WEBVTT Kind: captions Language: en 00:00:06.990 --> 00:00:11.010 John Ossanna: Welcome from the U.S. Fish & Wildlife Service National Conservation Training Center 00:00:11.010 --> 00:00:12.160 here in Shepherdstown, West Virginia. 00:00:12.160 --> 00:00:13.910 My name is John Ossanna. 00:00:13.910 --> 00:00:17.900 I'd like to welcome you to our webinar series held in partnership with the U.S. Geological 00:00:17.900 --> 00:00:21.310 Survey of National Climate Change and Wildlife Science Center. 00:00:21.310 --> 00:00:27.289 Today's webinar is titled, "Can Prescribed Fire Help Forests Survive Drought in the Sierra 00:00:27.289 --> 00:00:28.289 Nevada Mountains?" 00:00:28.289 --> 00:00:29.289 Very pertinent obviously. 00:00:29.289 --> 00:00:33.860 Also, this is the last of the drought series, so be on the lookout for the next series we're 00:00:33.860 --> 00:00:35.130 going to be holding. 00:00:35.130 --> 00:00:37.680 I can't believe it's already through with this. 00:00:37.680 --> 00:00:38.680 Let's get started. 00:00:38.680 --> 00:00:43.120 To start things off, please join me in welcoming Shawn Carter with the National Climate Change 00:00:43.120 --> 00:00:47.519 and Wildlife Science Center who will be introducing our speaker today. 00:00:47.519 --> 00:00:48.519 Shawn? 00:00:48.519 --> 00:00:49.940 Shawn Carter: Great, thank you. 00:00:49.940 --> 00:00:52.370 Thanks, everyone, for joining us today. 00:00:52.370 --> 00:01:00.010 Yeah, it's with a little bit of a tight throat and tear in my eye that I welcome you to our 00:01:00.010 --> 00:01:06.200 last installment of this series on ecological drought. 00:01:06.200 --> 00:01:09.850 We have a great talk lined up today. 00:01:09.850 --> 00:01:15.580 I'm happy to introduce Dr. James Thorne, who's adjunct faculty and research scientist at 00:01:15.580 --> 00:01:19.820 the Department of Environmental Science and Policy at UC Davis. 00:01:19.820 --> 00:01:24.009 He has expertise in biogeography, conservation, biology, and ecology. 00:01:24.009 --> 00:01:29.579 Some of his recent work is focused on the success of forest thinning and tree health. 00:01:29.579 --> 00:01:35.079 Also, vulnerability assessments for mammals, trees, and vegetation types in California. 00:01:35.079 --> 00:01:46.420 He has his degrees in ecology from UC Davis and also degrees in geology from UC Santa 00:01:46.420 --> 00:01:50.569 Barbara and environmental studies from UC Santa Cruz. 00:01:50.569 --> 00:01:55.829 Without further ado, the floor is yours, James. 00:01:55.829 --> 00:02:02.490 James Thorne: Thank you so much for the opportunity to present to the group. 00:02:02.490 --> 00:02:04.139 Welcome to everybody online. 00:02:04.139 --> 00:02:08.420 John, Shawn, Elda, thank you for your invitation. 00:02:08.420 --> 00:02:11.840 We'll just dive right in. 00:02:11.840 --> 00:02:21.000 I need to recognize my co PIs and collaborators on this project, including Phil van Mantgem 00:02:21.000 --> 00:02:26.780 from the U.S. Geological Survey and a whole list of people that you see down there at 00:02:26.780 --> 00:02:31.920 the bottom of this particular slide, many of whom, this work would not have been possible 00:02:31.920 --> 00:02:37.620 without their collaboration and good help. 00:02:37.620 --> 00:02:50.140 This presentation comes out of a project that's funded by the Southwest Climate Science Center. 00:02:50.140 --> 00:02:59.390 We wanted to try to take advantage of the drought in California to examine whether forest 00:02:59.390 --> 00:03:08.870 treatments led to better forest condition as the drought progressed. 00:03:08.870 --> 00:03:17.560 As much as this is a results project, or presentation, it's probably going to be more about the project 00:03:17.560 --> 00:03:25.240 itself because we're still in the process of developing our final results. 00:03:25.240 --> 00:03:29.090 It's taking place in California, Sierra Nevada. 00:03:29.090 --> 00:03:44.420 I'm sitting right now at that little Red Star over in Davis, California. 00:03:44.420 --> 00:03:51.900 We wanted to basically see if the canopy, if we could measure tree condition to be healthier 00:03:51.900 --> 00:03:58.410 via remote sensing in areas that had been treated versus not treated. 00:03:58.410 --> 00:04:07.460 As you know, California's drought was a very intensive one and has led to the direct or 00:04:07.460 --> 00:04:11.850 indirect mortality of many millions of trees. 00:04:11.850 --> 00:04:18.250 One of the reasons that it was a more intensive drought than many of the droughts that are 00:04:18.250 --> 00:04:25.110 in our historical record, that we can observe, is because it was much warmer. 00:04:25.110 --> 00:04:32.900 Here you see minimum temperatures and how they really climbed during the 2013 to 2016 00:04:32.900 --> 00:04:43.439 period, at the same time that we had remarkable decreases in the amount of precipitation. 00:04:43.439 --> 00:04:50.729 To try to take advantage of this terrible thing, we wanted to do a large mashup of different 00:04:50.729 --> 00:04:52.379 types of information. 00:04:52.379 --> 00:04:57.939 This slide is really the "Here's The Progression of The Talk" slide. 00:04:57.939 --> 00:05:00.530 We have a remote sensing component. 00:05:00.530 --> 00:05:02.810 We have a plot based component. 00:05:02.810 --> 00:05:06.550 We have a leaf based component, and a GIS mashup. 00:05:06.550 --> 00:05:09.449 We want to try to combine all of those. 00:05:09.449 --> 00:05:11.419 I'll walk you through that a bit. 00:05:11.419 --> 00:05:20.520 I'm going to then talk about the results from a number of different spatial scales. 00:05:20.520 --> 00:05:29.180 There are these two flight boxes in which hyper spectral data has been flown every year 00:05:29.180 --> 00:05:36.919 for now five years going on six years, starting in 2013. 00:05:36.919 --> 00:05:40.879 Each box is about 13,000 square kilometers. 00:05:40.879 --> 00:05:48.660 One of them covers from the Central Valley of California up over the Sierras and the 00:05:48.660 --> 00:05:50.380 Lake Tahoe Basin. 00:05:50.380 --> 00:05:55.300 The other includes a transect that includes most of Yosemite National Park, as you can 00:05:55.300 --> 00:05:57.770 see there. 00:05:57.770 --> 00:06:03.900 At those areas, a fixed wing aircraft flew and took AVIRIS plus data, which has just 00:06:03.900 --> 00:06:14.759 under 300 bands of record, and at 18 meters. 00:06:14.759 --> 00:06:20.680 Here, you can see them laid out against the national forest and national parks in the 00:06:20.680 --> 00:06:22.240 Sierras. 00:06:22.240 --> 00:06:27.520 We wanted to try to use those data in conjunction with other types of land management that was 00:06:27.520 --> 00:06:36.280 brought in through GIS, and in some cases with LIDAR data, to look at differences in 00:06:36.280 --> 00:06:40.990 canopy water content in places that had been treated or not. 00:06:40.990 --> 00:06:45.309 On this slide, you can see the yellow flight boxes. 00:06:45.309 --> 00:06:50.479 The red are locations where we know LIDAR was flown, at one point or another, that might 00:06:50.479 --> 00:06:54.129 be able to be used to look at structure. 00:06:54.129 --> 00:06:59.020 On the left inset, you can see some brown and orange polygons. 00:06:59.020 --> 00:07:05.389 Those would be representative of locations where prescribed fire or mechanical thinning 00:07:05.389 --> 00:07:07.860 had been applied. 00:07:07.860 --> 00:07:17.349 Those locations would represent places where, perhaps, trees might be less impacted by the 00:07:17.349 --> 00:07:23.610 increasing ratcheting of the drought stress than in other places which had not been treated, 00:07:23.610 --> 00:07:30.589 and therefore had a continuation of the 70 years of fire suppression and denser stands, 00:07:30.589 --> 00:07:34.759 than we might expect otherwise. 00:07:34.759 --> 00:07:39.669 I should jump back for just a second. 00:07:39.669 --> 00:07:42.339 I guess it doesn't show on that one. 00:07:42.339 --> 00:07:50.199 Three of these sites form an elevational transect that we looked at for a local level approach. 00:07:50.199 --> 00:07:56.219 I tried to break up the landscape and our analysis approach into three spatial scales 00:07:56.219 --> 00:08:05.669 a local level, or stand level, a mesoscale, which I'll show you in a minute, and then 00:08:05.669 --> 00:08:07.889 a full landscape scale. 00:08:07.889 --> 00:08:13.360 At the stand level, we looked at these three locations, the San Joaquin Experimental Range, 00:08:13.360 --> 00:08:19.210 Soaproot Saddle which is mid elevation, and Teakettle Experimental Forest, which is getting 00:08:19.210 --> 00:08:24.589 up into the upper elevations of conifers in the Sierra Nevada. 00:08:24.589 --> 00:08:30.759 At Teakettle, in the upper level where it's predominantly red fir/white fir forest, there's 00:08:30.759 --> 00:08:39.550 a 10 year, ongoing experiment that is overseen by Malcolm North of the U.S. Forest Service. 00:08:39.550 --> 00:08:41.300 They have these 10 acre blocks. 00:08:41.300 --> 00:08:46.610 The 10 acre blocks represent, the different colors here, represent different treatments 00:08:46.610 --> 00:08:47.610 that have been applied. 00:08:47.610 --> 00:08:55.910 They have three replicates for a control, or for over story thinning or understory thinning, 00:08:55.910 --> 00:09:01.060 or prescribed burn with understory thinning or over story thinning. 00:09:01.060 --> 00:09:05.040 We thought, "OK, here's a place where this is about as good as we can get. 00:09:05.040 --> 00:09:07.899 We know what the prescriptions have been. 00:09:07.899 --> 00:09:14.459 We know pretty well where these locations are, and we have LIDAR for this location. 00:09:14.459 --> 00:09:17.110 Let's look at that in more detail." 00:09:17.110 --> 00:09:22.750 We used it as a location to develop some of the methods for the analyses. 00:09:22.750 --> 00:09:27.210 Here's an image of the LIDAR from those locations. 00:09:27.210 --> 00:09:35.610 Each of these boxes has three lines in it representing the three plots at that location. 00:09:35.610 --> 00:09:40.810 As you go to the right on the x axis, you're going to increasing height. 00:09:40.810 --> 00:09:46.649 The vertical is the proportion of the canopy that is at that height. 00:09:46.649 --> 00:09:52.601 The upper left understory thinning, we can see there's actually quite a bit...Even though 00:09:52.601 --> 00:09:57.600 the understory was thinned, there's quite a bit of material to the left of the 15 meter 00:09:57.600 --> 00:10:02.630 height line, which in this case, is shrubs that grew back. 00:10:02.630 --> 00:10:04.130 We can see some differences. 00:10:04.130 --> 00:10:09.339 For example, the right hand side prescribed burn with over story thinning, you don't see 00:10:09.339 --> 00:10:15.460 a hump out to the right end, which would be where the larger tree canopies would be more 00:10:15.460 --> 00:10:16.529 frequent. 00:10:16.529 --> 00:10:24.519 We can see that the different treatments showed a slightly different structural profile at 00:10:24.519 --> 00:10:27.160 this location. 00:10:27.160 --> 00:10:36.620 This image basically is showing the combinations of three colors, or the amount of NDWI, Normalized 00:10:36.620 --> 00:10:40.600 Difference Water Index, to NDVI. 00:10:40.600 --> 00:10:48.370 You can see that it ratchets down from 2013 to 2015, and then each column, or each collection 00:10:48.370 --> 00:10:54.170 of three, is a different plot with treatments. 00:10:54.170 --> 00:11:00.190 The message that we got when we did this was that, "Yeah, we could see a loss of canopy 00:11:00.190 --> 00:11:02.560 water by this index." 00:11:02.560 --> 00:11:05.880 By the way, the NDWI is a unitless index. 00:11:05.880 --> 00:11:08.770 We'll talk a little bit more about that later on. 00:11:08.770 --> 00:11:13.750 We could see it ratcheting down, but unfortunately there was almost as much noise within the 00:11:13.750 --> 00:11:16.889 treatments as there was among the treatments. 00:11:16.889 --> 00:11:25.710 It was a little bit unsatisfactory with regards to we were hoping that perhaps a prescribed 00:11:25.710 --> 00:11:33.950 burn with an understory thin would come out at losing less water overall than others. 00:11:33.950 --> 00:11:38.029 We weren't really able to show that. 00:11:38.029 --> 00:11:40.610 We asked, "Well, why might that be?" 00:11:40.610 --> 00:11:44.870 We know that things have dried down. 00:11:44.870 --> 00:11:50.230 One of the answers might be visible in this series of images. 00:11:50.230 --> 00:11:56.420 The upper row is the location where the Teakettle Experimental Forest is located, in those red 00:11:56.420 --> 00:11:58.649 squares. 00:11:58.649 --> 00:12:05.860 You can see that those three images in green, the green stays about the same level of saturation. 00:12:05.860 --> 00:12:12.410 It gets slightly paler through time, but that's telling us that this entire area is not drying 00:12:12.410 --> 00:12:18.339 down as much as we might expect under the drought. 00:12:18.339 --> 00:12:24.329 By contrast, the lower row, which is at our lowest elevation site in the San Joaquin Experimental 00:12:24.329 --> 00:12:29.360 Station and it's in Oak Savannah, you can see the green is really paling out quite a 00:12:29.360 --> 00:12:30.410 bit. 00:12:30.410 --> 00:12:34.380 The intensity of the red in the right hand image is much heavier. 00:12:34.380 --> 00:12:41.230 That's showing us that there's a much greater impact of decline in this index of canopy 00:12:41.230 --> 00:12:47.000 water at that location than there was at the Teakettle location. 00:12:47.000 --> 00:12:54.480 We came around to noticing that the Teakettle location, it sits somewhat in a topographic 00:12:54.480 --> 00:12:57.019 bowl. 00:12:57.019 --> 00:13:02.720 There may be some groundwater dynamics that are going on at this location that buffer 00:13:02.720 --> 00:13:11.360 it, that essentially insulate it from the increasing effect of the drought. 00:13:11.360 --> 00:13:15.940 Let's take a look at the mesoscale approach then. 00:13:15.940 --> 00:13:19.240 Here's our two flight boxes. 00:13:19.240 --> 00:13:26.459 The red and yellow little candy wrappers that are out there, since it's Halloween, those 00:13:26.459 --> 00:13:34.600 represent locations where we tried to match places that had had a prescribed burn or a 00:13:34.600 --> 00:13:43.420 mechanical thin that were available via GIS data with matching locations that had the 00:13:43.420 --> 00:13:50.640 same vegetation and slope that had not been treated. 00:13:50.640 --> 00:13:53.980 Here's what that might look like. 00:13:53.980 --> 00:14:00.519 It's essentially a paired plot, or a paired polygon type of an analysis. 00:14:00.519 --> 00:14:08.639 Here, in the lower image, is a burn from 2008, a prescribed burn, and nearby, not terribly 00:14:08.639 --> 00:14:14.779 far away, a control of about the same area, same vegetation type, same elevation, similar 00:14:14.779 --> 00:14:17.990 slope aspect. 00:14:17.990 --> 00:14:28.589 We want to start to compare these things in some paired plot approach. 00:14:28.589 --> 00:14:32.709 It gets a little complicated because you have prescribed burns in many different years, 00:14:32.709 --> 00:14:38.529 and you have the image years of 2013, '14, and '15. 00:14:38.529 --> 00:14:46.360 What we would expect to see, or our hypothesis, is illustrated in the right hand most column. 00:14:46.360 --> 00:14:48.610 There's 2011. 00:14:48.610 --> 00:14:54.980 You can see that there's a burn and a control column there. 00:14:54.980 --> 00:15:05.399 We would hope that water canopy content would be... 00:15:05.399 --> 00:15:08.339 The red shows the opposite of what we would expect. 00:15:08.339 --> 00:15:12.899 We would hope that the prescribed burn would have a higher water canopy content than the 00:15:12.899 --> 00:15:14.540 control. 00:15:14.540 --> 00:15:20.279 For 2011, the control had a higher canopy water content than the burn. 00:15:20.279 --> 00:15:24.670 If you look at the far left, it's doing more of what we would expect. 00:15:24.670 --> 00:15:33.040 The burn has a slightly higher value than the control for each of the three years at 00:15:33.040 --> 00:15:34.910 that location. 00:15:34.910 --> 00:15:42.610 What this suggests is that the timing, the historical timing by year of a treatment might 00:15:42.610 --> 00:15:49.800 have an effect on forests given the onset of a drought. 00:15:49.800 --> 00:15:55.230 If you did a prescribed burn and, that same summer, it went into a drought, the trees 00:15:55.230 --> 00:16:02.980 might have become somewhat stressed by the prescribed burn and be adversely impacted, 00:16:02.980 --> 00:16:09.379 whereas perhaps, if the burn was several years prior, it would be a better solution. 00:16:09.379 --> 00:16:17.839 We were also not terribly happy with this initial set of results from burns and treatments 00:16:17.839 --> 00:16:21.970 because they seemed quite noisy as well. 00:16:21.970 --> 00:16:27.240 It may be also that, because we're going across a large elevational gradient, which we'll 00:16:27.240 --> 00:16:34.089 talk about in a moment, that the same treatments could have different health effects depending 00:16:34.089 --> 00:16:39.550 on the level of background stress that a location is subjected to. 00:16:39.550 --> 00:16:43.680 Now, let's move up to the landscape approach. 00:16:43.680 --> 00:16:51.610 With the landscape approach, we learned that it takes a long time to process hyper spectral 00:16:51.610 --> 00:16:57.990 imagery to be at the point where you can look across pixels from year to year and have the 00:16:57.990 --> 00:17:04.579 same 18 meter pixels lining up across 13,000 square kilometers. 00:17:04.579 --> 00:17:08.560 Indeed, you can see here, there are flight lines. 00:17:08.560 --> 00:17:13.209 There's about 11 or 12 flight lines in each box. 00:17:13.209 --> 00:17:20.439 We had to take each flight line, which was geo referenced by the Jet Propulsion Laboratory, 00:17:20.439 --> 00:17:27.510 but we had to further geo reference them by cutting each flight line into small pieces, 00:17:27.510 --> 00:17:32.620 locking it onto the landscape, and then locking the years from one to the next. 00:17:32.620 --> 00:17:37.529 At the landscape scale, there's some things that I see that make me confident that we 00:17:37.529 --> 00:17:42.250 could use these data at this spatial scale. 00:17:42.250 --> 00:17:44.350 The first is on the left image here. 00:17:44.350 --> 00:17:49.980 You can see that down in the valley at the bottom of the box, the canopy water index 00:17:49.980 --> 00:17:55.620 for a single year is drier in browns and reds. 00:17:55.620 --> 00:18:02.909 It moves into blue colors in our conifer zone, which is a more productive and moister zone. 00:18:02.909 --> 00:18:10.100 As you get to the other side of the Sierras and into Nevada, past Lake Tahoe, you can 00:18:10.100 --> 00:18:11.990 see that it dries down again. 00:18:11.990 --> 00:18:19.640 The major moisture gradient of these mountains is captured by these images. 00:18:19.640 --> 00:18:26.779 On the right hand image, in the back behind those three flight lines, is the vegetation 00:18:26.779 --> 00:18:32.020 map for this area derived from other satellite imagery. 00:18:32.020 --> 00:18:37.909 The patterns that we see in the hyper spectral imagery and the patterns of the vegetation 00:18:37.909 --> 00:18:39.990 line up very well. 00:18:39.990 --> 00:18:42.750 That also suggests to me that we could do things. 00:18:42.750 --> 00:18:46.490 We could look at this by different vegetation type, different forest type. 00:18:46.490 --> 00:18:53.480 We go from oak savannas to mixed conifer hardwood to conifers, to sub alpine conifers. 00:18:53.480 --> 00:19:02.090 We could probably look at all of those in turn, and see what type of dynamics they display. 00:19:02.090 --> 00:19:08.840 Here's what one of our rendered images of the multiple flight lines looks like for, 00:19:08.840 --> 00:19:11.660 what I call, the Tahoe Box. 00:19:11.660 --> 00:19:20.169 The nice thing about the NDWI is, although it's unitless, we've tried to address that, 00:19:20.169 --> 00:19:26.470 I'll present that in a minute, it does allow you to compare from one location to another 00:19:26.470 --> 00:19:33.860 and across years, because it's all on a single index. 00:19:33.860 --> 00:19:37.450 Here's the change from 2013 to 2015. 00:19:37.450 --> 00:19:40.760 The tannish color shows the drying down. 00:19:40.760 --> 00:19:44.710 There's some speckling in that. 00:19:44.710 --> 00:19:48.690 The white locations are places where there was cloud or snow cover, and we weren’t 00:19:48.690 --> 00:19:56.460 able to calculate the differences for those particular combination of years. 00:19:56.460 --> 00:20:03.309 Here's the same image again, now with some of the wildfires for this region overlaid 00:20:03.309 --> 00:20:04.840 on it. 00:20:04.840 --> 00:20:12.049 What you can see here is where the intense red is located on the left side of this image, 00:20:12.049 --> 00:20:13.700 is the King Fire. 00:20:13.700 --> 00:20:17.779 The King Fire took place during this sequence. 00:20:17.779 --> 00:20:23.130 Some of the greatest drying down are within the footprint of that wildfire. 00:20:23.130 --> 00:20:32.130 We can start to bring in things like the wildfires, perhaps other disturbances, and look at them 00:20:32.130 --> 00:20:38.570 in relationship to these types of data. 00:20:38.570 --> 00:20:40.669 Now we switch over to the Yosemite Box. 00:20:40.669 --> 00:20:43.710 You can see Mono Lake in the upper right hand side. 00:20:43.710 --> 00:20:48.240 Here's the 2014 and 2015 images side by side. 00:20:48.240 --> 00:20:51.399 The stars are the field locations. 00:20:51.399 --> 00:20:55.230 We're going to come back to those in a minute. 00:20:55.230 --> 00:21:03.010 You can see that the band of blue above that heavy red in the left image is paling out 00:21:03.010 --> 00:21:07.590 and getting much less pronounced by 2015. 00:21:07.590 --> 00:21:13.970 That would be the increasing sequence of the drought. 00:21:13.970 --> 00:21:16.480 Here's the change in canopy water content. 00:21:16.480 --> 00:21:24.100 We can see that almost the entire location is moving into drier condition, rather than 00:21:24.100 --> 00:21:25.660 into wetter condition. 00:21:25.660 --> 00:21:31.270 Again, I've put the prescribed fires on there with the crosshatch black locations. 00:21:31.270 --> 00:21:39.710 They match up pretty well with the heavy duty red emerging locations. 00:21:39.710 --> 00:21:47.760 One of the things that we're planning to do with these data are to develop a topographic 00:21:47.760 --> 00:21:48.760 model. 00:21:48.760 --> 00:21:54.450 There's a scientist named Jenifer Cartwright who works for the U.S. Geological Survey. 00:21:54.450 --> 00:22:01.621 She is using these types of imagery along with topographic modeling to try to identify 00:22:01.621 --> 00:22:08.720 where refugia are located on the landscape within the drought. 00:22:08.720 --> 00:22:10.210 We think we can do that here. 00:22:10.210 --> 00:22:15.289 We could either create a topographic model not using any of these data, but say, "Here's 00:22:15.289 --> 00:22:22.070 where we think the refuge, the least impacted locations will be for each vegetation type, 00:22:22.070 --> 00:22:29.100 and then look at the change in the canopy water index at those locations." 00:22:29.100 --> 00:22:33.200 Or we could actually use the imagery to say where are the locations that had the least 00:22:33.200 --> 00:22:37.610 impact and go in and look at what conditions are at those locations. 00:22:37.610 --> 00:22:41.510 That's currently the major thrust of the research. 00:22:41.510 --> 00:22:48.899 I've talked about the remote sensing and the three scales of analysis, but we've also tried 00:22:48.899 --> 00:22:55.790 to address the issue that the NDWI is unitless, and it would be nice to know that the millimeters 00:22:55.790 --> 00:22:58.080 of water in the canopy were. 00:22:58.080 --> 00:23:04.180 While we were doing this project, we originally were only going to process three years, but 00:23:04.180 --> 00:23:09.450 we learned that NASA decided to fly the imagery again in 2016, and indeed they're actually 00:23:09.450 --> 00:23:12.310 going for another several years. 00:23:12.310 --> 00:23:28.210 In 2016, we fielded a field campaign to get out on the ground and try to collect 00:23:28.210 --> 00:23:34.880 information about leaf water potential and field measurements of the spectral indices 00:23:34.880 --> 00:23:41.830 of trees so that we could come up with a canopy water content that was derived in the field 00:23:41.830 --> 00:23:45.500 at the same time that the planes were doing the over flights. 00:23:45.500 --> 00:23:52.170 We could link the values from the field to the remotely sensed ones and that that would 00:23:52.170 --> 00:23:59.340 get us to getting to some actual measurement of the canopy water content. 00:23:59.340 --> 00:24:04.840 Here are some of the folks who worked on that project. 00:24:04.840 --> 00:24:08.960 We collected from these four sites in June of 2016. 00:24:08.960 --> 00:24:13.660 The Blodgett Forest, the San Joaquin, Teakettle, and Soaproot. 00:24:13.660 --> 00:24:21.650 Notice all of the tree mortality at the Soaproot level elevation. 00:24:21.650 --> 00:24:25.299 Those are an elevational transect. 00:24:25.299 --> 00:24:33.971 We collected leaf reflectants, leaf water content, leaf mass, leaf thickness, water 00:24:33.971 --> 00:24:39.871 potential from pre dawn and midday, and at the leaf level and at the canopy level, leaf 00:24:39.871 --> 00:24:48.779 area index, canopy cover or gap fractions, and species and tree data. 00:24:48.779 --> 00:24:53.919 We would lay out a nine unit plot. 00:24:53.919 --> 00:25:00.169 Within that, three plots would be selected and four dominant trees would be measured 00:25:00.169 --> 00:25:01.640 in each of those locations. 00:25:01.640 --> 00:25:10.429 I'm going to talk a little bit about the three elevational locations, starting at the San 00:25:10.429 --> 00:25:14.640 Joaquin Experimental Range, which is the lowest elevation one. 00:25:14.640 --> 00:25:23.250 Here we have two species of oaks, the blue oak and valley oak, and the lowest elevation 00:25:23.250 --> 00:25:24.250 pine. 00:25:24.250 --> 00:25:28.740 We can see that the water potential versus the leaf water content of the pine and the 00:25:28.740 --> 00:25:30.380 oaks are quite different. 00:25:30.380 --> 00:25:37.940 Also, the leaf water content against the normalized difference water index as measured in the 00:25:37.940 --> 00:25:41.150 field is also quite different. 00:25:41.150 --> 00:25:45.630 This is the location at which our results are the most far along. 00:25:45.630 --> 00:25:53.600 Leaf water content against the NDWI is showing less of a distinct pattern. 00:25:53.600 --> 00:26:02.990 For the oaks, we can actually identify...our three different plots show us that topographic 00:26:02.990 --> 00:26:12.240 position is an important consideration for the condition of the canopy of those trees. 00:26:12.240 --> 00:26:20.169 You can see these three groups are broken out here, and that they have different topographic 00:26:20.169 --> 00:26:21.169 position. 00:26:21.169 --> 00:26:30.870 We were able to use that with some repeat plot data to identify mortality that is associated 00:26:30.870 --> 00:26:32.990 with the drying down of the remote sensing. 00:26:32.990 --> 00:26:41.620 The little red circles here are showing you the nine locations that we have repeated field 00:26:41.620 --> 00:26:45.059 measurements for, and then the imagery. 00:26:45.059 --> 00:26:52.140 There is a fairly good correspondence between the percentage of dead trees and the canopy 00:26:52.140 --> 00:26:58.290 water decrease that is shown in the statistical chart. 00:26:58.290 --> 00:27:03.840 This is particularly of interest at the low elevation in these blue oaks, because blue 00:27:03.840 --> 00:27:11.600 oaks may have, they probably have, less pathogens and pests that are attacking them than is 00:27:11.600 --> 00:27:13.610 occurring at higher elevations. 00:27:13.610 --> 00:27:21.029 At higher elevations, the predominant cause of the mortality has been beetle outbreaks. 00:27:21.029 --> 00:27:28.049 It's hard to get to a direct physiological link to mortality, but at this lowest elevation, 00:27:28.049 --> 00:27:34.470 in the hottest, driest places, we think that we might be closer to identifying the physiological 00:27:34.470 --> 00:27:38.610 tolerance of these trees. 00:27:38.610 --> 00:27:41.269 We jump up to the middle elevation. 00:27:41.269 --> 00:27:46.799 Here, there's five different species, and they array themselves pretty well with regards 00:27:46.799 --> 00:27:53.710 to water potential and leaf water content. 00:27:53.710 --> 00:28:00.490 They also break out fairly well of leaf water content against the handheld NDWI. 00:28:00.490 --> 00:28:08.970 In this case, we might be able to go after dynamics with the individual trees. 00:28:08.970 --> 00:28:15.730 However our 18 meter pixel of the over flight is still a little bit coarse for being able 00:28:15.730 --> 00:28:22.309 to extract out individual species from the canopy as has been done successfully in the 00:28:22.309 --> 00:28:28.059 leaf to landscape project that the Sequoia National Park and Professor Greg Aznar have 00:28:28.059 --> 00:28:32.480 done in the southern Sierras. 00:28:32.480 --> 00:28:39.380 We can identify that the oaks and the ponderosa pine are really quite different with their 00:28:39.380 --> 00:28:45.620 LWC and NDWI. 00:28:45.620 --> 00:28:50.149 At the highest elevation, this is getting up into the Teakettle, you can see the species 00:28:50.149 --> 00:28:57.669 are quite mixed with their leaf water potential in NDWI. 00:28:57.669 --> 00:29:02.290 This is perhaps emblematic at the site level of some of the problems that we were running 00:29:02.290 --> 00:29:13.279 into with regards to the noise within the plots by treatment being as high as the noise 00:29:13.279 --> 00:29:16.500 between the plots on 10 year old treatments. 00:29:16.500 --> 00:29:23.380 We do see that the incense cedar, the CADE, is perhaps the most differentiated, those 00:29:23.380 --> 00:29:28.620 purple dots going off to the left. 00:29:28.620 --> 00:29:36.039 While the individual site results, to my mind, leave something to be desired, when we put 00:29:36.039 --> 00:29:42.260 them all together, we do find that the leaf water content versus the spectral indices 00:29:42.260 --> 00:29:50.080 of the species from the San Joaquin and the Soaproot and the Teakettle can track fairly 00:29:50.080 --> 00:29:59.230 well between a leaf water content of grams per cm squared and the NDWI values. 00:29:59.230 --> 00:30:07.110 This does revert back to the concept that at the landscape scale, we have a fairly robust 00:30:07.110 --> 00:30:09.320 measure. 00:30:09.320 --> 00:30:15.890 If we had more data, we might be able to get better estimates of the actual leaf water 00:30:15.890 --> 00:30:18.630 content in the canopy. 00:30:18.630 --> 00:30:25.480 That's perhaps another step in future research here. 00:30:25.480 --> 00:30:31.491 While I've mentioned a number of things that are still in progress, Phil Van Mantgem, who 00:30:31.491 --> 00:30:39.409 is a co PI on this project, has some results from long term demographic data that I would 00:30:39.409 --> 00:30:41.549 like to bring to your attention. 00:30:41.549 --> 00:30:54.230 These are locations that have been measured, I think, quite a few times, and pre and post 00:30:54.230 --> 00:30:56.179 fire. 00:30:56.179 --> 00:31:06.289 Here's what the stand density looks like when you stratify by those values, and the probability 00:31:06.289 --> 00:31:13.630 of death being lower in burn stands after accounting for tree size and groups. 00:31:13.630 --> 00:31:21.080 You can see that the probability declines for the pines after the prescribed fire in 00:31:21.080 --> 00:31:24.200 these demographic plots. 00:31:24.200 --> 00:31:32.230 Contrary to the results from Teakettle, here we're seeing that perhaps these fires are 00:31:32.230 --> 00:31:45.889 beneficial in trying to insulate the pines from the ongoing drought. 00:31:45.889 --> 00:31:52.350 Jumping back now to the GIS data for just a few more ideas of our next steps. 00:31:52.350 --> 00:31:57.880 We have stand treatments, we have climate data and models, we have environmental data, 00:31:57.880 --> 00:32:05.860 and building those into a landscape model and using the remote sensing to try to validate 00:32:05.860 --> 00:32:11.669 that, or using the remote sensing to build the model and then trying to translate it 00:32:11.669 --> 00:32:19.660 over to perhaps landsat are steps that we would like to take next. 00:32:19.660 --> 00:32:21.120 There's other data. 00:32:21.120 --> 00:32:30.450 Here's a view of the 2016 tree mortality surveys, the aerial surveys, the ADS surveys, which 00:32:30.450 --> 00:32:36.360 also occur in Oregon and Washington and many parts of the United States flown by the Forest 00:32:36.360 --> 00:32:37.360 Service. 00:32:37.360 --> 00:32:42.120 In California, they’re a combined state and federal initiative. 00:32:42.120 --> 00:32:47.110 Here in the background is a change in climatic water deficit. 00:32:47.110 --> 00:32:52.190 This is a GIS model that we produced here. 00:32:52.190 --> 00:33:00.000 The change from a standard 30 year average to the average of the 2013 to 2015 drought, 00:33:00.000 --> 00:33:07.100 that's the red to blue in the background, and then the black is the level of tree mortality, 00:33:07.100 --> 00:33:10.059 conifer mortality that has occurred. 00:33:10.059 --> 00:33:17.039 You can see that there are, in the Southern Sierra, down at the bottom of the image, the 00:33:17.039 --> 00:33:22.980 black is occurring in some of the reddest locations, and the black just south of lake 00:33:22.980 --> 00:33:29.940 Tahoe is occurring in less intensively drought stricken areas, and perhaps, there may be 00:33:29.940 --> 00:33:36.269 a difference in the proportion of trees killed by beetles versus by direct physiological 00:33:36.269 --> 00:33:39.910 impacts between those two areas. 00:33:39.910 --> 00:33:46.159 This could be a way that we could start to identify different types of impacts in different 00:33:46.159 --> 00:33:48.120 locations. 00:33:48.120 --> 00:33:54.950 Finally, here's that same climatic water deficit change in the background for the whole Sierra 00:33:54.950 --> 00:34:01.180 Nevada, and here are our two flight lines with the change in canopy water content between 00:34:01.180 --> 00:34:06.149 2013 and 2015. 00:34:06.149 --> 00:34:12.750 Looking to see how we might be able to link, or how good is the correspondence between 00:34:12.750 --> 00:34:19.200 the loss of canopy water index as measured through our hyper spectral, and a model of 00:34:19.200 --> 00:34:24.020 landscape hydrology and the change in the background hydrology could be another way 00:34:24.020 --> 00:34:29.540 that we might be able to get into predictive modeling about future levels of physiological 00:34:29.540 --> 00:34:36.190 stress on these different tree species or on these different vegetation types, I should 00:34:36.190 --> 00:34:37.190 say. 00:34:37.190 --> 00:34:43.760 Now, a lot of this was censored on the hypothesis that if we are able to somehow reintroduce 00:34:43.760 --> 00:34:49.579 a land management techniques like prescribed burning to large areas, that those areas would 00:34:49.579 --> 00:34:53.600 become more resilient to these types of perturbations. 00:34:53.600 --> 00:35:01.020 Of course, there will be barriers to those that can include the funding, and in California, 00:35:01.020 --> 00:35:09.050 air quality is a big issue because state air quality agencies regulate how much federal 00:35:09.050 --> 00:35:12.500 forest lands are permitted to burn. 00:35:12.500 --> 00:35:19.710 The timing of the burning, the site accessibility, rough terrain is often…cannot be treated. 00:35:19.710 --> 00:35:25.460 Prescribed fire may not be sufficiently severe and hotter the droughts may produce stresses 00:35:25.460 --> 00:35:28.750 that exceed the potential management responses. 00:35:28.750 --> 00:35:33.890 These are some of the management challenges and questions. 00:35:33.890 --> 00:35:39.210 I'd like to leave you then with, perhaps, a framework for how we're thinking about all 00:35:39.210 --> 00:35:47.030 of these, that we have forest plots with the water potential in remote sensing by experimental 00:35:47.030 --> 00:35:51.830 sites or remote sensing in landscape samples, landscape levels, GIS integrations. 00:35:51.830 --> 00:35:54.050 I talked a bit about those. 00:35:54.050 --> 00:35:59.240 There's fire suppression, no treatment, prescribed burns, mechanical fitting, are there other 00:35:59.240 --> 00:36:02.530 techniques that we might be able to use? 00:36:02.530 --> 00:36:04.619 What trend information is critical? 00:36:04.619 --> 00:36:09.430 What condition information is important? and what future projections would be needed to 00:36:09.430 --> 00:36:11.100 pull those together? 00:36:11.100 --> 00:36:17.250 Finally, the bottom box of some other questions, what experimental treatments, and at what 00:36:17.250 --> 00:36:23.440 scale, and what monitoring would ratchet forward our understanding so that we don't merely 00:36:23.440 --> 00:36:29.640 look at the response of perturbations or the projections of future impact. 00:36:29.640 --> 00:36:36.760 We try to integrate those so that we have a way to advance our understanding even when 00:36:36.760 --> 00:36:43.079 management techniques might not do what we would hope they would do. 00:36:43.079 --> 00:36:52.390 With that, I'll thank you for your attention and take any questions that may be out there. 00:36:52.390 --> 00:36:54.730 John: Thank you, Jim. 00:36:54.730 --> 00:36:56.770 I see a few people typing away. 00:36:56.770 --> 00:36:58.870 I just want to say real quick, thank you for the presentation. 00:36:58.870 --> 00:37:08.880 I also like to thank USGS for continuing these webinars series with us in the last 10 months 00:37:08.880 --> 00:37:11.690 with this particular drought specific series. 00:37:11.690 --> 00:37:16.970 I want to thank everybody there, Holly, Kate, and Shawn and everyone who's participated 00:37:16.970 --> 00:37:19.970 in this and help put this on. 00:37:19.970 --> 00:37:22.230 We will be taking a two month break. 00:37:22.230 --> 00:37:24.760 Be on lookout for future webinars. 00:37:24.760 --> 00:37:27.010 We have another series that we're planning right now. 00:37:27.010 --> 00:37:29.920 We got our first question from Johnny. 00:37:29.920 --> 00:37:37.970 "Did you see any interaction with times since burn and drought effects in terms of mortality 00:37:37.970 --> 00:37:38.970 response?" 00:37:38.970 --> 00:37:44.950 James: We mostly have been trying to look at the flip side of that, and the flip side 00:37:44.950 --> 00:37:55.660 would be the idea of resilience or locations that had less impact. 00:37:55.660 --> 00:37:57.819 There are some publications that are out there for sure. 00:37:57.819 --> 00:38:00.490 I'm going to back up. 00:38:00.490 --> 00:38:06.710 Here's the mortality as measured across the state. 00:38:06.710 --> 00:38:13.290 Greg Asner has a nice paper in PNAS where they made projections about mortality, and 00:38:13.290 --> 00:38:21.819 these aerial detection surveys from the forest service, the annual tree mortality that the 00:38:21.819 --> 00:38:25.619 2016 says 102 million trees. 00:38:25.619 --> 00:38:28.300 It’s probably surpassed that now. 00:38:28.300 --> 00:38:32.319 The great majority of those died by insect. 00:38:32.319 --> 00:38:42.859 I would say, we don't have a good measure of that, but we recognize that we need it 00:38:42.859 --> 00:38:50.990 because the NDWI can be saturated by, or can be affected by the number of trees that are 00:38:50.990 --> 00:38:57.910 actually in a pixel or the proportion of a pixel that is in a green, similar to NDVI. 00:38:57.910 --> 00:39:06.660 We recognize that that is a potentially confounding factor in these measurements. 00:39:06.660 --> 00:39:17.160 John: If anybody’s on the phone line, if you like to throw out a question, you can 00:39:17.160 --> 00:39:22.569 press *6 on your phone and that should unmute you, if you have any questions. 00:39:22.569 --> 00:39:27.380 Toni Lyn: Hey, James. 00:39:27.380 --> 00:39:32.200 This is Toni Lyn. 00:39:32.200 --> 00:39:33.930 Great to hear this work. 00:39:33.930 --> 00:39:42.100 I was just wondering if you could talk a little bit more about any signs of fire refugia that 00:39:42.100 --> 00:39:43.910 you found. 00:39:43.910 --> 00:39:48.280 There's really neat work happening in the Pacific Northwest on this. 00:39:48.280 --> 00:39:54.450 Maybe you just know about, in general, what is being seen in the state even not just from 00:39:54.450 --> 00:39:56.370 your work. 00:39:56.370 --> 00:39:58.980 James: Fire refugia? 00:39:58.980 --> 00:40:01.980 Toni Lyn: Yeah. 00:40:01.980 --> 00:40:07.520 Thinking about not just managing places so they don't burn as much, but places that are 00:40:07.520 --> 00:40:09.609 naturally not burning as much. 00:40:09.609 --> 00:40:15.380 Do we put our prescribed burns there or think about putting them elsewhere in fact? 00:40:15.380 --> 00:40:16.470 James: Yeah. 00:40:16.470 --> 00:40:18.380 Great question. 00:40:18.380 --> 00:40:23.869 Good to hear your voice. 00:40:23.869 --> 00:40:30.339 We're interested in the work that is here, these resources. 00:40:30.339 --> 00:40:36.510 One of the things we learned last thanksgiving, almost a year ago now, from a stakeholder 00:40:36.510 --> 00:40:41.380 meeting was that, "Hey, wow, if you can just create the entire flight box and give us four 00:40:41.380 --> 00:40:47.990 years of consecutive June values that we might be able to explore various different applications 00:40:47.990 --> 00:40:50.820 of these hyper spectral data." 00:40:50.820 --> 00:40:59.320 That's been one of the main focuses, and going after refugia in that location is something 00:40:59.320 --> 00:41:07.220 that we're interested to do starting from a climatic perspective. 00:41:07.220 --> 00:41:14.170 From a perspective of are there places that seem to have just had less canopy moisture 00:41:14.170 --> 00:41:20.970 loss than others, and what are the physical characteristics of those places because that 00:41:20.970 --> 00:41:26.030 will be, of course, interacting with the management of those locations. 00:41:26.030 --> 00:41:35.680 If we can do that by veg type, then we might be able to identify some fire refugia. 00:41:35.680 --> 00:41:39.630 Fire is keenly on the minds of people in California. 00:41:39.630 --> 00:41:45.530 It's quite smokey outside here in Davis today even though the coastal fires have died down 00:41:45.530 --> 00:41:47.440 now a bit. 00:41:47.440 --> 00:41:57.599 The combination of four years of drought and then double precip that brought the thatch 00:41:57.599 --> 00:42:05.440 up in many of our more arid systems, so there was a huge annual fuel load that was out there 00:42:05.440 --> 00:42:12.931 and then very dry conditions have led to some of the sweeping wildfires that have been in 00:42:12.931 --> 00:42:14.849 the news. 00:42:14.849 --> 00:42:21.831 Those types of combinations in California, it makes me wonder whether, perhaps, we're 00:42:21.831 --> 00:42:31.020 moving out of our models of gradual climate change and the increasing stress that that 00:42:31.020 --> 00:42:38.339 brings, and into some of the more stochastic events that are broadly predicted under climate 00:42:38.339 --> 00:42:40.210 change. 00:42:40.210 --> 00:42:45.950 Four years of drought and then double rain and then an extremely dry and fire season 00:42:45.950 --> 00:42:56.140 and high winds, those things within a five, six year period seem to be pointing us to 00:42:56.140 --> 00:42:59.270 those greater extremes. 00:42:59.270 --> 00:43:08.869 One thing that I am interested to do is to try to look for spatial correspondence between 00:43:08.869 --> 00:43:18.050 measures of, say, future climate exposure like some of the climate exposure maps that 00:43:18.050 --> 00:43:27.310 we've developed in California, lay those over the annual maps and, say, what was the 2016, 00:43:27.310 --> 00:43:35.210 2017 year, what did that look like relative to our projections into the future of increasing 00:43:35.210 --> 00:43:37.200 stress? 00:43:37.200 --> 00:43:46.160 Was 2016-17 the equivalent of a mean condition in 2050 or something like that. 00:43:46.160 --> 00:43:54.119 We might be able to start to get a handle on what some of these outlier conditions bring 00:43:54.119 --> 00:43:55.690 to us. 00:43:55.690 --> 00:44:03.900 I guess, you could say that the places that retain the most canopy moisture might become 00:44:03.900 --> 00:44:11.880 the fire refugia, but we could go around on that one in a couple of different directions. 00:44:11.880 --> 00:44:14.720 Toni Lyn: Totally. 00:44:14.720 --> 00:44:15.720 Thanks. 00:44:15.720 --> 00:44:20.069 There's just…Meg and some others up in the Northwest, they're thinking about this in 00:44:20.069 --> 00:44:25.860 a way that's very different than I think about climate change refugia, but taking into account 00:44:25.860 --> 00:44:31.701 topography and then looking at historical data to see places that have remained unburned, 00:44:31.701 --> 00:44:34.530 and then what's the pattern there. 00:44:34.530 --> 00:44:36.609 It's cool. 00:44:36.609 --> 00:44:39.920 Maybe, we can all join forces or something. 00:44:39.920 --> 00:44:42.359 [laughs] James: That would be great. 00:44:42.359 --> 00:44:47.540 I’d love to work with Meg and with you. 00:44:47.540 --> 00:44:49.160 Thanks so much for the question. 00:44:49.160 --> 00:44:51.680 Toni Lyn: Thanks, Jim. 00:44:51.680 --> 00:44:53.579 John: Thanks, Tony. 00:44:53.579 --> 00:44:57.140 Alright, I don't see questions in the chat box. 00:44:57.140 --> 00:45:03.800 Once again, I'd like to thank Mr. Thorne for your presentation and, like I said, it will 00:45:03.800 --> 00:45:07.790 be available shortly on the USGS website. 00:45:07.790 --> 00:45:11.430 Be on the lookout for that if you guys know somebody that didn't have the chance to view 00:45:11.430 --> 00:45:12.430 this. 00:45:12.430 --> 00:45:14.230 Thank you very much for participating. 00:45:14.230 --> 00:45:15.520 Have a good day. 00:45:15.520 --> 00:45:16.849 James: Great, thank you, sir.