1 00:00:04,916 --> 00:00:05,583 Welcome to 2 00:00:05,583 --> 00:00:10,958 the EROS User Experience webinar series, where we talk to staff at EROS 3 00:00:10,958 --> 00:00:16,041 to learn more about the data, tools and services coming out of the USGS 4 00:00:16,083 --> 00:00:20,041 Earth Resources Observation Science or EROS Center. 5 00:00:20,583 --> 00:00:23,708 Today's webinar is titled RCMAP Long-term 6 00:00:23,708 --> 00:00:27,125 and Near-Real time Rangeland Vegetation Monitoring. 7 00:00:27,208 --> 00:00:31,208 I'm your host, Danielle Golon, the remote sensing user services 8 00:00:31,208 --> 00:00:34,333 lead here at EROS in Sioux Falls, South Dakota. 9 00:00:34,375 --> 00:00:36,416 The time is currently 12 p.m. central. 10 00:00:36,416 --> 00:00:38,125 So let's get started. 11 00:00:38,125 --> 00:00:42,000 First, a few logistics to ensure the best audio experience. 12 00:00:42,041 --> 00:00:44,333 All participants have been muted. 13 00:00:44,333 --> 00:00:47,291 If you have any questions or comments during the webinar, please 14 00:00:47,291 --> 00:00:50,833 use the Q&A feature, which can be found under the more settings 15 00:00:50,833 --> 00:00:54,750 if you do not see it currently or if this feature does not work for you. 16 00:00:54,791 --> 00:00:58,000 Please feel free to enter your questions or feedback into the chat, 17 00:00:58,000 --> 00:01:01,166 and we will address the questions and feedback at the end of the webinar. 18 00:01:01,375 --> 00:01:05,375 If you cannot use the Q&A feature or the chat due to your organization 19 00:01:05,375 --> 00:01:10,541 settings, please feel free to email your questions to custserv@USGS.gov. 20 00:01:10,958 --> 00:01:20,875 That is custserv@usgs.gov and we will answer them there. 21 00:01:21,458 --> 00:01:23,416 Today's webinar is being recorded. 22 00:01:23,416 --> 00:01:24,916 The recording will be available 23 00:01:24,916 --> 00:01:28,875 later on the USGS EROS User Experience webinar webpage. 24 00:01:28,916 --> 00:01:32,916 We'll post a link to that webpage in the chat later on, and today's webinar 25 00:01:32,916 --> 00:01:36,708 will consist of a presentation and then a question and answer session. 26 00:01:37,250 --> 00:01:39,458 Today's speaker is Matthew Rigge. 27 00:01:39,458 --> 00:01:41,458 Matthew is an ecologist with the U.S. 28 00:01:41,458 --> 00:01:44,083 Geological Survey here at EROS. 29 00:01:44,083 --> 00:01:46,833 His work focuses on quantifying the current 30 00:01:46,833 --> 00:01:50,875 and historical condition of rangelands across the western United States. 31 00:01:51,208 --> 00:01:54,625 He accomplishes this by producing and analyzing fractional 32 00:01:54,625 --> 00:01:58,375 vegetation cover maps derived from remote sensing and AI. 33 00:01:59,000 --> 00:02:03,375 These data sets support investigations into the drivers of change in rangeland 34 00:02:03,375 --> 00:02:08,416 ecosystems, including long term trends, interannual weather variability, 35 00:02:08,583 --> 00:02:11,583 and the effects of land management practices. 36 00:02:11,625 --> 00:02:14,625 We also have several EROS staff members from User Services 37 00:02:14,625 --> 00:02:18,458 and the RCMAP team here at EROS on the line, to help 38 00:02:18,458 --> 00:02:22,375 answer any questions you may have after Matthew has finished his presentation. 39 00:02:22,416 --> 00:02:24,958 Again, please feel free to add your questions or feedback 40 00:02:24,958 --> 00:02:28,958 throughout the webinar using that Q&A feature in teams or the webinar chat. 41 00:02:29,375 --> 00:02:32,083 We'll try to answer all questions within the time allotted, 42 00:02:32,083 --> 00:02:35,333 but if we're not able to address your question during the final Q&A 43 00:02:35,333 --> 00:02:38,416 portion of the webinar, we will follow up with you offline. 44 00:02:38,750 --> 00:02:41,458 And if there's a future webinar topic you would like us to cover. 45 00:02:41,458 --> 00:02:43,833 Please feel free to suggest that as well. 46 00:02:43,833 --> 00:02:47,875 With that, it's my pleasure to introduce today's speaker, Matthew Rigge. 47 00:02:47,958 --> 00:02:49,291 Take it away, Matthew. 48 00:02:49,291 --> 00:02:52,375 Thanks, everyone for taking your lunch hour 49 00:02:52,375 --> 00:02:57,250 or they're about to learn more about RCMAP, the Rangeland Condition 50 00:02:57,250 --> 00:03:00,333 Monitoring Assessment and Projection project. 51 00:03:00,375 --> 00:03:05,958 I'm going to talk today about our long term monitoring data and near real time. 52 00:03:05,958 --> 00:03:09,958 This work is supported by both the BLM, the Bureau of Land Management 53 00:03:09,958 --> 00:03:13,250 and the USGS National Land Imaging Program. 54 00:03:13,333 --> 00:03:18,458 Also would like to acknowledge support from our KBR contract here at EROS. 55 00:03:18,458 --> 00:03:23,958 Brett, Dev, Logan, Trenton, Jordan, Audra and Steve with RCMAP. 56 00:03:24,000 --> 00:03:28,375 Our goal is to capture both gradual and abrupt 57 00:03:28,375 --> 00:03:32,583 changes in rangeland condition over the Landsat time series. 58 00:03:32,916 --> 00:03:35,916 So that's 1985 until present. 59 00:03:36,125 --> 00:03:41,083 A lot of that change is related to interannual weather variation, 60 00:03:41,250 --> 00:03:46,833 but also disturbance, land management practices, all sorts of things 61 00:03:46,833 --> 00:03:51,875 going on in the landscape that are causing change worthy of detection. 62 00:03:51,916 --> 00:03:56,875 The overall goal of the project is that monitoring changes to vegetation 63 00:03:56,875 --> 00:04:00,166 composition in the past, evaluating 64 00:04:00,375 --> 00:04:03,375 the effect of management practices 65 00:04:03,833 --> 00:04:06,333 helps us to better target future 66 00:04:06,333 --> 00:04:09,833 improvements and better understand how the landscape is changing, 67 00:04:09,958 --> 00:04:13,833 ultimately improving wildlife habitat and landscape health. 68 00:04:14,083 --> 00:04:18,458 So that's one of the real emphasis of this project, is to make the data 69 00:04:18,833 --> 00:04:22,500 as tangible to real world applications as possible. 70 00:04:22,500 --> 00:04:26,958 So we do this by combining a series of field observations 71 00:04:26,958 --> 00:04:32,541 with machine learning and AI Landsat data, a bunch of other ancillary data 72 00:04:32,625 --> 00:04:36,333 sets like topography, vegetation indices, etc. 73 00:04:36,625 --> 00:04:40,708 to make a series of ten cover maps 74 00:04:41,041 --> 00:04:44,958 on an annual basis across the western US. 75 00:04:45,250 --> 00:04:50,000 So we have percent cover maps for bare ground litter or basis 76 00:04:50,041 --> 00:04:53,541 perennial, herbaceous annual or basis shrub, 77 00:04:54,125 --> 00:04:56,666 sagebrush, non sagebrush, 78 00:04:56,666 --> 00:04:59,833 shrub tree and the height of shrubs. 79 00:04:59,875 --> 00:05:04,000 So this is an example of what the data would look like for a pixel 80 00:05:04,041 --> 00:05:08,375 on the landscape were operating at the Landsat scale, which is 30 meter 81 00:05:08,666 --> 00:05:12,791 spatial resolution and gives us a pretty robust understanding 82 00:05:12,791 --> 00:05:18,125 of how that pixel has changed through time and helps us to sort of understand 83 00:05:18,291 --> 00:05:21,291 what the driving factors of that change are. 84 00:05:21,291 --> 00:05:24,333 Provide an example here in Montana, 85 00:05:24,375 --> 00:05:29,833 the Bridger Fire, which occurred in 2020, showing the benefit of our sea 86 00:05:29,875 --> 00:05:34,416 map data compared to some other ways of understanding change. 87 00:05:34,416 --> 00:05:37,833 So we have on the top left a Landsat 88 00:05:37,875 --> 00:05:41,250 image pre fire and top right post-fire. 89 00:05:41,708 --> 00:05:44,250 We can clearly see the burn impact. 90 00:05:44,250 --> 00:05:49,666 These darker brown areas within the burn perimeter in red indicating burn. 91 00:05:49,708 --> 00:05:54,666 The image does provide some context into how the landscape has changed. 92 00:05:55,041 --> 00:05:59,666 But looking at the sea map tree cover from the same years 19 and 21, 93 00:06:00,250 --> 00:06:03,541 we can understand more in depth 94 00:06:04,000 --> 00:06:07,125 what the magnitude of that change was. 95 00:06:07,125 --> 00:06:10,541 Also, compared to data like NDVI. 96 00:06:11,375 --> 00:06:14,375 That same fire scar here in Montana, 97 00:06:14,791 --> 00:06:17,791 the fire indicated with that dashed red line, 98 00:06:18,375 --> 00:06:21,916 we can see that the year after fire in 21, 99 00:06:21,958 --> 00:06:26,083 the NDVI was lower than pre fire, 100 00:06:26,250 --> 00:06:28,833 but it quickly recovers and it really doesn't provide 101 00:06:28,833 --> 00:06:33,250 a whole lot of information on how that landscape changed post-fire. 102 00:06:33,500 --> 00:06:36,500 Compare that again to our see map 103 00:06:37,041 --> 00:06:39,375 fire indicated in that dashed red line. 104 00:06:39,375 --> 00:06:44,958 We can see tree cover has gone down a lot post-fire 105 00:06:46,041 --> 00:06:49,083 and or cover has gone up. 106 00:06:49,291 --> 00:06:55,083 So we can understand how the vegetation community has redistributed post-fire. 107 00:06:55,083 --> 00:06:58,916 In this case, with herbicides increasing. 108 00:07:01,375 --> 00:07:02,333 Training data 109 00:07:02,333 --> 00:07:06,666 is really important to produce a model like this. 110 00:07:06,708 --> 00:07:12,416 One of the unique things about our map is our series of high resolution 111 00:07:12,416 --> 00:07:16,666 satellite image sites, where we sent out field 112 00:07:16,666 --> 00:07:22,125 crews to the ground and observe the cover of various patches of vegetation 113 00:07:22,666 --> 00:07:26,333 on different types of geology, all the different 114 00:07:26,333 --> 00:07:30,125 permutations of things that could exist on these image footprints, 115 00:07:30,125 --> 00:07:34,208 and then use those data to predict 116 00:07:34,208 --> 00:07:37,291 a two meter scale, a high resolution scale 117 00:07:37,541 --> 00:07:40,541 map of all the vegetation components I listed before, 118 00:07:41,125 --> 00:07:43,958 and then scale that up to 30m 119 00:07:43,958 --> 00:07:47,125 to serve as a key element of our training data. 120 00:07:47,166 --> 00:07:50,625 So those sites are indicated by all of these red dots here, 121 00:07:50,625 --> 00:07:55,208 which are approximately 15 by 15 kilometer image footprint. 122 00:07:55,250 --> 00:08:00,458 Those would be worldview two, worldview three primarily satellite images. 123 00:08:00,583 --> 00:08:06,625 So the benefit of that approach is that it gives us a huge N 124 00:08:06,750 --> 00:08:12,541 to operate with nearly 69,000,030 meter scale training points to operate with. 125 00:08:12,875 --> 00:08:18,541 With a model like this, we're using a neural net model, really data hungry. 126 00:08:18,541 --> 00:08:21,833 So having a high end is really important here. 127 00:08:21,916 --> 00:08:25,750 We also pull in all of the Bureau 128 00:08:25,750 --> 00:08:30,125 of Land Management aim the analysis inventory and monitoring data 129 00:08:30,166 --> 00:08:33,291 set and their LMF data as well. 130 00:08:33,875 --> 00:08:36,625 So these data are the gold 131 00:08:36,625 --> 00:08:39,916 standard monitoring data collected by the BLM. 132 00:08:40,125 --> 00:08:45,750 Most of those plots are randomly located across the domain in the west western 133 00:08:45,750 --> 00:08:50,958 part of the US, designed to represent essentially a single Landsat pixel 134 00:08:51,583 --> 00:08:54,583 measured with boots on the ground as well. 135 00:08:54,916 --> 00:08:58,833 So that's definitely a key data set we've brought in here as well, 136 00:08:59,333 --> 00:09:03,666 pulling in data from land fire, their public reference database, other 137 00:09:03,791 --> 00:09:09,125 sort of ad hoc additional data sets as we can get Ahold of it from the G. 138 00:09:09,125 --> 00:09:09,416 Biff. 139 00:09:09,416 --> 00:09:13,875 As an example, the Global Biodiversity Information Facility, 140 00:09:14,375 --> 00:09:18,916 some UAV classified imagery 141 00:09:19,125 --> 00:09:22,500 from Aaron Johnston in western South Dakota. 142 00:09:22,583 --> 00:09:26,458 We will take nearly any fractional component 143 00:09:26,458 --> 00:09:30,458 cover field observation as training data. 144 00:09:30,458 --> 00:09:33,833 So if you do have any data available that might fit the bill, 145 00:09:34,125 --> 00:09:35,958 please feel free to reach out altogether. 146 00:09:35,958 --> 00:09:40,375 We've got nearly 70 million training points to choose from, covering 147 00:09:40,666 --> 00:09:47,250 the broad temporal extent and capturing a lot of the spatial variability. 148 00:09:47,291 --> 00:09:51,958 We do note that there is higher density of training data in public lands, 149 00:09:51,958 --> 00:09:56,083 especially in BLM country, as opposed to like the Great Plains, 150 00:09:56,083 --> 00:09:59,083 as an example, where there's simply less public land. 151 00:09:59,125 --> 00:10:03,666 So in RCMAP, we produce two key series of data. 152 00:10:04,500 --> 00:10:08,708 One of them being the RCMAP set of annual products, 153 00:10:08,708 --> 00:10:12,000 those ten cover components I mentioned before, 154 00:10:12,750 --> 00:10:17,333 and the second being a series of RCMAP e.g. 155 00:10:17,625 --> 00:10:21,250 that is exotic annual grass near real time 156 00:10:21,375 --> 00:10:24,416 weekly and also annual products. 157 00:10:24,416 --> 00:10:29,416 So the annual products span the full Landsat archive 158 00:10:29,666 --> 00:10:34,125 going back to 1985, stretching through 2025. 159 00:10:34,291 --> 00:10:38,125 Currently, we have plans in place to extend that through 26 160 00:10:38,166 --> 00:10:42,666 and annual releases updating additional years at the end. 161 00:10:43,041 --> 00:10:44,000 Moving forward. 162 00:10:44,000 --> 00:10:48,750 Ten Cover Components I mentioned before the key input data 163 00:10:48,791 --> 00:10:54,375 here are Landsat ARD derived percentile composite. 164 00:10:54,750 --> 00:10:58,291 So we look across all the clear observations 165 00:10:58,791 --> 00:11:01,708 within each calendar year. 166 00:11:01,708 --> 00:11:04,708 Find the 10th, 50th, and 90th percentile 167 00:11:05,041 --> 00:11:09,625 of all the clear observations in that pixel and derived composites, 168 00:11:09,708 --> 00:11:14,416 the idea there being that those percentiles help us to capture 169 00:11:14,458 --> 00:11:18,708 the range of phenology conditions going on within that year. 170 00:11:18,791 --> 00:11:22,583 Also, pulling in spectral indices, topography features, 171 00:11:23,000 --> 00:11:25,791 and some texture metrics as well. 172 00:11:25,791 --> 00:11:31,208 Already talked about the training data for RCMAP moving over to the EAG side. 173 00:11:31,375 --> 00:11:32,416 The spring. 174 00:11:32,416 --> 00:11:37,666 We completed some major updates to that series of data. 175 00:11:37,708 --> 00:11:40,708 The previous PM of that project, Steve Boyd. 176 00:11:40,708 --> 00:11:45,083 We've been investigating updates to that release that this spring. 177 00:11:45,750 --> 00:11:48,958 So major updates to the methodology. 178 00:11:49,125 --> 00:11:52,541 Got a paper in the works describing those updates, 179 00:11:52,625 --> 00:11:55,625 and those are weekly products 180 00:11:55,750 --> 00:12:01,375 designed to be released within a week of the latest satellite 181 00:12:01,375 --> 00:12:06,625 observation going into the model produce for 2016 through 2026 currently. 182 00:12:06,625 --> 00:12:10,750 So we're in the midst of releasing 2026 data at the moment. 183 00:12:11,416 --> 00:12:14,416 And this data set we produce 184 00:12:14,625 --> 00:12:19,375 component cover similar as the RCMAP products fractional cover 185 00:12:19,708 --> 00:12:22,916 focusing on the herbs component, 186 00:12:23,125 --> 00:12:25,958 especially invasive grasses. 187 00:12:25,958 --> 00:12:28,958 So we have cover of total urban 188 00:12:28,958 --> 00:12:32,500 green and sinister cover, 189 00:12:32,833 --> 00:12:37,458 cheatgrass and medusa head, which are two of the key 190 00:12:37,500 --> 00:12:41,208 invasive grasses impacting a lot of the western United States. 191 00:12:41,875 --> 00:12:43,166 Field brome. 192 00:12:43,166 --> 00:12:47,083 Sandburg's, a native perennial grass that has similar phonology 193 00:12:47,166 --> 00:12:49,875 to many of the exotics, which is Sandburg's bluegrass, 194 00:12:49,875 --> 00:12:56,416 and the total cover of a list of 16 species of exotic annual grasses that are 195 00:12:57,416 --> 00:12:58,708 problematic. 196 00:12:58,708 --> 00:13:03,750 And with the data we're pulling in instead of Landsat data, 197 00:13:04,041 --> 00:13:09,041 we use harmonized Landsat Sentinel data, which gives us better 198 00:13:09,416 --> 00:13:13,750 temporal resolution, allowing us to better capture 199 00:13:14,208 --> 00:13:19,458 the logical patterns across the landscape, which is especially important 200 00:13:19,458 --> 00:13:24,625 for understanding the differences between a site with perennial grasses 201 00:13:24,625 --> 00:13:29,125 versus invasive grasses, where the phenology tends to be insights 202 00:13:29,125 --> 00:13:32,541 with a lot of invasive grasses otherwise similar 203 00:13:32,708 --> 00:13:35,708 inputs to the annual products. 204 00:13:36,000 --> 00:13:38,041 Similar training as well. 205 00:13:38,041 --> 00:13:39,208 I mentioned this before. 206 00:13:39,208 --> 00:13:45,083 Both of these products use neural network models for classification. 207 00:13:45,291 --> 00:13:49,958 Both of these data sets fit within a broader mapping hierarchy. 208 00:13:51,375 --> 00:13:52,541 A lot going on here. 209 00:13:52,541 --> 00:13:54,250 I'll give the basics. 210 00:13:54,250 --> 00:13:58,833 So in RCMAP we've got five primary components 211 00:13:58,875 --> 00:14:02,791 or basis shrub, tree, bare ground and litter. 212 00:14:03,125 --> 00:14:09,125 The sum of those five will equal 100% cover in each and every pixel 213 00:14:09,291 --> 00:14:14,250 that is enforced through post-processing as needed. 214 00:14:14,625 --> 00:14:19,625 We produce a series of secondary products for some of the primary components 215 00:14:20,083 --> 00:14:23,791 that add relevant floristic detail. 216 00:14:23,791 --> 00:14:27,791 For example, for sage grouse applications, understanding 217 00:14:28,083 --> 00:14:33,250 what portion of the shrub component is sagebrush is highly relevant. 218 00:14:34,500 --> 00:14:35,958 Those nest 219 00:14:35,958 --> 00:14:38,958 within their respective primary component. 220 00:14:39,208 --> 00:14:43,708 So on the total or basis side, we separate broadly into annuals and perennials, 221 00:14:44,500 --> 00:14:48,833 and these weekly and annual map EAG products 222 00:14:48,833 --> 00:14:52,708 all fit within the total or basis hierarchy. 223 00:14:52,875 --> 00:14:56,875 The overall scheme here is to capture 224 00:14:56,958 --> 00:15:02,375 the diversity of the landscape free of contradictions. 225 00:15:02,375 --> 00:15:05,375 And in this hierarchy, 226 00:15:06,583 --> 00:15:08,375 looking at our annual components, 227 00:15:08,375 --> 00:15:12,708 this is our most recent data release from the spring. 228 00:15:12,750 --> 00:15:15,750 Our version eight. 229 00:15:15,916 --> 00:15:18,541 These animations are showing how 230 00:15:18,541 --> 00:15:22,416 the component cover has changed from 1985 231 00:15:22,958 --> 00:15:26,458 until 2025 on an annual time step, 232 00:15:28,375 --> 00:15:30,583 showing that they are quite dynamic, 233 00:15:30,583 --> 00:15:34,291 especially components like bare ground and or basis. 234 00:15:35,375 --> 00:15:38,125 A lot to absorb at this level, 235 00:15:38,125 --> 00:15:41,541 which is why it's nice to condense them 236 00:15:42,041 --> 00:15:45,666 these temporal patterns into trends. 237 00:15:46,125 --> 00:15:49,125 And to me, time is the most interesting 238 00:15:49,166 --> 00:15:52,166 thing going on here with RCMAP. 239 00:15:52,250 --> 00:15:55,250 Understanding how the landscape has changed through time 240 00:15:55,375 --> 00:15:58,458 is really the take home message for all of this. 241 00:15:59,166 --> 00:16:02,416 So what I've done here is summarized 242 00:16:03,000 --> 00:16:07,041 the 1985 to 2025 trend per 243 00:16:07,041 --> 00:16:10,875 component per level three ecoregion, 244 00:16:12,083 --> 00:16:13,541 where ecoregions in 245 00:16:13,541 --> 00:16:17,333 blue have a net decreasing trend 246 00:16:17,583 --> 00:16:20,666 in that component through time, and the opposite 247 00:16:20,666 --> 00:16:23,666 for ecoregions in red. 248 00:16:24,375 --> 00:16:28,458 Some of the big stories here are related to 249 00:16:28,458 --> 00:16:32,375 annual basis, which would include invasive grasses. 250 00:16:32,916 --> 00:16:35,500 That problem is expanding 251 00:16:35,500 --> 00:16:38,500 across much of the West and the Great Basin. 252 00:16:38,958 --> 00:16:42,833 Some new areas of invasion of annual grasses 253 00:16:44,166 --> 00:16:47,166 into Montana and Wyoming as well. 254 00:16:47,500 --> 00:16:50,500 And that increase in annual 255 00:16:50,500 --> 00:16:54,791 invasive grasses is driving an increase in the fire regime, 256 00:16:55,666 --> 00:17:00,041 leading to more burned area, which results in a declining 257 00:17:00,083 --> 00:17:04,250 trend in sagebrush since 85 across 258 00:17:04,250 --> 00:17:07,250 most of the West. 259 00:17:07,375 --> 00:17:10,250 And also on the flip side, 260 00:17:10,250 --> 00:17:14,333 an increasing trend in herb cover overall 261 00:17:15,791 --> 00:17:19,708 as the grasses expand in burned areas. 262 00:17:19,750 --> 00:17:22,750 A lot of that increase in is related 263 00:17:22,958 --> 00:17:26,458 to increases in the annual portion of that. 264 00:17:26,458 --> 00:17:28,625 So not so great news. 265 00:17:28,625 --> 00:17:31,041 And another side effect of all of this 266 00:17:31,041 --> 00:17:34,166 is a declining pattern in bare ground. 267 00:17:34,500 --> 00:17:38,458 So the sagebrush biome in particular typically 268 00:17:38,458 --> 00:17:44,333 had a lot of bare ground between the inner spaces of shrubs. 269 00:17:44,375 --> 00:17:47,375 After invasion and following fire. 270 00:17:47,375 --> 00:17:51,500 Those spaces get filled in with invasive grasses, often 271 00:17:51,958 --> 00:17:55,375 leading to a declining pattern in bare ground. 272 00:17:56,375 --> 00:17:59,375 Another interesting pattern to note here 273 00:17:59,791 --> 00:18:02,791 in the Great Plains, this is really well documented. 274 00:18:03,666 --> 00:18:06,875 There's an increasing pattern in tree cover, 275 00:18:07,958 --> 00:18:12,625 especially in the southern portion of the Great Plains, as trees, 276 00:18:12,666 --> 00:18:17,708 especially eastern red cedar, are expanding due to fire suppression 277 00:18:17,708 --> 00:18:20,708 and other management impacts. 278 00:18:21,541 --> 00:18:24,541 How is Arcee map data been used in the past? 279 00:18:24,666 --> 00:18:27,083 These are examples from the literature. 280 00:18:27,083 --> 00:18:32,541 Lots of diverse applications of the data in wildlife management, 281 00:18:32,708 --> 00:18:37,291 climate analysis, work on energy development, all sorts of things. 282 00:18:37,291 --> 00:18:40,291 I think my favorite is probably a study on lampreys 283 00:18:40,291 --> 00:18:43,500 in the Goose Lake Basin in Oregon. 284 00:18:43,666 --> 00:18:47,875 There's a broad array of applications for these sort of data. 285 00:18:48,333 --> 00:18:50,875 I'm going to focus on three today. 286 00:18:50,875 --> 00:18:55,375 So mentioned the tree cover expansion in the Great Plains. 287 00:18:55,458 --> 00:18:58,000 This is an example of exactly that pattern. 288 00:18:58,000 --> 00:19:01,750 So in southwest Nebraska here 289 00:19:03,375 --> 00:19:06,375 I've got the tree cover slope 290 00:19:06,541 --> 00:19:09,541 between 85 and present 291 00:19:10,666 --> 00:19:11,250 here. 292 00:19:11,250 --> 00:19:15,000 So increases in red and decreases in blue 293 00:19:15,000 --> 00:19:18,291 and or basis cover same pattern 294 00:19:18,875 --> 00:19:21,875 blue showing decreased red showing increase. 295 00:19:22,791 --> 00:19:24,791 So in this area 296 00:19:24,791 --> 00:19:28,458 tree cover has been expanding since 85. 297 00:19:28,833 --> 00:19:32,125 We can see that's the general pattern here in red. 298 00:19:32,291 --> 00:19:37,125 And as one component goes up something else has to go down. 299 00:19:37,458 --> 00:19:40,583 As I mentioned before everything it adds up to 100. 300 00:19:40,666 --> 00:19:42,625 So it's a zero some game. 301 00:19:42,625 --> 00:19:47,166 And in this case what's going down is herb cover grass cover. 302 00:19:47,458 --> 00:19:52,125 The exception to that being this area that burned in 2002, 303 00:19:52,416 --> 00:19:57,541 we can clearly see that impact on the tree cover slope, where that is 304 00:19:57,541 --> 00:20:02,416 an exception to the general rule in this area of increase, 305 00:20:03,125 --> 00:20:07,291 and similarly an increasing trend in our basis. 306 00:20:07,291 --> 00:20:11,166 They're looking at a wildlife management application 307 00:20:11,208 --> 00:20:17,125 in the northern portion of Montana stretching into southern Canada. 308 00:20:17,416 --> 00:20:20,416 We did expand our study area a couple years ago 309 00:20:20,625 --> 00:20:26,500 to capture the sagebrush biome extent into Alberta and Saskatchewan as well. 310 00:20:26,541 --> 00:20:29,291 So Julie Heinrichs et al. 311 00:20:29,291 --> 00:20:32,833 In this case, used our data to understand 312 00:20:32,833 --> 00:20:36,875 long term changes in sagebrush connectivity. 313 00:20:37,000 --> 00:20:42,000 Put those data into a sage grouse resource use analysis model. 314 00:20:42,000 --> 00:20:47,541 Considering that as as one of several factors in that model, ultimately leading 315 00:20:48,000 --> 00:20:50,833 to an understanding of sage grouse 316 00:20:50,833 --> 00:20:53,833 mortality risk in this landscape. 317 00:20:53,916 --> 00:20:59,000 Another wildlife focused case study here, the U.S. 318 00:20:59,000 --> 00:21:04,375 Fish and Wildlife Service leads an effort called the Sagebrush Conservation Design. 319 00:21:04,625 --> 00:21:07,916 This effort aims to 320 00:21:09,041 --> 00:21:11,625 categorize the sagebrush biome 321 00:21:11,625 --> 00:21:14,916 into three priority classes core 322 00:21:15,666 --> 00:21:19,750 sort of the best of the best remaining sagebrush habitat, 323 00:21:19,875 --> 00:21:23,583 where the sagebrush cover is intact, 324 00:21:23,833 --> 00:21:26,833 not very badly fragmented, 325 00:21:27,041 --> 00:21:30,041 and little cover of invasive grasses. 326 00:21:30,750 --> 00:21:35,375 Growth Opportunity area is sort of a moderate degree of intactness. 327 00:21:35,375 --> 00:21:39,166 Maybe some invasives presence in that landscape 328 00:21:39,416 --> 00:21:43,458 and other rangeland areas where the sagebrush habitat 329 00:21:43,500 --> 00:21:47,166 has been badly degraded by invasives, 330 00:21:47,166 --> 00:21:50,333 fire, fragmentation, etc.. 331 00:21:50,375 --> 00:21:55,666 So the key map data both the sagebrush cover or basics 332 00:21:55,833 --> 00:22:00,500 cover a bare ground cover all feed into this classification, 333 00:22:00,541 --> 00:22:06,375 and this helps guide landscape scale management actions. 334 00:22:06,875 --> 00:22:09,583 Recognizing that we have limited 335 00:22:09,583 --> 00:22:14,708 conservation dollars available, we want to apply those dollars 336 00:22:14,708 --> 00:22:17,833 to the places that are most likely 337 00:22:17,833 --> 00:22:21,291 to see the greatest net benefit from them. 338 00:22:22,875 --> 00:22:25,875 Another important thing to note here is 339 00:22:26,791 --> 00:22:30,291 in the most recent version of the sagebrush conservation design, 340 00:22:30,291 --> 00:22:34,541 which is currently in development, the authors have decided 341 00:22:34,541 --> 00:22:39,250 to pull in data from the rangeland analysis platform as well. 342 00:22:39,333 --> 00:22:45,041 The wrap products are similar in many respects to what RCMAP does, 343 00:22:45,083 --> 00:22:50,916 and in this case, the authors of the Sagebrush Conservation Design 344 00:22:51,083 --> 00:22:54,916 found a benefit from doing 345 00:22:54,916 --> 00:22:59,041 a consensus of both our products and the wrap products. 346 00:22:59,041 --> 00:23:02,416 So that's one thing I really want to highlight here, is it's 347 00:23:02,416 --> 00:23:06,625 not a choice of either or necessarily which data set you use. 348 00:23:06,625 --> 00:23:10,916 There is a benefit of, in this case, a geometric 349 00:23:10,958 --> 00:23:14,875 average of both the Wrap and RCMAP products. 350 00:23:14,875 --> 00:23:17,500 I found the same thing in this before. 351 00:23:17,500 --> 00:23:22,416 Definitely less air in a consensus product than a single product by itself. 352 00:23:22,416 --> 00:23:23,625 Speaking of error, 353 00:23:24,875 --> 00:23:26,166 that's one thing 354 00:23:26,166 --> 00:23:30,791 we focus a lot on in RCMAP is accuracy assessment. 355 00:23:30,958 --> 00:23:34,208 So I mentioned the BLM Aim data previously. 356 00:23:34,791 --> 00:23:39,375 We withhold a 10% set 357 00:23:39,416 --> 00:23:43,833 of all of that Aim data for the purposes of validation, 358 00:23:44,041 --> 00:23:48,541 which is about 6600 points in the current iteration of RCMAP, 359 00:23:49,375 --> 00:23:52,166 the rangeland analysis platform folks withhold 360 00:23:52,166 --> 00:23:56,416 that same set of data for cross product comparisons. 361 00:23:56,625 --> 00:24:00,666 Any other project who wants to participate could do so as well. 362 00:24:00,750 --> 00:24:03,833 So in this graphic I am showing 363 00:24:03,958 --> 00:24:07,291 the accuracy stats the arms 364 00:24:07,375 --> 00:24:10,833 which is the root mean squared error on the left and the correlation, 365 00:24:10,916 --> 00:24:15,250 the R value on the right of our component 366 00:24:15,250 --> 00:24:18,916 cover relative to those withheld points. 367 00:24:19,125 --> 00:24:24,500 So the model did not see these points whatsoever in development or version eight 368 00:24:24,791 --> 00:24:29,291 is the most recently released product in May this year. 369 00:24:30,208 --> 00:24:34,791 Version seven was the prior year, six before that, and so on. So 370 00:24:35,750 --> 00:24:36,208 looking 371 00:24:36,208 --> 00:24:41,500 at our relative degree of enhancements through time over the last five years, 372 00:24:42,333 --> 00:24:45,333 so we can see in both our M.Sc. 373 00:24:45,666 --> 00:24:48,083 Where lower values would be better 374 00:24:48,083 --> 00:24:52,375 and in correlation were higher values would be better. 375 00:24:52,833 --> 00:24:55,375 We are definitely trending the right direction. 376 00:24:55,375 --> 00:24:59,791 There are some components that are just easier to produce than others. 377 00:24:59,916 --> 00:25:00,500 Their ground. 378 00:25:00,500 --> 00:25:05,958 As an example, trees an example tend to be more accurate overall, 379 00:25:06,166 --> 00:25:10,375 just a more clear spectral signal with those components 380 00:25:10,375 --> 00:25:14,750 as compared to something like shrub or litter as an example. 381 00:25:14,833 --> 00:25:21,000 So we spent a lot of time improving our products between each iteration, 382 00:25:21,375 --> 00:25:25,333 pulling in new data sets, new modeling techniques, 383 00:25:25,583 --> 00:25:28,375 new training data, etc. 384 00:25:28,375 --> 00:25:32,500 each new iteration of RCMAP is a completely new time series. 385 00:25:32,500 --> 00:25:36,250 So for example, when we do the V8 predictions, 386 00:25:36,375 --> 00:25:39,583 we would redo not just 2025. 387 00:25:39,833 --> 00:25:42,833 We would go back all the way to 1985 because 388 00:25:43,375 --> 00:25:47,708 new training data in 2025 from a new site 389 00:25:48,000 --> 00:25:51,833 would have impacts going all the way back through the time series. 390 00:25:51,916 --> 00:25:56,333 Probably the most common question I get from users is 391 00:25:56,375 --> 00:26:01,541 what is the appropriate spatial scale to apply your product? 392 00:26:01,625 --> 00:26:05,541 And we've always known that bigger is better. 393 00:26:06,166 --> 00:26:08,083 What do I mean by that? 394 00:26:08,083 --> 00:26:12,958 Is the mean of a 1000 acre pasture likely 395 00:26:12,958 --> 00:26:18,125 to be closer to truth than a single random pixel within that pasture? 396 00:26:18,166 --> 00:26:20,666 Yes, but that's not very clear guidance. 397 00:26:20,666 --> 00:26:25,250 So we undertook an analysis to get a better quantitative 398 00:26:25,250 --> 00:26:29,250 idea of what the appropriate scale would be. 399 00:26:29,583 --> 00:26:32,583 We did this by scaling our 400 00:26:32,833 --> 00:26:35,458 high resolution training sites 401 00:26:35,458 --> 00:26:38,416 to a variety of resolutions starting at 30m, 402 00:26:38,416 --> 00:26:42,625 which is the native resolution of our products all the way up to 1500 403 00:26:42,625 --> 00:26:47,791 meter resolution, and compared the accuracy of our products 404 00:26:47,791 --> 00:26:52,708 relative to that training data, which is approximately truth. 405 00:26:53,291 --> 00:26:58,166 And not surprisingly, we found that aggregating 406 00:26:58,208 --> 00:27:02,208 the map predictions to a 90 meter resolution 407 00:27:02,250 --> 00:27:07,708 improves the correlation and RMSE relative to the native 30 meter resolution. 408 00:27:07,708 --> 00:27:13,916 And that affects sort of plateaus as you go beyond about 210 meter resolution. 409 00:27:13,916 --> 00:27:18,375 Just to show that if you can get by with data 410 00:27:18,416 --> 00:27:23,875 at a coarser resolution than 30m, you can produce a more accurate product. 411 00:27:23,875 --> 00:27:26,875 All of this varies by landscape complexity, 412 00:27:26,916 --> 00:27:32,375 how heterogeneous landscape is, what component you're looking at, etc. 413 00:27:32,416 --> 00:27:34,833 but even at a 90 meter scale, 414 00:27:34,833 --> 00:27:38,708 the accuracy is quite a bit better than a 30 meter scale. 415 00:27:38,708 --> 00:27:39,958 And I should also note that 416 00:27:39,958 --> 00:27:44,458 that Aim validation I showed before is at a 30 meter scale. 417 00:27:44,458 --> 00:27:48,041 So we are comparing single pixel truth 418 00:27:48,125 --> 00:27:51,208 tag to our modeled predictions. 419 00:27:51,250 --> 00:27:54,666 Some other important things to consider in terms of accuracy. 420 00:27:54,708 --> 00:27:59,541 First, more common values are predicted more accurately. 421 00:27:59,541 --> 00:28:04,750 If we look at the frequency of a cover value 422 00:28:04,833 --> 00:28:07,958 per component combination versus 423 00:28:08,000 --> 00:28:11,708 the RMS of that cover combination. 424 00:28:11,708 --> 00:28:15,375 In our model predictions, we see a clear pattern here 425 00:28:15,375 --> 00:28:19,291 that the more frequent that cover been is, 426 00:28:19,708 --> 00:28:23,208 the less error there is in that prediction. 427 00:28:23,208 --> 00:28:26,041 So something really common, like a 428 00:28:27,458 --> 00:28:30,375 10% stage brush cover 429 00:28:30,375 --> 00:28:35,750 value, will all else equal be more accurate than something 430 00:28:35,750 --> 00:28:40,416 less common, like a 99% cover sagebrush bin? 431 00:28:40,458 --> 00:28:42,458 That would be very, very uncommon. 432 00:28:42,458 --> 00:28:45,583 Another important thing to think about is the distinction 433 00:28:45,583 --> 00:28:48,916 between absolute error and relative error. 434 00:28:49,000 --> 00:28:51,375 Those can be at odds at times. 435 00:28:51,375 --> 00:28:54,375 For example, looking at bare ground, the peak 436 00:28:54,416 --> 00:28:58,375 RMSE is at about 80% cover, but the peak 437 00:28:58,375 --> 00:29:02,333 relative error is at about to 10% cover. 438 00:29:02,333 --> 00:29:05,875 So considering the ratio of error 439 00:29:06,375 --> 00:29:09,916 of absolute error to the actual predicted value, 440 00:29:10,041 --> 00:29:14,041 aka relative error can be important as well. 441 00:29:14,208 --> 00:29:18,416 Also, should note that our models, like most models, 442 00:29:18,708 --> 00:29:21,458 suffer from regression to the mean. 443 00:29:21,458 --> 00:29:22,958 What does that look like? 444 00:29:22,958 --> 00:29:27,375 It looks like inflation of predicted values at the low end, 445 00:29:27,541 --> 00:29:30,750 and a deflation of values at the high end. 446 00:29:30,833 --> 00:29:33,833 So the range of values can be truncated 447 00:29:33,833 --> 00:29:37,166 relative to training data as an example. 448 00:29:37,208 --> 00:29:41,291 So we have enough withheld Aim data at this point 449 00:29:41,333 --> 00:29:45,166 to look at spatial patterns in accuracy. 450 00:29:45,750 --> 00:29:51,083 In this case I'm looking at independent correlation per level three ecoregion. 451 00:29:51,125 --> 00:29:57,333 Some patterns to note here are the more common that component 452 00:29:57,333 --> 00:30:02,333 is per ecoregion, the more accurate that prediction tends to be. 453 00:30:02,541 --> 00:30:06,416 So as an example, sagebrush cover is very common 454 00:30:06,416 --> 00:30:11,541 in the central Great Plains, much less common in the Mojave Desert, 455 00:30:12,083 --> 00:30:14,875 and our accuracy of sagebrush is higher 456 00:30:14,875 --> 00:30:18,291 in the central Great Basin then in the Mojave Desert. 457 00:30:18,666 --> 00:30:24,208 That's the general pattern that dominates the spatial patterns we see here. 458 00:30:24,250 --> 00:30:30,125 Other things like the background, mineralogy and geology of the site 459 00:30:30,166 --> 00:30:34,791 do complicate our predictions as well, especially in places with lots of bare 460 00:30:34,833 --> 00:30:39,416 ground like portions of the Mojave Desert, Great Basin, etc. 461 00:30:39,458 --> 00:30:44,166 in a lot of cases, the primary thing you see with Landsat imagery 462 00:30:44,166 --> 00:30:50,291 is that background mineralogy, which can, like I said, complicate predictions. 463 00:30:50,458 --> 00:30:50,833 Okay. 464 00:30:50,833 --> 00:30:55,875 Moving over to the other major data set that we produce 465 00:30:56,291 --> 00:30:59,291 are exotic annual grass product 466 00:30:59,666 --> 00:31:03,125 already talked about the invasive grass 467 00:31:03,541 --> 00:31:06,458 acceleration of the fire cycle, 468 00:31:06,458 --> 00:31:09,500 being a key driver of habitat loss 469 00:31:10,041 --> 00:31:14,416 and degradation across a lot of the western portion of the US. 470 00:31:15,708 --> 00:31:18,708 So again, we're using harmonized Landsat Sentinel, 471 00:31:18,833 --> 00:31:23,041 the BLM Aim data, our high resolution training sites 472 00:31:23,125 --> 00:31:27,750 to model weekly cover of basis, 473 00:31:28,375 --> 00:31:33,375 invasive grasses and some species specific maps like cheatgrass 474 00:31:33,375 --> 00:31:38,541 and medusa head near real time classification of cover conditions 475 00:31:38,625 --> 00:31:44,250 helps in rapid decision making related to grazing and fire. 476 00:31:44,250 --> 00:31:46,375 So that's one of our key focuses here. 477 00:31:46,375 --> 00:31:50,708 Again, in releasing these data as quickly as possible 478 00:31:51,125 --> 00:31:55,416 for near real time application mentioned this already. 479 00:31:55,458 --> 00:32:00,458 We did a major overhaul of our mapping methodology in the most recent 480 00:32:00,458 --> 00:32:01,333 data release. 481 00:32:01,333 --> 00:32:06,375 The key problem in the prior version of the data was associating 482 00:32:06,875 --> 00:32:09,625 the yearly Aim observations 483 00:32:09,625 --> 00:32:14,208 with a weekly ndVi curve, resulting in a one 484 00:32:14,208 --> 00:32:18,750 to many relationship instead of a 1 to 1 relationship as we do now. 485 00:32:19,375 --> 00:32:22,375 Therefore, the old product represented 486 00:32:22,458 --> 00:32:27,333 a forecast of peak cover, a forecast that changed 487 00:32:27,750 --> 00:32:30,500 as we got better and better information 488 00:32:30,500 --> 00:32:33,500 towards the peak of each year. 489 00:32:33,541 --> 00:32:38,000 In addition to how we've trained the model, we've expanded 490 00:32:38,000 --> 00:32:41,333 the study area for these products, expanded the mapping window. 491 00:32:41,375 --> 00:32:44,916 Previously, the maps were released March through June. 492 00:32:45,000 --> 00:32:47,833 Now we're going March through September. 493 00:32:47,833 --> 00:32:51,666 We've added additional components, made the data more consistent, 494 00:32:51,750 --> 00:32:54,083 improved input variables to the model. 495 00:32:54,083 --> 00:32:58,416 We are now participating in that 10% withholding aim validation 496 00:32:58,416 --> 00:33:03,375 I talked about before, and offered the data in a lot of new places 497 00:33:03,375 --> 00:33:08,500 in terms of user access like Google Earth Engine, MRC, etc. 498 00:33:09,416 --> 00:33:11,750 all of these changes have 499 00:33:11,750 --> 00:33:15,250 dramatically improved our validation results. 500 00:33:15,333 --> 00:33:21,166 So the top panel here is showing the accuracy of our e g products 501 00:33:21,166 --> 00:33:25,375 relative to that same set of 10% aim withheld points 502 00:33:25,375 --> 00:33:29,958 I talked about before, compared to the accuracy of our prior set of data 503 00:33:30,958 --> 00:33:32,166 total basis, 504 00:33:32,166 --> 00:33:35,666 we did not have that product before, so it can't make that comparison. 505 00:33:36,083 --> 00:33:41,291 But for all the other components, we are significantly better than previously. 506 00:33:41,666 --> 00:33:45,958 Also, the prior version of the data, in this case, it's 507 00:33:45,958 --> 00:33:49,375 not a fully fair comparison because we did 508 00:33:49,375 --> 00:33:53,250 not withhold these points from training the old version. 509 00:33:53,250 --> 00:33:57,375 So it's a cross-validation on the bottom compared to independent validation. 510 00:33:57,375 --> 00:33:58,875 And yet we're still doing better. 511 00:33:58,875 --> 00:34:03,041 So really good news there in terms of improved product accuracy 512 00:34:03,125 --> 00:34:04,958 based on how the model is trained. 513 00:34:04,958 --> 00:34:09,750 Our model will initially predict green cover of all of our components, 514 00:34:09,750 --> 00:34:14,666 and we derive a total and thinnest portion of 515 00:34:15,666 --> 00:34:19,375 cover through a post-processing approach that calculates 516 00:34:19,625 --> 00:34:23,791 maximum year to date green cover as total, 517 00:34:23,791 --> 00:34:30,041 and then cover would be the difference between weekly total and green cover. 518 00:34:30,083 --> 00:34:33,083 Here's an example of what that phenology looks like. 519 00:34:33,750 --> 00:34:36,416 We're total and green are synonymous. 520 00:34:36,416 --> 00:34:41,791 Until we see a decrease in green cover total would stay flat. 521 00:34:41,791 --> 00:34:44,750 In that scenario, senescent would increase. 522 00:34:44,750 --> 00:34:48,500 And then eventually, once vegetation is growth 523 00:34:48,500 --> 00:34:51,875 is done that year, all the cover would go to senescence. 524 00:34:52,958 --> 00:34:54,750 I think this crossing point here, 525 00:34:54,750 --> 00:34:59,375 where green and nest meat and eventually nest is higher 526 00:34:59,375 --> 00:35:04,000 than green, would have important fire implications. 527 00:35:06,708 --> 00:35:08,958 So looking at the weekly time 528 00:35:08,958 --> 00:35:11,958 series here from 2025 529 00:35:12,000 --> 00:35:15,666 for cheatgrass on the left and total e.g. 530 00:35:15,833 --> 00:35:18,875 on the right, we can see a rapid increase 531 00:35:18,875 --> 00:35:22,375 early in the spring and then a plot tailing effect. 532 00:35:22,750 --> 00:35:27,125 So we're looking here at total cover, which would not decrease 533 00:35:28,083 --> 00:35:31,083 throughout the course of the year. 534 00:35:32,500 --> 00:35:32,875 Moving 535 00:35:32,875 --> 00:35:35,875 to a different set here again from 25, 536 00:35:36,125 --> 00:35:39,125 we've got green total and senescent. 537 00:35:39,666 --> 00:35:42,666 Green is going to increase 538 00:35:43,083 --> 00:35:46,083 starting in the south and at low elevation. 539 00:35:46,291 --> 00:35:49,250 And the green will work its way further north 540 00:35:49,250 --> 00:35:52,625 and to higher elevation as the season progresses. 541 00:35:53,541 --> 00:35:58,500 Total or will accumulate and not decline. 542 00:35:59,041 --> 00:36:03,500 And the difference between total and green will go to senescent. 543 00:36:04,375 --> 00:36:06,541 The brown or dead 544 00:36:06,541 --> 00:36:10,125 plant material that was previously alive that year. 545 00:36:11,625 --> 00:36:13,833 Looking at the 546 00:36:13,833 --> 00:36:18,208 patterns across some select ecoregions of our study area. 547 00:36:18,250 --> 00:36:20,291 This is the green component. 548 00:36:20,291 --> 00:36:23,791 So again we would expect an increase and then decrease. 549 00:36:24,666 --> 00:36:28,041 So these patterns follow the expected 550 00:36:28,041 --> 00:36:33,500 phenology here in terms of when the peak green cover is 551 00:36:33,541 --> 00:36:37,791 and the timing of the start and end of season. 552 00:36:38,000 --> 00:36:42,416 Interesting also to compare across years. 553 00:36:42,833 --> 00:36:45,416 So in this case I've plotted the study 554 00:36:45,416 --> 00:36:48,583 area wide means by year 555 00:36:48,791 --> 00:36:52,375 for six of our EEG components showing. 556 00:36:52,541 --> 00:36:54,916 And then the lines are color coded by year. 557 00:36:54,916 --> 00:36:57,916 So focusing on cheatgrass as an example here 558 00:36:59,208 --> 00:37:01,416 we can see there's a lot of variability 559 00:37:01,416 --> 00:37:05,791 between years in terms of when green up occurs. 560 00:37:06,041 --> 00:37:12,041 And the relative amount of cheatgrass is pretty variable, especially compared to 561 00:37:12,041 --> 00:37:17,125 something like total or basis, where the the degree of variability is much lower. 562 00:37:17,916 --> 00:37:22,208 All of this underscores what we see in the literature that cheatgrass 563 00:37:22,500 --> 00:37:27,125 and invasive grass cover in general, tends to be much more dynamic temporarily 564 00:37:27,375 --> 00:37:30,375 than perennials are that e.g., data 565 00:37:30,500 --> 00:37:33,625 reflect fire nearly immediately. 566 00:37:33,875 --> 00:37:35,250 So this is an example. 567 00:37:35,250 --> 00:37:38,250 In California, the cornucopia fire. 568 00:37:38,583 --> 00:37:40,000 And that occurred 569 00:37:41,083 --> 00:37:42,750 about week 28. 570 00:37:42,750 --> 00:37:45,750 Here we can see as soon as that fire occurred 571 00:37:45,750 --> 00:37:50,125 we well, actually even prior to that fire occurring, green was going down 572 00:37:50,375 --> 00:37:53,625 making that eventual fire more likely. 573 00:37:54,083 --> 00:37:57,291 And then after fire, we lose all our green cover. 574 00:37:57,541 --> 00:37:59,583 And that goes to senescence. 575 00:37:59,583 --> 00:38:05,250 That's one caveat in these products, really, the fire would consume that 576 00:38:05,708 --> 00:38:09,375 to cover instead of it going up, it should go down as well. 577 00:38:09,375 --> 00:38:12,375 That's something we're working on in future generations 578 00:38:12,458 --> 00:38:15,875 in terms of improving that post-fire representation. 579 00:38:15,916 --> 00:38:17,208 Change accuracy. 580 00:38:17,208 --> 00:38:22,916 So all the accuracy statistics I've shown so far are comparing the accuracy 581 00:38:22,916 --> 00:38:26,666 of our map, a single date of our map 582 00:38:26,666 --> 00:38:30,583 to a single date of validation data. 583 00:38:30,625 --> 00:38:35,750 The BLM aim database does contain lots of examples of plots 584 00:38:35,750 --> 00:38:41,916 that have been observed multiple times, both across years and even within a year. 585 00:38:41,916 --> 00:38:45,791 So here's an example of an aim point in Nevada 586 00:38:45,833 --> 00:38:50,000 that's been observed six times starting in 2013. 587 00:38:50,666 --> 00:38:53,416 We've got plot photos here showing the conditions 588 00:38:53,416 --> 00:38:56,416 of what that plot looked like through time. 589 00:38:56,500 --> 00:38:59,958 And I've got the Aim data plotted in these orange dots, 590 00:39:00,125 --> 00:39:04,083 the RCMAP components at that same location 591 00:39:05,041 --> 00:39:06,833 with the blue line. 592 00:39:06,833 --> 00:39:11,250 So we can see in both annular basis and in bare ground. 593 00:39:11,875 --> 00:39:15,666 The sea map components do a nice job of capturing 594 00:39:15,750 --> 00:39:19,791 that temporal pattern observed in AME data. 595 00:39:20,375 --> 00:39:24,958 This is an example of within year change from AME data. 596 00:39:25,416 --> 00:39:28,208 This is in the Mojave Desert in Nevada. 597 00:39:28,208 --> 00:39:31,208 Looking at the year 2020, 598 00:39:31,375 --> 00:39:35,375 this site has been observed five times within 2022, 599 00:39:35,416 --> 00:39:39,541 rather in June and September of 22. 600 00:39:39,750 --> 00:39:43,875 Quite a bit of difference even in that shorter time period. 601 00:39:45,208 --> 00:39:49,166 A little hard to see, but there's a patch of cover here 602 00:39:49,166 --> 00:39:53,125 that really is less dense in June, so they must 603 00:39:53,125 --> 00:39:58,583 have had a pretty good monsoon season this year to get more cover. 604 00:39:59,000 --> 00:40:01,416 So in this case we can see the aim. 605 00:40:01,416 --> 00:40:06,541 Observations of urban cover have increased from here 606 00:40:06,541 --> 00:40:10,625 to here, and our predictions have done the same. 607 00:40:10,625 --> 00:40:14,750 They have increased nearly synonymously with AME data. 608 00:40:14,875 --> 00:40:17,958 In this example, if we plot these out through time, 609 00:40:18,000 --> 00:40:23,833 all the course bonding AME observations versus the course week of our 610 00:40:24,875 --> 00:40:25,833 cover, we 611 00:40:25,833 --> 00:40:28,833 see a pretty nice correlation there. 612 00:40:29,041 --> 00:40:31,208 Moving over to Oregon, 613 00:40:31,208 --> 00:40:33,750 focusing on phenology here. 614 00:40:33,750 --> 00:40:38,333 This is an Aim site that was observed in May and June of 2025. 615 00:40:38,958 --> 00:40:43,125 You can see the phenology is much more progressed on the right. 616 00:40:43,583 --> 00:40:48,375 We've lost most of that green cover in this month span, 617 00:40:48,916 --> 00:40:51,500 and our data correspond 618 00:40:51,500 --> 00:40:54,791 pretty well with that in terms of less green cover 619 00:40:54,916 --> 00:40:57,916 in June as compared to May. 620 00:40:58,583 --> 00:41:00,875 One thing to note here is that 621 00:41:00,875 --> 00:41:04,166 satellite products tend to lag reality 622 00:41:04,166 --> 00:41:10,125 by about two weeks, so probably more fair to move this green curve a little bit 623 00:41:10,125 --> 00:41:13,916 to the left in this comparison, which would better align the products. 624 00:41:13,916 --> 00:41:18,541 And if we plot all of these out, there's like 8000 examples of interannual 625 00:41:18,583 --> 00:41:22,958 change in AME data and about a couple 626 00:41:23,000 --> 00:41:25,958 thousand examples within your change. 627 00:41:25,958 --> 00:41:29,791 So focusing on the cover component from RCMAP, 628 00:41:30,166 --> 00:41:34,375 we can see that aim change on the 629 00:41:34,375 --> 00:41:37,875 x axis versus map change on the y axis. 630 00:41:38,541 --> 00:41:41,666 We do a pretty good job of capturing 631 00:41:41,666 --> 00:41:45,791 that Interannual change in the mapping product 632 00:41:46,833 --> 00:41:49,833 also should highlight, as I mentioned before, 633 00:41:49,916 --> 00:41:54,541 the map change slope is less than ame, 634 00:41:55,041 --> 00:41:59,041 meaning that the map change tends to be less 635 00:41:59,041 --> 00:42:02,041 than the real world observed change. 636 00:42:02,916 --> 00:42:06,541 A pretty nice correlation with the interannual 637 00:42:06,541 --> 00:42:10,583 within season change as well with e.g. 638 00:42:10,666 --> 00:42:11,375 cover. 639 00:42:11,375 --> 00:42:17,458 We've done some work with the weekly EEG products to understand 640 00:42:17,666 --> 00:42:22,916 how the landscape condition currently differs from the long term mean. 641 00:42:22,916 --> 00:42:24,666 So what we've done here. 642 00:42:24,666 --> 00:42:27,208 This is an example from 2026. 643 00:42:27,208 --> 00:42:29,458 Week 23. 644 00:42:29,458 --> 00:42:34,333 We took the mean of all prior week 23 in our products. 645 00:42:34,333 --> 00:42:40,250 So the mean of the basis cover from 2016 through 2025. 646 00:42:40,250 --> 00:42:46,041 And then compare that from our 2026 week 647 00:42:46,083 --> 00:42:49,708 23 map and mapped out the spatial patterns here. 648 00:42:49,708 --> 00:42:53,083 So blue meaning we in week 23 of 649 00:42:53,083 --> 00:42:58,083 26 had more cover than the long term mean. 650 00:42:58,833 --> 00:43:03,291 And red being we have less verbatim cover than the long term mean. 651 00:43:03,958 --> 00:43:07,166 So this would be about June of this year. 652 00:43:07,875 --> 00:43:10,916 I did some field work in this part of the world around Gillette, 653 00:43:11,000 --> 00:43:15,833 Wyoming about that time, and indeed it checks out in the field. 654 00:43:15,833 --> 00:43:20,791 It was very, very dry in this part of the world at that time. 655 00:43:22,416 --> 00:43:23,875 Another interesting thing to 656 00:43:23,875 --> 00:43:28,958 point out here is all these blue dots in Oregon, Washington, etc. 657 00:43:29,000 --> 00:43:31,666 are burned areas. 658 00:43:31,666 --> 00:43:35,333 So places where there was tree cover at some point 659 00:43:35,333 --> 00:43:38,833 in that reference period between 16 and 25. 660 00:43:39,041 --> 00:43:41,208 And now we're a post burn. 661 00:43:41,208 --> 00:43:44,208 So or cover has increased 662 00:43:44,375 --> 00:43:47,208 as tree cover was reduced with fire 663 00:43:47,208 --> 00:43:51,458 leading to that positive deviation in herbaceous cover. 664 00:43:52,000 --> 00:43:55,125 So on the left we can see what that looks like temporarily. 665 00:43:56,083 --> 00:43:59,875 This is from 2026 focusing on total or basis. 666 00:44:00,291 --> 00:44:03,625 I've got the long term mean per week plotted in blue 667 00:44:03,666 --> 00:44:07,166 with standard deviation in that shade. 668 00:44:07,833 --> 00:44:12,791 We started the year above normal, probably as a result of a warm 669 00:44:12,791 --> 00:44:16,375 winter across a lot of the West, and as the season progressed, 670 00:44:16,916 --> 00:44:21,625 we eventually went below the long term mean by June 671 00:44:21,666 --> 00:44:25,625 our data both the RCMAP and RCMAP EAG. 672 00:44:25,750 --> 00:44:30,583 The weekly products are available on the MRC Rangeland Viewer, 673 00:44:30,625 --> 00:44:35,666 where you can download custom extents of the products, do time series 674 00:44:35,666 --> 00:44:40,208 plotting by a series of different polygon types, so plot long 675 00:44:40,250 --> 00:44:44,125 term trends by watershed by a lot meant, etc. 676 00:44:44,250 --> 00:44:47,375 the data are also available for static download on MRL. 677 00:44:47,375 --> 00:44:50,666 See we've got w w CS services 678 00:44:50,750 --> 00:44:54,041 on MRC, also on ArcGIS online. 679 00:44:54,125 --> 00:44:57,041 The data are also in the Google Earth 680 00:44:57,041 --> 00:45:00,833 Community Catalog, specifically the awesome Community catalog 681 00:45:00,875 --> 00:45:06,166 there on the Desert Research Institute Climate engine there on Science Base. 682 00:45:06,166 --> 00:45:10,083 We try to make these as broadly accessible as possible 683 00:45:10,125 --> 00:45:11,875 and lots of different venues. 684 00:45:11,875 --> 00:45:17,125 So we are in the midst of releasing 2026 data for the E.g. 685 00:45:17,333 --> 00:45:19,083 side of the project. 686 00:45:19,083 --> 00:45:20,375 We'll continue that through. 687 00:45:20,375 --> 00:45:23,208 Next week is actually our last release in the background. 688 00:45:23,208 --> 00:45:26,875 We've been releasing the 2016 through 2025, E.g. 689 00:45:26,916 --> 00:45:27,375 data. 690 00:45:27,375 --> 00:45:30,750 We've got a paper summarizing the updated e.g. 691 00:45:30,833 --> 00:45:36,291 methods in works, another paper summarizing the entire RCMAP project, 692 00:45:36,333 --> 00:45:39,375 and a third one looking at regional accuracy 693 00:45:39,375 --> 00:45:44,125 patterns of RCMAP will produce a new generation of e.g. 694 00:45:44,166 --> 00:45:46,291 products starting early next year. 695 00:45:46,291 --> 00:45:50,708 Same with a new generation of RCMAP early next year. 696 00:45:50,750 --> 00:45:53,958 We are looking into expanding the number 697 00:45:53,958 --> 00:45:57,000 of unique species we're mapping on the E.g. 698 00:45:57,041 --> 00:46:01,916 side, with some more emphasis on some of the species in the southwest 699 00:46:02,000 --> 00:46:05,750 like red brome, buffel grass, etc. 700 00:46:05,833 --> 00:46:08,916 we're also exploring additional modes 701 00:46:08,916 --> 00:46:14,041 of data input, so specifically hyperspectral data, 702 00:46:14,458 --> 00:46:18,333 SAR data, etc., further improving our products. 703 00:46:18,333 --> 00:46:21,875 And then lastly, this is still in its infancy, 704 00:46:22,000 --> 00:46:25,833 but we are looking into better incorporation 705 00:46:25,833 --> 00:46:30,833 of RCMAP data into both land fire and NLCD. 706 00:46:30,958 --> 00:46:34,500 And with that I am happy to take any questions. 707 00:46:34,500 --> 00:46:38,291 We've got contact information noted here, website link, 708 00:46:38,333 --> 00:46:42,500 viewer link and please reach out with any questions. 709 00:46:42,541 --> 00:46:43,916 Perfect. Thanks, Matthew. 710 00:46:43,916 --> 00:46:44,375 And Matthew 711 00:46:44,375 --> 00:46:45,583 is going to leave this slide up 712 00:46:45,583 --> 00:46:47,458 for the rest of the webinar, just so you can write this down 713 00:46:47,458 --> 00:46:49,208 if you need any of this contact information. 714 00:46:49,208 --> 00:46:53,208 But for those listening in, I have added a few final links to the chat 715 00:46:53,333 --> 00:46:55,500 which include more information about RCMAP 716 00:46:55,500 --> 00:46:58,083 as well as links to sign up for a few of our listeners, 717 00:46:58,083 --> 00:47:02,208 a link to watch the recorded webinars, and a few social media channels 718 00:47:02,208 --> 00:47:03,500 to follow up. You're interested. 719 00:47:03,500 --> 00:47:06,958 I do want to thank Matthew for a really great presentation at this time. 720 00:47:06,958 --> 00:47:11,625 Please add any final questions you have about the data or tools covered today, or 721 00:47:11,625 --> 00:47:15,208 perhaps any future data or tutorial ideas you'd like to see with RCMAP? 722 00:47:15,208 --> 00:47:18,958 Please feel free to add those to the Q&A feature and teams or the chat now 723 00:47:19,041 --> 00:47:22,333 you can also email them into serve at USGS gov. 724 00:47:22,416 --> 00:47:25,000 If the chat and QA feature does not work for you, 725 00:47:25,000 --> 00:47:28,500 but with that, we will transition over to the Q&A portion of the webinar. 726 00:47:28,583 --> 00:47:32,416 The first question we have is from Scott. 727 00:47:32,541 --> 00:47:38,333 How does RCMAP reconcile low temporal or spatial support for a given state? 728 00:47:38,375 --> 00:47:42,583 For example, states east of Colorado or sampled substantially less frequently 729 00:47:42,583 --> 00:47:43,666 or spatially? 730 00:47:43,666 --> 00:47:47,083 Even with 70 million training points, there are magnitudes 731 00:47:47,083 --> 00:47:50,875 more unmonitored pixels and potentially unobserved vegetation. 732 00:47:50,875 --> 00:47:52,541 Community distributions. 733 00:47:52,541 --> 00:47:54,083 Yeah, that's a good question. 734 00:47:54,083 --> 00:47:55,625 The density of training data 735 00:47:55,625 --> 00:47:59,416 is definitely lower in the Great Plains than it is further west. 736 00:47:59,416 --> 00:48:03,625 So we do have a number of our high resolution 737 00:48:03,625 --> 00:48:06,458 satellite sites in the Great Plains. 738 00:48:06,458 --> 00:48:09,458 We've added a few more in Texas, 739 00:48:09,583 --> 00:48:12,625 South Dakota, North Dakota, Nebraska, 740 00:48:12,750 --> 00:48:17,625 and we are investigating always incorporating new lines of data there. 741 00:48:17,625 --> 00:48:20,875 There are some land fire observations in the Great Plains. 742 00:48:20,875 --> 00:48:25,000 We're looking into getting some data from the National Park Service there. 743 00:48:25,041 --> 00:48:27,625 I am a data I believe it's called. 744 00:48:27,625 --> 00:48:31,500 Yes, that is a bit of a weakness that the training 745 00:48:31,500 --> 00:48:34,541 data density is lower in that part of the world. 746 00:48:34,541 --> 00:48:38,333 Also, in general, the training data is less dense 747 00:48:38,375 --> 00:48:42,166 earlier in the time series relative to later in the time series. 748 00:48:42,166 --> 00:48:45,166 The BLM Aim data come online in about 2008. 749 00:48:45,250 --> 00:48:48,666 Our high res site data come online in about 2013. 750 00:48:48,750 --> 00:48:51,875 We do have some data going back through the whole archive 751 00:48:51,916 --> 00:48:54,916 through 85, but it is less dense there as well. 752 00:48:54,916 --> 00:48:57,416 So that is something to note. 753 00:48:57,416 --> 00:48:58,750 All right. Thanks, Matthew. 754 00:48:58,750 --> 00:49:00,875 Our next question comes from Megan. 755 00:49:00,875 --> 00:49:02,041 She had two questions. 756 00:49:02,041 --> 00:49:05,000 The first was if we can share where the webinar will be posted. 757 00:49:05,000 --> 00:49:06,833 So I did add that link to the chat. 758 00:49:06,833 --> 00:49:07,916 You know, she had several partners 759 00:49:07,916 --> 00:49:11,125 that were not able to attend but are very interested in our sea map. 760 00:49:11,125 --> 00:49:12,291 So excited to hear that. 761 00:49:12,291 --> 00:49:15,166 And then Megan also had a question for you, Matthew. 762 00:49:15,166 --> 00:49:18,708 Was there any exploration of changes in non or excuse me, 763 00:49:18,875 --> 00:49:20,083 sorry if I'm mispronouncing that. 764 00:49:20,083 --> 00:49:22,583 Shrub cover and height in the woody encroachment. 765 00:49:22,583 --> 00:49:24,708 Partners in northeast have expressed interest 766 00:49:24,708 --> 00:49:28,125 in both encroachment of trees and shrubs, and climate dependency 767 00:49:28,125 --> 00:49:32,416 and treatment efficacy will likely vary across the functional groups. 768 00:49:32,500 --> 00:49:37,541 Yeah, they are asking about Artemisia, which is the sagebrush genus. 769 00:49:37,708 --> 00:49:41,166 And the question is have we explored non 770 00:49:41,166 --> 00:49:45,208 artemisia changes in the Great Plains. 771 00:49:45,208 --> 00:49:45,791 Was that it. 772 00:49:45,791 --> 00:49:49,625 Was there any exploration of the changes in non artemisia shrub 773 00:49:49,625 --> 00:49:51,958 cover and height in the woody encroachment. 774 00:49:51,958 --> 00:49:54,958 Yes. So and Megan note in the chat in Nebraska. 775 00:49:55,000 --> 00:49:56,041 Okay. Yes. 776 00:49:56,041 --> 00:50:01,666 So that would be captured in our shrub cover 777 00:50:01,791 --> 00:50:06,750 trend in places where there are no sagebrush present. 778 00:50:06,875 --> 00:50:10,750 The shrub cover trend would be that precisely. 779 00:50:10,750 --> 00:50:13,875 So we can see the woody expansion in the Great Plains. 780 00:50:14,000 --> 00:50:19,500 You can kind of see this pattern where it starts off as an increase in shrubs. 781 00:50:19,875 --> 00:50:23,291 What those shrubs really are are juvenile trees. 782 00:50:23,333 --> 00:50:28,375 Trees less than three meters tall would be called shrubs in our product. 783 00:50:28,375 --> 00:50:32,291 And then eventually, once the tree individuals who pass that height 784 00:50:32,333 --> 00:50:36,500 threshold, then that becomes a tree cover trend. 785 00:50:36,791 --> 00:50:39,833 So all of that being said, in the Great Plains, 786 00:50:40,125 --> 00:50:43,708 the first early warning sign of this woody 787 00:50:43,708 --> 00:50:48,458 expansion would typically be increasing trend in shrubs, 788 00:50:48,583 --> 00:50:51,583 which would be, again, those juvenile trees. 789 00:50:51,583 --> 00:50:52,958 All right. Thank you, Matthew. 790 00:50:52,958 --> 00:50:55,833 The next question we have and I will note it is 1:00, 791 00:50:55,833 --> 00:50:58,208 but we're going to extend the webinar for about five more minutes 792 00:50:58,208 --> 00:50:59,958 while we can get through all these questions. 793 00:50:59,958 --> 00:51:02,916 So if you need to hop off. Thank you all for joining us. 794 00:51:02,916 --> 00:51:05,000 But our next question is from Karen. 795 00:51:05,000 --> 00:51:07,333 Have you been able to map yet? 796 00:51:07,333 --> 00:51:10,875 Yeah. It's not a it's another is another. 797 00:51:10,875 --> 00:51:11,208 Yeah. 798 00:51:11,208 --> 00:51:14,583 The botanists like to make pronunciation difficult. 799 00:51:14,625 --> 00:51:17,708 Vince Nata is another important invasive grass. 800 00:51:17,708 --> 00:51:20,375 We have looked into that. 801 00:51:20,375 --> 00:51:25,166 The challenge we're facing there is it is just not very well 802 00:51:25,166 --> 00:51:29,708 represented in the BLM Aim database at this point. 803 00:51:29,875 --> 00:51:33,500 I think there was just a couple hundred maybe observations 804 00:51:33,500 --> 00:51:36,166 total within that Aim database. We are trying. 805 00:51:36,166 --> 00:51:39,583 I mentioned the red brome and buffel 806 00:51:39,583 --> 00:51:43,041 grass being ones that we're going to look into as well. 807 00:51:43,083 --> 00:51:48,500 A lot of our invasive grass species are really Great Basin centric. 808 00:51:48,625 --> 00:51:51,875 The cheatgrass medusa head, but also the field brome 809 00:51:51,875 --> 00:51:55,083 component includes Japanese brome, 810 00:51:55,083 --> 00:51:59,875 which is a pretty common invasive grass across a lot of the Great Plains. 811 00:51:59,958 --> 00:52:03,541 So definitely looking into expanding that list, 812 00:52:03,666 --> 00:52:06,625 I would say the less common 813 00:52:06,625 --> 00:52:10,583 that species is in the training data, the more difficulty 814 00:52:10,625 --> 00:52:14,333 we will have mapping it, but definitely looking into it. 815 00:52:14,375 --> 00:52:15,125 All right. Excellent. 816 00:52:15,125 --> 00:52:16,875 Our next question is 817 00:52:16,875 --> 00:52:20,875 can this product or model be available to use outside of the US? 818 00:52:21,208 --> 00:52:24,125 I want to use it for another one that I'm going 819 00:52:24,125 --> 00:52:27,375 to have to pronounce Pentagon Harlem mapping. 820 00:52:27,458 --> 00:52:29,500 Okay. That one I'm not familiar with. 821 00:52:29,500 --> 00:52:33,125 And no, it's not currently available outside of the US, 822 00:52:33,208 --> 00:52:38,083 aside from that little portion of Canada that's within the sagebrush biome, 823 00:52:38,208 --> 00:52:43,083 we are considering expanding the study area to cover all of Conus. 824 00:52:43,125 --> 00:52:43,833 At least. 825 00:52:43,833 --> 00:52:47,500 I'm not sure if that would cover the users study area of interest. 826 00:52:47,500 --> 00:52:52,750 There are some global fractional cover products available based on Motus. 827 00:52:52,750 --> 00:52:57,458 If I recall correctly, it wouldn't be as detailed as what we do. 828 00:52:57,500 --> 00:53:01,500 It would just be shrub or basis, maybe bare ground, 829 00:53:01,541 --> 00:53:04,541 I think maybe three components and maybe an NPV 830 00:53:04,583 --> 00:53:07,583 and non photosynthetic veg component as well. 831 00:53:07,583 --> 00:53:08,500 And of course 832 00:53:08,500 --> 00:53:13,791 or facial resolution to my knowledge that would be available at a global scale. 833 00:53:13,791 --> 00:53:18,625 But we currently don't have any plans to expand RCMAP at a global level. 834 00:53:18,666 --> 00:53:19,916 Okay. Thank you Matthew. 835 00:53:19,916 --> 00:53:23,625 And our last question is broadly, how do RCMAP data 836 00:53:23,666 --> 00:53:27,541 compare to RAP data or Rangeland Analysis Platform? 837 00:53:27,625 --> 00:53:29,625 Apologies if you covered this and I missed it. 838 00:53:29,625 --> 00:53:31,708 Yeah that's a really great question. 839 00:53:31,708 --> 00:53:33,500 And it's a big topic. 840 00:53:33,500 --> 00:53:38,125 So the rangeland analysis platform data, the RAP data produced by the ARS 841 00:53:38,458 --> 00:53:42,708 and University of Montana and others is similar 842 00:53:42,708 --> 00:53:46,625 in many respects to our data. 843 00:53:46,875 --> 00:53:49,625 The key difference is our training data. 844 00:53:49,625 --> 00:53:54,041 We've got those high resolution sites that are not used in the rangeland 845 00:53:54,041 --> 00:53:55,291 analysis platform. 846 00:53:55,291 --> 00:53:58,291 Our post-processing methods differ. 847 00:53:58,291 --> 00:54:00,500 Some of our modeling methods differ. 848 00:54:00,500 --> 00:54:04,041 All of this difference is good news for users. 849 00:54:04,041 --> 00:54:08,958 It's good that the products are different in that you get more statistical power 850 00:54:09,166 --> 00:54:12,250 in having a different modeling framework. 851 00:54:12,250 --> 00:54:15,333 So generally speaking, the two data 852 00:54:15,375 --> 00:54:20,500 sets are pretty similar, especially the bigger and temporal scale 853 00:54:20,541 --> 00:54:23,541 you look at, the more similar they will become. 854 00:54:23,833 --> 00:54:27,583 There are some important differences that I don't have time to go into. 855 00:54:27,625 --> 00:54:32,875 We do have a paper in the works with rap people as coauthors 856 00:54:32,875 --> 00:54:35,875 talking about some of these differences, so look out for that. 857 00:54:35,875 --> 00:54:40,166 Did also mention earlier that there is benefit from looking 858 00:54:40,166 --> 00:54:44,500 at a consensus of both Rap data and or C map data. 859 00:54:44,500 --> 00:54:48,583 So for example, if shrub cover was of interest, a mean 860 00:54:48,958 --> 00:54:52,416 or some other statistic, considering the data from both RAP 861 00:54:52,416 --> 00:54:56,750 and RCMAP could be of benefit and often is of benefit. 862 00:54:56,791 --> 00:54:57,958 Excellent. Thank you Matthew. 863 00:54:57,958 --> 00:55:01,458 We are at 105 so we are going to conclude the webinar. 864 00:55:01,500 --> 00:55:04,708 Clarinda, I do see that you had a question about some open spaces. 865 00:55:04,708 --> 00:55:07,625 So I'll make sure we touch base with you offline on that one. 866 00:55:07,625 --> 00:55:10,333 But I did want to thank everyone for attending the webinar. 867 00:55:10,333 --> 00:55:14,458 If you have any additional questions later on about see map, the RCMAP Viewer, 868 00:55:14,458 --> 00:55:18,416 or any data, tools or services the USGS EROS provides, 869 00:55:18,500 --> 00:55:21,791 you can always email our team at custserv@USGS.gov. 870 00:55:22,041 --> 00:55:23,083 I'd like to once again 871 00:55:23,083 --> 00:55:26,333 thank our speaker, Matthew for his time and great presentation 872 00:55:26,416 --> 00:55:28,375 and our team in the background that helped answer 873 00:55:28,375 --> 00:55:30,833 some of those great questions you all had today. 874 00:55:30,833 --> 00:55:34,291 If you'd like to be notified of when the next webinar will occur, please 875 00:55:34,291 --> 00:55:36,166 sign up for our EROS User Services 876 00:55:36,166 --> 00:55:39,875 listserv by emailing that custserv@USGS.gov email. 877 00:55:40,166 --> 00:55:42,875 Again, thank you all for taking the time to join us today. 878 00:55:42,875 --> 00:55:44,750 We hope you have a wonderful rest of the day. 879 00:55:44,750 --> 00:55:46,208 We'll see you next time. Thank you.