Land cover mapping for tropical forest rehabilitation planning using remotely-sensed data

Abstract

Lack of reliable and up-to-date maps relating to land cover (among other themes) constitute a weakness in land resource surveys and cause costly failures to many forest rehabilitation projects in the tropics. This study evaluated the utility of satellite imagery for land cover mapping for forest rehabilitation planning in a case study in Mindoro, Philippines. Using Landsat TM data, visual and digital image processing techniques were performed using the GRID module of ARC/INFO and the microBRIAN image processing software. Crown cover density is found as the most useful and the most important detail of information the image could provide. Detailed mapping at the species and forest type levels is unreliable, as is the delineation of water bodies and some cultural features in rugged terrain. Clustering of the NDVI image is found more applicable in producing land cover maps depicting crown cover classes than classifying raw TM-3, -4, and -5.

Biomass variation across selectively logged forest within a 225-km2 region of Borneo and its prediction by Landsat TM

“…Within forest management, these data are required to assess the volume of timber available within potential harvesting coupes (MacLean and Krabill, 1986;Jusoff and Souza, 1996;Trotter et al, 1997). Within tropical rain forests already disturbed by selective felling, forestry organizations need to quantify the rate of biomass recovery to judge the impact of previous operations and the timing of the next phase of selective logging (Pinard and Putz, 1996;Apan, 1997;Asner et al, 2002). Assessments of impacts of forest disturbance and rehabilitation on carbon sequestration (Chambers et al, 2007;Saatchi et al, 2007), would similarly benefit from regional biomass estimates.…”

Mapping and analysis of changes in the riparian landscape structure of the Lockyer Valley catchment, Queensland, Australia

“…Singh, 1987;Apan, 1997). The contrasting reflectance properties of these two land use/cover classes in the visible and infrared bands allow their easy differentiation using digital approaches.…”

Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM

“…El autor Apan (1997) ha estudiado bosques tropicales complejos con una fiabilidad de 66,7 % para el usuario y 56,6 % para el productor. Por su parte, Liberti et al (2009) discriminan bosques con una precisión global entre 53 % a 72 %.…”