Cerrar

No. de sistema: 000059742

LDR _ _ 00000nab^^22^^^^^za^4500
008 _ _ 200107m20199999xx^^r^p^o^^^^z0^^^a0eng^d
040 _ _ a| ECO
c| ECO
043 _ _ a| n-mx-yu
044 _ _ a| xx
245 0 0 a| Assessment of the CLASlite forest monitoring system in detecting disturbance from selective logging in the Selva Maya, Mexico
506 _ _ a| Acceso en línea sin restricciones
520 1 _ a| Detecting and monitoring forest disturbance from selective logging is necessary to develop effective strategies and polices that conserve tropical forests and mitigate climate change. We assessed the potential of using the remote sensing tool, CLASlite forest monitoring system, to detect disturbance from timber harvesting in four community forests (ejidos) of the Selva Maya on the Yucatan Peninsula, Mexico. Selective logging impacts (e.g. felling gaps, skid trails, logging roads and log landings) were mapped using GPS in the 2014 annual cutting areas (ACAs) of each ejido. We processed and analyzed two pre-harvest Landsat images (2001 and 2013) and one post-harvest image (November 2014) with the CLASlite system, producing maps of degraded, deforested and unlogged areas in each ACA. Based on reference points of disturbed (felling and skidding), deforested (log landings and roads) and unlogged areas in each ACA, we applied accuracy assessments which showed very low overall accuracies (<19.1%). Selective logging impacts, mainly from log landings and new logging road construction, were detected in only one ejido which had the highest logging intensity (7 m³ ha–¹).
530 _ _ a| Disponible en línea
533 _ _ a| Reproducción electrónica en formato PDF
538 _ _ a| Adobe Acrobat profesional 6.0 o superior
700 1 _ a| Hernández Gómez, Irving Uriel
e| autor
700 1 _ a| Cerdán Cabrera, Carlos Roberto
e| autor
700 1 _ a| Navarro Martínez, María Angélica
c| Doctora
e| autora
700 1 _ a| Vázquez Luna, Dinora
e| autora
700 1 _ a| Armenta Montero, Samaria
e| autora
700 1 _ a| Ellis, Edward Alan
e| autora
773 0 _
t| Silva Fennica
g| Vol. 53, no. 1, id 10012 (2019), p. 1-10
x| 2242-4075
856 4 1 u| https://www.silvafennica.fi/article/10012
z| Artículo electrónico
856 _ _ u| http://aleph.ecosur.mx:8991/F?func=service&doc_library=CFS01&local_base=CFS01&doc_number=000059742&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
y| Artículo electrónico
902 _ _ a| BG / GOG
904 _ _ a| Diciembre 2020
905 _ _ a| Artecosur
905 _ _ a| Artfrosur
905 _ _ a| Biblioelectrónica
LNG eng
Cerrar
Assessment of the CLASlite forest monitoring system in detecting disturbance from selective logging in the Selva Maya, Mexico
Hernández Gómez, Irving Uriel (autor)
Cerdán Cabrera, Carlos Roberto (autor)
Navarro Martínez, María Angélica (autora)
Vázquez Luna, Dinora (autora)
Armenta Montero, Samaria (autora)
Ellis, Edward Alan (autora)
Nota: Disponible en línea
Acceso en línea sin restricciones
Contenido en: Silva Fennica. Vol. 53, no. 1, id 10012 (2019), p. 1-10. ISSN: 2242-4075
No. de sistema: 59742
Tipo: Artículo
PDF PDF
  • Consulta (1)




Inglés

"Detecting and monitoring forest disturbance from selective logging is necessary to develop effective strategies and polices that conserve tropical forests and mitigate climate change. We assessed the potential of using the remote sensing tool, CLASlite forest monitoring system, to detect disturbance from timber harvesting in four community forests (ejidos) of the Selva Maya on the Yucatan Peninsula, Mexico. Selective logging impacts (e.g. felling gaps, skid trails, logging roads and log landings) were mapped using GPS in the 2014 annual cutting areas (ACAs) of each ejido. We processed and analyzed two pre-harvest Landsat images (2001 and 2013) and one post-harvest image (November 2014) with the CLASlite system, producing maps of degraded, deforested and unlogged areas in each ACA. Based on reference points of disturbed (felling and skidding), deforested (log landings and roads) and unlogged areas in each ACA, we applied accuracy assessments which showed very low overall accuracies (<19.1%). Selective logging impacts, mainly from log landings and new logging road construction, were detected in only one ejido which had the highest logging intensity (7 m³ ha–¹)."


  • Adobe Acrobat profesional 6.0 o superior