doing in Vermont. 340 00:16:06,410 --> 00:16:09,480 So that takes the data and creates kind of a trend analysis 341 00:16:09,480 --> 00:16:10,900 and this year's score, 342 00:16:10,900 --> 00:16:14,530 how well are we doing this year on a red, yellow green scale 343 00:16:14,530 --> 00:16:16,180 for those data sets. 344 00:16:16,180 --> 00:16:18,610 So I think that's a good snapshot of forest condition 345 00:16:18,610 --> 00:16:20,880 that links to the data underneath it. 346 00:16:20,880 --> 00:16:22,150 And we'll be having dashboards 347 00:16:22,150 --> 00:16:24,020 for New York and New Hampshire stood up here 348 00:16:24,020 --> 00:16:25,220 pretty soon, I'm hoping. 349 00:16:27,320 --> 00:16:29,340 - Okay, next question. 350 00:16:29,340 --> 00:16:33,727 David Brin, "Water quality is impacted significantly 351 00:16:33,727 --> 00:16:35,227 "by access roads. 352 00:16:35,227 --> 00:16:37,047 "Is there a data set on the existing 353 00:16:37,047 --> 00:16:39,567 "woodland access networks and their compliance 354 00:16:39,567 --> 00:16:43,207 "with UPMs, AMPs or OCPs?" 355 00:16:45,350 --> 00:16:48,960 - So I am not the best placed to answer that 356 00:16:48,960 --> 00:16:51,820 except to say that I know that that's an issue that 357 00:16:51,820 --> 00:16:53,570 is both a focus of research 358 00:16:53,570 --> 00:16:55,853 and kind of management monitoring. 359 00:16:57,210 --> 00:16:58,483 So I know that, 360 00:17:00,380 --> 00:17:02,480 I think that would really come down to 361 00:17:02,480 --> 00:17:05,140 the type of ownership you're dealing with 362 00:17:05,140 --> 00:17:06,810 and the level of reclassification. 363 00:17:06,810 --> 00:17:09,190 So it might be more available at some ownerships 364 00:17:09,190 --> 00:17:10,930 and others, but that's not something that I'm aware, 365 00:17:10,930 --> 00:17:12,350 something that FEMC has, 366 00:17:12,350 --> 00:17:14,870 and I'm not certain that there's a single place 367 00:17:14,870 --> 00:17:16,223 to gather that information.