Temporal stability of stratifications using different dendrometric variables and geostatistical interpolation

Reis, Aliny Aparecida dos; Ribeiro, Andressa; Mayrinck, Rafaella Carvalho; Mello, José Marcio de; Batista, Anderson Pedro Bernardina; Ferraz Filho, Antonio Carlos

doi:10.5902/1980509843274

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Artigo • Ciênc. Florest. 32 (1) • Jan-Mar 2022 • https://doi.org/10.5902/1980509843274 copy

Temporal stability of stratifications using different dendrometric variables and geostatistical interpolation

Estabilidade temporal das estratificações utilizando diferentes variáveis dendrométricas e interpolação geoestatística

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Stratifying a forest results in more precise and cheaper inventories. This study aimed to select the stratifying variable that estimates more precise and stable inventory over the years for a eucalyptus plantation in Minas Gerais state, Brazil. The continuous forest inventory was performed annually from 2.7 to 6.8 years, and based on the field measurements, arithmetic mean diameter (d), height (h), dominant height (Hdom), basal area (G), volume (V), and mean annual increment in volume (MAI) were calculated. Semivariograms were generated and the exponential, spherical and Gaussian models were fit for each stratifying variable for each measurement date. The models were assessed by the reduced mean error and its deviation, being the exponential model selected. Maps showing the spatial distribution of all variables were generated for each measurement age, using ordinary kriging. Next, the study area was divided in four strata based on each stratifying variable for each measurement age. The stability of each stratifying variables for each measurement age were assessed by: 1) coincident strata area; 2) stability of total strata area; 3) plot permanency on each stratum; and 4) inventory error using stratified random sampling procedures. All variables in all ages presented spatial dependence structure. G and Hdom were the stratifying variables that generated the most and the least coincident strata area over the years, respectively. G and height (h and Hdom) were the stratifying variables yielding the least and most plot stratum changes, respectively. The same trend was observed for the total strata area stability. Stratifying based on MAI and V yielded the smaller inventory error, and h and Hdom yielded the largest. G was selected as the best stratifying variable because it yielded small inventory errors and was the most stable variable in terms of coincident strata area, total strata area and plot stratum changes over the years.

Keywords:
Stratified random sampling; Eucalyptus; Forest inventory; Forest management

Variable	Age (year)	Mean	SD	CV (%)
d (cm)	2.7	11.37	0.84	7.39
	3.7	13.92	0.81	5.79
	4.8	15.14	0.75	4.96
	5.8	16.04	0.75	4.68
	6.8	16.33	0.77	4.72
h (m)	2.7	15.31	1.72	11.22
	3.7	19.88	1.65	8.30
	4.8	22.70	2.05	9.02
	5.8	25.11	1.94	7.74
	6.8	26.09	2.22	8.49
Hdom (m)	2.7	16.41	1.73	10.54
	3.7	21.00	1.61	7.64
	4.8	24.14	1.93	7.99
	5.8	26.86	2.01	7.49
	6.8	27.84	2.33	8.36
G (m² ha^-1)	2.7	10.93	1.59	14.52
	3.7	16.33	1.81	11.09
	4.8	19.24	1.81	9.42
	5.8	21.52	1.85	8.58
	6.8	22.23	1.84	8.30
V (m³ ha^-1)	2.7	80.31	17.23	21.45
	3.7	150.36	24.45	16.26
	4.8	202.90	31.70	15.62
	5.8	253.60	36.02	14.20
	6.8	276.26	39.38	14.25
MAI (m³ ha^-1 year^-1)	2.7	29.01	5.66	19.51
	3.7	40.38	6.21	15.37
	4.8	41.98	6.27	14.94
	5.8	43.39	6.01	13.84
	6.8	40.69	5.79	14.23

Variable	d						h					Hdom					G					MAI					V
Variable	Age	2.7	3.7	4.8	5.8	6.8	2.7	3.7	4.8	5.8	6.8	2.7	3.7	4.8	5.8	6.8	2.7	3.7	4.8	5.8	6.8	2.7	3.7	4.8	5.8	6.8	2.7	3.7	4.8	5.8	6.8
d	2.7	100
	3.7	63	100
	4.8	59	70	100
	5.8	55	80	75	100
	6.8	54	70	78	83	100
h	2.7	62	48	43	40	41	100
	3.7	58	58	41	50	49	64	100
	4.8	63	57	48	50	51	62	75	100
	5.8	50	60	39	56	53	43	70	66	100
	6.8	49	59	38	55	53	42	70	66	89	100
Hdom	2.7	63	49	42	42	43	80	69	66	50	50	100
	3.7	59	55	45	49	52	66	75	67	52	51	70	100
	4.8	60	57	44	50	50	60	77	84	66	66	67	73	100
	5.8	53	60	45	55	55	50	74	71	84	82	56	60	74	100
	6.8	50	46	53	49	54	55	54	59	43	43	62	65	61	54	100
G	2.7	72	44	49	42	46	60	46	49	35	35	59	55	48	40	50	100
	3.7	69	64	72	59	61	53	44	54	37	35	56	55	51	44	57	68	100
	4.8	58	51	77	56	63	44	33	45	26	25	46	45	41	35	57	62	85	100
	5.8	61	62	74	62	65	51	44	54	38	35	54	53	51	44	58	62	84	81	100
	6.8	54	60	71	63	70	52	48	55	43	42	53	56	52	47	57	58	75	73	84	100
MAI	2.7	58	36	42	36	42	68	44	44	26	27	59	56	43	33	54	81	59	55	54	54	100
	3.7	73	60	61	53	56	63	61	62	43	44	65	70	62	51	57	73	79	69	74	72	62	100
	4.8	71	63	64	57	59	63	63	74	50	50	65	68	69	57	59	65	76	64	71	71	55	81	100
	5.8	65	64	59	61	63	57	68	68	62	60	64	70	70	65	62	59	65	55	68	71	50	77	77	100
	6.8	59	66	53	65	69	56	74	68	72	73	63	68	70	72	62	49	51	40	53	62	46	63	67	78	100
V	2.7	70	43	47	39	43	74	57	54	36	37	70	65	56	43	57	77	64	57	59	55	80	72	65	62	53	100
	3.7	64	66	62	59	61	57	67	62	50	51	63	71	66	58	60	62	73	63	72	71	54	84	77	80	68	66	100
	4.8	67	65	66	60	63	59	65	74	52	52	62	68	71	59	62	61	74	63	72	71	54	78	89	80	68	65	82	100
	5.8	56	66	54	66	68	50	68	63	74	70	57	64	67	71	57	50	55	44	58	64	42	64	66	84	82	52	74	71	100
	6.8	57	60	61	62	73	57	65	62	59	59	61	68	62	63	70	54	59	52	63	71	55	66	67	77	85	61	71	72	78	100

Variable	Number of plots by class of change
Variable	0x	1x	2x	3x	4x	Score
d	51	31	15	18	1	119
h	38	38	28	12	0	130
Hdom	30	36	35	13	2	153
G	44	45	24	3	0	102
V	39	46	21	10	0	118
MAI	42	41	24	7	2	118

Age (years)	d	h	Hdom	G	V	MAI
2.7	1.68	1.95	2.24	1.33	1.26	1.36
3.7	1.44	1.55	1.74	1.51	0.95	1.16
4.8	1.92	1.25	1.93	1.94	0.89	0.94
5.5	1.45	0.84	1.21	1.46	0.77	0.75
6.8	1.66	0.97	1.58	1.38	0.74	0.99
Mean	1.63	1.31	1.74	1.53	0.92	1.04
Standard deviation	0.20	0.45	0.39	0.24	0.21	0.23
CV (%)	12.06	34.42	22.16	15.87	22.38	22.15

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E-mail: cienciaflorestal@ufsm.br

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[1] Authorship Contribution

1 - Aliny Aparecida dos Reis Contribution: Conceptualization, Formal Analysis, Methodology, Visualization, Writing - original draft, Writing - review & editing 2 - Andressa Ribeiro Contribution: Visualization, Writing - review & editing 3 - Rafaella Carvalho Mayrinck Contribution: Visualization, Writing - review & editing 4 - José Marcio de Mello Contribution: Visualization, Resources, Supervision, Writing - review & editing 5 - Anderson Pedro Bernardina Batista Contribution: Visualization, Writing - review & editing 6 - Antonio Carlos Ferraz Filho Contribution: Visualization, Validation, Supervision, Writing - review & editing