Remote-sensing support for the Estonian National Forest Inventory, facilitating the construction of maps for forest height, standing-wood volume, and tree species composition

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Predicted dominant species and field observations on 6,239 NFI sample plots_Tabel 5_ Prognoositud enamuspuuliik võrrelduna 6239 SMI proovitüki andmetega_

NFI / SMI	Predicted dominant species* / Prognoositud enamuspuuliik*
	KS	KU	MA	LV	LM	HB	XK
KS	1,110	186	114	153	137	106	16
KU	201	694	162	47	12	37	6
MA	173	211	1,532	4	8	15	2
LV	93	25	3	334	14	23	7
LM	67	10	7	25	123	20	4
HB	81	34	5	65	23	108	13
XK	47	13	15	72	30	35	17

Standing-wood volume M (m3 ha−1) prediction errors under model (2), by dominant tree species, with ALS data distinguished according to spring (SP) and summer (SU)_Tabel 3_ Esimese rinde tüvemahu M (m3 ha−1) prognoosmudeli (2) vead peapuuliikide järgi_ Eristatud on suvised (SU) ja kevadised (SP) laserskaneerimise lennud_

ALS data ALS andmed	Target areaPiirkond	Variable*Tunnus*	Dominant species**/ Enamuspuuliik**
			HB	KS	KU	LM	LV	MA	XK
SP 2017	SW	M̅	190	161	179	263	152	250	233
SP 2017	SW	RSE	77	67	70	116	96	83	81
SP 2017	SW	MRE	−37	11	18	−45	25	−1	−59
SP 2017	SW	N	22	93	43	13	7	106	7
SU 2017	SE	M̅	265	190	287	331	199	302	87
SU 2017	SE	RSE	102	92	106	90	76	98	73
SU 2017	SE	MRE	−3	37	−13	−51	22	−26	57
SU 2017	SE	N	18	83	57	8	18	92	4
SP 2018	NE	M̅	157	197	235	188	145	237	166
SP 2018	NE	RSE	95	60	73	78	53	56	30
SP 2018	NE	MRE	−37	−3	6	−26	−9	10	−26
SP 2018	NE	N	19	59	66	10	31	120	3
SU 2018	NW	M̅	239	162	248	314	158	211	235
SU 2018	NW	RSE	104	59	91	125	59	72	144
SU 2018	NW	MRE	54	31	−29	−17	12	−24	144
SU 2018	NW	N	9	99	54	16	36	93	1

Parameters for the forest-height H (dm) prediction model (1)_ ALS data are from spring (SP) and summer (SU), with RSE the model residual standard error, R2 the coefficient of determination, and DF the number of degrees of freedom_ Insignificant values (p > 0_05) are in italics_Tabel 1_ Metsa kõrguse H (dm) prognoosmudeli (1) parameetrid_ Eristatud on suvised (SU) ja kevadised (SP) laserskaneerimise lennud_ RSE on mudeli jääkhälve, R2 on determinatsioonikordaja ja DF on vabadusastmete arv_ Mitteolulised väärtused (p > 0,05) on kursiivis_

ALS flightALS lend	BlockAla	Model parameters* / Mudeli parameetrid*
		a	b	RSE (dm)	R2 %	DF
SP 2017	SW	9.05	11.86	24	89.5	292
SU 2017	SE	6.09	11.58	21	94.7	281
SP 2018	NE	13.80	11.47	17	94.8	313
SU 2018	NW	3.94	12.07	21	91.8	312

The average difference between predicted forest height Ĥ (m) and standing-wood volume Mˆ(m3 ha-1)\hat M\left( {{{\rm{m}}^3}\;{\rm{h}}{{\rm{a}}^{ - 1}}} \right) in the case of both evergreen (EGR) and deciduous (DEC) stands, upon comparing ALS springtime (SP) against summer (SU) data_Tabel 4_ Prognoositud kõrguse Ĥ (m) ja esimese rinde tüvemahuMˆ(m3 ha-1)\hat M\left( {{m^3}\;h{a^{ - 1}}} \right)keskmine erinevus okaspuupuistutes (EGR) ja lehtpuupuistutes (DEC) kasutades suvised (SU) ja kevadisi (SP) laser-skaneerimise andmeid_

Block SPAla SP	Block SUAla SU	(HˆSP-HˆSU)¯, m\overline {\left( {{{\hat H}_{SP}} - {{\hat H}_{SU}}} \right)} ,\;{\rm{m}}		(MˆSP-MˆSU)¯, m3 ha-1\overline {\left( {{{\hat M}_{SP}} - {{\hat M}_{SU}}} \right)} ,\;{{\rm{m}}^3}\;{\rm{h}}{{\rm{a}}^{ - 1}}
		EGR	DEC	EGR	DEC
SW	SE	0.53	−0.90	34.5	−46.5
NE	NW	0.13	−0.71	40.4	−38.0

Parameters for the standing-wood volume M (m3 ha−1) prediction model (2) for the upper layer_ ALS data are from springtime (SP) and summer (SU)_ RSE is the model residual standard error, R2 the coefficient of determination, and DF the number of degrees of freedom_ Insignificant values (p > 0_05) are in italics_Tabel 2_ Esimese rinde tüvemahu M (m3 ha−1) prognoosmudeli (2) parameetrid_ Eristatud on suvised (SU) ja kevadised (SP) laserskaneerimise lennud_ RSE on mudeli jääkhälve, R2 on determinatsioonikordaja ja DF on vabadusastmete arv_ Mitteolulised väärtused (p > 0_05) on kursiivis_

ALS flightALS lend	Model parameters* / Mudeli parameetrid*
	a	b	c	d	RSE (m3 ha−1)	R2	DF
SP 2017	1.438	1.264	0.200	0.416	78.6	84.2	287
SU 2017	0.083	1.452	0.121	0.861	97.0	85.2	276
SP 2018	1.277	1.233	0.308	0.454	65.7	91.7	308
SU 2018	0.298	1.492	0.003	0.598	76.3	85.1	304