Selective Logging Detection in the Brazilian Amazon

Costa, Olívia Bueno da; Matricardi, Eraldo Aparecido Trondoli; Pedlowski, Marcos Antonio; Miguel, Eder Pereira; Gaspar, Ricardo de Oliveira

doi:10.1590/2179-8087.063417

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Original Article, Forest Management • Floresta Ambient. 26 (2) • 2019 • https://doi.org/10.1590/2179-8087.063417 copy

Selective Logging Detection in the Brazilian Amazon

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Selective logging activities are commonly observed in the Brazilian Amazon and are responsible for high forest impact. In this study, selective logging detection techniques and the spatiotemporal extension of forests impacted by logging activities between 2003 and 2014 in portions of the states of Mato Grosso, Pará, and Rondônia were assessed using remotely sensing data. Based on results obtained, it was estimated that the overall accuracies are greater than 91% for techniques applied to detect forests impacted by selective logging in the study areas. Forests impacted by selective logging increased in Western state of Mato Grosso and Northern state of Rondônia, which indicates a stage high forest activity in these regions. In contrast, in Eastern state of Pará, a decrease in forests impacted by logging activities was observed, which indicates collapsed stage of logging activities resulting from deforestation and predatory logging in that region.

Keywords:
Legal Amazon; geoprocessing and forest degradation

Satellite/Sensor	Mato Grosso		Pará		Rondônia
Satellite/Sensor	Scene	Date	Scene	Date	Scene	Date
Landsat (OLI)	226/68	Oct 23	223/62	Dec 21	232/66	Oct 1
Landsat (OLI)	226/69	Aug 20	223/63	Dec 21
RapidEye	2133426	May 6	2237328	June 26	2035216	June 24
	2133527	May 6	2237427	June 26	2035215	June 24
	2133427	July 5	2237428	June 26
	2133526	July 5	2237327	June 26
	2133525	May 1
	2133425	Aug 13

	Usage Classes	Rapid Eye image			Producer Accuracy	User Accuracy
	Usage Classes	Selective logging	Other uses	Total	Producer Accuracy	User Accuracy
Classified image	Selective logging	96	10	106	88.1%	91%
	Other uses	13	141	154	93%	92%
	Total	109	151	260
Omission errors		11.9%	7%
Commission errors		9.4%	8.4%
Overall accuracy		91%

	Usage Classes	Rapid Eye image			Producer Accuracy	User Accuracy
	Usage Classes	Selective logging	Other uses	Total	Producer Accuracy	User Accuracy
Classified image	Selective logging	87	7	94	92.6%	92.6%
	Other uses	7	99	106	93%	93%
	Total	94	106	200
Omission errors		7.4%	7%
Commission errors		7.4%	7%
Overall accuracy		93%

	Usage Classes	Rapid Eye image			Producer Accuracy	User Accuracy Selective logging
	Usage Classes	Selective logging	Other uses	Total	Producer Accuracy	User Accuracy Selective logging
Classified image	Selective logging	58	6	64	98%	90.6%
	Other uses	1	35	36	85.4%	97%
	Total	59	41	100
Omission errors		2%	14.6%
Commission errors		9.4%	3%
Overall accuracy		93%

State	Landsat Images	Year
State	Landsat Images	2003	2006	2010	2014
Mato Grosso	226/68	4,900.60	5,191.70	3,508.10	6,180.40
Mato Grosso	226/69	1,220.40	1,394.50	1,067.20	613.4
Pará	223/62	4,030.20	3,660.00	4,868.10	2,354.10
Pará	223/63	4,308.60	3,208.80	3,500.80	1,623.90
Rondônia	232/66	43.5	209.4	470	1,745.10

Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro Rodovia BR 465 Km 7, CEP 23897-000, Tel.: (21) 2682 0558 | (21) 3787-4033 - Seropédica - RJ - Brazil
E-mail: floram@ufrrj.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] Eraldo Aparecido Trondoli Matricardi Faculdade de Tecnologia, Universidade de Brasília – UnB, Campus Darcy Ribeiro, Asa Norte, CEP 70910-900, Brasília, DF, Brasil e- mail: ematricardi@gmail.com