DOI: 10.5281/zenodo.12646047
title: High-resolution harvester data for estimating rolling resistance and forest trafficability

description: This study describes an extensive, high-resolution data on rolling resistance coefficient collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50~km of harvester routes and used in the following study:

Salmivaara, A., Holmström, E., Kulju, S., Ala-Ilomäki, J., Virjonen, P., Nevalainen, P., Heikkonen, J., and Launiainen, S. 2024.High-resolution harvester data for estimating rolling resistance and forest trafficability. European Journal of Forest Reseach. doi: 10.1007/s10342-024-01717-6

The coordinates are in metric system, and the relative position between sites are maintained. However, they have been converted and do not refer to actual locations.

Two datasets collected by a Ponsse Ergo harvester are available: 

1) All non-frost period of 2021, with 8m*8m grid means of rolling resistance ('grid mean rr'), along with grid mean longitudinal inclination of the machine ('grid mean pitch'), time, topographic wetness index ('twi') and binary indication whether a road is within 4m radius from the grid centerpoint ('roads'). 

2) Data including all 11 test sites with all first pass harvester machine data ('time', 'E_conv', 'N_conv', 'dtorq', 'drpm', 'peng', 'cooling', 'phydr', 'pwheels', 'v_ms', 'fmotive', 'slopeforce', 'normalforce', 'pitch', 'rr') and grid means of spatial data ('grid', 'grid mean rr', 'grid mean pitch', 'roads', 'site', 'slope_rad_terrain', 'bmroot_all'. 'c_bmroot_ratio', 'broadlv_rootbm_ratio', 'p_bmroot_ratio','s_bmroot_ratio', 'age', 'diameter', 'standheight', 'b_vol', 'b_pulp_vol', 'b_timber_vol', 's_vol', 's_pulp_vol', 's_timber_vol', 'p_vol', 'p_pulp_vol', 'p_timber_vol', 'broadlv_vol','broadlv_pulp_vol', 'broadlv_timber_vol', 'canopy_cov', 'broadlv_canopy_cov', 'ba', 'volume', 'dem', 'devMeanElev16', 'devMeanElev8', 'diffMeanElev8', 'erosion risk', 'tri', 'roughness_mag', 'roughness_scale', 'dtw00_5ha', 'dtw_01_0ha', 'dtw_04_0ha', 'dtw_10_0ha', 'twi', 'sat_deficit_doy', vol_moisture_doy, 'bare_rock_areas', 'lakes', 'streams', 'peat', 'topsoil', 'aspect', 'aspect_sin', 'aspect_cos', 'main_class_fertility_class_comb')

 
description: README Arcnet-data

'time' = time stamp (yyyymmdd_hhmmss)

'dtorq' = diesel torque (Nm)

'drpm' = diesel RPM (1/s)

'peng' = power output by diesel engine (kW)

'cooling' =hydraulic system cooling current (mA)

'phydr' =power output by hydraulic engine (kW)

'pwheels' =power on wheels (kW)

'v_ms' =speed (m/s)

'fmotive' = motive force (kN)

'slopeforce' =slope force (kN)

'normalforce' =normal force (kN)

'pitch' =longitudinal inclination angle (deg) (measured by an inclinator)

 

README Geospatial data, feature names described in Table S1 of the article  (Salmivaara, A., Holmström, E., Kulju, S., Ala-Ilomäki, J., Virjonen, P., Nevalainen, P., Heikkonen, J., and Launiainen, S. 2024.High-resolution harvester data for estimating rolling resistance and forest trafficability. European Journal of Forest Reseach. doi: 10.1007/s10342-024-01717-6).

 

LUKE ms-NFI, https://kartta.luke.fi/index.html,  MÃ¤kisara, K., Katila, M., PerÃ¤saari, J.(2019): The Multi-Source National Forest Inventory of Finland - methods and results 2015.

 

bm_kuusi_elavatoksat = Biomass, spruce, living branches 2019 (10 kg/ha)

bm_kuusi_juuret = Biomass, spruce, roots, d > 1 cm 2019 (10 kg/ha)

bm_kuusi_kanto = Biomass, spruce, stump 2019 (10 kg/ha)

bm_kuusi_kuolleetoksat = Biomass, spruce, dead branches 2019 (10 kg/ha)

bm_kuusi_latva = Biomass, spruce, stem residual 2019 (10 kg/ha)

bm_kuusi_neulaset = Biomass, spruce, foliage 2019 (10 kg/ha)

bm_kuusi_runkokuori  = Biomass, spruce, stem and bark 2019 (10 kg/ha)

bm_lehtip_elavatoksat = Biomass, broad-leaved trees, living branches 2019 (10 kg/ha)

bm_lehtip_juuret = Biomass, broad-leaved trees, roots, d > 1 cm 2019 (10 kg/ha)

bm_lehtip_kanto = Biomass, broad-leaved trees, stump 2019 (10 kg/ha)

bm_lehtip_kuolleetoksat = Biomass, broad-leaved trees, dead branches 2019 (10 kg/ha)

bm_lehtip_latva = Biomass, broad-leaved trees, stem residual 2019 (10 kg/ha)

bm_lehtip_neulaset = Biomass, broad-leaved trees, foliage 2019 (10 kg/ha)

bm_lehtip_runkokuori = Biomass, broad-leaved trees, stem and bark 2019 (10 kg/ha)

bm_manty_elavatoksat = Biomass, pine, living branches 2019 (10 kg/ha)

bm_manty_juuret = Biomass, pine, roots, d > 1 cm 2019 (10 kg/ha)

bm_manty_kanto = Biomass, pine, stump 2019 (10 kg/ha)

bm_manty_kuolleetoksat = Biomass, pine, dead branches 2019 (10 kg/ha)

bm_manty_latva = Biomass, pine, stem residual 2019 (10 kg/ha)

bm_manty_neulaset = Biomass, pine, foliage 2019 (10 kg/ha)

bm_manty_runkokuori = Biomass, pine, stem and bark 2019 (10 kg/ha)

fra_luokka = Land class based on FAO FRA (1-4)

1=metsÃ¤ forest

2=muu puustoinen maa, other land with tree cover

3=muu maa, other land cover

4=muu maa, muu puita kasvava maa, other land cover with tree cover

ika = Stand age 2019 (year)

kasvupaikka = Site fertility class 2019 (1-10): 

1=lehto tai vastaava suo (tvs) herb-rich forest /herb-rich hw-spruce swamps, pine mires, fens,  

2=lehtomainen kangas tvs,herb-rich heat f. /V.myrtillus / tall sedge spruce swamps, tall sedge pine fens, tall sedge fens, 

3=tuore kangas tvs, mesic heath f. / Carex clobularis / V.vitis-idaea swamps, Carex globularis pine swamps, low sedge (oligotrophic) fens,

4=kuivahko kangas tvs, sub-xeric heath f./Low sedge, dwarf-shrub & cottongrass pine bogs, ombo-oligotrophic bogs,

5= kuiva kangas tvs , xeric heath f. / S.fuscum pine bogs, ombotrophic and S.fuscum low sedge bogs.

6=karukkokangas tvs, barren heath f. / 

7=kalliomaa, hietikko tai vesijÃ¤ttÃ¶maa, rock,cliff or sand f. 

8=lakimetsÃ¤, 

9=tunturikoivikko, 

10=avotunturi

keskilapimitta = Stand mean diameter of 2019 (cm)

keskipituus = Stand mean height 2019 (dm)

koivukuitu = Volume, birch pulpwood 2019 (m3/ha)

koivutukki = Volume, birch saw timber 2019 (m3/ha)

koivu = Volume, birch 2019 (m3/ha)

kuusikuitu = Volume, spruce pulpwood 2019 (m3/ha)

kuusitukki = Volume, spruce saw timber 2019 (m3/ha)

kuusi = Volume, spruce 2019 (m3/ha)

latvuspeitto = Canopy cover 2019 (%)

lehtip_latvuspeitto = Canopy cover of broad-leaved trees 2019 (%)

maaluokka Land class 2019 (1-3)

1= metsÃ¤maa, forest land

2= kitumaa,

3= joutomaa,

mantykuitu = Volume, pine pulpwood 2019 (m3/ha)

mantytukki = Volume, pine saw timber 2019 (m3/ha)

manty = Volume, pine 2019 (m3/ha)

muulpkuitu = Volume, other broad-leaved trees pulpwood 2019 (m3/ha)

muulptukki = Volume, other broad-leaved trees saw timber 2019 (m3/ha)

muulp = Volume, other broad-leaved trees 2019 (m3/ha)

paatyyppi = Site main class 2019 (1-4); 1=mineral soil, 2= spruce mire, 3= pine bog, 4=open peatland

ppa = Stand basal area 2019 (m2/ha)

tilavuus = Volume, the growing stock 2019 (m3/ha)

mista = Data source index, MSNFI 2019

1?=2019 ennuste

2=017 ennuste

3=2015 ennuste

4=2013 ennuste

bmroot_all = sum of all root biomass [tons/ha] 

bmroot_pine_ratio = ratio of pine root biomass to all biomass [varying between 0-1]

bmroot_conifer_ratio = ratio of conifer roots biomass (spruce+pine) to all root biomass [varying between 0-1]

bmroot_spruce_ratio = ratio of spruce roots biomass (spruce+pine) to all root biomass [varying between 0-1]

bmroot_decid_ratio= ratio of deciduous tree roots biomass (spruce+pine) to all root biomass [varying between 0-1]

site_type_comb = combination of site main type and site fertility class as integer, where first number indicates site main type (1=mineral, 2=peatland (spruce fen), 3= peatland (pine bog), 4= open peat land and 

second number indicates the fertility class, the smaller the number the more fertile the site is. 

Fertility classes (1–7) of tmineral land sites are based mainly on the classi?cation of Cajander (1926, 1949) / Muukkonen & Mäkipää 2006 (Empirical_biomass_models_of_understorey_vegetation_in_boreal_forests_according_to_stand_and_site_attributes):

11.  Herb-rich forest

12.  Herb-rich heath forest

13.  Mesic heath forest

14.  Sub-xeric heath forest

15.  Xeric heath forest.

16.  Barren heath forest.

17.  Rock, cliff, or sand

Fertility classes (1–5) of peatland sites follow the classi?cation:

21.  Herb-rich hardwood-spruce swamps, 

31.  herb-rich pine fens; herb-rich fens (mesotrophic).

22.  Vaccinium myrtillus or tall sedge spruce swamps; 

32. tall sedge pine fens; tall sedge fens (meso-oligotrophic).

23.  Carex  globularis and  Vaccinium  vitis-idaea  spruce  swamps and spruce-pine swamps; 

33. Carex globularis  pine swamps; low sedge fens (oligotrophic).

24. low  sedge  Sphagnum papillosum  bogs  (ombro-oligo-trophic)

34.  Low  sedge,  dwarf-shrub,  and  cottongrass pine  bogs;  low  sedge  Sphagnum papillosum  bogs  (ombro-oligo-trophic).

35.  Sphagnum fuscum pine bogs; ombrotrophic and Sphagnum fuscum low sedge bogs (ombrotrophic)

 

in all ms-NFI:

32766 is a missing value: the pixel belongs to forestry land but without satellite image cover

32767 is a null value: the pixel does not belong to forestry land or is outside of the country

-9999 is nodata value

 

SPAFHY outputs

sat_deficit // saturation deficit (units m). TWI adjusted based on prevailing hydrological state. sat_deficit == 0 where groundwater at the surface, positive elsewhere. IN ML MODEL, USE INSTEAD OF TWI OR DTW!

 

vol_moisture.asc // volumetric soil moisture in top 30cm layer (root zone). IN ML MODEL, USE AS ADDITIONAL INDEPENDENT VARIABLE

 

 

kallio, source: National Land Survey of Finland Topographic Database 2021, https://docplayer.fi/6838792-Maanmittauslaitoksen-maastotietokohteet.html

34100 = kalliomaa

-9999 = nodata

 

soil_merged_pintamaa_cl, source: GSF The superficial deposits 1:20 000/1:50 000/1:200 000, https://tupa.gtk.fi/paikkatieto/meta/maapera_200k.html, https://hakku.gtk.fi/fi/

195601;Taytemaa (Ta)

195111;Kalliomaa (Ka) RT

195603;Vesi (Ve)

195214;Hiekkamoreeni (Mr) RT

195413;Savi (Sa) RT

195411;hieno Hieta (HHt) RT

195512;Saraturve (Ct) RT

195314;Hiekka (Hk) RT

195313;Sora (Sr) RT

195511;Lieju (Lj) RT

195315;karkea Hieta (KHt) RT

195513;Rahkaturve (St) RT

195412;Hiesu (Hs) RT

195602;Kartoittamaton (0)

195215;Hienoainesmoreeni (HMr) RT

19541321;Liejusavi (LjSa) RT

19541121;liejuinen hieno Hieta (LjHHt) RT

19541221;Liejuhiesu (LjHs) RT

19531521;liejuinen Hieta (karkea) (LjHt) RT

195311;Lohkareita (Lo) RT

195514;Turvetuotantoalue (Tu) RT

195312;Kivia (Ki) RT

195113;Rapakallio (RpKa) RT

195213;Soramoreeni (SrMr) RT

19531421;liejuinen Hiekka (LjHk) RT

195112;Rakka (RaKa) RT

19551892;Paksu turvekerros (Tvp) RT, turvekerros >0.6m

195210;Sekalajitteinen maalaji, pÃ¤Ã¤lajitetta ei selvitetty (SY) RT

195310;Karkearakeinen maalaji, pÃ¤Ã¤lajitetta ei selvitetty (KY) RT

19551822;Soistuma (Tvs) RT, turvekerros <0.3m

19551891;Ohut turvekerros (Tvo) RT, turvekerros 0.3-0.6m

195110;Kalliopaljastuma (KaPa) RT

195410;Hienojakoinen maalaji, pÃ¤Ã¤lajitetta ei selvitetty (HY) RT

195618;Liejuinen hienorakeinen maalaji RT

#Classification to following 4 classes can be applied:

FineTextured = [19531521, 195412, 19541221, 195511, 195413, 195410,

                    19541321, 195618]

    Peats = [195512, 195513, 195514, 19551822, 19551891, 19551892]

    CoarseTextured = [195213, 195314, 19531421, 195313, 195310, 195111, 195311, 195312, 195113, 195112, 195110] 

    MediumTextured = [195315, 19531521, 195215, 195214, 195601, 195411, 195210] 

    Water = [195603]

 

 

soistuma, source: National Land Survey of Finland Topographic Database 2021, https://docplayer.fi/6838792-Maanmittauslaitoksen-maastotietokohteet.html

35300 soistuma, shallow peat layer

   -9999 nodata

 

suo, source: National Land Survey of Finland Topographic Database 2021 

35412 =Suo, helppokulkuinen metsÃ¤Ã¤ kasvava /peatland

35411 =Suo, helppokulkuinen puuton  

35421 =Suo, vaikeakulkuinen puuton 

35422 =Suo, vaikeakulkuinen metsÃ¤Ã¤ kasvava 

   -9999 nodata

 

roads, source: National Land Survey of Finland Topographic Database 2021 

12111 = Autotie Ia 

12112 = Autotie Ib

12121 = Autotie IIa 

12122 = Autotie IIb 

12131 = Autotie IIIa 

12132 = Autotie IIIb 

12141 = Ajotie

12151 = Lautta

12152 = Lossi

12312 = Talvitie

12313 = Polku

12314 = KÃ¤vely- ja pyÃ¶rÃ¤tie

12316 = Ajopolku 

 

railroads, source: National Land Survey of Finland Topographic Database 2021 

14111, 14112 = rautatie/railroad

14121 = Kapearaiteinen rautatie

14131 = metro

 

lakes, source: National Land Survey of Finland Topographic Database 2021 

36200

 

sea, source: National Land Survey of Finland Topographic Database 2021 

36211

 

streams1, naroow stream,source: National Land Survey of Finland Topographic Database 2021 

36311 = Virtavesi/stream, < 2m

36312 = Virtavesi/stream  2-5m

 

streams2, wider streams, >5m wide river, ditch, stream (joet, purot, ojat),Luonnollisen vedenkierron osana oleva pysyvÃ¤sti tai ajoittain (metsÃ¤ojat) veden peittÃ¤mÃ¤ alue, jossa on virtaama. source: National 

Land Survey of Finland Topographic Database 2021 

36313 >5m wide stream, river, ditch

 

sahkolinja,source: National Land Survey of Finland Topographic Database 2021

22311 = SuurjÃ¤nnite (110 kV ja sitÃ¤ suurempi)  

22312 = JakelujÃ¤nnite (20 kV ja sitÃ¤ suurempi mutta alle 110 kV) 

 

twi, source: Salmivaara, A. Topographical Wetness Index for Finland, 16m, 2016. CSC - IT Center for Science Ltd. http://urn.fi/urn:nbn:fi:csc-kata20170511114638598124

TWI index [unitless], the higher the wetter

 

 

korjuukelpoisuus, source: Suomen metsÃ¤keskus

1 = kelirikko / thawing period; frost damage period / frost heaving / poor sleighing conditions

2 = normaali kesÃ¤, kivennÃ¤ismaa / normal summer, mineral soil site

3 = kuiva kesÃ¤, kivennÃ¤ismaa / dry summe mineral soil site

4 = normaali kesÃ¤, turvemaa / normal summer, peatland site

5 = kuiva kesÃ¤, turvemaa / dry summer peatland site 

6 = talvi / winter site

254 = vesistÃ¶, water

255 nodata

 

erosion_risk,  the average susceptibility of surface soil to water erosion (t/ha/y), source: : RÃ¤sÃ¤nen, T.A., TÃ¤htikarhu, M., Uusi-KÃ¤mppÃ¤, J., Piirainen, S., Turtola, E., 2021. Improving the agricultural erosion management in Finland through high-resolution data. Hydrol. Earth Syst. Sci. Discuss. 1â€“30. https://doi.org/10.5194/hess-2021-457 

(t/ha/y)

 

dtw10_0ha, source: Salmivaara, A. (2020). DTW-kosteusindeksikartta, 2m, CSC â€“ Tieteen

tietotekniikan keskus Oy, http://urn.fi/urn:nbn:fi:att:3403a010-b9d0- 4948-8f9f-2bc4ca763897  

Depth-to-water soil moisture index [m] (the lower the wetter) calculated with 10 ha threshold indicating dryer conditions

 

dtw04_0ha, source: Salmivaara, A. (2020). DTW-kosteusindeksikartta, 2m, CSC â€“ Tieteen

tietotekniikan keskus Oy, http://urn.fi/urn:nbn:fi:att:3403a010-b9d0- 4948-8f9f-2bc4ca763897  

Depth-to-water soil moisture index [m] (the lower the wetter) calculated with 4 ha threshold indicating normal/dry summer conditions

 

dtw01_0ha, source: Salmivaara, A. (2020). DTW-kosteusindeksikartta, 2m, CSC â€“ Tieteen

tietotekniikan keskus Oy, http://urn.fi/urn:nbn:fi:att:3403a010-b9d0- 4948-8f9f-2bc4ca763897  

Depth-to-water soil moisture index [m] (the lower the wetter) calculated with 1 ha threshold indicating moist conditions

 

dtw00_5ha, source: Salmivaara, A. (2020). DTW-kosteusindeksikartta, 2m, CSC â€“ Tieteen

tietotekniikan keskus Oy, http://urn.fi/urn:nbn:fi:att:3403a010-b9d0- 4948-8f9f-2bc4ca763897 

Depth-to-water soil moisture index [m] (the lower the wetter) calculated with 0.5 ha threshold indicating very moist conditions

 

dem, source: NLS Topographic Database

mean altitude above sea level,[m]

 

aspect, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

slope aspect (i.e. slope orientation in degrees clockwise from north) for each grid cell in an input digital elevation model (DEM).

 

slope_deg, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

slope gradient (i.e. slope steepness in DEGREES) for each grid cell in an input digital elevation model (DEM).

 

slope_rad, slope in radians, calculated from  slope_deg by multiplying with (pi/180).

 

devMeanElev16, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

the difference between the elevation of each grid cell and the mean elevation of the centering local neighbourhood, normalized by standard deviation. 16m window

 

devMeanElev8, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

the difference between the elevation of each grid cell and the mean elevation of the centering local neighbourhood, normalized by standard deviation. 8m window

 

diffMeanElev8, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

calculate the difference between the elevation of each grid cell and the mean elevation of the centering local neighbourhood. 8m window

 

roughness_scale, roughness scale, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

Calculates surface roughness over a range of spatial scales, Minimum search neighbourhood radius in grid cells: 2 maximum search neighbourhood radius in grid cells: 16, Step size 2

 

roughness_mag,roughness magnitude, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

Calculates surface roughness over a range of spatial scales, Minimum search neighbourhood radius in grid cells: 2 maximum search neighbourhood radius in grid cells: 16, Step size 2

 

tri, calculated from dem with whiteboxtools (https://docs.csc.fi/apps/whiteboxtools/), https://jblindsay.github.io/ghrg/WhiteboxTools/index.html

The terrain ruggedness index (TRI) is a measure of local topographic relief. The TRI calculates the root-mean-square-deviation (RMSD) for each grid cell in a digital elevation model (DEM), calculating the residuals (i.e. elevation differences) between a grid cell and its eight neighbours.