Forest structure and individual tree inventories of north-eastern Siberia along climatic gradients

Miesner, Timon; Herzschuh, Ulrike; Pestryakova, Luidmila A.; Wieczorek, Mareike; Zakharov, Evgenii S.; Kolmogorov, Alexei I.; Davydova, Paraskovya V.; Kruse, Stefan

doi:https://doi.org/10.5194/essd-2022-152

Preprints

https://doi.org/10.5194/essd-2022-152

Preprints

24 May 2022

Status: this preprint is currently under review for the journal ESSD.

Forest structure and individual tree inventories of north-eastern Siberia along climatic gradients

Timon Miesner^1,2, Ulrike Herzschuh^1,2,3, Luidmila A. Pestryakova⁴, Mareike Wieczorek¹, Evgenii S. Zakharov^5,4, Alexei I. Kolmogorov⁴, Paraskovya V. Davydova⁴, and Stefan Kruse¹ Timon Miesner et al.

Show author details

¹Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Telegrafenberg A45, 14473 Potsdam, Germany
²Institute of Environmental Sciences and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany
³Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam-Golm, Germany
⁴Institute of Natural Sciences, North-Eastern Federal University, 677000 Yakutsk, Russia
⁵Institute for Biological Problems of Cryolithozone, 677000 Yakutsk, Russia

Received: 16 May 2022 – Discussion started: 24 May 2022

Abstract. We compile a data set of forest surveys from expeditions to the north-east of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia) and the Chukotka Autonomous Okrug (59-73° N, 97-169° E). The region is characterized by permafrost soils, and forests dominated by larch (Larix gmelinii RUPR., Larix cajanderi MAYR).

Our dataset consists of a plot data base describing 226 georeferenced vegetation survey sites, and of a tree data base with information about all trees on these plots. The tree data base contains information on height, species and vitality of 40,289 trees. A subset of the trees was subject to a more detailed inventory, recording stem diameter at base and at breast height, crown diameter and height of the beginning of the crown.

We recorded heights up to 28.5 m (median = 2.5 m) and stand densities up to 120,000 trees per ha (median = 1197 ha⁻¹), both values tending to be higher in the more southerly areas. Observed taxa include Larix MILL., Pinus L., Picea A.DIETR., Abies MILL., Salix L., Betula L., Populus L., Alnus MILL. and Ulmus L..

In this study, we present the forest inventory data aggregated per site. Additionally, we connect it with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of ten species groups, allometries depend also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here.

The data can be used for studying the forest structure of north-eastern Siberia, and for the calibration and validation of remotely sensed data.

Timon Miesner et al.

Status: open (until 19 Jul 2022)

Post a comment Subscribe to comment alert

RC1: 'Comment on essd-2022-152', Anonymous Referee #1, 07 Jun 2022 reply

The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-152/essd-2022-152-RC1-supplement.pdf
Reply

Citation: https://doi.org/10.5194/essd-2022-152-RC1
RC2: 'Comment on essd-2022-152', Anonymous Referee #2, 16 Jun 2022 reply

The paper by Miesner et al presents a database of forest surveys distributed across a large area characterized by ecoclimatic gradients in northeastern Siberia. This geographic region has important influences on global carbon and energy dynamics due to its large area and sensitivity to climate change. Despite this importance there is a relative paucity of freely available and easily accessible data that can be used to inform observational and modeling studies. From this perspective, this manuscript describes an important dataset that is worthy of publication and dissemination. Some revision is required before the paper and data can be considered further for publication.

The overall structure of the manuscript is appropriate. The data are described reasonably well, and the comparison with various gridded data products is useful for understanding the utility and limitations of the data set. There are a number of areas where additional detail and/or discussion are warranted. Some of these are noted in my specific comments below, but in general the discussion seems a bit superficial. In particular, it would be useful to have a deeper discussion of errors associated with the use of height as the primary unifying measurement, as well as the visual estimates of height. To my mind, DBH is a more common and useful metric than height, and seems an easier measure than the several required to triangulate height using a clinometer. Related, it seems that there is a high potential for error, that is hard to quantify, associated with the visual estimates of height. More critical discussion here would be nice.

Regarding the data, I found the files a bit unwieldy to work with. There is a lot of awkwardly structured metadata at the top of each file, before the actual data, making it difficult to read the files into a program like R. There also seems to be some redundant data here, in terms of site names, campaign, PI, etc. It may be more appropriate to have separate metadata files in order to make the data more user friendly/analysis ready.

These are primarily suggestions - having the data described and available is important, and this paper accomplishes that. The edits/revisions I suggest would improve the utility of the data set.

Below are a number of specific/minor comments, more editorial in nature.

L120: Perhaps Gridded Data Products would be a more appropriate term here. The CHELSA data is downscaled reanalysis/climate data, not a remote sensing data set.

L161: What variable is suitable for comparison with biomass? A little more detail/information here would be nice.

L242-3: Are the field data consistent with this? Are vegetation conditions consistent with recent disturbance?

L246: Areas with tree loss in the Hansen data set hold more standing dead than those without? Please clarify.

L247: Plots indicated as having forest loss do not have any disturbed trees? Please clarify.

L256: It would be good to discuss in a bit more depth the error implications of visual height estimates. Also, since DBH is a common measure that is often allometrically related to height, biomass, LAI and other ecologically important processes it would be good to discuss the tradeoffs associated with using height instead.

L261-4: These sentences are almost too vague to be helpful. What does it mean that the plots are not weighed accordingly? I’m not sure what the last sentence is supposed to mean.

L270-1: Are there any patterns here, geographic or otherwise? Are sites recently affected by fire indicated in the database?

L288-9: What about variables produced at 30m resolution? How to explain the mismatch for these?

L311: Extend should be extent

L325: See also papers by Kropp et al and Walker et al for evidence of drought stress in Siberian larch.

L349: I didn’t think the WorldClim data set was used in this study, please correct/clarify.

L355: I’m not sure this conclusion warrants a stand alone paragraph.

Figure 2 - panels should be labeled a, b, c, etc. and referred to as such in the text.

Figure 4 - it would be helpful if the figure capture indicated that these specific plots were selected to show examples of different size class distributions.

Figure 7 - are all of these results significant, and if so to what level?

Kropp, H., Loranty, M., Alexander, H. D., Berner, L. T., Natali, S. M., & Spawn, S. A. (2017). Environmental constraints on transpiration and stomatal conductance in a Siberian Arctic boreal forest. , (3), 487–497. https://doi.org/10.1002/2016JG003709

Kropp, H., Loranty, M. M., Natali, S. M., Kholodov, A. L., Alexander, H. D., Zimov, N. S., Mack, M. C., & Spawn, S. A. (2019). Tree density influences ecohydrological drivers of plant–water relations in a larch boreal forest in Siberia. , (7), e2132. https://doi.org/10.1002/eco.2132

Walker, X., Alexander, H. D., Berner, L., Boyd, M. A., Loranty, M. M., Natali, S., & Mack, M. C. (2021). Positive response of tree productivity to warming is reversed by increased tree density at the Arctic tundra-taiga ecotone. , cjfr-2020-0466. https://doi.org/10.1139/cjfr-2020-0466

Reply

Citation: https://doi.org/10.5194/essd-2022-152-RC2
RC3: 'Comment on essd-2022-152', Anonymous Referee #3, 19 Jun 2022 reply

Miesner and coauthors compile a data set of forest surveys from expeditions to the north-east of the Russian Federation. Data collection spans a long time period and includes about 10 tree species. They reported forest attributes, such as tree height, DBH, etc. They also compared their data with remote sensing datasets of forest height, biomass, and forest loss, and found the limitations in these remote sensing data. This dataset is invaluable to understand boreal forest conditions and their impacts on high-latitude carbon dynamics. In boreal forests, many tree species are short and are often classified as shrubs. In this work, how did the authors separate forests from shrublands?

In the abstract, it is necessary to indicate the time period of data collection.

Reply

Citation: https://doi.org/10.5194/essd-2022-152-RC3

Timon Miesner et al.

Data sets

Tree data set from forest inventories in north-eastern Siberia Miesner, Timon; Herzschuh, Ulrike; Pestryakova, Luidmila A; Wieczorek, Mareike; Kolmogorov, Alexei; Heim, Birgit; Zakharov, Evgenii S; Shevtsova, Iuliia; Epp, Laura Saskia; Niemeyer, Bastian; Jacobsen, Inga; Schröder, Julius; Trense, Darjona; Schnabel, Ellen; Schreiber, Xenia; Bernhardt, Nadine; Stuenzi, Simone Maria; Brieger, Frederic; Schulte, Luise; Smirnikov, Viktor; Gloy, Josias; von Hippel, Barbara; Jackisch, Robert; Kruse, Stefan https://www.pangaea.de/tok/45fd6ddb6a15ac79a71d0bf9a8e5bc492dda507a

Timon Miesner et al.

Viewed

Total article views: 356 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
274	68	14	356	3	2

HTML: 274
PDF: 68
XML: 14
Total: 356
BibTeX: 3
EndNote: 2

Views and downloads (calculated since 24 May 2022)

Month	HTML	PDF	XML	Total
May 2022	118	27	3	148
Jun 2022	152	41	11	204
Jul 2022	4	0	4

Cumulative views and downloads (calculated since 24 May 2022)

Month	HTML	PDF	XML	Total
May 2022	118	27	3	148
Jun 2022	152	41	11	204
Jul 2022	4	0	4

Viewed (geographical distribution)

Total article views: 334 (including HTML, PDF, and XML) Thereof 334 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 04 Jul 2022

Short summary

We present data which were collected on expeditions to the north-east of the Russian Federation: One table describes the 226 locations we visited during those expeditions, and the other one describes 40,2895 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements, or use data like the ones we present here.


Total:	0
HTML:	0
PDF:	0
XML:	0