Effects of flight and smoothing parameters of number of trees with aerial imagery in a native Brazilian atlantic forest remnant

Pertille, Carla Talita; Gomes, Karla Mayara Almada; Santos, Darcy Maria da Conceição Laura dos; Veras, Hudson Franklin Pessoa; Mohan, Midhun; Sanquetta, Carlos Roberto; Behling, Alexandre; Corte, Ana Paula Dalla

doi:10.1590/01047760202330013338

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FOREST MANAGEMENT • CERNE 30 • 2024 • https://doi.org/10.1590/01047760202330013338 copy

Effects of flight and smoothing parameters of number of trees with aerial imagery in a native Brazilian atlantic forest remnant

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Background:

The objective of this study was to detect native trees from different flight configurations and smoothing techniques in Canopy Height Models (CHMs) in a native remnant in the municipality of Curitiba, State of Paraná, Brazil. To do so, eight flights were carried out with a Phantom 4, with two flight planning applications (Litchi and Pix4Dcapture) and two flight arrangements (single and double), totaling four flights for each application. All flights were processed using the Pix4Dmapper program. The LiDAR database was obtained with a DJI Matrice 300 system and from this data, the Digital Terrain Model (DTM) of the area was extracted. From the UAV data, the Digital Surface Model (DSM) of each flight was obtained. Subtracting each DSM from the DTM resulted in the CHMs for each UAV flight flown. The CHMs were smoothed with the CHMsmoothing function and three search window sizes were tested (6.5 x 6.5, 8 x 8, and 10 x 10).

Results:

The results of the ITD approach revealed that in unsmoothed and smoothed CHMs, the search window of size 8 resulted in the best precision metrics, with the highest recall, precision, and F-score values. In the smallest window size, there was the highest number of false positives while in the largest window size, the omitted trees were more representative.

Conclusion:

The best combination between flight parameters and smoothing techniques was with the Litchi application, with a single flight and 80% lateral and longitudinal overlap, resulting in individuals detected with an F-score of 0.94.

Keywords:
Remote sensing; LM algorithm; canopy height model.

HIGHLIGHTS

The lateral and longitudinal overlap evaluated as well as the applications tested did not differ in terms of the number of trees detected. Flights with a double design showed greater drag in the images generated. In the tested algorithm, the smaller search window size resulted in more falsely detected trees and the larger search window size caused greater omission of individuals. The simple flight with 80% lateral and longitudinal overlap detected trees with an F-score of 0.94.

Parameter	DF1	DF2	SF1	SF2
Speed (m/s)	8	8	8	8
Height (m)	120	120	120	120
Distance between lines (m)	51	33	33	25
Theoretical GSD (cm)	3.16	3.16	3.16	3.16
Side overlap (%)	70	80	80	85
Longitudinal overlap (%)	70	80	80	85

F	WS	NT	TP	FN	FP	R	P	F	NT	TP	FN	FP	R	P	F
DF1	6.5	465	364	164	45	0.69	0.89	0.78	461	267	199	63	0.57	0.81	0.67
DF2	6.5	462	299	261	81	0.53	0.79	0.64	460	245	260	58	0.49	0.81	0.61
SF1	6.5	462	254	288	70	0.47	0.78	0.59	462	227	225	53	0.50	0.81	0.62
SF2	6.5	462	230	287	62	0.44	0.79	0.57	462	208	235	42	0.47	0.83	0.60
DF1	8	465	315	150	69	0.68	0.82	0.74	460	350	47	26	0.88	0.93	0.91
DF2	8	460	340	86	33	0.80	0.91	0.85	461	334	55	28	0.86	0.92	0.89
SF1	8	460	378	28	22	0.93	0.95	0.94	461	304	106	30	0.74	0.91	0.82
SF2	8	460	318	87	24	0.79	0.93	0.85	462	301	107	26	0.74	0.92	0.82
DF1	10	465	105	329	18	0.24	0.85	0.38	462	139	179	27	0.44	0.84	0.57
DF2	10	462	96	245	23	0.28	0.81	0.42	462	118	203	25	0.37	0.83	0.51
SF1	10	462	106	226	17	0.32	0.86	0.47	461	120	213	27	0.36	0.82	0.50
SF2	10	463	107	233	24	0.31	0.82	0.45	460	125	222	22	0.36	0.85	0.51

F	WS	NT	TP	FN	FP	R	P	F	NT	TP	FN	FP	R	P	F
DF1	462	237	305	66	0.44	0.78	0.56	462	199	292	66	0.41	0.75	0.53	462
DF2	460	218	308	80	0.41	0.73	0.53	461	211	295	28	0.42	0.88	0.57	460
SF1	461	264	244	44	0.52	0.86	0.65	460	251	249	37	0.5	0.87	0.64	461
SF2	460	244	301	37	0.45	0.87	0.59	461	207	213	31	0.49	0.87	0.63	460
DF1	460	353	71	21	0.83	0.94	0.88	460	272	141	25	0.66	0.92	0.77	460
DF2	460	236	176	45	0.57	0.84	0.68	461	343	44	28	0.89	0.92	0.91	460
SF1	462	369	43	20	0.9	0.95	0.92	460	326	30	18	0.92	0.95	0.93	462
SF2	463	362	38	61	0.91	0.86	0.88	462	293	102	15	0.74	0.95	0.83	463
DF1	461	92	105	37	0.47	0.71	0.56	460	134	180	25	0.43	0.84	0.57	461
DF2	462	99	239	22	0.29	0.82	0.43	460	111	205	25	0.35	0.82	0.49	462
SF1	460	139	188	26	0.43	0.84	0.57	462	119	294	27	0.29	0.82	0.43	460
SF2	461	106	232	24	0.31	0.82	0.45	460	129	176	20	0.42	0.87	0.57	461

F	WS	NT	TP	FN	FP	R	P	F	NT	TP	FN	FP	R	P	F
DF1	6.5	462	223	322	76	0.41	0.75	0.53	461	152	325	110	0.32	0.58	0.41
DF2	6.5	460	213	339	84	0.39	0.72	0.50	461	218	304	36	0.42	0.86	0.56
SF1	6.5	461	245	275	41	0.47	0.86	0.61	460	195	325	47	0.38	0.81	0.51
SF2	6.5	462	188	356	72	0.35	0.72	0.47	462	214	314	29	0.41	0.88	0.56
DF1	8	460	364	84	41	0.81	0.90	0.85	460	364	84	41	0.81	0.90	0.85
DF2	8	461	339	142	69	0.70	0.83	0.76	461	339	142	69	0.70	0.83	0.76
SF1	8	462	314	102	27	0.75	0.92	0.83	462	314	102	27	0.75	0.92	0.83
SF2	8	461	342	70	33	0.83	0.91	0.87	461	342	70	33	0.83	0.91	0.87
DF1	10	462	100	220	17	0.31	0.85	0.46	461	132	182	27	0.42	0.83	0.56
DF2	10	463	94	235	23	0.29	0.80	0.42	460	115	199	28	0.37	0.80	0.50
SF1	10	462	139	188	26	0.43	0.84	0.57	462	122	207	26	0.37	0.82	0.51
SF2	10	461	107	236	24	0.31	0.82	0.45	461	125	219	23	0.36	0.84	0.51

Litchi
Source of Variation	DF	SS	MS	F-value	p-value
Trat	2	0.0007	0.0038	0.1228	0.8848
Residuals	33	1.0239	0.0310	-	-
Pix4D
Source of Variation	DF	SS	MS	F-value	p-value
Trat	2	0.0150	0.0075	0.2940	0.7472
Residuals	33	0.8445	0.0255	-	-
Single flight
Source of Variation	DF	SS	MS	F-value	p-value
Trat	2	0.0124	0.0062	0.2143	0.8082
Residuals	33	0.9577	0.0290	-	-
Double flight
Source of Variation	DF	SS	MS	F-value	p-value
Trat	2	0.0318	0.0159	0.5992	0.5551
Residuals	33	0.8770	0.0265	-	-

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[1] Corresponding author: carla.pertille@ufpr.br