This paper reviews the expectations for forestry’s contribution to rural development – and for its special contributions to the most disadvantaged, to women and the landless users of the forest commons. A growing literature challenges some of these expectations; in particular, certain expectations about cultural differences and physical stocks as explanatory factors for patterns of household behavior. This literature could also be used to support a call for sharper definitions of deforestation, improved indicators of the effects of forest resources on the rural poor, and improved design of forest policy interventions. Our paper reviews the literature, suggests some unifying themes, and identifies the critical issues that remain unanswered. The primary contention arising from this literature is that households follow systematic patterns of economic behavior in their consumption and production of forest resources, and that policy interventions in social forestry should be analyzed with regard to markets, policies, and institutions. Markets for forest resources generally exist in some form – although they may be thin. Successful forestry projects and policies require careful identification of the target populations and careful estimation of market and market-related effects on the household behavior of these populations. Institutional structures that assure secure rights for scarce forest resources are uniquely important in a forest enviornment often characterized by open access resources and weak government administration. Social and community forestry, improved stoves, improved strains of multi-purpose trees, and even private commercial forest operations can all improve local welfare, but only where scarcity is correctly identified and the appropriate institutions are in place. An increasing number of observations of afforestation from developing countries around the world is evidence that forestry activities do satisfy these conditions in selective important cases. The critical point for policy is to identify the characteristics of these successful cases that are predictive of other cases where new forestry activities can be welfare enhancing.

Hyde, Centre for International Forestry Research, Bogor, Indonesia E-mail: wfhyde@aol.com
Köhlin, Göteborg University, Göteborg, Sweden E-mail: gk@nn.se

Category : Research note

article id 10012, category Research note

Irving U. Hernández-Gómez, Carlos R. Cerdán, Angélica Navarro-Martínez, Dinora Vázquez-Luna, Samaria Armenta-Montero, Edward A. Ellis. (2019). Assessment of the CLASlite forest monitoring system in detecting disturbance from selective logging in the Selva Maya, Mexico. Silva Fennica vol. 53 no. 1 article id 10012. https://doi.org/10.14214/sf.10012

Keywords: community forestry; forest degradation; tropical forest; Yucatan Peninsula

Highlights: The accuracy of CLASlite to detect forest disturbance from selective logging using Landsat imagery was very low (<19.1%); Selective logging impacts was only detected in one case with the highest logging intensity (7 m³ ha^–1); CLASlite shows potential in monitoring forest disturbance from tree biomass impacts greater than 900 m².

Abstract | Full text in HTML | Full text in PDF | Author Info

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).

Hernández-Gómez, Facultad de Ciencias Agrícolas, Universidad Veracruzana. Circuito Gonzalo Aguirre Beltrán, Isleta, Xalapa, Veracruz. C.P. 91000, Mexico E-mail: urielxal@gmail.com
Cerdán, Facultad de Ciencias Agrícolas, Universidad Veracruzana. Circuito Gonzalo Aguirre Beltrán, Isleta, Xalapa, Veracruz. C.P. 91000, Mexico E-mail: ccerdan@uv.mx
Navarro-Martínez, El Colegio de la Frontera Sur Av. Centenario km 5.5, Col. Pacto Obrero Campesino s/n. Chetumal, Quintana Roo. C.P. 77014, Mexico E-mail: manavaster@gmail.com
Vázquez-Luna, Facultad de Ingeniería en Sistemas de Producción Agropecuaria, Universidad Veracruzana. Carretera Costera del Golfo Km. 220, C. Agrícola y Ganadera Michapan, Acayucan, Veracruz. C.P. 96000, Mexico E-mail: divazquez@uv.mx
Armenta-Montero, Centro de Investigaciones Tropicales (CITRO), Universidad Veracruzana. Morelos No. 44 y 46, Zona Centro, Xalapa, Veracruz. C.P. 91000, Mexico E-mail: samaria.am@gmail.com
Ellis, Centro de Investigaciones Tropicales (CITRO), Universidad Veracruzana. Morelos No. 44 y 46, Zona Centro, Xalapa, Veracruz. C.P. 91000, Mexico E-mail: eellis@uv.mx

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